diff --git a/AGENTS.md b/AGENTS.md
index 908c2b6..7c74e95 100644
--- a/AGENTS.md
+++ b/AGENTS.md
@@ -80,6 +80,29 @@ A2/A3 probability adapters and five-fold Adult integration; full Covertype runs
 and remaining adapters/searches/D5 are open. Sprint 049 records all five full
 Covertype folds timing out at the 90-second fit cap. Next profile the current
 CPU path on that same input before expanding adapters; A3 validation is incomplete.
+Sprint 050 identifies histogram aggregation and repeated candidate row hashing
+in a bounded full-input profile. Next hoist invariant candidate row hashing with
+exact identity/candidate checks, then rerun the bounded workload. Sprint 051
+completes that change: fold zero passes in 87.4 seconds with exact fresh replay;
+the other folds were pending at that revision. Sprint 052 reuses selected
+histogram statistics with exact conformance checks;
+all five full Covertype folds pass within the unchanged cap and replay exactly.
+Sprint 053 connects A5 independent quantiles to all five frozen Bike origins
+with exact fresh replay and independently recomputed pinball scores. Sprint 054
+adds A7 explicit count/exposure binding on all five frozen frequency folds with
+exact replay. Sprint 055 adds A8 claim severity on all five frozen grouped folds.
+Sprint 056 adds direct A9 annualized Tweedie on all five aggregate folds, with
+verified exposure weights and period conversion. Sprint 057 binds matched paid
+events to all five frozen A9 input folds. Sprint 058 trains and replays the public
+composition on all five packets with independent component selection. Sprint 059
+adds A10 fixed-scale survival on all five frozen folds. Sprint 060 adds A12
+structured Formula with age separated from tree features on all five folds.
+Sprint 061 adds the current A4 query-aware adapter with synthetic direct/fresh
+parity; MSLR source/binding remains open. [Sprint 062](v1-sprints/062-cpu-exit-and-gpu-entry.md)
+reviews CPU exit and GPU entry. Next: installed D5 probes for distinct run-ID RNG
+streams and stale preparation rejection, then source/workflow and gate reconciliation.
+Real searches, joint A9 selection and formal E5 remain open. F3 has not started;
+the review defines its first vertical path without changing phase ordering.
 These internal trials
 do not establish E5/E7.
 Independent validation stopping is implemented in Sprint 039. A6 CPU workflows and shared
diff --git a/benchmarks/v1/composition_smoke.py b/benchmarks/v1/composition_smoke.py
new file mode 100644
index 0000000..11612ff
--- /dev/null
+++ b/benchmarks/v1/composition_smoke.py
@@ -0,0 +1,161 @@
+"""Five hash-bound composition fits and inference replays; no quality gate."""
+
+import argparse
+import importlib.metadata
+import json
+import os
+import platform
+import subprocess
+import sys
+from pathlib import Path
+
+import numpy as np
+
+from benchmarks.v1.paid_event_data import digest
+from benchmarks.v1.process_runner import execute
+
+
+def run(manifest, directory):
+    manifest, directory = Path(manifest).resolve(), Path(directory).resolve()
+    bound = json.loads(manifest.read_text())
+    if len(bound["cells"]) != 5 or {c["fold"] for c in bound["cells"]} != set(range(5)):
+        raise ValueError("all five bound folds required")
+    directory.mkdir(parents=True, exist_ok=True)
+    if any(directory.iterdir()):
+        raise ValueError("fresh output directory required")
+    repo = Path(__file__).resolve().parents[2]
+    script = Path(__file__).with_name("composition_worker.py")
+    sources = [
+        *sorted((repo / "src/openboost").glob("*.py")),
+        Path(__file__),
+        script,
+        Path(__file__).with_name("process_runner.py"),
+        Path(__file__).with_name("paid_event_data.py"),
+    ]
+    report = dict(
+        scope="Component-selected composition validation only; no joint selection or quality gate",
+        revision=subprocess.check_output(["git", "rev-parse", "HEAD"], text=True).strip(),
+        dirty=bool(subprocess.check_output(["git", "status", "--porcelain"])),
+        argv=[
+            sys.executable,
+            "-m",
+            "benchmarks.v1.composition_smoke",
+            str(manifest),
+            str(directory),
+        ],
+        binding_sha256=digest(manifest),
+        python=platform.python_version(),
+        os=platform.platform(),
+        packages={n: importlib.metadata.version(n) for n in ["numpy", "openboost"]},
+        cpu_count=os.cpu_count(),
+        threads=1,
+        memory_cap=None,
+        gpu=None,
+        sources={str(p.relative_to(repo)): digest(p) for p in sources},
+        cells=[],
+    )
+    for cell in bound["cells"]:
+        packet = manifest.parent / cell["path"]
+        if digest(packet) != cell["sha256"]:
+            raise ValueError("composition packet hash differs")
+        job = dict(
+            seed=cell["fold"],
+            patience=3,
+            config=dict(rounds=4, learning_rate=0.1, bins=32, max_depth=2, reg_lambda=1),
+        )
+        job_path = directory / f"job-{cell['fold']}.json"
+        job_path.write_text(json.dumps(job, indent=2) + "\n")
+        output = directory / str(cell["fold"])
+        record = execute(
+            [sys.executable, str(script), "fit", str(job_path), str(packet)],
+            output,
+            timeout_s=90,
+            threads=1,
+        )
+        record.update(fold=cell["fold"], job=job, input_sha256=digest(packet))
+        if record["status"] == "pass":
+            try:
+                with np.load(packet) as a:
+                    np.savez(
+                        output / "features.npz",
+                        x=a["x_validation"],
+                        row_ids=a["row_ids_validation"],
+                        exposure=a["exposure_validation"],
+                    )
+                command = [
+                    sys.executable,
+                    str(script),
+                    "predict",
+                    str(output / "model.bin"),
+                    str(output / "features.npz"),
+                    str(output / "replay.npz"),
+                ]
+                record["replay_command"] = command
+                subprocess.run(
+                    command,
+                    check=True,
+                    capture_output=True,
+                    timeout=30,
+                    env=dict(
+                        os.environ,
+                        OMP_NUM_THREADS="1",
+                        OPENBLAS_NUM_THREADS="1",
+                        MKL_NUM_THREADS="1",
+                    ),
+                )
+                with (
+                    np.load(output / "predictions.npz") as a,
+                    np.load(output / "replay.npz") as b,
+                    np.load(output / "features.npz") as f,
+                ):
+                    assert (
+                        set(a.files)
+                        == set(b.files)
+                        == {
+                            "row_ids",
+                            "paid_count_rate",
+                            "paid_count_mean",
+                            "severity_mean",
+                            "annualized_mean",
+                            "period_mean",
+                        }
+                    )
+                    for k in a.files:
+                        np.testing.assert_array_equal(a[k], b[k])
+                    np.testing.assert_array_equal(a["row_ids"], f["row_ids"])
+                    for k in set(a.files) - {"row_ids"}:
+                        assert (
+                            a[k].shape == a["row_ids"].shape
+                            and np.isfinite(a[k]).all()
+                            and (a[k] > 0).all()
+                        )
+                    np.testing.assert_allclose(
+                        a["annualized_mean"], a["paid_count_rate"] * a["severity_mean"], rtol=1e-12
+                    )
+                    np.testing.assert_allclose(
+                        a["period_mean"], a["annualized_mean"] * f["exposure"], rtol=1e-12
+                    )
+                    np.testing.assert_allclose(
+                        a["paid_count_mean"], a["paid_count_rate"] * f["exposure"], rtol=1e-12
+                    )
+                record.update(
+                    fresh_process_exact=True,
+                    products_and_units=True,
+                    replay_sha256=digest(output / "replay.npz"),
+                    training=json.loads((output / "training.json").read_text()),
+                )
+            except Exception as error:
+                record.update(status="error", reason=str(error))
+        report["cells"].append(record)
+        (directory / "summary.json").write_text(json.dumps(report, indent=2) + "\n")
+        print(f"Fold {cell['fold']}: {record['status']}", flush=True)
+    return report
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("manifest", type=Path)
+    parser.add_argument("directory", type=Path)
+    args = parser.parse_args()
+    if any(c["status"] != "pass" for c in run(args.manifest, args.directory)["cells"]):
+        raise SystemExit(1)
diff --git a/benchmarks/v1/composition_worker.py b/benchmarks/v1/composition_worker.py
new file mode 100644
index 0000000..52f2a3a
--- /dev/null
+++ b/benchmarks/v1/composition_worker.py
@@ -0,0 +1,105 @@
+"""Current CPU matched-payment composition fit and inference-only replay."""
+
+import argparse
+import json
+from dataclasses import asdict
+from pathlib import Path
+
+import numpy as np
+
+from openboost import NumericData, RunContext
+from openboost.composition import FrequencySeverity, paid_loss_problems
+
+
+def fit(job, arrays):
+    from openboost.recipes import gamma, poisson
+
+    if (
+        set(job) != {"seed", "config", "patience"}
+        or type(job["seed"]) is not int
+        or job["seed"] < 0
+    ):
+        raise ValueError("explicit composition seed/config/patience required")
+    cfg = job["config"]
+    if set(cfg) != {"rounds", "learning_rate", "bins", "max_depth", "reg_lambda"}:
+        raise ValueError("unsupported composition configuration")
+    if type(cfg["rounds"]) is not int or cfg["rounds"] <= 0:
+        raise ValueError("positive round budget required")
+    roles = {"x", "row_ids", "paid_count", "paid_total", "exposure"}
+    if set(arrays) != {f"{r}_{p}" for r in roles for p in ("train", "validation")}:
+        raise ValueError("matched training/validation roles only")
+    problems = []
+    names = None
+    for part in ("train", "validation"):
+        x, ids = arrays["x_" + part], arrays["row_ids_" + part]
+        if x.ndim != 2 or not len(x) or not np.isfinite(x).all():
+            raise ValueError("nonempty finite encoded features required")
+        names = tuple(f"x{i}" for i in range(x.shape[1])) if names is None else names
+        data = NumericData(x, ids, names)
+        problems.append(
+            paid_loss_problems(
+                data,
+                arrays["paid_count_" + part],
+                arrays["paid_total_" + part],
+                arrays["exposure_" + part],
+            )
+        )
+    if np.intersect1d(arrays["row_ids_train"], arrays["row_ids_validation"]).size:
+        raise ValueError("policy partitions overlap")
+    models, components = [], {}
+    selection = "final" if job["patience"] is None else "best_component_validation"
+    for i, (name, recipe) in enumerate((("frequency", poisson), ("severity", gamma))):
+        result = recipe(
+            problems[0][i],
+            problems[1][i],
+            context=RunContext(name, job["seed"]),
+            patience=job["patience"],
+            **cfg,
+        )
+        model = result.state.model if job["patience"] is None else result.state.best_model
+        models.append(model)
+        components[name] = dict(
+            stop={**asdict(result.stop), "reason": result.stop.reason},
+            selected_identity=model.identity,
+            best_score=result.state.best_score,
+            accepted_commits=result.state.version,
+        )
+    model = FrequencySeverity(*models)
+    data = problems[1][0].data
+    outputs = model.predict(data, data, arrays["exposure_validation"])
+    return model, outputs, dict(selection=selection, joint_selection=False, components=components)
+
+
+def replay(model_path, packet):
+    if set(packet) != {"x", "row_ids", "exposure"}:
+        raise ValueError("inference features, policy IDs and exposure only")
+    model = FrequencySeverity.load(model_path)
+    data = NumericData(packet["x"], packet["row_ids"], model.frequency.feature_names)
+    return model.predict(data, data, packet["exposure"])
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("mode", choices=("fit", "predict"))
+    parser.add_argument("spec", type=Path)
+    parser.add_argument("packet", type=Path)
+    parser.add_argument("output", type=Path, nargs="?")
+    args = parser.parse_args()
+    with np.load(args.packet, allow_pickle=False) as a:
+        arrays = dict(a)
+    if args.mode == "predict":
+        if args.output is None:
+            raise ValueError("prediction output path required")
+        outputs = replay(args.spec, arrays)
+        np.savez(args.output, row_ids=arrays["row_ids"], **outputs)
+    else:
+        if args.output is not None:
+            raise ValueError("fit writes to its dedicated working directory")
+        model, outputs, training = fit(json.loads(args.spec.read_text()), arrays)
+        model.save("model.bin")
+        np.savez("predictions.npz", row_ids=arrays["row_ids_validation"], **outputs)
+        Path("training.json").write_text(json.dumps(training, indent=2, allow_nan=False) + "\n")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/benchmarks/v1/evidence/aggregate-056/A9/0/execution.json b/benchmarks/v1/evidence/aggregate-056/A9/0/execution.json
new file mode 100644
index 0000000..7bc8389
--- /dev/null
+++ b/benchmarks/v1/evidence/aggregate-056/A9/0/execution.json
@@ -0,0 +1,19 @@
+{
+  "artifacts": {
+    "model.bin": "05cb37e2b36082e4258cea1ee81d26d048d677dcc265df9f06887cf59bec40a2",
+    "predictions.npz": "106aa1fc8ff5625ddc993ae7dedf00a86eef64ba429f5bda7b93616b9d21c6b5",
+    "training.json": "73c8cf78fd6b2886c56ae11ccf1cd4c5913541482d811562d36423dd8c252c8e",
+    "worker.log": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855"
+  },
+  "command": [
+    "/Users/jiaruixu/work_space/openboost/.venv/bin/python3",
+    "/Users/jiaruixu/work_space/openboost/benchmarks/v1/openboost_worker.py",
+    "/private/tmp/openboost-aggregate-056/A9/0/job.json"
+  ],
+  "exit_code": 0,
+  "reason": "",
+  "status": "pass",
+  "threads": 1,
+  "timeout_s": 90,
+  "wall_s": 19.495609958001296
+}
diff --git a/benchmarks/v1/evidence/aggregate-056/A9/0/model.bin b/benchmarks/v1/evidence/aggregate-056/A9/0/model.bin
new file mode 100644
index 0000000..40a0a9a
--- /dev/null
+++ b/benchmarks/v1/evidence/aggregate-056/A9/0/model.bin
@@ -0,0 +1 @@
+{"format": "openboost-evaluation-v1", "application": "A9", "output": "annualized_paid_mean", "model": {"format": "openboost-ensemble-v2", "feature_names": ["x0", "x1", "x2", "x3", "x4", "x5", "x6", "x7", "x8", "x9", "x10", "x11", "x12", "x13", "x14", "x15", "x16", "x17", "x18", "x19", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28", "x29", "x30", "x31", "x32", "x33", "x34", "x35", "x36", "x37", "x38", "x39", "x40", "x41", "x42", "x43", "x44", "x45", "x46", "x47", "x48", "x49", "x50", "x51", "x52", "x53", "x54"], "base": [5.149670587545257], "terms": [{"kind": "tree", "learner": {"format": "openboost-tree-v3", "feature_names": ["x0", "x1", "x2", "x3", "x4", "x5", "x6", "x7", "x8", "x9", "x10", "x11", "x12", "x13", "x14", "x15", "x16", "x17", "x18", "x19", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28", "x29", "x30", "x31", "x32", "x33", "x34", "x35", "x36", "x37", "x38", "x39", "x40", "x41", "x42", "x43", "x44", "x45", "x46", "x47", "x48", "x49", "x50", 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diff --git a/benchmarks/v1/evidence/aggregate-056/A9/4/period-predictions.npz b/benchmarks/v1/evidence/aggregate-056/A9/4/period-predictions.npz
new file mode 100644
index 0000000..a1ce245
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diff --git a/benchmarks/v1/evidence/aggregate-056/A9/4/predictions.npz b/benchmarks/v1/evidence/aggregate-056/A9/4/predictions.npz
new file mode 100644
index 0000000..4629bf3
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diff --git a/benchmarks/v1/evidence/aggregate-056/A9/4/replay.npz b/benchmarks/v1/evidence/aggregate-056/A9/4/replay.npz
new file mode 100644
index 0000000..4629bf3
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diff --git a/benchmarks/v1/evidence/aggregate-056/A9/4/training.json b/benchmarks/v1/evidence/aggregate-056/A9/4/training.json
new file mode 100644
index 0000000..aeabd8f
--- /dev/null
+++ b/benchmarks/v1/evidence/aggregate-056/A9/4/training.json
@@ -0,0 +1,22 @@
+{
+  "selection": "best_validation",
+  "stop": {
+    "rounds": 4,
+    "patience": 3,
+    "min_delta": 0.0,
+    "reference_score": 46.51041983102714,
+    "last_score": 46.51041983102714,
+    "completed_rounds": 4,
+    "stale_rounds": 0,
+    "reason": "budget"
+  },
+  "accepted_commits": 4,
+  "selected_model_identity": "6b26ac22d0be34cf25b677ed1c99903854b947676468045a4c32298e6813536b",
+  "best_validation_score": 46.51041983102714,
+  "output": "annualized_paid_mean",
+  "selection_metric": "weighted_tweedie_objective",
+  "power": 1.5,
+  "target_units": "annualized_paid_total",
+  "weight_role": "exposure_once",
+  "selected_validation_tweedie_objective": 46.51041983102714
+}
diff --git a/benchmarks/v1/evidence/aggregate-056/A9/4/worker.log b/benchmarks/v1/evidence/aggregate-056/A9/4/worker.log
new file mode 100644
index 0000000..e69de29
diff --git a/benchmarks/v1/evidence/aggregate-056/README.md b/benchmarks/v1/evidence/aggregate-056/README.md
new file mode 100644
index 0000000..fb9fa34
--- /dev/null
+++ b/benchmarks/v1/evidence/aggregate-056/README.md
@@ -0,0 +1,30 @@
+# Current A9 direct annualized aggregate integration
+
+All five frozen aggregate folds pass the current CPU Tweedie worker and exact
+fresh-process replay. Annualized paid totals use positive exposure weights once,
+with fixed power 1.5 and no extra offset. Output retains annualized units.
+
+```sh
+UV_CACHE_DIR=/tmp/openboost-research-uv-cache uv run --no-sync python -m benchmarks.v1.openboost_worker_smoke /tmp/openboost-aggregate-056 --applications A9
+```
+
+Requires pinned insurance files in build/v1-data and committed preprocessing
+freeze. Four rounds, depth two, 32 bins, learning rate 0.1, patience three and one
+CPU thread; 90-second fit/30-second replay caps. No test labels scored, memory
+cap, profiler or concurrent regression. Export is outside fit timing. Host:
+Apple M4 Max, 51539607552 bytes RAM (same host as Sprint 052); Python reports
+x86_64. No CUDA or measured peak memory.
+
+`summary.json` retains source/revision/dirty state, population/split/packet hashes,
+commands, environment and outcomes. Each fold retains raw model, predictions,
+replay, training, execution and log artifacts. All source/output hashes match.
+`verification.json` records independent weighted Tweedie objectives and exact
+source-ID/exposure-weight agreement with the hashed validation-period packet.
+Annualized targets times exposure reproduce paid totals. Additional raw
+period-predictions.npz files retain annualized predictions times exposure.
+Numerical checks use rtol=1e-12/atol=1e-14; fresh replay is exact.
+
+This is direct aggregate integration, not a complete A9 search, calibrated
+compound distribution or performance comparison. Frequency-severity composition
+still requires matched positive-payment counts and totals; A7 raw claim counts
+cannot be substituted. No required scope is dropped.
diff --git a/benchmarks/v1/evidence/aggregate-056/summary.json b/benchmarks/v1/evidence/aggregate-056/summary.json
new file mode 100644
index 0000000..399970c
--- /dev/null
+++ b/benchmarks/v1/evidence/aggregate-056/summary.json
@@ -0,0 +1,601 @@
+{
+  "scope": "Current A1/A2/A3/A5/A6/A7/A8/A9/A11 real-data validation plumbing only; four rounds, no test scores, quality or performance claim",
+  "revision": "d7622f4c1d3494d5e6b5019b66db1d80708e6c23",
+  "dirty": true,
+  "argv": [
+    "/Users/jiaruixu/work_space/openboost/.venv/bin/python3",
+    "-m",
+    "benchmarks.v1.openboost_worker_smoke",
+    "/private/tmp/openboost-aggregate-056",
+    "--applications",
+    "A9"
+  ],
+  "python": "3.12.12",
+  "os": "macOS-26.3-x86_64-i386-64bit",
+  "machine": "x86_64",
+  "cpu_count": 16,
+  "device": "cpu",
+  "gpu": null,
+  "threads": 1,
+  "memory_cap": null,
+  "packages": {
+    "numpy": "2.3.5",
+    "openboost": "1.0.0rc1"
+  },
+  "sources": {
+    "src/openboost/__init__.py": "b0a76b68a1824abef45bf30d3d094dce7de2ec9deef3d5cbe862a6b3f6038788",
+    "src/openboost/artifacts.py": "fc5232f29d2b9d55676035bbd5bb9fe726babba5fcdfbd84ff18133de3e9a817",
+    "src/openboost/binning.py": "e77bdbaf42121ba5dde8ec681371bd0c7d1e31fada19618d796279cc0b322a7d",
+    "src/openboost/composition.py": "a52dc4d09582031441dbf2c4a3ed889851783a2f4fece50506fa5e0e0501ccec",
+    "src/openboost/data.py": "93b4eb6487e7fb21c960d19906c7a49b3df9fa28b2a2bacdc8ea6437889f2343",
+    "src/openboost/leaves.py": "3bff04ff4b1eff1a1d58f6ce5e29f15391e8b2e9a80a696c6fee988d1be95b75",
+    "src/openboost/multioutput.py": "98c192719bb9ee787ec385a2d219a2f52aacebc161cc9fc1275006f49daad76d",
+    "src/openboost/objectives.py": "fcf847bda2917354093e03ce42bd22e0fd85e391185d3141318cb7ccecd721f7",
+    "src/openboost/ops.py": "c82134d466e6ebf660582d9895ee268b7c00fa999ec8ccdbc0c70634d429599f",
+    "src/openboost/outputs.py": "f10f3a68b4513cb93c222cd1e39ca238c686c903daa8c8035c417b5916365115",
+    "src/openboost/ranking.py": "beddf8fb6eeecbed54acdb55147bc251392a6f9fb7b46304e5d80d1d0ee642b3",
+    "src/openboost/recipes.py": "4fc1cd9971cde4bad95922c0f4e241d169a8a9fa08730070f02033c3405d265c",
+    "src/openboost/results.py": "b33bb8c658678bf5319a1e76b9a420112debea4f26361acad166f6f1e641585d",
+    "src/openboost/runs.py": "5a00eead8d6d4d6ddc99a646a0131f04918c619c5061b647ec4fc3c386459dc4",
+    "src/openboost/runtime.py": "8962e0204d867a1b700ed15e92b155cd6144927b651a79b8083634a0c315ad2d",
+    "src/openboost/stats.py": "4edbeba39e727f60213f04b5d3592bc82fb02cb1ebd9603ca9f8cee66b932cc9",
+    "src/openboost/stopping.py": "2374873a01dba8d7b359cd484337de228ca6c805d6a43b23bcf51598416746a1",
+    "src/openboost/survival.py": "cb168551a5aa026d6c501f4370c7ddc998d3cc7269ce6f17d93258ceadef89e9",
+    "src/openboost/tree.py": "60dd8cbc56d78913e868a3788bd08c91e858febc4f4c0534831077df640d7581",
+    "benchmarks/v1/openboost_worker.py": "f659290d0190e6c507876c715bbbc94d11729c3b8f43fa70634ec42c1ae96494",
+    "benchmarks/v1/openboost_predict.py": "7b0fa9e9231a5259750359f7f191664b93f1fd0160ad85fdb9d7a45c4e790233",
+    "benchmarks/v1/openboost_worker_smoke.py": "225da1c92963e963e38e8cca47fc7da3ae4c69a9c66a8e49a06e4050cd3bd8de",
+    "benchmarks/v1/worker_data.py": "6d2b09303abaace59b22aebd666317b2da57a0efd37f8cbfb138766a933749f1",
+    "benchmarks/v1/preprocessing.py": "62561effccd6856bc404339dddbc8c2daa3a1d0b011be59edd8fd3435802bc30",
+    "benchmarks/v1/process_runner.py": "5ccc984919d2932974f89cfc16691e79dd0e2c35a5da145993d5c9b1a0cd8eab"
+  },
+  "data": {
+    "A9": {
+      "application": "A9",
+      "dataset": "insurance",
+      "folds": [
+        {
+          "seed": 0,
+          "artifacts": {
+            "worker-input": {
+              "path": "0/worker-input.npz",
+              "sha256": "1e3cd8d7e7bd157745bff7db14534bd1fbcecff3abef8f4341fa1f58a9583f0f"
+            },
+            "train-rows": {
+              "path": "0/train-rows.npz",
+              "sha256": "89ca61cb3b97a660065989fc29d4083af85a8b78081ff71dba6f316710421bbb"
+            },
+            "validation": {
+              "path": "0/validation.npz",
+              "sha256": "e9d6286fffd61b5c7dd91b7a027684fa561d72fe0d57a14f0b846b3eae1e8c20"
+            },
+            "test-features": {
+              "path": "0/test-features.npz",
+              "sha256": "6dcf4429e95ed60936e9fce3a8ce1ad4cfe23524dc1ec3115f5ab2ea2a686153"
+            },
+            "test-truth": {
+              "path": "0/test-truth.npz",
+              "sha256": "0b43c2d68dcea2f014cc1ef202e89fce4055b61723a241d71dbefd6be62efcaf"
+            },
+            "validation-period": {
+              "path": "0/validation-period.npz",
+              "sha256": "38b3c8adea524a454dbceb35cb3b18f97a77e332da742607b505cb263d38bdc0"
+            },
+            "test-period": {
+              "path": "0/test-period.npz",
+              "sha256": "4a65bf4de6a61bd5434bc9301092c35e4cd0cc636675978d48e0b703978c9a36"
+            }
+          },
+          "metadata": {
+            "target_units": "annualized paid total; exposure weight once, no offset"
+          }
+        },
+        {
+          "seed": 1,
+          "artifacts": {
+            "worker-input": {
+              "path": "1/worker-input.npz",
+              "sha256": "e8c4d49a1008615af63446d04a049fadb9eb2c65243942defc4765272d1e8dac"
+            },
+            "train-rows": {
+              "path": "1/train-rows.npz",
+              "sha256": "34d0dd6b71e9c4c95461c3eb03fd32da250352b89fc9c113f9c44ff6b9f2f8b9"
+            },
+            "validation": {
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+              "sha256": "0e80cdbb30a5f39d7a0aa66133f65d5f59bdc9a0cdab73560c3606ff09130f4e"
+            },
+            "test-features": {
+              "path": "1/test-features.npz",
+              "sha256": "8db330118e63840ca6ba192426d475f17c43478418a684fa1283698c80f789b4"
+            },
+            "test-truth": {
+              "path": "1/test-truth.npz",
+              "sha256": "722b45843d3dfa5494bdf2ad027a398b74d0dbe3ca3ffe3f432d12549bed3a40"
+            },
+            "validation-period": {
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+              "sha256": "cc873cd194e217b151323af8c6ca5078357f5b8fc45c97d0c286d1cd624417a4"
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+            "test-period": {
+              "path": "1/test-period.npz",
+              "sha256": "627c93a4f32751fc739ac8a6f1d1b9f6ee36d3213ade9cf5c31847073abdcf7b"
+            }
+          },
+          "metadata": {
+            "target_units": "annualized paid total; exposure weight once, no offset"
+          }
+        },
+        {
+          "seed": 2,
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index 0000000..ddab343
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diff --git a/benchmarks/v1/evidence/composition-058/1/model.bin b/benchmarks/v1/evidence/composition-058/1/model.bin
new file mode 100644
index 0000000..a7e64da
--- /dev/null
+++ b/benchmarks/v1/evidence/composition-058/1/model.bin
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diff --git a/benchmarks/v1/evidence/composition-058/2/replay.npz b/benchmarks/v1/evidence/composition-058/2/replay.npz
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diff --git a/benchmarks/v1/evidence/composition-058/2/training.json b/benchmarks/v1/evidence/composition-058/2/training.json
new file mode 100644
index 0000000..c0c7314
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diff --git a/benchmarks/v1/evidence/composition-058/3/execution.json b/benchmarks/v1/evidence/composition-058/3/execution.json
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index 0000000..202c581
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diff --git a/benchmarks/v1/evidence/composition-058/4/predictions.npz b/benchmarks/v1/evidence/composition-058/4/predictions.npz
new file mode 100644
index 0000000..c4aa425
Binary files /dev/null and b/benchmarks/v1/evidence/composition-058/4/predictions.npz differ
diff --git a/benchmarks/v1/evidence/composition-058/4/replay.npz b/benchmarks/v1/evidence/composition-058/4/replay.npz
new file mode 100644
index 0000000..c4aa425
Binary files /dev/null and b/benchmarks/v1/evidence/composition-058/4/replay.npz differ
diff --git a/benchmarks/v1/evidence/composition-058/4/training.json b/benchmarks/v1/evidence/composition-058/4/training.json
new file mode 100644
index 0000000..4e2165a
--- /dev/null
+++ b/benchmarks/v1/evidence/composition-058/4/training.json
@@ -0,0 +1,36 @@
+{
+  "selection": "best_component_validation",
+  "joint_selection": false,
+  "components": {
+    "frequency": {
+      "stop": {
+        "rounds": 4,
+        "patience": 3,
+        "min_delta": 0.0,
+        "reference_score": 0.16359409804271152,
+        "last_score": 0.16359409804271152,
+        "completed_rounds": 4,
+        "stale_rounds": 0,
+        "reason": "budget"
+      },
+      "selected_identity": "eabcb8c2e91852747b0e18011f563434b292dd14a5dac19ff6c8b460757b08cd",
+      "best_score": 0.16359409804271152,
+      "accepted_commits": 4
+    },
+    "severity": {
+      "stop": {
+        "rounds": 4,
+        "patience": 3,
+        "min_delta": 0.0,
+        "reference_score": 8.536981751551068,
+        "last_score": 8.537179094305912,
+        "completed_rounds": 4,
+        "stale_rounds": 2,
+        "reason": "budget"
+      },
+      "selected_identity": "fd102c9f3971e5184365af24aab61b2234ce638cca6d0e70637a5652014b369c",
+      "best_score": 8.536981751551068,
+      "accepted_commits": 4
+    }
+  }
+}
diff --git a/benchmarks/v1/evidence/composition-058/4/worker.log b/benchmarks/v1/evidence/composition-058/4/worker.log
new file mode 100644
index 0000000..e69de29
diff --git a/benchmarks/v1/evidence/composition-058/README.md b/benchmarks/v1/evidence/composition-058/README.md
new file mode 100644
index 0000000..95d9f79
--- /dev/null
+++ b/benchmarks/v1/evidence/composition-058/README.md
@@ -0,0 +1,26 @@
+# Matched frequency-severity execution and replay
+
+All five Sprint 057 hash-bound policy packets pass combined Poisson/Gamma fits
+and exact fresh-process replay. Frequency uses matched positive-payment counts
+with exposure; severity uses positive policy averages weighted by paid count.
+No business weights or offsets are added. Separate component validation selects
+models; the recorded joint_selection flag is false.
+
+```sh
+UV_CACHE_DIR=/tmp/openboost-research-uv-cache uv run --no-sync python -m benchmarks.v1.composition_smoke /tmp/openboost-paid-events-057/manifest.json /tmp/openboost-composition-058
+```
+
+Regenerate Sprint 057 packets if absent. Binding hash matches its committed
+manifest. Four rounds per component, depth two, 32 bins, learning rate 0.1,
+patience three and one CPU thread. Both component fits share the 90-second cap;
+fresh inference has 30 seconds. No profiler, concurrent regression, memory cap,
+test labels or CUDA. Host: Apple M4 Max, 51539607552 bytes RAM (same host as
+Sprint 052); Python reports x86_64. Peak memory was not measured.
+
+The summary retains revision/dirty state, source and input hashes, exact commands,
+environment, statuses and component stopping/model identities. Each fold retains
+raw two-model persistence, all five named prediction arrays, replay, training,
+execution and log records. All source/output hashes match. All arrays replay
+exactly, preserve policy IDs, and are finite and positive. Products and exposure
+conversions pass at rtol=1e-12. This does not establish joint aggregate selection,
+full A9 quality/search, a compound distribution or comparative performance.
diff --git a/benchmarks/v1/evidence/composition-058/summary.json b/benchmarks/v1/evidence/composition-058/summary.json
new file mode 100644
index 0000000..78f19d3
--- /dev/null
+++ b/benchmarks/v1/evidence/composition-058/summary.json
@@ -0,0 +1,455 @@
+{
+  "scope": "Component-selected composition validation only; no joint selection or quality gate",
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new file mode 100644
index 0000000..a78fcb1
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diff --git a/benchmarks/v1/evidence/count-054/A7/4/predictions.npz b/benchmarks/v1/evidence/count-054/A7/4/predictions.npz
new file mode 100644
index 0000000..6bf040f
Binary files /dev/null and b/benchmarks/v1/evidence/count-054/A7/4/predictions.npz differ
diff --git a/benchmarks/v1/evidence/count-054/A7/4/replay.npz b/benchmarks/v1/evidence/count-054/A7/4/replay.npz
new file mode 100644
index 0000000..6bf040f
Binary files /dev/null and b/benchmarks/v1/evidence/count-054/A7/4/replay.npz differ
diff --git a/benchmarks/v1/evidence/count-054/A7/4/training.json b/benchmarks/v1/evidence/count-054/A7/4/training.json
new file mode 100644
index 0000000..074d116
--- /dev/null
+++ b/benchmarks/v1/evidence/count-054/A7/4/training.json
@@ -0,0 +1,22 @@
+{
+  "selection": "best_validation",
+  "stop": {
+    "rounds": 4,
+    "patience": 3,
+    "min_delta": 0.0,
+    "reference_score": 0.20964191911099933,
+    "last_score": 0.20964191911099933,
+    "completed_rounds": 4,
+    "stale_rounds": 0,
+    "reason": "budget"
+  },
+  "accepted_commits": 4,
+  "selected_model_identity": "2367e6b5f493d1396b1419938dd956102fdb6b939aeb874ea1b2c96fc094fd19",
+  "best_validation_score": 0.20964191911099933,
+  "output": "period_count_mean",
+  "selection_metric": "weighted_poisson_nll",
+  "exposure_role": "likelihood_once",
+  "target_units": "period_count",
+  "raw_units": "log_rate",
+  "selected_validation_nll": 0.20964191911099933
+}
diff --git a/benchmarks/v1/evidence/count-054/A7/4/worker.log b/benchmarks/v1/evidence/count-054/A7/4/worker.log
new file mode 100644
index 0000000..e69de29
diff --git a/benchmarks/v1/evidence/count-054/README.md b/benchmarks/v1/evidence/count-054/README.md
new file mode 100644
index 0000000..79e40b7
--- /dev/null
+++ b/benchmarks/v1/evidence/count-054/README.md
@@ -0,0 +1,32 @@
+# Current A7 count/exposure integration
+
+All five frozen insurance frequency folds pass the current CPU worker and exact
+fresh-process replay. Period counts and separate positive exposure vectors bind
+to the public Poisson recipe. Exposure enters likelihood once; the real packets
+use unit sample weights. Prediction requires exposure and returns period count
+means, preserving source policy IDs.
+
+```sh
+UV_CACHE_DIR=/tmp/openboost-research-uv-cache uv run --no-sync python -m benchmarks.v1.openboost_worker_smoke /tmp/openboost-count-054 --applications A7
+```
+
+Requires pinned source files in build/v1-data and the committed preprocessing
+freeze. The exporter validates all source, group split and encoded fold hashes.
+Four rounds, depth two, 32 bins, learning rate 0.1, patience three, one CPU thread;
+90-second worker and 30-second replay caps. Source export is outside fit timing.
+No test labels scored, profiler, concurrent regression suite, CUDA or memory cap.
+Host: Apple M4 Max, 51539607552 bytes physical RAM (same host as Sprint 052);
+Python reports x86_64. Peak memory was not measured.
+
+`summary.json` retains source/revision/dirty state, dataset and packet identities,
+commands, environment and all outcomes. Each fold retains raw model, predictions,
+replay, training metadata, process record and log. All source/output hashes match.
+`verification.json` retains independently recomputed mean Poisson NLL including
+log-factorial terms, math.fsum checks and exact source IDs. The maximum absolute
+NLL discrepancy is approximately 2.4e-14 across different reduction/exp-log paths,
+consistent with floating-point rounding. An initial rtol=1e-13 check failed on
+fold two; retained final checks use rtol=1e-12 and atol=1e-14. Replay stays exact.
+
+These are integration outcomes, not a complete search, calibrated quality
+acceptance or comparative performance claim. Weighted synthetic tests separately
+verify sample weights and exposure are applied independently.
diff --git a/benchmarks/v1/evidence/count-054/summary.json b/benchmarks/v1/evidence/count-054/summary.json
new file mode 100644
index 0000000..ac6c26d
--- /dev/null
+++ b/benchmarks/v1/evidence/count-054/summary.json
@@ -0,0 +1,561 @@
+{
+  "scope": "Current A1/A2/A3/A5/A6/A7/A11 real-data validation plumbing only; four rounds, no test scores, quality or performance claim",
+  "revision": "003fdac127ed782c77e2766f2082a3d33c665077",
+  "dirty": true,
+  "argv": [
+    "/Users/jiaruixu/work_space/openboost/.venv/bin/python3",
+    "-m",
+    "benchmarks.v1.openboost_worker_smoke",
+    "/private/tmp/openboost-count-054",
+    "--applications",
+    "A7"
+  ],
+  "python": "3.12.12",
+  "os": "macOS-26.3-x86_64-i386-64bit",
+  "machine": "x86_64",
+  "cpu_count": 16,
+  "device": "cpu",
+  "gpu": null,
+  "threads": 1,
+  "memory_cap": null,
+  "packages": {
+    "numpy": "2.3.5",
+    "openboost": "1.0.0rc1"
+  },
+  "sources": {
+    "src/openboost/__init__.py": "b0a76b68a1824abef45bf30d3d094dce7de2ec9deef3d5cbe862a6b3f6038788",
+    "src/openboost/artifacts.py": "fc5232f29d2b9d55676035bbd5bb9fe726babba5fcdfbd84ff18133de3e9a817",
+    "src/openboost/binning.py": "e77bdbaf42121ba5dde8ec681371bd0c7d1e31fada19618d796279cc0b322a7d",
+    "src/openboost/composition.py": "a52dc4d09582031441dbf2c4a3ed889851783a2f4fece50506fa5e0e0501ccec",
+    "src/openboost/data.py": "93b4eb6487e7fb21c960d19906c7a49b3df9fa28b2a2bacdc8ea6437889f2343",
+    "src/openboost/leaves.py": "3bff04ff4b1eff1a1d58f6ce5e29f15391e8b2e9a80a696c6fee988d1be95b75",
+    "src/openboost/multioutput.py": "98c192719bb9ee787ec385a2d219a2f52aacebc161cc9fc1275006f49daad76d",
+    "src/openboost/objectives.py": "fcf847bda2917354093e03ce42bd22e0fd85e391185d3141318cb7ccecd721f7",
+    "src/openboost/ops.py": "c82134d466e6ebf660582d9895ee268b7c00fa999ec8ccdbc0c70634d429599f",
+    "src/openboost/outputs.py": "f10f3a68b4513cb93c222cd1e39ca238c686c903daa8c8035c417b5916365115",
+    "src/openboost/ranking.py": "beddf8fb6eeecbed54acdb55147bc251392a6f9fb7b46304e5d80d1d0ee642b3",
+    "src/openboost/recipes.py": "4fc1cd9971cde4bad95922c0f4e241d169a8a9fa08730070f02033c3405d265c",
+    "src/openboost/results.py": "b33bb8c658678bf5319a1e76b9a420112debea4f26361acad166f6f1e641585d",
+    "src/openboost/runs.py": "5a00eead8d6d4d6ddc99a646a0131f04918c619c5061b647ec4fc3c386459dc4",
+    "src/openboost/runtime.py": "8962e0204d867a1b700ed15e92b155cd6144927b651a79b8083634a0c315ad2d",
+    "src/openboost/stats.py": "4edbeba39e727f60213f04b5d3592bc82fb02cb1ebd9603ca9f8cee66b932cc9",
+    "src/openboost/stopping.py": "2374873a01dba8d7b359cd484337de228ca6c805d6a43b23bcf51598416746a1",
+    "src/openboost/survival.py": "cb168551a5aa026d6c501f4370c7ddc998d3cc7269ce6f17d93258ceadef89e9",
+    "src/openboost/tree.py": "60dd8cbc56d78913e868a3788bd08c91e858febc4f4c0534831077df640d7581",
+    "benchmarks/v1/openboost_worker.py": "6b34bfaf017e6817f87b29aa748150ed54b2d8fe06c92e17eccb7312ebdf6de5",
+    "benchmarks/v1/openboost_predict.py": "ca01fc530e46fd3446711138858f57e29760ba99869c6bddbda4fa25c99758c6",
+    "benchmarks/v1/openboost_worker_smoke.py": "dfdd1955d0f6ed4d8b4fcca33e44ebcb92c38ac51c2663c60e69d254b0dc4080",
+    "benchmarks/v1/worker_data.py": "6d2b09303abaace59b22aebd666317b2da57a0efd37f8cbfb138766a933749f1",
+    "benchmarks/v1/preprocessing.py": "62561effccd6856bc404339dddbc8c2daa3a1d0b011be59edd8fd3435802bc30",
+    "benchmarks/v1/process_runner.py": "5ccc984919d2932974f89cfc16691e79dd0e2c35a5da145993d5c9b1a0cd8eab"
+  },
+  "data": {
+    "A7": {
+      "application": "A7",
+      "dataset": "insurance",
+      "folds": [
+        {
+          "seed": 0,
+          "artifacts": {
+            "worker-input": {
+              "path": "0/worker-input.npz",
+              "sha256": "0ae4d0a206449d6df81dbc470fd1654d8f33e72f0f5fbd52bec89c27257671ac"
+            },
+            "train-rows": {
+              "path": "0/train-rows.npz",
+              "sha256": "ae02673c24506bbcab8b6aa80affd2631d19bd11e5dc14065b23f987beecac59"
+            },
+            "validation": {
+              "path": "0/validation.npz",
+              "sha256": "72af1635e878c0524add52ee4c8d8f70cfb0c4705c10f75eb8d0d66a4fd35c56"
+            },
+            "test-features": {
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diff --git a/benchmarks/v1/evidence/count-054/verification.json b/benchmarks/v1/evidence/count-054/verification.json
new file mode 100644
index 0000000..b7ac558
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diff --git a/benchmarks/v1/evidence/histogram-052/A3/0/execution.json b/benchmarks/v1/evidence/histogram-052/A3/0/execution.json
new file mode 100644
index 0000000..b598e1a
--- /dev/null
+++ b/benchmarks/v1/evidence/histogram-052/A3/0/execution.json
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diff --git a/benchmarks/v1/evidence/histogram-052/A3/0/model.bin b/benchmarks/v1/evidence/histogram-052/A3/0/model.bin
new file mode 100644
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diff --git a/benchmarks/v1/evidence/histogram-052/A3/0/predictions.npz b/benchmarks/v1/evidence/histogram-052/A3/0/predictions.npz
new file mode 100644
index 0000000..e3e023c
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diff --git a/benchmarks/v1/evidence/histogram-052/A3/0/replay.npz b/benchmarks/v1/evidence/histogram-052/A3/0/replay.npz
new file mode 100644
index 0000000..e3e023c
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diff --git a/benchmarks/v1/evidence/histogram-052/A3/0/training.json b/benchmarks/v1/evidence/histogram-052/A3/0/training.json
new file mode 100644
index 0000000..1e1017b
--- /dev/null
+++ b/benchmarks/v1/evidence/histogram-052/A3/0/training.json
@@ -0,0 +1,27 @@
+{
+  "selection": "best_validation",
+  "stop": {
+    "rounds": 4,
+    "patience": 3,
+    "min_delta": 0.0,
+    "reference_score": 1.4960895873593474,
+    "last_score": 1.4960895873593474,
+    "completed_rounds": 4,
+    "stale_rounds": 0,
+    "reason": "budget"
+  },
+  "accepted_commits": 4,
+  "selected_model_identity": "277e32543ddf98ed52048c6dc0b95dac6ad4f3838cee6e9042d668c4f0ab5f5c",
+  "best_validation_score": 1.4960895873593474,
+  "output": "class_probabilities",
+  "class_order": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6
+  ],
+  "selection_metric": "logloss"
+}
diff --git a/benchmarks/v1/evidence/histogram-052/A3/0/worker.log b/benchmarks/v1/evidence/histogram-052/A3/0/worker.log
new file mode 100644
index 0000000..e69de29
diff --git a/benchmarks/v1/evidence/histogram-052/A3/1/execution.json b/benchmarks/v1/evidence/histogram-052/A3/1/execution.json
new file mode 100644
index 0000000..fa9a3a3
--- /dev/null
+++ b/benchmarks/v1/evidence/histogram-052/A3/1/execution.json
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index 0000000..ec70052
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diff --git a/benchmarks/v1/evidence/histogram-052/A3/2/execution.json b/benchmarks/v1/evidence/histogram-052/A3/2/execution.json
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+}
diff --git a/benchmarks/v1/evidence/histogram-052/A3/2/model.bin b/benchmarks/v1/evidence/histogram-052/A3/2/model.bin
new file mode 100644
index 0000000..047f33e
--- /dev/null
+++ b/benchmarks/v1/evidence/histogram-052/A3/2/model.bin
@@ -0,0 +1 @@
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diff --git a/benchmarks/v1/evidence/histogram-052/A3/4/predictions.npz b/benchmarks/v1/evidence/histogram-052/A3/4/predictions.npz
new file mode 100644
index 0000000..22329d5
Binary files /dev/null and b/benchmarks/v1/evidence/histogram-052/A3/4/predictions.npz differ
diff --git a/benchmarks/v1/evidence/histogram-052/A3/4/replay.npz b/benchmarks/v1/evidence/histogram-052/A3/4/replay.npz
new file mode 100644
index 0000000..22329d5
Binary files /dev/null and b/benchmarks/v1/evidence/histogram-052/A3/4/replay.npz differ
diff --git a/benchmarks/v1/evidence/histogram-052/A3/4/training.json b/benchmarks/v1/evidence/histogram-052/A3/4/training.json
new file mode 100644
index 0000000..028aa72
--- /dev/null
+++ b/benchmarks/v1/evidence/histogram-052/A3/4/training.json
@@ -0,0 +1,27 @@
+{
+  "selection": "best_validation",
+  "stop": {
+    "rounds": 4,
+    "patience": 3,
+    "min_delta": 0.0,
+    "reference_score": 1.4955556215694166,
+    "last_score": 1.4955556215694166,
+    "completed_rounds": 4,
+    "stale_rounds": 0,
+    "reason": "budget"
+  },
+  "accepted_commits": 4,
+  "selected_model_identity": "70e82e75bc80b3975da9da29dd4a283731d5dc5db97d28b84c8d9807c25b31de",
+  "best_validation_score": 1.4955556215694166,
+  "output": "class_probabilities",
+  "class_order": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6
+  ],
+  "selection_metric": "logloss"
+}
diff --git a/benchmarks/v1/evidence/histogram-052/A3/4/worker.log b/benchmarks/v1/evidence/histogram-052/A3/4/worker.log
new file mode 100644
index 0000000..e69de29
diff --git a/benchmarks/v1/evidence/histogram-052/README.md b/benchmarks/v1/evidence/histogram-052/README.md
new file mode 100644
index 0000000..eebe303
--- /dev/null
+++ b/benchmarks/v1/evidence/histogram-052/README.md
@@ -0,0 +1,29 @@
+# Full Covertype replay after histogram gather reuse
+
+All five unchanged full-data jobs pass their original 90-second process cap:
+folds 0–4 take 70.3, 69.9, 68.1, 67.8 and 67.9 seconds respectively. Each saved
+model reproduces validation probabilities and source row IDs exactly in a fresh
+process under the 30-second replay cap. Fold zero's model bytes and prediction
+arrays also exactly match Sprint 051. This is bounded validation integration,
+not a quality search, matched-quality speed comparison or A3 acceptance.
+
+`summary.json` retains revision/dirty state, source hashes, jobs, packet hashes,
+commands, environment and execution outcomes. `input-summary.json` retains the
+original Sprint 049 source/version/split/packet provenance and failed outcomes;
+its bytes match the prior-summary digest. Per-fold raw models, predictions,
+replays, training metadata, execution records and logs are retained.
+`verification.json` records hash/equality checks and supplemental CPU/RAM metadata.
+
+Regenerate the frozen packets with the Sprint 049 export procedure if absent,
+then rerun from that generated summary into a fresh output directory:
+
+```sh
+UV_CACHE_DIR=/tmp/openboost-research-uv-cache uv run --no-sync python -m benchmarks.v1.replay_current_packets /tmp/openboost-covertype-049/summary.json /tmp/openboost-histogram-052
+```
+
+Four rounds, depth two, 32 bins, seven classes and one CPU thread remain unchanged.
+Source export is outside worker timing. No profiler or concurrent regression
+suite ran during these fits. No memory cap, measured peak memory, CUDA or test
+label scoring. The implementation retains a contiguous selected-statistic buffer;
+transpose construction can transiently hold two rows-by-fields arrays. No memory
+improvement or stable speed ratio is inferred from these local reruns.
diff --git a/benchmarks/v1/evidence/histogram-052/input-summary.json b/benchmarks/v1/evidence/histogram-052/input-summary.json
new file mode 100644
index 0000000..54680af
--- /dev/null
+++ b/benchmarks/v1/evidence/histogram-052/input-summary.json
@@ -0,0 +1,371 @@
+{
+  "scope": "Current A1/A2/A3/A6/A11 real-data validation plumbing only; four rounds, no test scores, quality or performance claim",
+  "revision": "0def101a0f91e52a654aa4d3d734144d0a98411f",
+  "dirty": true,
+  "argv": [
+    "/Users/jiaruixu/work_space/openboost/.venv/bin/python3",
+    "-m",
+    "benchmarks.v1.openboost_worker_smoke",
+    "/private/tmp/openboost-covertype-049",
+    "--applications",
+    "A3"
+  ],
+  "python": "3.12.12",
+  "os": "macOS-26.3-x86_64-i386-64bit",
+  "machine": "x86_64",
+  "cpu_count": 16,
+  "device": "cpu",
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diff --git a/benchmarks/v1/evidence/histogram-052/verification.json b/benchmarks/v1/evidence/histogram-052/verification.json
new file mode 100644
index 0000000..c2b0009
--- /dev/null
+++ b/benchmarks/v1/evidence/histogram-052/verification.json
@@ -0,0 +1,9 @@
+{
+  "all_source_and_artifact_hashes_match": true,
+  "fold_zero_model_bytes_equal_sprint_051": true,
+  "fold_zero_prediction_arrays_equal_sprint_051": true,
+  "all_fresh_replays_exact": true,
+  "cpu": "Apple M4 Max",
+  "physical_ram_bytes": 51539607552,
+  "metadata_note": "CPU and RAM read with sysctl; Python reports x86_64 as recorded in summary; peak memory not measured"
+}
diff --git a/benchmarks/v1/evidence/paid-events-057/README.md b/benchmarks/v1/evidence/paid-events-057/README.md
new file mode 100644
index 0000000..dd3c15b
--- /dev/null
+++ b/benchmarks/v1/evidence/paid-events-057/README.md
@@ -0,0 +1,23 @@
+# Matched positive-payment input binding
+
+All five A9 training/validation folds bind to matched positive-payment counts,
+totals and exposure. Independent claim iteration exactly reproduces the source
+reader's bincount aggregates. Frozen source array hashes, original worker packet
+hashes and training-ID hashes match. Source policy IDs map in exact frozen order;
+eligibility, annualized targets and exposure weights are checked before output.
+
+```sh
+UV_CACHE_DIR=/tmp/openboost-research-uv-cache uv run --no-sync python -m benchmarks.v1.paid_event_data /tmp/openboost-aggregate-056/summary.json /tmp/openboost-paid-events-057
+```
+
+Regenerate Sprint 056 packets first if absent. The parent summary digest in
+manifest.json matches the committed aggregate-056/summary.json. Pinned insurance
+files are read from build/v1-data. Large generated feature packets are not copied;
+the manifest retains their hashes, row/event counts and reconstruction command.
+It also records revision/dirty state, source implementation hashes, source audit,
+Python/NumPy/OS and input identities. No performance or memory measurement.
+
+Only training/validation feature and matched aggregate roles are emitted. No
+test label packet is opened; the preparer reads the full underlying source.
+This is input binding, not a composition fit,
+independent author result, quality/search acceptance or an OS access boundary.
diff --git a/benchmarks/v1/evidence/paid-events-057/manifest.json b/benchmarks/v1/evidence/paid-events-057/manifest.json
new file mode 100644
index 0000000..b91bb81
--- /dev/null
+++ b/benchmarks/v1/evidence/paid-events-057/manifest.json
@@ -0,0 +1,113 @@
+{
+  "scope": "Matched paid-event binding only; no composition fit or test scores",
+  "revision": "a041f1b1f3ad37c8dc78b6cfc3a63bb79ee7af19",
+  "dirty": true,
+  "argv": [
+    "/Users/jiaruixu/work_space/openboost/.venv/bin/python3",
+    "-m",
+    "benchmarks.v1.paid_event_data",
+    "/private/tmp/openboost-aggregate-056/summary.json",
+    "/private/tmp/openboost-paid-events-057"
+  ],
+  "python": "3.12.12",
+  "os": "macOS-26.3-x86_64-i386-64bit",
+  "numpy": "2.3.5",
+  "previous_summary_sha256": "44c953a83f5f3119f46407bb0cabf21bddbf45f5d5bb017a74200de8d1586646",
+  "source_manifest_sha256": "ff95579dd1b95595e87027f38afd512cd986b3182f08f50a7a2139ffb63f291f",
+  "sources": {
+    "benchmarks/v1/paid_event_data.py": "e8c807f7f7f484e8f9132836a994e05c1238851f1cb6a42890ca3cd1fc4ecaa8",
+    "benchmarks/v1/real_data.py": "e796be3be35202fe688235b40798ba98793605adcdcd6194f2729fb869eac0b9"
+  },
+  "source_audit": {
+    "frequency_rows": 678013,
+    "severity_rows": 26639,
+    "nonpositive_claims": 0,
+    "orphan_claims": 195,
+    "retained_claims": 26444,
+    "positive_count_without_payment": 9116,
+    "zero_count_with_payment": 0,
+    "aggregate_retained": 668897
+  },
+  "cells": [
+    {
+      "fold": 0,
+      "status": "pass",
+      "path": "fold-0.npz",
+      "sha256": "0c992d9e5c7c77191f4253c3d762f938ce8ec8020010aabb052d6a2ba460584d",
+      "input_sha256": "1e3cd8d7e7bd157745bff7db14534bd1fbcecff3abef8f4341fa1f58a9583f0f",
+      "train_ids_sha256": "89ca61cb3b97a660065989fc29d4083af85a8b78081ff71dba6f316710421bbb",
+      "rows": {
+        "train": 401236,
+        "validation": 133882
+      },
+      "paid_events": {
+        "train": 15935,
+        "validation": 5253
+      }
+    },
+    {
+      "fold": 1,
+      "status": "pass",
+      "path": "fold-1.npz",
+      "sha256": "fe912bb90a41e2ad9722949399d6fb8056120e254bb09e900f0629ddb89ffd24",
+      "input_sha256": "e8c4d49a1008615af63446d04a049fadb9eb2c65243942defc4765272d1e8dac",
+      "train_ids_sha256": "34d0dd6b71e9c4c95461c3eb03fd32da250352b89fc9c113f9c44ff6b9f2f8b9",
+      "rows": {
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+      "paid_events": {
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+    },
+    {
+      "fold": 2,
+      "status": "pass",
+      "path": "fold-2.npz",
+      "sha256": "e2be0ee857dbe10cc36d12721e5f213df28750c1e861a66ea03961e98bcc27ad",
+      "input_sha256": "c2a2c40926e231d1e596f7e4c662219d923e6f926a96df27b4717abd7445f420",
+      "train_ids_sha256": "605ce72c18c2ab8b1b00045782ae01f5a7f22bdb38d4ac24074715e39628a3ab",
+      "rows": {
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+      "paid_events": {
+        "train": 16097,
+        "validation": 5156
+      }
+    },
+    {
+      "fold": 3,
+      "status": "pass",
+      "path": "fold-3.npz",
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+}
diff --git a/benchmarks/v1/evidence/profile-050/README.md b/benchmarks/v1/evidence/profile-050/README.md
new file mode 100644
index 0000000..e80ffc1
--- /dev/null
+++ b/benchmarks/v1/evidence/profile-050/README.md
@@ -0,0 +1,23 @@
+# Bounded full-input Covertype profile
+
+This is a deliberately interrupted instrumented diagnostic, not a successful
+training run or a speed comparison. The full Sprint 049 fold-zero job was run
+for 60 seconds with a 90-second outer cap and one thread. Exit 124 is expected.
+No core/recipe changes or reduced dataset were used.
+
+`manifest.json` records exact command, job/input hashes, source hashes and outer
+execution outcome. `profile.json` contains the top 100 cumulative call records;
+`profile.txt` is a readable view and `profile.pstats` the trusted local binary
+profile. `stacks.txt` contains periodic Python stack traces. Cumulative call times
+overlap. cProfile overhead and an initial overlapping local regression run mean
+these timings are diagnostic, not fair performance measurements.
+
+To reproduce, regenerate the Sprint 049 packets if needed, then invoke the
+recorded `profile_worker.py JOB --seconds 60` command in a fresh output directory
+through `process_runner.execute` with timeout_s=90 and threads=1. The runner must
+retain its hard cap because Python alarms can be delayed in native code. Source
+export time is outside the timed worker. No test labels were scored.
+
+The profile prioritizes histogram aggregation and repeated per-candidate row
+hashing. Prediction-time transforms are present but are not the leading measured
+path. See Sprint 050 for the next exact-semantics optimization.
diff --git a/benchmarks/v1/evidence/profile-050/execution.json b/benchmarks/v1/evidence/profile-050/execution.json
new file mode 100644
index 0000000..9b82e3b
--- /dev/null
+++ b/benchmarks/v1/evidence/profile-050/execution.json
@@ -0,0 +1,22 @@
+{
+  "artifacts": {
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+    "profile.pstats": "cb8599a012537eab59b75256f6e2587d54dc556c26b47e89c13c8a9fd3163093",
+    "profile.txt": "a24170558d579ba07ee6003182d914999e057f5aa73e114bf69545b3912f493d",
+    "stacks.txt": "62f5c52374ead85eb9468f2a3858cc3be277fa1cfd1c2e410728405c277c4863",
+    "worker.log": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855"
+  },
+  "command": [
+    "/Users/jiaruixu/work_space/openboost/.venv/bin/python3",
+    "/Users/jiaruixu/work_space/openboost/benchmarks/v1/profile_worker.py",
+    "/tmp/openboost-covertype-049/A3/0/job.json",
+    "--seconds",
+    "60"
+  ],
+  "exit_code": 124,
+  "reason": "worker exited nonzero",
+  "status": "error",
+  "threads": 1,
+  "timeout_s": 90,
+  "wall_s": 60.219372749998
+}
diff --git a/benchmarks/v1/evidence/profile-050/manifest.json b/benchmarks/v1/evidence/profile-050/manifest.json
new file mode 100644
index 0000000..fad762a
--- /dev/null
+++ b/benchmarks/v1/evidence/profile-050/manifest.json
@@ -0,0 +1,70 @@
+{
+  "command": [
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+    "/Users/jiaruixu/work_space/openboost/benchmarks/v1/profile_worker.py",
+    "/tmp/openboost-covertype-049/A3/0/job.json",
+    "--seconds",
+    "60"
+  ],
+  "timeout_s": 90,
+  "threads": 1,
+  "status": "error",
+  "exit_code": 124,
+  "reason": "worker exited nonzero",
+  "wall_s": 60.219372749998,
+  "artifacts": {
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+    "stacks.txt": "62f5c52374ead85eb9468f2a3858cc3be277fa1cfd1c2e410728405c277c4863",
+    "profile.json": "6e6eb241ebb6b23e2e1e12161903ca52055367f5fd05f6cbec2d1e8c951dda8c",
+    "profile.txt": "a24170558d579ba07ee6003182d914999e057f5aa73e114bf69545b3912f493d"
+  },
+  "job": {
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+    "classes": 7
+  },
+  "job_sha256": "af893941106b23a112a930cfc5a4b3c968c8f8d68a889cbcbe9ff89836cb5437",
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+  "revision": "078ca618604ad38e01a2913bf2cffe0fb8f1c593",
+  "dirty": true,
+  "python": "3.12.12",
+  "os": "macOS-26.3-x86_64-i386-64bit",
+  "sources": {
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+    "benchmarks/v1/profile_worker.py": "fd070e691537ca093530b81b495792f83b619aa14df073c94bed6656a12d3218",
+    "benchmarks/v1/openboost_worker.py": "d53f4796353430002ca2231de1852c139492fd7de1c7e4936374da1befebd33d",
+    "benchmarks/v1/openboost_predict.py": "b0c21aca3046eb3ad40f52f4c845dc159872d10261a0afca2d1cd01357354d04",
+    "benchmarks/v1/process_runner.py": "5ccc984919d2932974f89cfc16691e79dd0e2c35a5da145993d5c9b1a0cd8eab"
+  }
+}
diff --git a/benchmarks/v1/evidence/profile-050/profile.json b/benchmarks/v1/evidence/profile-050/profile.json
new file mode 100644
index 0000000..ebb3008
--- /dev/null
+++ b/benchmarks/v1/evidence/profile-050/profile.json
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+{
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diff --git a/benchmarks/v1/evidence/profile-050/profile.txt b/benchmarks/v1/evidence/profile-050/profile.txt
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diff --git a/benchmarks/v1/evidence/quantile-053/A5/4/predictions.npz b/benchmarks/v1/evidence/quantile-053/A5/4/predictions.npz
new file mode 100644
index 0000000..39f472e
Binary files /dev/null and b/benchmarks/v1/evidence/quantile-053/A5/4/predictions.npz differ
diff --git a/benchmarks/v1/evidence/quantile-053/A5/4/replay.npz b/benchmarks/v1/evidence/quantile-053/A5/4/replay.npz
new file mode 100644
index 0000000..39f472e
Binary files /dev/null and b/benchmarks/v1/evidence/quantile-053/A5/4/replay.npz differ
diff --git a/benchmarks/v1/evidence/quantile-053/A5/4/training.json b/benchmarks/v1/evidence/quantile-053/A5/4/training.json
new file mode 100644
index 0000000..31f8b3d
--- /dev/null
+++ b/benchmarks/v1/evidence/quantile-053/A5/4/training.json
@@ -0,0 +1,64 @@
+{
+  "selection": "best_validation",
+  "output": "quantiles",
+  "quantiles": [
+    0.1,
+    0.5,
+    0.9
+  ],
+  "quantile_runs": [
+    {
+      "q": 0.1,
+      "stop": {
+        "rounds": 4,
+        "patience": 3,
+        "min_delta": 0.0,
+        "reference_score": 25.97250694063927,
+        "last_score": 25.97250694063927,
+        "completed_rounds": 4,
+        "stale_rounds": 0,
+        "reason": "budget"
+      },
+      "accepted_commits": 4,
+      "selected_model_identity": "46f4db4fd7879c01dcc786ba3665706e054205f9727047b88eba659a8ef3cad2",
+      "best_validation_score": 25.97250694063927,
+      "selected_validation_pinball": 25.97250694063927
+    },
+    {
+      "q": 0.5,
+      "stop": {
+        "rounds": 4,
+        "patience": 3,
+        "min_delta": 0.0,
+        "reference_score": 90.80059272260272,
+        "last_score": 90.80059272260272,
+        "completed_rounds": 4,
+        "stale_rounds": 0,
+        "reason": "budget"
+      },
+      "accepted_commits": 4,
+      "selected_model_identity": "e8043b16ec964f260382c0b64c873f971b06f34db709f8595276e301657cbced",
+      "best_validation_score": 90.80059272260272,
+      "selected_validation_pinball": 90.80059272260272
+    },
+    {
+      "q": 0.9,
+      "stop": {
+        "rounds": 4,
+        "patience": 3,
+        "min_delta": 0.0,
+        "reference_score": 55.61411142694065,
+        "last_score": 55.61411142694065,
+        "completed_rounds": 4,
+        "stale_rounds": 0,
+        "reason": "budget"
+      },
+      "accepted_commits": 4,
+      "selected_model_identity": "b3a8202c86529a5f12ae430b91a81da31d340474ec0a4dd0850896c1f22edba9",
+      "best_validation_score": 55.61411142694065,
+      "selected_validation_pinball": 55.61411142694065
+    }
+  ],
+  "selection_metric": "independent_weighted_pinball",
+  "crossing_rows": 0
+}
diff --git a/benchmarks/v1/evidence/quantile-053/A5/4/worker.log b/benchmarks/v1/evidence/quantile-053/A5/4/worker.log
new file mode 100644
index 0000000..e69de29
diff --git a/benchmarks/v1/evidence/quantile-053/README.md b/benchmarks/v1/evidence/quantile-053/README.md
new file mode 100644
index 0000000..aa19000
--- /dev/null
+++ b/benchmarks/v1/evidence/quantile-053/README.md
@@ -0,0 +1,29 @@
+# Current A5 quantile integration on frozen Bike origins
+
+All five frozen calendar-only Bike rolling origins pass the current OpenBoost
+CPU worker and exact fresh-process inference. Each job composes three independent
+scalar quantile recipes at 0.1, 0.5 and 0.9, with four rounds, depth two, 32 bins,
+learning rate 0.1 and patience three. Selection and stopping remain per quantile.
+
+```sh
+UV_CACHE_DIR=/tmp/openboost-research-uv-cache uv run --no-sync python -m benchmarks.v1.openboost_worker_smoke /tmp/openboost-quantile-053 --applications A5
+```
+
+Requires the pinned archive at /tmp/openboost-v1-bike.zip and committed preprocessing
+freeze. The exporter checks source hashes, calendar-only features, train-fitted
+encoding and whole-date chronological prefixes. Later origin rows are unused.
+The worker receives training and validation only; no test labels were scored.
+
+`summary.json` retains revision/dirty state, source hashes, source/split/packet
+identities, exact jobs/commands, environment, stopping and output metadata. Every
+fold retains its raw model, predictions, replay, training record, process record
+and log. `verification.json` records independently recomputed selected validation
+pinball scores and exact source-row alignment. All source/output hashes match.
+Supplemental host metadata is Apple M4 Max, 51539607552 bytes physical RAM
+(the same host as Sprint 052); Python reports x86_64. One process thread, 90-second
+fit cap, 30-second replay cap; no memory cap, peak-memory measurement or CUDA.
+
+No observed crossings in these short runs does not imply a noncrossing guarantee.
+No sorting is applied. This is validation plumbing, not a 16-trial search,
+calibration/quality acceptance or performance comparison. Source export is outside
+fit timing, and a small focused test ran during the integration invocation.
diff --git a/benchmarks/v1/evidence/quantile-053/summary.json b/benchmarks/v1/evidence/quantile-053/summary.json
new file mode 100644
index 0000000..0636e04
--- /dev/null
+++ b/benchmarks/v1/evidence/quantile-053/summary.json
@@ -0,0 +1,766 @@
+{
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diff --git a/benchmarks/v1/evidence/quantile-053/verification.json b/benchmarks/v1/evidence/quantile-053/verification.json
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index 0000000..53ebbf3
--- /dev/null
+++ b/benchmarks/v1/evidence/quantile-053/verification.json
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diff --git a/benchmarks/v1/evidence/row-hash-051/README.md b/benchmarks/v1/evidence/row-hash-051/README.md
new file mode 100644
index 0000000..6e9b23c
--- /dev/null
+++ b/benchmarks/v1/evidence/row-hash-051/README.md
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+# Covertype fold zero after invariant row-hash hoisting
+
+The unchanged four-round/depth-two/32-bin/seven-class job completes in 87.4 seconds
+under the original 90-second cap. Fresh-process seven-class probabilities and row
+IDs match exactly. This is one bounded rerun, not a comparative speed benchmark
+or full five-fold A3 acceptance. The prior original run timed out at 90 seconds.
+
+`manifest.json` records the exact job/command, input hash, source/revision identity,
+process result and fresh-replay command/hash. Raw model, validation predictions,
+replay, training metadata and process records/log are retained. Regenerate the
+Sprint 049 packets if needed, then run the recorded command through process_runner
+with timeout_s=90 and threads=1 in a fresh output directory. Replay has a 30-second
+cap. Source export time is outside this worker measurement.
+
+Only candidate row-digest hoisting changed. No profiling or concurrent regression
+run was active during this fit. No memory cap or CUDA. A single result near the
+cap does not establish reproducibility, quality parity or a stable speed ratio.
diff --git a/benchmarks/v1/evidence/row-hash-051/execution.json b/benchmarks/v1/evidence/row-hash-051/execution.json
new file mode 100644
index 0000000..72a07a5
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diff --git a/benchmarks/v1/evidence/row-hash-051/manifest.json b/benchmarks/v1/evidence/row-hash-051/manifest.json
new file mode 100644
index 0000000..35a6815
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+++ b/benchmarks/v1/evidence/row-hash-051/manifest.json
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+{
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+}
diff --git a/benchmarks/v1/evidence/row-hash-051/model.bin b/benchmarks/v1/evidence/row-hash-051/model.bin
new file mode 100644
index 0000000..1a306f3
--- /dev/null
+++ b/benchmarks/v1/evidence/row-hash-051/model.bin
@@ -0,0 +1 @@
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index 0000000..1e1017b
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new file mode 100644
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+  "selected_model_identity": "fa6ec9b84c3c47568710f5962e08842a112928b0cbaa256d461dd4208635eabc",
+  "best_validation_score": 8.53698175155106,
+  "output": "positive_claim_mean",
+  "selection_metric": "weighted_gamma_objective",
+  "target_units": "positive_claim_amount",
+  "raw_units": "log_mean",
+  "selected_validation_gamma_objective": 8.53698175155106
+}
diff --git a/benchmarks/v1/evidence/severity-055/A8/4/worker.log b/benchmarks/v1/evidence/severity-055/A8/4/worker.log
new file mode 100644
index 0000000..e69de29
diff --git a/benchmarks/v1/evidence/severity-055/README.md b/benchmarks/v1/evidence/severity-055/README.md
new file mode 100644
index 0000000..77bac39
--- /dev/null
+++ b/benchmarks/v1/evidence/severity-055/README.md
@@ -0,0 +1,29 @@
+# Current A8 positive-claim severity integration
+
+All five frozen policy-grouped severity folds pass the current CPU worker and
+exact fresh-process replay. Targets remain individual eligible positive joined
+claims, with unit claim weights in the real packets. Output is positive claim
+mean; no exposure or policy-average substitution is applied.
+
+```sh
+UV_CACHE_DIR=/tmp/openboost-research-uv-cache uv run --no-sync python -m benchmarks.v1.openboost_worker_smoke /tmp/openboost-severity-055 --applications A8
+```
+
+Requires pinned insurance source files in build/v1-data and the committed
+preprocessing freeze. The exporter verifies source, grouped population, row and
+encoding hashes. Four rounds, depth two, 32 bins, learning rate 0.1, patience
+three and one CPU thread; 90-second fit/30-second replay caps. Source export is
+outside fit timing. No test labels scored, memory cap, profiler or concurrent
+regression suite. Host: Apple M4 Max, 51539607552 bytes RAM (same host as Sprint
+052); Python reports x86_64. No CUDA or measured peak memory.
+
+`summary.json` retains source/revision/dirty state, dataset/packet identities,
+commands, environment and results. Raw models, predictions, replay, training,
+execution records and logs are retained for every fold. All source/output hashes
+match. `verification.json` records independently recomputed Gamma objectives
+(y/mean+log(mean)), positive means and exact source IDs. Numerical comparisons
+use rtol=1e-12 and atol=1e-14; prediction replay is exact.
+
+These are bounded integration outcomes, not full quality searches, dispersion
+estimation, calibration or comparative performance results. Synthetic weighted
+checks separately verify original sample-weight semantics.
diff --git a/benchmarks/v1/evidence/severity-055/summary.json b/benchmarks/v1/evidence/severity-055/summary.json
new file mode 100644
index 0000000..ab0fde4
--- /dev/null
+++ b/benchmarks/v1/evidence/severity-055/summary.json
@@ -0,0 +1,556 @@
+{
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+    "--applications",
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diff --git a/benchmarks/v1/evidence/structured-060/4/predictions.npz b/benchmarks/v1/evidence/structured-060/4/predictions.npz
new file mode 100644
index 0000000..0d07079
Binary files /dev/null and b/benchmarks/v1/evidence/structured-060/4/predictions.npz differ
diff --git a/benchmarks/v1/evidence/structured-060/4/replay.npz b/benchmarks/v1/evidence/structured-060/4/replay.npz
new file mode 100644
index 0000000..0d07079
Binary files /dev/null and b/benchmarks/v1/evidence/structured-060/4/replay.npz differ
diff --git a/benchmarks/v1/evidence/structured-060/4/training.json b/benchmarks/v1/evidence/structured-060/4/training.json
new file mode 100644
index 0000000..b2f2287
--- /dev/null
+++ b/benchmarks/v1/evidence/structured-060/4/training.json
@@ -0,0 +1,20 @@
+{
+  "selection": "best_validation",
+  "stop": {
+    "rounds": 4,
+    "patience": 3,
+    "min_delta": 0.0,
+    "reference_score": 127.67160192185678,
+    "last_score": 127.67160192185678,
+    "completed_rounds": 4,
+    "stale_rounds": 0,
+    "reason": "budget"
+  },
+  "accepted_commits": 4,
+  "selected_model_identity": "5ff9c74bdb0e6db51ec400f1c99d7a84210e8907038e9746e7189edfea0513ba",
+  "best_validation_score": 127.67160192185678,
+  "output": "saturation_age28_mean",
+  "selection_metric": "weighted_half_squared_error",
+  "structure_units": "age_days_divided_by_28",
+  "selected_validation_half_squared_error": 127.67160192185676
+}
diff --git a/benchmarks/v1/evidence/structured-060/4/worker.log b/benchmarks/v1/evidence/structured-060/4/worker.log
new file mode 100644
index 0000000..e69de29
diff --git a/benchmarks/v1/evidence/structured-060/README.md b/benchmarks/v1/evidence/structured-060/README.md
new file mode 100644
index 0000000..43ea7ff
--- /dev/null
+++ b/benchmarks/v1/evidence/structured-060/README.md
@@ -0,0 +1,28 @@
+# Current A12 structured saturation integration
+
+All five frozen concrete folds pass the public Formula worker and exact fresh
+replay. Encoded composition predictors feed parameter trees; age in days divided
+by 28 is a separate positive structure role. Ordinary GBDT packets retain age as
+a feature. Dedicated formula-input packets remove that appended column only.
+
+```sh
+UV_CACHE_DIR=/tmp/openboost-research-uv-cache uv run --offline --no-sync --with xlrd python -m benchmarks.v1.openboost_worker_smoke /tmp/openboost-structured-060-retry --applications A12
+```
+
+The initial run without xlrd failed during source export before any fit. Its
+failure and the retry dependency version/CLI are retained in verification.json.
+The retry used cached xlrd in an ephemeral uv environment, without modifying
+project dependencies. Pinned concrete source and preprocessing remain unchanged.
+
+Four rounds, depth two, 32 bins, learning rate 0.1, patience three, public Formula
+backtracking defaults and one CPU thread; unchanged 90/30-second fit/replay caps.
+No test scoring, profiler, concurrent regression, memory cap or CUDA. Host: Apple
+M4 Max, 51539607552 bytes RAM (same host as Sprint 052); Python reports x86_64.
+Peak memory is unmeasured; export is outside fit timing.
+
+Summary retains revision/dirty state, source/data/packet hashes, commands,
+environment and results. Each fold retains raw model/predictions/replay/training,
+execution records and log. All hashes match. Independent softplus/saturation and
+weighted half-squared-error calculations match outputs/scores; age separation
+and source IDs match exactly. This is integration, not extrapolation or A12
+quality/search acceptance, baseline parity, or a performance claim.
diff --git a/benchmarks/v1/evidence/structured-060/summary.json b/benchmarks/v1/evidence/structured-060/summary.json
new file mode 100644
index 0000000..809db3b
--- /dev/null
+++ b/benchmarks/v1/evidence/structured-060/summary.json
@@ -0,0 +1,626 @@
+{
+  "scope": "Current A1/A2/A3/A5/A6/A7/A8/A9/A10/A11/A12 real-data validation plumbing only; four rounds, no test scores, quality or performance claim",
+  "revision": "3ff97c3d249271f6c8423b95562ccf03d773b676",
+  "dirty": true,
+  "argv": [
+    "/tmp/openboost-research-uv-cache/builds-v0/.tmp3iI6Gk/bin/python",
+    "-m",
+    "benchmarks.v1.openboost_worker_smoke",
+    "/private/tmp/openboost-structured-060-retry",
+    "--applications",
+    "A12"
+  ],
+  "python": "3.12.12",
+  "os": "macOS-26.3-x86_64-i386-64bit",
+  "machine": "x86_64",
+  "cpu_count": 16,
+  "device": "cpu",
+  "gpu": null,
+  "threads": 1,
+  "memory_cap": null,
+  "packages": {
+    "numpy": "2.3.5",
+    "openboost": "1.0.0rc1"
+  },
+  "sources": {
+    "src/openboost/__init__.py": "b0a76b68a1824abef45bf30d3d094dce7de2ec9deef3d5cbe862a6b3f6038788",
+    "src/openboost/artifacts.py": "fc5232f29d2b9d55676035bbd5bb9fe726babba5fcdfbd84ff18133de3e9a817",
+    "src/openboost/binning.py": "e77bdbaf42121ba5dde8ec681371bd0c7d1e31fada19618d796279cc0b322a7d",
+    "src/openboost/composition.py": "a52dc4d09582031441dbf2c4a3ed889851783a2f4fece50506fa5e0e0501ccec",
+    "src/openboost/data.py": "93b4eb6487e7fb21c960d19906c7a49b3df9fa28b2a2bacdc8ea6437889f2343",
+    "src/openboost/leaves.py": "3bff04ff4b1eff1a1d58f6ce5e29f15391e8b2e9a80a696c6fee988d1be95b75",
+    "src/openboost/multioutput.py": "98c192719bb9ee787ec385a2d219a2f52aacebc161cc9fc1275006f49daad76d",
+    "src/openboost/objectives.py": "fcf847bda2917354093e03ce42bd22e0fd85e391185d3141318cb7ccecd721f7",
+    "src/openboost/ops.py": "c82134d466e6ebf660582d9895ee268b7c00fa999ec8ccdbc0c70634d429599f",
+    "src/openboost/outputs.py": "f10f3a68b4513cb93c222cd1e39ca238c686c903daa8c8035c417b5916365115",
+    "src/openboost/ranking.py": "beddf8fb6eeecbed54acdb55147bc251392a6f9fb7b46304e5d80d1d0ee642b3",
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index 0000000..3faee65
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new file mode 100644
index 0000000..f6470da
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index 0000000..c290a7c
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new file mode 100644
index 0000000..0ba68f5
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diff --git a/benchmarks/v1/evidence/survival-059/3/predictions.npz b/benchmarks/v1/evidence/survival-059/3/predictions.npz
new file mode 100644
index 0000000..c3a96e3
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diff --git a/benchmarks/v1/evidence/survival-059/3/replay.npz b/benchmarks/v1/evidence/survival-059/3/replay.npz
new file mode 100644
index 0000000..c3a96e3
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diff --git a/benchmarks/v1/evidence/survival-059/3/training.json b/benchmarks/v1/evidence/survival-059/3/training.json
new file mode 100644
index 0000000..0e4b504
--- /dev/null
+++ b/benchmarks/v1/evidence/survival-059/3/training.json
@@ -0,0 +1,20 @@
+{
+  "selection": "best_validation",
+  "stop": {
+    "rounds": 4,
+    "patience": 3,
+    "min_delta": 0.0,
+    "reference_score": 5.437543155714646,
+    "last_score": 5.437543155714646,
+    "completed_rounds": 4,
+    "stale_rounds": 0,
+    "reason": "budget"
+  },
+  "accepted_commits": 4,
+  "selected_model_identity": "8d4a7492f58fdf71500d98ec5d94da276f940e5e4757d53e7a957410b19eb87c",
+  "best_validation_score": 5.437543155714646,
+  "output": "lognormal_location_scale",
+  "selection_metric": "weighted_censored_nll",
+  "sigma": 1.0,
+  "selected_validation_censored_nll": 5.437543155714646
+}
diff --git a/benchmarks/v1/evidence/survival-059/3/worker.log b/benchmarks/v1/evidence/survival-059/3/worker.log
new file mode 100644
index 0000000..e69de29
diff --git a/benchmarks/v1/evidence/survival-059/4/execution.json b/benchmarks/v1/evidence/survival-059/4/execution.json
new file mode 100644
index 0000000..8795f72
--- /dev/null
+++ b/benchmarks/v1/evidence/survival-059/4/execution.json
@@ -0,0 +1,19 @@
+{
+  "artifacts": {
+    "model.bin": "1839c453b9c1388d7e85f821e18ffabb3e485417b9c9e68f7b8bb6c095c52017",
+    "predictions.npz": "d2ddf282a5c847726c85dd936f28ada7174939982b45d1a1c158b94c3935293a",
+    "training.json": "aa63164b90e06b10354e14d7255ed45566f756c8a7d0f6f889540c70c0b583b6",
+    "worker.log": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855"
+  },
+  "command": [
+    "/Users/jiaruixu/work_space/openboost/.venv/bin/python3",
+    "/Users/jiaruixu/work_space/openboost/benchmarks/v1/openboost_worker.py",
+    "/private/tmp/openboost-survival-059/A10/4/job.json"
+  ],
+  "exit_code": 0,
+  "reason": "",
+  "status": "pass",
+  "threads": 1,
+  "timeout_s": 90,
+  "wall_s": 0.24594029098807368
+}
diff --git a/benchmarks/v1/evidence/survival-059/4/model.bin b/benchmarks/v1/evidence/survival-059/4/model.bin
new file mode 100644
index 0000000..d667fd0
--- /dev/null
+++ b/benchmarks/v1/evidence/survival-059/4/model.bin
@@ -0,0 +1 @@
+{"format": "openboost-evaluation-v1", "application": "A10", "output": "lognormal_location_scale", "model": {"format": "openboost-ensemble-v2", "feature_names": ["x0", "x1", "x2", "x3", "x4", "x5", "x6", "x7", "x8", "x9", "x10", "x11", "x12", "x13", "x14", "x15", "x16"], "base": [4.270689754721605], "terms": [{"kind": "tree", "learner": {"format": "openboost-tree-v3", "feature_names": ["x0", "x1", "x2", "x3", "x4", "x5", "x6", "x7", "x8", "x9", "x10", "x11", "x12", "x13", "x14", "x15", "x16"], "cuts": [[20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0], [2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 8.5, 9.0, 10.5, 11.0, 12.0, 13.0, 14.5, 18.0, 21.0, 28.0, 32.5], [38.0, 39.0, 41.5, 43.0, 46.5, 49.0, 49.5, 52.0, 54.0, 56.0, 58.0, 60.0, 60.5, 62.0, 63.0, 64.0, 65.5, 66.0, 67.0, 68.0, 69.0, 71.0], [], [], [], [0.0], [0.0], [0.0], [0.0], [], [0.0], [0.0], [], [0.0], [0.0], []], "categories": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], "feature": [0, 2, 8, -1, -1, -1, -1], "threshold": [3, 9, 0, -1, -1, -1, -1], "missing_left": [false, false, false, false, false, false, false], "left": [1, 3, 5, -1, -1, -1, -1], "right": [2, 4, 6, -1, -1, -1, -1], "value": [[0.04829745793020143], [-0.7239653980198609], [0.4435217508203625], [0.04447013958109991], [-1.0165820254693227], [0.6672879510012071], [-0.07914207619250511]]}, "mapping": [[1.0]], "coefficient": 0.1}, {"kind": "tree", "learner": {"format": "openboost-tree-v3", "feature_names": ["x0", "x1", "x2", "x3", "x4", "x5", "x6", "x7", "x8", "x9", "x10", "x11", "x12", "x13", "x14", "x15", "x16"], "cuts": [[20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0], [2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 8.5, 9.0, 10.5, 11.0, 12.0, 13.0, 14.5, 18.0, 21.0, 28.0, 32.5], [38.0, 39.0, 41.5, 43.0, 46.5, 49.0, 49.5, 52.0, 54.0, 56.0, 58.0, 60.0, 60.5, 62.0, 63.0, 64.0, 65.5, 66.0, 67.0, 68.0, 69.0, 71.0], [], [], [], [0.0], [0.0], [0.0], [0.0], [], [0.0], [0.0], [], [0.0], [0.0], []], "categories": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], "feature": [0, 2, 8, -1, -1, -1, -1], "threshold": [3, 9, 0, -1, -1, -1, -1], "missing_left": [false, false, false, false, false, false, false], "left": [1, 3, 5, -1, -1, -1, -1], "right": [2, 4, 6, -1, -1, -1, -1], "value": [[0.04303345377333415], [-0.6551240537745735], [0.40063267285038456], [0.0405485965422459], [-0.9200067330497369], [0.6030973548027836], [-0.07169341019791667]]}, "mapping": [[1.0]], "coefficient": 0.1}, {"kind": "tree", "learner": {"format": "openboost-tree-v3", "feature_names": ["x0", "x1", "x2", "x3", "x4", "x5", "x6", "x7", "x8", "x9", "x10", "x11", "x12", "x13", "x14", "x15", "x16"], "cuts": [[20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0], [2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 8.5, 9.0, 10.5, 11.0, 12.0, 13.0, 14.5, 18.0, 21.0, 28.0, 32.5], [38.0, 39.0, 41.5, 43.0, 46.5, 49.0, 49.5, 52.0, 54.0, 56.0, 58.0, 60.0, 60.5, 62.0, 63.0, 64.0, 65.5, 66.0, 67.0, 68.0, 69.0, 71.0], [], [], [], [0.0], [0.0], [0.0], [0.0], [], [0.0], [0.0], [], [0.0], [0.0], []], "categories": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], "feature": [0, 2, 0, -1, -1, -1, -1], "threshold": [3, 9, 6, -1, -1, -1, -1], "missing_left": [false, false, false, false, false, false, false], "left": [1, 3, 5, -1, -1, -1, -1], "right": [2, 4, 6, -1, -1, -1, -1], "value": [[0.038332658590237906], [-0.5928259879283654], [0.3618659109593453], [0.0369724790815509], [-0.8326060934100117], [0.23495834399506332], [1.0950510022676028]]}, "mapping": [[1.0]], "coefficient": 0.1}, {"kind": "tree", "learner": {"format": "openboost-tree-v3", "feature_names": ["x0", "x1", "x2", "x3", "x4", "x5", "x6", "x7", "x8", "x9", "x10", "x11", "x12", "x13", "x14", "x15", "x16"], "cuts": [[20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0], [2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 8.5, 9.0, 10.5, 11.0, 12.0, 13.0, 14.5, 18.0, 21.0, 28.0, 32.5], [38.0, 39.0, 41.5, 43.0, 46.5, 49.0, 49.5, 52.0, 54.0, 56.0, 58.0, 60.0, 60.5, 62.0, 63.0, 64.0, 65.5, 66.0, 67.0, 68.0, 69.0, 71.0], [], [], [], [0.0], [0.0], [0.0], [0.0], [], [0.0], [0.0], [], [0.0], [0.0], []], "categories": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], "feature": [0, 2, 8, -1, -1, -1, -1], "threshold": [3, 9, 0, -1, -1, -1, -1], "missing_left": [false, false, false, false, false, false, false], "left": [1, 3, 5, -1, -1, -1, -1], "right": [2, 4, 6, -1, -1, -1, -1], "value": [[0.03519999270582112], [-0.5364496945830091], [0.32840480955200485], [0.03371142458653273], [-0.7535085145360605], [0.509789083748791], [-0.09211889019219294]]}, "mapping": [[1.0]], "coefficient": 0.1}], "classes": null}, "sigma": 1.0}
diff --git a/benchmarks/v1/evidence/survival-059/4/predictions.npz b/benchmarks/v1/evidence/survival-059/4/predictions.npz
new file mode 100644
index 0000000..8638332
Binary files /dev/null and b/benchmarks/v1/evidence/survival-059/4/predictions.npz differ
diff --git a/benchmarks/v1/evidence/survival-059/4/replay.npz b/benchmarks/v1/evidence/survival-059/4/replay.npz
new file mode 100644
index 0000000..8638332
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diff --git a/benchmarks/v1/evidence/survival-059/4/training.json b/benchmarks/v1/evidence/survival-059/4/training.json
new file mode 100644
index 0000000..2803008
--- /dev/null
+++ b/benchmarks/v1/evidence/survival-059/4/training.json
@@ -0,0 +1,20 @@
+{
+  "selection": "best_validation",
+  "stop": {
+    "rounds": 4,
+    "patience": 3,
+    "min_delta": 0.0,
+    "reference_score": 5.421118163064362,
+    "last_score": 5.421118163064362,
+    "completed_rounds": 4,
+    "stale_rounds": 0,
+    "reason": "budget"
+  },
+  "accepted_commits": 4,
+  "selected_model_identity": "2642c10434015170142db57467d6391b4fe601b250e433102a7ab7ee6a90ce9d",
+  "best_validation_score": 5.421118163064362,
+  "output": "lognormal_location_scale",
+  "selection_metric": "weighted_censored_nll",
+  "sigma": 1.0,
+  "selected_validation_censored_nll": 5.421118163064362
+}
diff --git a/benchmarks/v1/evidence/survival-059/4/worker.log b/benchmarks/v1/evidence/survival-059/4/worker.log
new file mode 100644
index 0000000..e69de29
diff --git a/benchmarks/v1/evidence/survival-059/README.md b/benchmarks/v1/evidence/survival-059/README.md
new file mode 100644
index 0000000..22c5491
--- /dev/null
+++ b/benchmarks/v1/evidence/survival-059/README.md
@@ -0,0 +1,25 @@
+# Current A10 event/right-censored AFT integration
+
+All five frozen Veteran folds pass the current CPU AFT worker and exact fresh
+inference. Events become equal positive bounds; right-censored times become
+positive lower/infinite upper bounds. Output is [log-time location, sigma] with
+fixed sigma=1, matching the evaluation contract. No censoring-as-event substitution.
+
+```sh
+UV_CACHE_DIR=/tmp/openboost-research-uv-cache uv run --no-sync python -m benchmarks.v1.openboost_worker_smoke /tmp/openboost-survival-059 --applications A10
+```
+
+Requires pinned Veteran source in build/v1-data and committed preprocessing
+freeze. Four rounds, depth two, 32 bins, learning rate 0.1, patience three, one
+CPU thread and unchanged 90-second fit/30-second replay caps. No test scoring,
+profiler, concurrent regression, memory cap or CUDA. Host: Apple M4 Max,
+51539607552 bytes RAM (same host as Sprint 052); Python reports x86_64.
+Peak memory was not measured. Existing source licensing closure remains open.
+
+Summary retains revision/dirty state, source/data/split/packet hashes, commands,
+environment and results. Raw model, predictions, replay, training metadata,
+execution and log records are retained per fold. All source/output hashes match.
+Verification records independently computed event density/right-survival NLL
+using math.erfc, with rtol=1e-12/atol=1e-14, and exact source-ID agreement.
+These are bounded integration checks, not IPCW/calibration, quality/search,
+learned-scale, other-censoring or comparative performance acceptance.
diff --git a/benchmarks/v1/evidence/survival-059/summary.json b/benchmarks/v1/evidence/survival-059/summary.json
new file mode 100644
index 0000000..6dafdb6
--- /dev/null
+++ b/benchmarks/v1/evidence/survival-059/summary.json
@@ -0,0 +1,1336 @@
+{
+  "scope": "Current A1/A2/A3/A5/A6/A7/A8/A9/A10/A11 real-data validation plumbing only; four rounds, no test scores, quality or performance claim",
+  "revision": "20d9b7c98cd63ea28b553538861923b58c9d34a6",
+  "dirty": true,
+  "argv": [
+    "/Users/jiaruixu/work_space/openboost/.venv/bin/python3",
+    "-m",
+    "benchmarks.v1.openboost_worker_smoke",
+    "/private/tmp/openboost-survival-059",
+    "--applications",
+    "A10"
+  ],
+  "python": "3.12.12",
+  "os": "macOS-26.3-x86_64-i386-64bit",
+  "machine": "x86_64",
+  "cpu_count": 16,
+  "device": "cpu",
+  "gpu": null,
+  "threads": 1,
+  "memory_cap": null,
+  "packages": {
+    "numpy": "2.3.5",
+    "openboost": "1.0.0rc1"
+  },
+  "sources": {
+    "src/openboost/__init__.py": "b0a76b68a1824abef45bf30d3d094dce7de2ec9deef3d5cbe862a6b3f6038788",
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+        "/Users/jiaruixu/work_space/openboost/benchmarks/v1/openboost_worker.py",
+        "/private/tmp/openboost-survival-059/A10/4/job.json"
+      ],
+      "timeout_s": 90,
+      "threads": 1,
+      "status": "pass",
+      "exit_code": 0,
+      "reason": "",
+      "wall_s": 0.24594029098807368,
+      "artifacts": {
+        "model.bin": "1839c453b9c1388d7e85f821e18ffabb3e485417b9c9e68f7b8bb6c095c52017",
+        "training.json": "aa63164b90e06b10354e14d7255ed45566f756c8a7d0f6f889540c70c0b583b6",
+        "worker.log": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+        "predictions.npz": "d2ddf282a5c847726c85dd936f28ada7174939982b45d1a1c158b94c3935293a"
+      },
+      "application": "A10",
+      "fold": 4,
+      "job": {
+        "application": "A10",
+        "library": "openboost",
+        "device": "cpu",
+        "threads": 1,
+        "seed": 4,
+        "early_stopping_rounds": 3,
+        "config": {
+          "rounds": 4,
+          "learning_rate": 0.1,
+          "max_depth": 2,
+          "reg_lambda": 1,
+          "bins": 32
+        },
+        "input_npz": "/private/tmp/openboost-survival-059/A10/4/worker-input.npz"
+      },
+      "replay_command": [
+        "/Users/jiaruixu/work_space/openboost/.venv/bin/python3",
+        "/Users/jiaruixu/work_space/openboost/benchmarks/v1/openboost_predict.py",
+        "/private/tmp/openboost-survival-059/A10/4/fit/model.bin",
+        "/private/tmp/openboost-survival-059/A10/4/fit/features.npz",
+        "/private/tmp/openboost-survival-059/A10/4/fit/replay.npz"
+      ],
+      "prediction_shape": [
+        28,
+        2
+      ],
+      "fresh_process_exact": true,
+      "training": {
+        "selection": "best_validation",
+        "stop": {
+          "rounds": 4,
+          "patience": 3,
+          "min_delta": 0.0,
+          "reference_score": 5.421118163064362,
+          "last_score": 5.421118163064362,
+          "completed_rounds": 4,
+          "stale_rounds": 0,
+          "reason": "budget"
+        },
+        "accepted_commits": 4,
+        "selected_model_identity": "2642c10434015170142db57467d6391b4fe601b250e433102a7ab7ee6a90ce9d",
+        "best_validation_score": 5.421118163064362,
+        "output": "lognormal_location_scale",
+        "selection_metric": "weighted_censored_nll",
+        "sigma": 1.0,
+        "selected_validation_censored_nll": 5.421118163064362
+      },
+      "replay_sha256": "d2ddf282a5c847726c85dd936f28ada7174939982b45d1a1c158b94c3935293a"
+    }
+  ]
+}
diff --git a/benchmarks/v1/evidence/survival-059/verification.json b/benchmarks/v1/evidence/survival-059/verification.json
new file mode 100644
index 0000000..2218e9e
--- /dev/null
+++ b/benchmarks/v1/evidence/survival-059/verification.json
@@ -0,0 +1,37 @@
+{
+  "source_and_artifact_hashes_match": true,
+  "rtol": 1e-12,
+  "atol": 1e-14,
+  "cells": [
+    {
+      "fold": 0,
+      "independent_censored_nll": 5.151468378018472,
+      "reported_nll": 5.151468378018473,
+      "source_ids_exact": true
+    },
+    {
+      "fold": 1,
+      "independent_censored_nll": 5.280621807552828,
+      "reported_nll": 5.280621807552828,
+      "source_ids_exact": true
+    },
+    {
+      "fold": 2,
+      "independent_censored_nll": 5.27085278893216,
+      "reported_nll": 5.270852788932159,
+      "source_ids_exact": true
+    },
+    {
+      "fold": 3,
+      "independent_censored_nll": 5.437543155714647,
+      "reported_nll": 5.437543155714646,
+      "source_ids_exact": true
+    },
+    {
+      "fold": 4,
+      "independent_censored_nll": 5.421118163064363,
+      "reported_nll": 5.421118163064362,
+      "source_ids_exact": true
+    }
+  ]
+}
diff --git a/benchmarks/v1/openboost_predict.py b/benchmarks/v1/openboost_predict.py
index e657a96..f3bb24f 100644
--- a/benchmarks/v1/openboost_predict.py
+++ b/benchmarks/v1/openboost_predict.py
@@ -9,30 +9,77 @@
 from openboost import NumericData
 from openboost.artifacts import Model
 from openboost.multioutput import MultiOutputModel, TargetScale
-from openboost.objectives import Normal
+from openboost.objectives import Formula, Normal
+from openboost.outputs import poisson_mean, positive_mean
+
+QUANTILES = (0.1, 0.5, 0.9)
 
 OUTPUTS = {
     "A1": "mean",
+    "A4": "ranking_score",
+    "A5": "quantiles",
     "A2": "positive_class_probability",
     "A3": "class_probabilities",
     "A11": "normal_mean_scale",
     "A6": "multioutput_original_units",
+    "A7": "period_count_mean",
+    "A8": "positive_claim_mean",
+    "A9": "annualized_paid_mean",
+    "A10": "lognormal_location_scale",
+    "A12": "saturation_age28_mean",
 }
 
 
-def predict_saved(saved, x):
+def predict_saved(saved, x, *, exposure=None, age=None):
+    if age is not None and (not isinstance(saved, dict) or saved.get("application") != "A12"):
+        raise ValueError("age structure only supported for A12")
+    if exposure is not None and (not isinstance(saved, dict) or saved.get("application") != "A7"):
+        raise ValueError("exposure is supported only for count inference")
+    if isinstance(saved, dict) and saved.get("application") == "A5":
+        if (
+            set(saved) != {"format", "application", "output", "quantiles", "models"}
+            or saved["format"] != "openboost-evaluation-v1"
+            or saved["output"] != OUTPUTS["A5"]
+            or saved["quantiles"] != list(QUANTILES)
+            or not isinstance(saved["models"], list)
+            or len(saved["models"]) != len(QUANTILES)
+        ):
+            raise ValueError("invalid frozen quantile bundle")
+        models = [Model.from_record(r) for r in saved["models"]]
+        if any(
+            m.base.shape != (1,)
+            or m.classes is not None
+            or m.feature_names != models[0].feature_names
+            for m in models
+        ):
+            raise ValueError("quantile model schema differs")
+        # Preserve raw level order, including crossings; no post-hoc sorting.
+        return np.column_stack(
+            [
+                predict_saved(
+                    dict(format=saved["format"], application="A1", output="mean", model=r), x
+                )
+                for r in saved["models"]
+            ]
+        )
     if (
         not isinstance(saved, dict)
         or set(saved)
         != (
             {"format", "application", "output", "model"}
             | ({"target_scale"} if saved.get("application") == "A6" else set())
+            | ({"power"} if saved.get("application") == "A9" else set())
+            | ({"sigma"} if saved.get("application") == "A10" else set())
         )
         or saved["format"] != "openboost-evaluation-v1"
         or saved["application"] not in OUTPUTS
         or saved["output"] != OUTPUTS[saved["application"]]
     ):
         raise ValueError("unsupported evaluation bundle")
+    if saved["application"] == "A9" and saved["power"] != 1.5:
+        raise ValueError("frozen aggregate power differs")
+    if saved["application"] == "A10" and saved["sigma"] != 1.0:
+        raise ValueError("frozen AFT scale differs")
     model = Model.from_record(saved["model"])
     scale = None
     if saved["application"] == "A6":
@@ -40,7 +87,13 @@ def predict_saved(saved, x):
         if not isinstance(record, dict) or set(record) != {"mean", "std", "constant"}:
             raise ValueError("invalid evaluation target scale")
         scale = TargetScale(record["mean"], record["std"], record["constant"])
-    width = len(scale.mean) if scale is not None else 1 if saved["application"] == "A1" else 2
+    width = (
+        len(scale.mean)
+        if scale is not None
+        else 1
+        if saved["application"] in {"A1", "A4", "A7", "A8", "A9", "A10"}
+        else 2
+    )
     classification = saved["application"] in {"A2", "A3"}
     if classification:
         if model.classes is None:
@@ -65,6 +118,21 @@ def predict_saved(saved, x):
     if scale is not None:
         return MultiOutputModel(model, scale).predict(data)
     raw = model.predict(data)
+    if saved["application"] == "A12":
+        if age is None:
+            raise ValueError("age/28 structure required for inference")
+        age = np.asarray(age)
+        if age.shape != (len(raw),):
+            raise ValueError("aligned age vector required")
+        return Formula.predict(raw, age[:, None])[:, 0]
+    if saved["application"] == "A10":
+        return np.column_stack([raw[:, 0], np.full(len(raw), saved["sigma"])])
+    if saved["application"] == "A7":
+        if exposure is None:
+            raise ValueError("prediction exposure required")
+        return poisson_mean(raw, exposure)["count_mean"]
+    if saved["application"] in {"A8", "A9"}:
+        return positive_mean(raw)
     return raw[:, 0] if width == 1 else Normal.parameters(raw)
 
 
@@ -74,14 +142,20 @@ def main():
     parser.add_argument("features", type=Path)
     parser.add_argument("output", type=Path)
     args = parser.parse_args()
+    saved = json.loads(args.model.read_text())
     with np.load(args.features, allow_pickle=False) as arrays:
-        if set(arrays.files) != {"x", "row_ids"}:
-            raise ValueError("prediction packet must contain only features and row IDs")
+        if set(arrays.files) != (
+            {"x", "row_ids"}
+            | ({"exposure"} if saved.get("application") == "A7" else set())
+            | ({"age"} if saved.get("application") == "A12" else set())
+        ):
+            raise ValueError("prediction packet differs from declared feature/exposure schema")
         x, ids = arrays["x"], arrays["row_ids"]
+        exposure = arrays.get("exposure")
+        age = arrays.get("age")
     if ids.ndim != 1 or len(ids) != len(x) or len(np.unique(ids)) != len(ids):
         raise ValueError("unique aligned prediction row IDs required")
-    saved = json.loads(args.model.read_text())
-    prediction = predict_saved(saved, x)
+    prediction = predict_saved(saved, x, exposure=exposure, age=age)
     np.savez(args.output, row_ids=ids, prediction=prediction)
 
 
diff --git a/benchmarks/v1/openboost_worker.py b/benchmarks/v1/openboost_worker.py
index 21ae1d7..bcdf2ca 100644
--- a/benchmarks/v1/openboost_worker.py
+++ b/benchmarks/v1/openboost_worker.py
@@ -1,4 +1,4 @@
-"""Current CPU A1/A2/A3/A6/A11 trials on frozen encoded train/validation packets.
+"""Current CPU A1/A2/A3/A4/A5/A6/A7/A8/A9/A10/A11/A12 trials on frozen encoded train/validation packets.
 
 Explicit validation targets are required even with fixed budgets. The caller
 controls process threads and resource limits. Test arrays are always rejected.
@@ -13,14 +13,29 @@
 
 from openboost import ClassSchema, NumericData, Problem, RunContext
 from openboost.multioutput import TargetScale
-from openboost.recipes import binary, multi_squared, multiclass, normal, squared
+from openboost.recipes import (
+    aft,
+    binary,
+    formula,
+    gamma,
+    multi_squared,
+    multiclass,
+    normal,
+    poisson,
+    quantile,
+    ranking,
+    squared,
+    tweedie,
+)
 
 if __package__:
-    from benchmarks.v1.openboost_predict import OUTPUTS, predict_saved
+    from benchmarks.v1.openboost_predict import OUTPUTS, QUANTILES, predict_saved
     from benchmarks.v1.preprocessing import fit_target_scale
+    from benchmarks.v1.ranking import validate as validate_ranking
 else:
-    from openboost_predict import OUTPUTS, predict_saved
+    from openboost_predict import OUTPUTS, QUANTILES, predict_saved
     from preprocessing import fit_target_scale
+    from ranking import validate as validate_ranking
 
 
 def fit(job, arrays):
@@ -42,8 +57,30 @@ def fit(job, arrays):
     if type(job["seed"]) is not int or job["seed"] < 0:
         raise ValueError("nonnegative integer seed required")
     needed = {"x_train", "y_train", "x_validation", "y_validation", "validation_row_ids"}
-    if not needed <= set(arrays) or set(arrays) - needed - {"weight_train", "weight_validation"}:
+    if job["application"] == "A7":
+        needed |= {"exposure_train", "exposure_validation"}
+    if job["application"] == "A9":
+        needed |= {"weight_train", "weight_validation"}
+    if job["application"] == "A10":
+        needed |= {"event_train", "event_validation"}
+    if job["application"] == "A12":
+        needed |= {"age_train", "age_validation"}
+    ranking_groups = None
+    optional = {"weight_train", "weight_validation"}
+    if job["application"] == "A4":
+        needed |= {"query_train", "query_validation", "train_row_ids"}
+        optional = {"query_weight_train", "query_weight_validation"}
+    if not needed <= set(arrays) or set(arrays) - needed - optional:
         raise ValueError("explicit train/validation arrays only; no test arrays")
+    if job["application"] == "A4":
+        ranking_groups = validate_ranking(arrays, 1)
+        if any(
+            np.asarray(arrays[k]).dtype.kind not in "iu"
+            for k in ("train_row_ids", "validation_row_ids")
+        ):
+            raise ValueError("ranking requires integer source row IDs for stable ties")
+        if np.intersect1d(arrays["train_row_ids"], arrays["validation_row_ids"]).size:
+            raise ValueError("ranking source rows overlap")
     external_ids = np.asarray(arrays["validation_row_ids"])
     if (
         external_ids.ndim != 1
@@ -56,6 +93,8 @@ def fit(job, arrays):
     if cfg.pop("seed_from_fold", True) is not True:
         raise ValueError("seed semantics differ")
     allowed = {"rounds", "learning_rate", "max_depth", "reg_lambda", "bins"}
+    if job["application"] == "A4":
+        allowed |= {"lambdas"}
     if job["application"] == "A11":
         allowed |= {"mode", "damping", "minimum_scale"}
     if job["application"] == "A6":
@@ -71,7 +110,7 @@ def fit(job, arrays):
             raise ValueError("explicit canonical classification count required")
         classes = ClassSchema(tuple(range(count)))
     problems = []
-    width = 1 if job["application"] == "A1" else 2
+    width = 1 if job["application"] in {"A1", "A4", "A5", "A7", "A8", "A9", "A10"} else 2
     if classification:
         width = 1 if job["application"] == "A2" else count
     multi = job["application"] == "A6"
@@ -95,24 +134,79 @@ def fit(job, arrays):
             target_scale = fit_target_scale(y)
             scale = TargetScale(target_scale["mean"], target_scale["std"], target_scale["constant"])
         names = tuple(f"x{i}" for i in range(x.shape[1])) if names is None else names
-        # Packet IDs remain in emitted artifacts; public data uses local integer rows.
-        ids = np.arange(len(x))
+        # Ranking preserves source IDs for ties; other tasks use local integer rows.
+        ids = (
+            arrays["train_row_ids" if part == "train" else "validation_row_ids"]
+            if ranking_groups is not None
+            else np.arange(len(x))
+        )
         data = NumericData(x, ids, names)
+        if job["application"] == "A9":
+            weight = np.asarray(arrays["weight_" + part])
+            if weight.shape != (len(x),) or not np.isfinite(weight).all() or np.any(weight <= 0):
+                raise ValueError("positive aligned aggregate exposure weights required")
+        structure = None
+        if ranking_groups is not None:
+            _, sizes, weights = ranking_groups[0 if part == "train" else 1]
+            structure = {
+                "query": np.repeat(np.arange(len(sizes)), sizes)[:, None],
+                "query_weight": np.repeat(weights, sizes)[:, None],
+            }
+        if job["application"] == "A7":
+            exposure = np.asarray(arrays["exposure_" + part])
+            if (
+                exposure.shape != (len(x),)
+                or not np.isfinite(exposure).all()
+                or np.any(exposure <= 0)
+            ):
+                raise ValueError("aligned positive finite exposure required")
+            structure = {"exposure": exposure[:, None]}
+        if job["application"] == "A12":
+            age = np.asarray(arrays["age_" + part])
+            if age.shape != y.shape or not np.isfinite(age).all() or np.any(age <= 0):
+                raise ValueError("aligned positive age/28 structure required")
+            structure = {"x": age[:, None]}
+        target = y if multi else y[:, None]
+        target_kind = "numeric"
+        if job["application"] == "A10":
+            event = np.asarray(arrays["event_" + part])
+            if event.shape != y.shape or not np.isin(event, [0, 1]).all() or np.any(y <= 0):
+                raise ValueError("positive times and aligned binary events required")
+            target = np.column_stack([y, np.where(event, y, np.inf)])
+            target_kind = "event_right"
         problems.append(
             Problem(
                 data,
-                y if multi else y[:, None],
+                target,
                 data.row_ids,
+                target_kind=target_kind,
                 weight=arrays.get("weight_" + part),
                 raw_width=width,
                 classes=classes,
+                structure=structure,
             )
         )
     if multi:
         problems = [scale.transform(p) for p in problems]
-    recipe = {"A1": squared, "A2": binary, "A3": multiclass, "A6": multi_squared, "A11": normal}[
-        job["application"]
-    ]
+    if job["application"] == "A5":
+        return fit_quantiles(job, cfg, problems, arrays["x_validation"])
+    recipe = {
+        "A1": squared,
+        "A2": binary,
+        "A3": multiclass,
+        "A4": ranking,
+        "A6": multi_squared,
+        "A7": poisson,
+        "A8": gamma,
+        "A9": tweedie,
+        "A10": aft,
+        "A11": normal,
+        "A12": formula,
+    }[job["application"]]
+    if job["application"] == "A9":
+        cfg["power"] = 1.5
+    if job["application"] == "A10":
+        cfg["sigma"] = 1.0
     result = recipe(
         *problems,
         context=RunContext("evaluation", job["seed"]),
@@ -127,9 +221,18 @@ def fit(job, arrays):
         output=OUTPUTS[job["application"]],
         model=model.record(),
     )
+    if job["application"] == "A9":
+        saved["power"] = 1.5
+    if job["application"] == "A10":
+        saved["sigma"] = 1.0
     if multi:
         saved["target_scale"] = target_scale
-    prediction = predict_saved(saved, arrays["x_validation"])
+    prediction = predict_saved(
+        saved,
+        arrays["x_validation"],
+        exposure=arrays.get("exposure_validation"),
+        age=arrays.get("age_validation"),
+    )
     training = dict(
         selection=selection,
         stop={**asdict(result.stop), "reason": result.stop.reason},
@@ -138,6 +241,66 @@ def fit(job, arrays):
         best_validation_score=result.state.best_score,
         output=saved["output"],
     )
+    if job["application"] == "A7":
+        from openboost.objectives import Poisson
+
+        training.update(
+            selection_metric="weighted_poisson_nll",
+            exposure_role="likelihood_once",
+            target_units="period_count",
+            raw_units="log_rate",
+            selected_validation_nll=Poisson().loss(problems[1], model.predict(problems[1].data)),
+        )
+    if job["application"] == "A8":
+        from openboost.objectives import Gamma
+
+        training.update(
+            selection_metric="weighted_gamma_objective",
+            target_units="positive_claim_amount",
+            raw_units="log_mean",
+            selected_validation_gamma_objective=Gamma().loss(
+                problems[1], model.predict(problems[1].data)
+            ),
+        )
+    if job["application"] == "A9":
+        from openboost.objectives import Tweedie
+
+        training.update(
+            selection_metric="weighted_tweedie_objective",
+            power=1.5,
+            target_units="annualized_paid_total",
+            weight_role="exposure_once",
+            selected_validation_tweedie_objective=Tweedie(1.5).loss(
+                problems[1], model.predict(problems[1].data)
+            ),
+        )
+    if job["application"] == "A10":
+        from openboost.survival import LogNormalAFT
+
+        training.update(
+            selection_metric="weighted_censored_nll",
+            sigma=1.0,
+            selected_validation_censored_nll=LogNormalAFT(1.0).loss(
+                problems[1], model.predict(problems[1].data)
+            ),
+        )
+    if job["application"] == "A12":
+        training.update(
+            selection_metric="weighted_half_squared_error",
+            structure_units="age_days_divided_by_28",
+            selected_validation_half_squared_error=float(
+                np.average(
+                    (prediction - arrays["y_validation"]) ** 2 / 2,
+                    weights=arrays.get("weight_validation"),
+                )
+            ),
+        )
+    if job["application"] == "A4":
+        training.update(
+            selection_metric="one_minus_query_weighted_ndcg_at_10",
+            lambdas=cfg.get("lambdas", False),
+            tie_break="integer_source_row_id",
+        )
     if classification:
         training.update(class_order=list(classes.values), selection_metric="logloss")
     if multi:
@@ -149,6 +312,53 @@ def fit(job, arrays):
     return prediction, saved, training
 
 
+def fit_quantiles(job, cfg, problems, x_validation):
+    """Independent frozen levels with separate validation stopping and selection."""
+    from openboost.objectives import Quantile
+
+    models, runs = [], []
+    selection = "final" if job.get("early_stopping_rounds") is None else "best_validation"
+    for q in QUANTILES:
+        result = quantile(
+            *problems,
+            context=RunContext("evaluation", job["seed"]),
+            q=q,
+            patience=job.get("early_stopping_rounds"),
+            **cfg,
+        )
+        model = result.state.model if selection == "final" else result.state.best_model
+        models.append(model.record())
+        runs.append(
+            dict(
+                q=q,
+                stop={**asdict(result.stop), "reason": result.stop.reason},
+                accepted_commits=result.state.version,
+                selected_model_identity=model.identity,
+                best_validation_score=result.state.best_score,
+                selected_validation_pinball=Quantile(q).loss(
+                    problems[1], model.predict(problems[1].data)
+                ),
+            )
+        )
+    saved = dict(
+        format="openboost-evaluation-v1",
+        application="A5",
+        output=OUTPUTS["A5"],
+        quantiles=list(QUANTILES),
+        models=models,
+    )
+    prediction = predict_saved(saved, x_validation)
+    training = dict(
+        selection=selection,
+        output=OUTPUTS["A5"],
+        quantiles=list(QUANTILES),
+        quantile_runs=runs,
+        selection_metric="independent_weighted_pinball",
+        crossing_rows=int(np.any(np.diff(prediction, axis=1) < 0, axis=1).sum()),
+    )
+    return prediction, saved, training
+
+
 def main():
     parser = argparse.ArgumentParser(description=__doc__)
     parser.add_argument("job", type=Path)
diff --git a/benchmarks/v1/openboost_worker_smoke.py b/benchmarks/v1/openboost_worker_smoke.py
index de5b970..7b06582 100644
--- a/benchmarks/v1/openboost_worker_smoke.py
+++ b/benchmarks/v1/openboost_worker_smoke.py
@@ -1,4 +1,4 @@
-"""Bounded current A1/A2/A3/A6/A11 worker integration on all five frozen folds."""
+"""Bounded current A1/A2/A3/A5/A6/A7/A8/A9/A10/A11/A12 worker integration on all five frozen folds."""
 
 import argparse
 import hashlib
@@ -20,7 +20,7 @@ def run(directory, applications=("A1", "A6", "A11")):
     if (
         not applications
         or len(set(applications)) != len(applications)
-        or set(applications) - {"A1", "A2", "A3", "A6", "A11"}
+        or set(applications) - {"A1", "A2", "A3", "A5", "A6", "A7", "A8", "A9", "A10", "A11", "A12"}
     ):
         raise ValueError("unique supported applications required")
     root = Path(directory).resolve()
@@ -29,7 +29,7 @@ def run(directory, applications=("A1", "A6", "A11")):
         raise ValueError("fresh output directory required")
     repo = Path(__file__).resolve().parents[2]
     report = dict(
-        scope="Current A1/A2/A3/A6/A11 real-data validation plumbing only; four rounds, no test scores, quality or performance claim",
+        scope="Current A1/A2/A3/A5/A6/A7/A8/A9/A10/A11/A12 real-data validation plumbing only; four rounds, no test scores, quality or performance claim",
         revision=subprocess.check_output(["git", "rev-parse", "HEAD"], text=True).strip(),
         dirty=bool(subprocess.check_output(["git", "status", "--porcelain"])),
         argv=[
@@ -62,6 +62,7 @@ def run(directory, applications=("A1", "A6", "A11")):
         "openboost_worker_smoke.py",
         "worker_data.py",
         "preprocessing.py",
+        "ranking.py",
         "process_runner.py",
     ):
         p = Path(__file__).with_name(name)
@@ -80,7 +81,10 @@ def run(directory, applications=("A1", "A6", "A11")):
                 seed=seed,
                 early_stopping_rounds=3,
                 config=dict(rounds=4, learning_rate=0.1, max_depth=2, reg_lambda=1, bins=32),
-                input_npz=str(packet / fold["artifacts"]["worker-input"]["path"]),
+                input_npz=str(
+                    packet
+                    / fold["artifacts"]["formula-input" if app == "A12" else "worker-input"]["path"]
+                ),
             )
             if app in {"A2", "A3"}:
                 job["classes"] = 2 if app == "A2" else 7
@@ -101,6 +105,8 @@ def run(directory, applications=("A1", "A6", "A11")):
                             output / "features.npz",
                             x=arrays["x_validation"],
                             row_ids=arrays["validation_row_ids"],
+                            **({"exposure": arrays["exposure_validation"]} if app == "A7" else {}),
+                            **({"age": arrays["age_validation"]} if app == "A12" else {}),
                         )
                     command = [
                         sys.executable,
@@ -132,8 +138,14 @@ def run(directory, applications=("A1", "A6", "A11")):
                         np.testing.assert_array_equal(actual["row_ids"], restored["row_ids"])
                         np.testing.assert_array_equal(actual["prediction"], restored["prediction"])
                         expected_width = (
-                            () if app in {"A1", "A2"} else (7,) if app == "A3" else (2,)
+                            ()
+                            if app in {"A1", "A2", "A7", "A8", "A9", "A12"}
+                            else (7,)
+                            if app == "A3"
+                            else (2,)
                         )
+                        if app == "A5":
+                            expected_width = (3,)
                         if app == "A6":
                             expected_width = (len(fold["metadata"]["target_scale"]["mean"]),)
                         assert actual["prediction"].shape == (
@@ -141,6 +153,10 @@ def run(directory, applications=("A1", "A6", "A11")):
                             *expected_width,
                         )
                         assert np.isfinite(actual["prediction"]).all()
+                        if app in {"A7", "A8", "A9"}:
+                            assert (actual["prediction"] > 0).all()
+                        if app == "A10":
+                            np.testing.assert_array_equal(actual["prediction"][:, 1], 1.0)
                         if app == "A11":
                             assert (actual["prediction"][:, 1] > 0).all()
                         if app in {"A2", "A3"}:
@@ -170,7 +186,7 @@ def run(directory, applications=("A1", "A6", "A11")):
     parser.add_argument(
         "--applications",
         nargs="+",
-        choices=("A1", "A2", "A3", "A6", "A11"),
+        choices=("A1", "A2", "A3", "A5", "A6", "A7", "A8", "A9", "A10", "A11", "A12"),
         default=["A1", "A6", "A11"],
     )
     args = parser.parse_args()
diff --git a/benchmarks/v1/paid_event_data.py b/benchmarks/v1/paid_event_data.py
new file mode 100644
index 0000000..28fff03
--- /dev/null
+++ b/benchmarks/v1/paid_event_data.py
@@ -0,0 +1,167 @@
+"""Bind matched positive-payment aggregates to existing frozen A9 worker rows."""
+
+import argparse
+import hashlib
+import json
+import platform
+import subprocess
+import sys
+from pathlib import Path
+
+import numpy as np
+
+from benchmarks.v1 import real_data
+
+
+def digest(path):
+    return hashlib.sha256(Path(path).read_bytes()).hexdigest()
+
+
+def verify_paid_events(raw):
+    """Independent claim iteration, not the source reader's bincount aggregation."""
+    size = len(raw["group"])
+    count = np.zeros(size, dtype=np.int64)
+    total = np.zeros(size)
+    rows, amounts = raw["severity_policy_row"], raw["severity_y"]
+    if rows.shape != amounts.shape or rows.ndim != 1 or rows.dtype.kind not in "iu":
+        raise ValueError("aligned integer claim-policy mapping required")
+    for row, amount in zip(rows, amounts, strict=True):
+        if row < 0 or row >= size or not np.isfinite(amount) or amount <= 0:
+            raise ValueError("invalid retained positive payment")
+        count[row] += 1
+        total[row] += amount
+    if not np.array_equal(count, raw["paid_count"]) or not np.array_equal(total, raw["paid_total"]):
+        raise ValueError("paid aggregates differ from retained claim records")
+    return count, total
+
+
+def bind(raw, arrays, train_ids):
+    """Require exact source units/rows; only emit train/validation composition roles."""
+    count, total = verify_paid_events(raw)
+    required = {
+        "x_train",
+        "y_train",
+        "weight_train",
+        "x_validation",
+        "y_validation",
+        "weight_validation",
+        "validation_row_ids",
+    }
+    if set(arrays) != required:
+        raise ValueError("exact annualized train/validation packet required")
+    ids = np.asarray(raw["group"])
+    if ids.ndim != 1 or len(np.unique(ids)) != len(ids):
+        raise ValueError("unique source policies required")
+    lookup = {v: i for i, v in enumerate(ids.tolist())}
+    output = {}
+    used = set()
+    for part, labels in (
+        ("train", np.asarray(train_ids)),
+        ("validation", arrays["validation_row_ids"]),
+    ):
+        if labels.ndim != 1 or len(np.unique(labels)) != len(labels) or not len(labels):
+            raise ValueError("unique nonempty partition IDs required")
+        if any(v not in lookup or v in used for v in labels.tolist()):
+            raise ValueError("foreign or overlapping policies")
+        used.update(labels.tolist())
+        rows = np.array([lookup[v] for v in labels.tolist()])
+        exposure = raw["exposure"][rows]
+        if not np.all(raw["aggregate_eligible"][rows]):
+            raise ValueError("ineligible aggregate policy")
+        if not np.array_equal(arrays["weight_" + part], exposure):
+            raise ValueError("exposure weights differ from source")
+        if not np.array_equal(arrays["y_" + part], total[rows] / exposure):
+            raise ValueError("annualized targets differ from matched payments")
+        x = arrays["x_" + part]
+        if x.ndim != 2 or len(x) != len(rows) or not np.isfinite(x).all():
+            raise ValueError("aligned finite encoded features required")
+        output.update(
+            {
+                "x_" + part: x,
+                "row_ids_" + part: labels,
+                "paid_count_" + part: count[rows],
+                "paid_total_" + part: total[rows],
+                "exposure_" + part: exposure,
+            }
+        )
+    return output
+
+
+def run(previous_summary, directory, source_root=Path("build/v1-data")):
+    previous_summary, directory = Path(previous_summary).resolve(), Path(directory).resolve()
+    previous = json.loads(previous_summary.read_text())
+    directory.mkdir(parents=True, exist_ok=True)
+    if any(directory.iterdir()):
+        raise ValueError("fresh output directory required")
+    repo = Path(__file__).resolve().parents[2]
+    source_path = repo / "benchmarks/v1/datasets/insurance.json"
+    freeze = json.loads(source_path.read_text())
+    raw, audit = real_data.insurance(source_root)
+    for name, record in freeze["arrays"].items():
+        if real_data.array_hash(raw[name]) != record["sha256"]:
+            raise ValueError("source array differs from freeze")
+    report = dict(
+        scope="Matched paid-event binding only; no composition fit or test scores",
+        revision=subprocess.check_output(["git", "rev-parse", "HEAD"], text=True).strip(),
+        dirty=bool(subprocess.check_output(["git", "status", "--porcelain"])),
+        argv=[
+            sys.executable,
+            "-m",
+            "benchmarks.v1.paid_event_data",
+            str(previous_summary),
+            str(directory),
+        ],
+        python=platform.python_version(),
+        os=platform.platform(),
+        numpy=np.__version__,
+        previous_summary_sha256=digest(previous_summary),
+        source_manifest_sha256=digest(source_path),
+        sources={
+            str(p.relative_to(repo)): digest(p) for p in [Path(__file__), Path(real_data.__file__)]
+        },
+        source_audit=audit,
+        cells=[],
+    )
+    folds = previous["data"]["A9"]["folds"]
+    cells = previous["cells"]
+    if {c["fold"] for c in cells} != set(range(5)) or len(cells) != 5:
+        raise ValueError("all five frozen folds required")
+    for cell in cells:
+        if cell["application"] != "A9":
+            raise ValueError("A9 summary required")
+        fold = next(f for f in folds if f["seed"] == cell["fold"])
+        packet = Path(cell["job"]["input_npz"])
+        train_path = packet.parent / "train-rows.npz"
+        for path, role in [(packet, "worker-input"), (train_path, "train-rows")]:
+            if digest(path) != fold["artifacts"][role]["sha256"]:
+                raise ValueError("frozen packet hash differs")
+        with np.load(packet, allow_pickle=False) as a, np.load(train_path, allow_pickle=False) as t:
+            bound = bind(raw, dict(a), t["row_ids"])
+        dest = directory / f"fold-{cell['fold']}.npz"
+        np.savez(dest, **bound)
+        report["cells"].append(
+            dict(
+                fold=cell["fold"],
+                status="pass",
+                path=dest.name,
+                sha256=digest(dest),
+                input_sha256=digest(packet),
+                train_ids_sha256=digest(train_path),
+                rows={p: len(bound["row_ids_" + p]) for p in ("train", "validation")},
+                paid_events={
+                    p: int(bound["paid_count_" + p].sum()) for p in ("train", "validation")
+                },
+            )
+        )
+    (directory / "manifest.json").write_text(json.dumps(report, indent=2) + "\n")
+    return report
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("previous_summary", type=Path)
+    parser.add_argument("directory", type=Path)
+    args = parser.parse_args()
+    print(
+        f"{len(run(args.previous_summary, args.directory)['cells'])}/5 paid-event bindings passed"
+    )
diff --git a/benchmarks/v1/profile_worker.py b/benchmarks/v1/profile_worker.py
new file mode 100644
index 0000000..7c95238
--- /dev/null
+++ b/benchmarks/v1/profile_worker.py
@@ -0,0 +1,101 @@
+"""Unix diagnostic wrapper retaining profiles at a soft worker deadline."""
+
+import argparse
+import cProfile
+import faulthandler
+import json
+import math
+import pstats
+import runpy
+import signal
+import sys
+import time
+from pathlib import Path
+
+
+class ProfileDeadline(TimeoutError):
+    """Soft diagnostic interruption; an outer runner must still enforce a hard cap."""
+
+
+def profile_call(callback, directory, seconds):
+    if (
+        isinstance(seconds, bool)
+        or not isinstance(seconds, (int, float))
+        or not math.isfinite(seconds)
+        or not 0 < seconds <= 60
+    ):
+        raise ValueError("profile duration must be finite and in (0,60] seconds")
+    root = Path(directory)
+    if signal.getitimer(signal.ITIMER_REAL)[0] != 0:
+        raise ValueError("profiling cannot replace an active process timer")
+    previous = signal.getsignal(signal.SIGALRM)
+    profiler = cProfile.Profile()
+    start = time.monotonic()
+    status = "error"
+
+    def expire(signum, frame):
+        raise ProfileDeadline("diagnostic soft deadline reached")
+
+    signal.signal(signal.SIGALRM, expire)
+    try:
+        signal.setitimer(signal.ITIMER_REAL, seconds)
+        profiler.enable()
+        result = callback()
+        status = "complete"
+        return result
+    except ProfileDeadline:
+        status = "deadline"
+        raise
+    finally:
+        profiler.disable()
+        signal.setitimer(signal.ITIMER_REAL, 0)
+        signal.signal(signal.SIGALRM, previous)
+        profiler.dump_stats(root / "profile.pstats")
+        with (root / "profile.txt").open("w") as stream:
+            pstats.Stats(profiler, stream=stream).strip_dirs().sort_stats("cumulative").print_stats(
+                60
+            )
+        stats = pstats.Stats(profiler)
+        rows = [
+            dict(
+                file=key[0],
+                line=key[1],
+                function=key[2],
+                primitive_calls=value[0],
+                calls=value[1],
+                self_s=value[2],
+                cumulative_s=value[3],
+            )
+            for key, value in stats.stats.items()
+        ]
+        record = dict(
+            status=status,
+            soft_limit_s=seconds,
+            wall_s=time.monotonic() - start,
+            scope="instrumented diagnostic; cumulative times overlap; not a fit pass or speed comparison",
+            functions=sorted(rows, key=lambda row: row["cumulative_s"], reverse=True)[:100],
+        )
+        (root / "profile.json").write_text(json.dumps(record, indent=2) + "\n")
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("job", type=Path)
+    parser.add_argument("--seconds", type=float, default=60)
+    args = parser.parse_args()
+    worker = Path(__file__).with_name("openboost_worker.py")
+    sys.argv = [str(worker), str(args.job.resolve())]
+    with Path("stacks.txt").open("w") as stream:
+        faulthandler.dump_traceback_later(20, repeat=True, file=stream)
+        try:
+            profile_call(
+                lambda: runpy.run_path(str(worker), run_name="__main__"), Path.cwd(), args.seconds
+            )
+        except ProfileDeadline:
+            raise SystemExit(124) from None
+        finally:
+            faulthandler.cancel_dump_traceback_later()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/benchmarks/v1/replay_current_packets.py b/benchmarks/v1/replay_current_packets.py
new file mode 100644
index 0000000..1c8953d
--- /dev/null
+++ b/benchmarks/v1/replay_current_packets.py
@@ -0,0 +1,140 @@
+"""Rerun current worker jobs from an existing frozen smoke summary, without export."""
+
+import argparse
+import hashlib
+import importlib.metadata
+import json
+import os
+import platform
+import subprocess
+import sys
+from pathlib import Path
+
+import numpy as np
+
+from benchmarks.v1.process_runner import execute
+
+
+def digest(path):
+    return hashlib.sha256(path.read_bytes()).hexdigest()
+
+
+def run(previous_path, directory):
+    previous_path = Path(previous_path).resolve()
+    previous = json.loads(previous_path.read_text())
+    root = Path(directory).resolve()
+    root.mkdir(parents=True, exist_ok=True)
+    if any(root.iterdir()):
+        raise ValueError("fresh output directory required")
+    repo = Path(__file__).resolve().parents[2]
+    report = dict(
+        scope="Unchanged full-data worker replay; no search, quality or comparative speed claim",
+        previous_summary=dict(path=str(previous_path), sha256=digest(previous_path)),
+        revision=subprocess.check_output(["git", "rev-parse", "HEAD"], text=True).strip(),
+        dirty=bool(subprocess.check_output(["git", "status", "--porcelain"])),
+        argv=[
+            sys.executable,
+            "-m",
+            "benchmarks.v1.replay_current_packets",
+            str(previous_path),
+            str(root),
+        ],
+        python=platform.python_version(),
+        os=platform.platform(),
+        cpu_count=os.cpu_count(),
+        packages={k: importlib.metadata.version(k) for k in ("openboost", "numpy")},
+        threads=1,
+        gpu=None,
+        memory_cap=None,
+        cells=[],
+        sources={},
+    )
+    paths = [
+        *sorted((repo / "src/openboost").rglob("*.py")),
+        Path(__file__),
+        Path(__file__).with_name("openboost_worker.py"),
+        Path(__file__).with_name("openboost_predict.py"),
+        Path(__file__).with_name("process_runner.py"),
+    ]
+    report["sources"] = {str(p.relative_to(repo)): digest(p) for p in paths}
+    for cell in previous["cells"]:
+        job = cell["job"]
+        if job["threads"] != 1 or job["device"] != "cpu" or job["library"] != "openboost":
+            raise ValueError("single-thread current CPU jobs required")
+        app, fold = cell["application"], cell["fold"]
+        frozen = next(f for f in previous["data"][app]["folds"] if f["seed"] == fold)
+        input_path = Path(job["input_npz"])
+        if digest(input_path) != frozen["artifacts"]["worker-input"]["sha256"]:
+            raise ValueError("worker packet differs from frozen hash")
+        job_path = root / f"{app}-{fold}-job.json"
+        job_path.write_text(json.dumps(job, indent=2) + "\n")
+        out = root / app / str(fold)
+        record = execute(
+            [sys.executable, str(repo / "benchmarks/v1/openboost_worker.py"), str(job_path)],
+            out,
+            timeout_s=90,
+            threads=1,
+        )
+        record.update(application=app, fold=fold, job=job, input_sha256=digest(input_path))
+        if record["status"] == "pass":
+            try:
+                with np.load(input_path, allow_pickle=False) as arrays:
+                    np.savez(
+                        out / "features.npz",
+                        x=arrays["x_validation"],
+                        row_ids=arrays["validation_row_ids"],
+                    )
+                command = [
+                    sys.executable,
+                    str(repo / "benchmarks/v1/openboost_predict.py"),
+                    str(out / "model.bin"),
+                    str(out / "features.npz"),
+                    str(out / "replay.npz"),
+                ]
+                record["replay_command"] = command
+                subprocess.run(
+                    command,
+                    check=True,
+                    capture_output=True,
+                    timeout=30,
+                    env=dict(
+                        os.environ,
+                        OMP_NUM_THREADS="1",
+                        OPENBLAS_NUM_THREADS="1",
+                        MKL_NUM_THREADS="1",
+                    ),
+                )
+                with (
+                    np.load(out / "predictions.npz") as a,
+                    np.load(out / "replay.npz") as b,
+                    np.load(out / "features.npz") as f,
+                ):
+                    np.testing.assert_array_equal(a["prediction"], b["prediction"])
+                    np.testing.assert_array_equal(a["row_ids"], b["row_ids"])
+                    np.testing.assert_array_equal(a["row_ids"], f["row_ids"])
+                    assert np.isfinite(a["prediction"]).all()
+                    if app == "A3":
+                        assert a["prediction"].shape == (len(a["row_ids"]), job["classes"])
+                        assert ((a["prediction"] >= 0) & (a["prediction"] <= 1)).all()
+                        np.testing.assert_allclose(a["prediction"].sum(axis=1), 1.0)
+                record.update(
+                    fresh_process_exact=True,
+                    replay_sha256=digest(out / "replay.npz"),
+                    training=json.loads((out / "training.json").read_text()),
+                )
+            except Exception as error:
+                record.update(status="error", reason=str(error))
+        report["cells"].append(record)
+        (root / "summary.json").write_text(json.dumps(report, indent=2) + "\n")
+        print(f"{app}/{fold}: {record['status']} ({record['wall_s']:.1f}s)", flush=True)
+    return report
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("previous_summary", type=Path)
+    parser.add_argument("directory", type=Path)
+    args = parser.parse_args()
+    result = run(args.previous_summary, args.directory)
+    if any(c["status"] != "pass" for c in result["cells"]):
+        raise SystemExit(1)
diff --git a/benchmarks/v1/worker_data.py b/benchmarks/v1/worker_data.py
index c272841..d8b2e40 100644
--- a/benchmarks/v1/worker_data.py
+++ b/benchmarks/v1/worker_data.py
@@ -178,6 +178,11 @@ def bind(application, data, parts, frozen):
         packets["validation"]["event"] = event[parts["validation"]]
         packets["test-truth"]["event"] = event[parts["test"]]
     if application == "A12":
+        formula_packet = dict(worker)
+        for part in ("train", "validation"):
+            formula_packet["x_" + part] = worker["x_" + part][:, :-1]
+            formula_packet["age_" + part] = age[parts[part]]
+        packets["formula-input"] = formula_packet
         for part in ("validation", "test"):
             packets[part + "-structure"] = dict(
                 row_ids=ids[parts[part]],
diff --git a/docs/v1/aft.md b/docs/v1/aft.md
index 6f693f0..a576102 100644
--- a/docs/v1/aft.md
+++ b/docs/v1/aft.md
@@ -54,3 +54,8 @@ binding. Tests cover bounds rejection, three-round geometry/tree parity,
 finite differences, censoring effects, monotone outputs and fresh-process
 mixed/missing/unseen inference. Real A10 NLL/IPCW comparisons, calibration,
 learned scale, other censoring forms and CUDA remain open.
+
+The current A10 worker converts frozen time/event arrays to event_right bounds,
+fixes sigma=1 and persists it with a log-normal output tag. Five frozen Veteran
+folds pass bounded validation and exact fresh location/scale replay in Sprint 059.
+Independent censored NLL checks pass. This is not full A10 quality acceptance.
diff --git a/docs/v1/formula-runs.md b/docs/v1/formula-runs.md
index d638871..2aa4417 100644
--- a/docs/v1/formula-runs.md
+++ b/docs/v1/formula-runs.md
@@ -66,3 +66,9 @@ same-ID independent and reordered execution; they establish no speed benefit.
 
 [Shared preparation](preparation.md) now supports explicit training-code reuse
 across independent runs.
+
+The current A12 worker uses a dedicated concrete packet: age/28 is structure,
+and encoded composition features alone feed the parameter trees. Ordinary GBDT
+packets retain age as a predictor. Five frozen Formula folds pass exact fresh
+inference in Sprint 060; real extrapolation and quality/search acceptance remain
+open. Inference requires new positive age values in the same normalized units.
diff --git a/docs/v1/frequency-severity.md b/docs/v1/frequency-severity.md
index 7c95506..5736d39 100644
--- a/docs/v1/frequency-severity.md
+++ b/docs/v1/frequency-severity.md
@@ -53,3 +53,8 @@ selection by aggregate-loss quality requires an explicit workflow evaluation.
 Tests verify three-round component training, products, units, row reordering,
 aggregate rejection and fresh-process mixed-feature persistence. Real A9
 quality/joins, joint selection, AFT and CUDA remain open.
+
+Sprint 057 binds matched positive-payment counts/totals to the frozen A9 policy
+rows. Sprint 058 trains both public recipes on all five packets and verifies exact
+fresh replay of all named outputs. Component selection remains independent and is
+recorded as such. These bounded runs do not establish joint aggregate quality.
diff --git a/docs/v1/gamma.md b/docs/v1/gamma.md
index 8c8e347..3110500 100644
--- a/docs/v1/gamma.md
+++ b/docs/v1/gamma.md
@@ -45,3 +45,9 @@ numeric/categorical/missing/unseen input composition.
 Independent tests verify derivatives, offset-aware base, three rounds, weight
 semantics, rejected updates and persistence. Real A8 quality/selection evaluation,
 dispersion/calibration, Tweedie/composition/A9, AFT/A10 and CUDA remain open.
+
+The current A8 evaluation worker binds individual eligible positive claims and
+original weights to Gamma, retaining explicit positive-claim output semantics.
+Five frozen policy-grouped severity folds pass bounded validation and exact fresh
+inference in Sprint 055. Selected scores are Gamma objectives, not estimated
+dispersion likelihoods. Full A8 quality/search acceptance remains open.
diff --git a/docs/v1/poisson.md b/docs/v1/poisson.md
index b10ffa6..1f267a7 100644
--- a/docs/v1/poisson.md
+++ b/docs/v1/poisson.md
@@ -51,3 +51,9 @@ nonfinite geometry and means outside positive float64 range fail explicitly.
 Tests verify independent geometry, offset-aware initialization, three-round
 trees, zero counts and exposure scaling. Real A7 deviance/calibration comparisons,
 Gamma/A8, Tweedie/composition/A9, AFT/A10 and CUDA remain required work.
+
+The current A7 evaluation worker binds frozen exposure vectors to the public
+structure role and persists a period-count output tag with the raw model.
+Prediction packets must supply new exposure; extra offsets and exposure on
+other tasks are rejected. Five frozen frequency folds pass bounded validation
+and exact fresh inference in Sprint 054. Full A7 quality/search gates stay open.
diff --git a/docs/v1/quantile.md b/docs/v1/quantile.md
index 9cc97b8..f859cee 100644
--- a/docs/v1/quantile.md
+++ b/docs/v1/quantile.md
@@ -48,3 +48,10 @@ Inference stores the solved scalar leaf values in the existing tree format,
 without residuals, training rows or a training objective. These tests establish
 CPU mechanics and D3 solver correctness, not real A5 quality, quantile coverage,
 noncrossing guarantees, agent-author effort, CUDA or performance results.
+
+The current evaluation worker composes three independent recipes at the frozen
+A5 levels 0.1, 0.5 and 0.9. Its evaluation bundle retains that level order, three
+scalar models and per-level stopping/selection diagnostics. Predictions remain
+raw; crossing rows are reported without sorting. All five calendar-only Bike
+rolling origins pass bounded validation and fresh inference in Sprint 053.
+This does not establish calibrated intervals or real-data quality acceptance.
diff --git a/docs/v1/ranking.md b/docs/v1/ranking.md
index 83eca9a..459afe7 100644
--- a/docs/v1/ranking.md
+++ b/docs/v1/ranking.md
@@ -52,3 +52,10 @@ Independent tests verify pair geometry, logistic finite differences, query
 isolation, rank tie permutations, three-round tree composition, offsets and
 fresh-process persistence. Real A4 evaluation, quantile/penalized leaves, CUDA
 and external-library quality/performance comparisons remain open.
+
+The current A4 evaluation adapter accepts contiguous disjoint integer/string
+query IDs, one weight per query and explicit integer training/validation row IDs.
+It maps query groups to public structure roles, preserves source row IDs for ties,
+and rejects ordinary row weights. Pairwise and lambda modes use the same public
+recipe; fresh inference needs features and row IDs only. Sprint 061 verifies
+synthetic weighted parity and replay. Real MSLR binding/quality remains open.
diff --git a/docs/v1/tweedie.md b/docs/v1/tweedie.md
index dfb7256..b1d6b9a 100644
--- a/docs/v1/tweedie.md
+++ b/docs/v1/tweedie.md
@@ -55,3 +55,9 @@ Independent tests cover three powers, zero-target finite differences, intercepts
 three rounds, annualized weights, rejected steps and persistence. Frequency–
 severity composition, real A9 quality, calibrated tails, AFT/A10 and CUDA remain
 separate required work.
+
+The current direct A9 worker requires annualized targets and positive exposure
+weights, fixes evaluation power at 1.5 and persists annualized output metadata.
+Five frozen aggregate folds pass bounded validation and exact fresh inference
+in Sprint 056. Independent checks verify weights and period-unit conversion.
+Frequency-severity evaluation and full A9 quality/search remain separate work.
diff --git a/learnings/2026-09-06-v1-aggregate-worker.md b/learnings/2026-09-06-v1-aggregate-worker.md
new file mode 100644
index 0000000..d9ed482
--- /dev/null
+++ b/learnings/2026-09-06-v1-aggregate-worker.md
@@ -0,0 +1,48 @@
+# 2026-09-06: Annualized aggregate targets use exposure weights
+
+## Context
+
+Parent d7622f4. A9's frozen exporter binds eligible annualized paid totals and
+exposure weights, but the current worker lacked a direct aggregate path.
+
+## Decision or Result
+
+Use public Tweedie at fixed evaluation power 1.5, positive exposure weights and
+no additional offset. Persist explicit annualized output and power metadata.
+Reject missing/nonpositive weights, negative targets and extra exposure/offsets.
+The worker consumes a verified packet; the exporter establishes the weight's
+source meaning. An arbitrary standalone packet cannot prove its own provenance.
+
+## Changes
+
+- Current worker, inference and smoke harness support direct A9 aggregate means.
+- Weighted direct parity, selected objective, final/best selection and fresh replay.
+- Invalid weight/target/unit inputs and altered persisted power are rejected.
+- [Sprint 056](../v1-sprints/056-aggregate-worker.md).
+
+## Verification
+
+- Both direct A9 cases failed on unsupported application before implementation.
+- Full CPU regression: 881 passed; current tests include independent weighted
+  objective recomputation and persisted power validation.
+- Five frozen folds pass exact fresh replay, independent weighted objectives and
+  exact exposure-weight checks; target/period conversions pass. Source/output
+  hashes match [evidence](../benchmarks/v1/evidence/aggregate-056/README.md).
+- Ruff, strict MkDocs and whitespace checks pass. Commands use
+  UV_CACHE_DIR=/tmp/openboost-research-uv-cache and uv run --no-sync;
+  macOS/Python 3.12.12/NumPy 2.3.5.
+
+## Failed Attempts
+
+Missing support was reproduced without changing the frozen input population.
+No second log-exposure offset or count-likelihood substitution was used.
+
+## Risks and Follow-ups
+
+Frequency-severity composition still needs eligible paid-event counts matched to
+payment totals. Raw A7 claim counts do not satisfy that requirement. Complete
+A9 quality/search, other adapters, D5 and GPU remain open.
+
+## Commits
+
+- This direct A9 aggregate slice; parent d7622f4. Local only, no push.
diff --git a/learnings/2026-09-06-v1-candidate-row-hash.md b/learnings/2026-09-06-v1-candidate-row-hash.md
new file mode 100644
index 0000000..b53839a
--- /dev/null
+++ b/learnings/2026-09-06-v1-candidate-row-hash.md
@@ -0,0 +1,51 @@
+# 2026-09-06: Hoist immutable row identity out of candidate enumeration
+
+## Context
+
+Parent d1dede6. Sprint 050 measured repeated row hashing as a major cost within
+candidate enumeration on the full Covertype input. Each candidate in one call
+uses the same immutable Histogram.rows array.
+
+## Decision or Result
+
+Compute the existing digest once per candidates invocation and reuse the identical
+string. No global cache, hash format, candidate ordering, statistics, feasibility
+or routing changes. Histogram aggregation is intentionally a separate optimization.
+
+## Changes
+
+- ops.candidates: one local invariant digest replaces repeated hashing.
+- Two tests with numeric/categorical/missing features, full/subset rows and zero
+  weights verify exact digest identity, aggregate conservation and one hash call.
+- [Sprint 051](../v1-sprints/051-candidate-row-hash.md).
+
+## Verification
+
+Use UV_CACHE_DIR=/tmp/openboost-research-uv-cache.
+
+- `uv run --no-sync pytest tests/v1/test_candidate_hash.py -q -o addopts=''`:
+  both initially failed with 16 calls rather than one; both pass after hoisting.
+- `uv run --no-sync pytest tests/ -m 'not gpu and not benchmark' -q --tb=short`:
+  848 passed, including independent histogram/tree references and recipe tests.
+- Uninstrumented Sprint 049 fold-zero job rerun under the unchanged 90-second
+  process cap and one thread, after the regression suite completed: passes in
+  87.4 seconds. Exact fresh seven-class predictions/source IDs and normalized
+  probabilities verified under a 30-second replay cap.
+- Source/raw artifact hashes match [row-hash-051](../benchmarks/v1/evidence/row-hash-051/README.md).
+  Ruff and strict MkDocs pass on macOS/Python 3.12.12/NumPy 2.3.5.
+
+## Failed Attempts
+
+The focused test reproduced redundant work without a mathematical failure.
+The same row bytes now produce the same digest once; no digest weakening or
+identity bypass was used.
+
+## Risks and Follow-ups
+
+One local rerun is not a fair speed comparison or full five-fold A3 acceptance.
+Histogram aggregation remains the other measured cost. Full required quality,
+D5, CUDA and adoption gates remain open. No push or publication.
+
+## Commits
+
+- This invariant-hash slice; parent d1dede6.
diff --git a/learnings/2026-09-06-v1-composition-worker.md b/learnings/2026-09-06-v1-composition-worker.md
new file mode 100644
index 0000000..600bb2b
--- /dev/null
+++ b/learnings/2026-09-06-v1-composition-worker.md
@@ -0,0 +1,46 @@
+# 2026-09-06: Execute matched frequency-severity through public components
+
+## Context
+
+Parent e603f1f. Sprint 057 verified matched positive-payment inputs; composition
+training and persisted real-data replay remained untested in the current workflow.
+
+## Decision or Result
+
+Use paid_loss_problems for Poisson paid-event counts and Gamma positive policy
+averages weighted by paid count. Select each component independently using its
+own validation objective; persist both roles with FrequencySeverity. Emit named
+rate/count/severity/annualized/period outputs. Do not claim joint selection.
+
+## Changes
+
+- Dedicated strict composition worker and inference-only replay command.
+- Five-fold hash-bound smoke harness, combined fit budget and exact named replay.
+- Direct public parity and invalid contract tests; no foundation code changes.
+- [Sprint 058](../v1-sprints/058-composition-worker.md).
+
+## Verification
+
+- Focused tests: 9 passed. Full CPU regression: 901 passed.
+- Tests compare component model identities, stopping and fresh-process outputs;
+  reject test/offset/weight injections, overlapping policies and invalid counts.
+- All five real fits and named-array replays pass; products and exposure units
+  pass. Binding/source/output hashes match
+  [evidence](../benchmarks/v1/evidence/composition-058/README.md).
+- Ruff, strict MkDocs and whitespace pass. Commands use uv run --no-sync with
+  UV_CACHE_DIR=/tmp/openboost-research-uv-cache; macOS/Python 3.12.12/NumPy 2.3.5.
+
+## Failed Attempts
+
+Lint corrected test import ordering. No source eligibility, units or performance
+caps were changed to obtain passing tests.
+
+## Risks and Follow-ups
+
+Independent component selection is not aggregate-loss joint selection. Full A9
+search/quality, remaining applications, D5 and CUDA remain open. The paired mean
+is not a calibrated compound distribution. Hashes are not OS access isolation.
+
+## Commits
+
+- This composition execution slice; parent e603f1f. Local only, no push.
diff --git a/learnings/2026-09-06-v1-count-worker.md b/learnings/2026-09-06-v1-count-worker.md
new file mode 100644
index 0000000..e5c7c38
--- /dev/null
+++ b/learnings/2026-09-06-v1-count-worker.md
@@ -0,0 +1,47 @@
+# 2026-09-06: Bind count exposure exactly once in current evaluation
+
+## Context
+
+Parent 003fdac. The frozen A7 worker packet contains period counts and separate
+exposure vectors. The public Poisson recipe already represents exposure as a
+structure role, but the current evaluation worker had no A7 path.
+
+## Decision or Result
+
+Bind exposure to the public structure role without adding it to weights or
+supplying a second log offset. Persist raw log-rate models with an explicit
+period-count output tag. Inference requires new positive aligned exposure.
+Extra offsets and exposure on foreign applications are rejected.
+
+## Changes
+
+- Current worker and inference support explicit A7 count/exposure semantics.
+- Real-data harness includes exposure in prediction-only replay packets.
+- Tests cover weighted final/best direct parity, independent likelihood,
+  exposure scaling, fresh persistence and malformed/foreign inputs.
+- [Sprint 054](../v1-sprints/054-count-worker.md).
+
+## Verification
+
+- Both new direct-parity cases failed on missing A7 support before implementation.
+- Current worker suite: 47 passed. Full CPU suite: 866 passed.
+- Commands use UV_CACHE_DIR=/tmp/openboost-research-uv-cache and uv run --no-sync.
+- All five full-data folds pass with exact fresh exposure-aware replay.
+- Independent NLL checks pass at rtol=1e-12/atol=1e-14; the initial 1e-13 relative
+  check failed at a 2.4e-14 absolute discrepancy. Raw differences and math.fsum
+  checks are retained in [evidence](../benchmarks/v1/evidence/count-054/README.md).
+- Ruff, strict MkDocs and diff whitespace checks passed.
+
+## Failed Attempts
+
+Missing adapter support was reproduced without altering the mathematical recipe.
+No exposure-as-weight approximation or implicit unit-exposure fallback was used.
+
+## Risks and Follow-ups
+
+Real-data integration is not a quality search or calibrated rate/deviance result.
+All remaining adapters, searches, D5, CUDA and adoption gates stay open.
+
+## Commits
+
+- This A7 worker slice; parent 003fdac. Local only, no push.
diff --git a/learnings/2026-09-06-v1-covertype-profile.md b/learnings/2026-09-06-v1-covertype-profile.md
new file mode 100644
index 0000000..9198dfe
--- /dev/null
+++ b/learnings/2026-09-06-v1-covertype-profile.md
@@ -0,0 +1,55 @@
+# 2026-09-06: Measure the full Covertype CPU bottleneck
+
+## Context
+
+Merged main 078ca61 contains Sprint 049's five 90-second Covertype timeouts.
+Work continues locally on codex/covertype-cpu-profile. A static hypothesis favored
+repeated prediction-time binning, but no timing evidence supported that priority.
+
+## Decision or Result
+
+A Unix diagnostic wrapper retains cProfile output on a 60-second soft alarm,
+with the existing process runner imposing a 90-second hard cap. Run the exact
+full-data fold-zero job under one thread. It exits 124 as intended; no fit pass.
+Histogram aggregation uses about 30 seconds and hash updates about 22 seconds
+in the interrupted instrumented window. Candidate generation repeatedly hashes
+the same immutable row array. Prediction transforms account for about 2.28 seconds
+cumulative. Prioritize invariant candidate row hashing before cache redesign.
+
+## Changes
+
+- profile_worker.py and two tests for result preservation, deadline artifacts
+  and restoration of the process alarm state. Unix diagnostic only.
+- [Sprint 050](../v1-sprints/050-covertype-profile.md) and
+  [raw statistics/stacks](../benchmarks/v1/evidence/profile-050/README.md).
+
+## Verification
+
+Use UV_CACHE_DIR=/tmp/openboost-research-uv-cache.
+
+- `uv run --no-sync pytest tests/v1/test_profile_worker.py -q -o addopts=''`: 2 passed.
+- `uv run --no-sync pytest tests/ -m 'not gpu and not benchmark' -q --tb=short`: 846 passed.
+- Changed-file Ruff and strict MkDocs pass. Source and raw artifact hashes match.
+- Exact diagnostic worker command, job/input hashes and outer outcome are recorded
+  in manifest.json. It uses Sprint 049 fold zero, 60-second soft/90-second hard
+  limits, one thread, local macOS/Python 3.12.12/NumPy 2.3.5. No memory cap or CUDA.
+
+## Failed Attempts
+
+The incomplete profile is intentionally not counted as a successful fit. Stack
+snapshots support the observed histogram/hash paths. The initial prediction-cache
+priority hypothesis is not supported by this window. Do not add overlapping
+cumulative timings or extrapolate an end-to-end speed ratio from this run.
+
+## Risks and Follow-ups
+
+cProfile adds overhead; a local regression run overlapped the beginning of the
+window. No performance acceptance or competitor comparison is implied. Next hoist
+the per-invocation row hash with exact conformance tests, then rerun the unchanged
+bounded workload. Histogram layout/aggregation is a separate follow-up. Required
+real quality, D5, CUDA and adoption remain open. No new push or PR is authorized
+by the earlier one-off merge request; this slice remains local.
+
+## Commits
+
+- This profiling slice; parent 078ca61.
diff --git a/learnings/2026-09-06-v1-cpu-exit-review.md b/learnings/2026-09-06-v1-cpu-exit-review.md
new file mode 100644
index 0000000..dbdbbb3
--- /dev/null
+++ b/learnings/2026-09-06-v1-cpu-exit-review.md
@@ -0,0 +1,43 @@
+# 2026-09-06: Distinguish CPU implementation from phase acceptance
+
+## Context
+
+Parent a2a03e4. Current CPU adapters now span A1–A12, while repeated continuation
+risked conflating validation plumbing with formal CPU readiness and GPU progress.
+
+## Decision or Result
+
+[This review](../v1-sprints/062-cpu-exit-and-gpu-entry.md) reconciles current
+capabilities with unfinished source, workflow, installed D5 and formal E5 gates.
+Keep current F3 ordering explicit; no GPU implementation exists. Name the first
+B12 vertical path and acceptance without declaring an unapproved phase overlap.
+
+## Changes
+
+- Current capability/application position and a finite ordered remaining list.
+- Next slice: installed same-seed/different-ID RNG and changed-data preparation
+  probes, complementing existing scheduling permutations/retries/failures.
+- GPU first-slice scope and parity/cost criteria; no new CPU objective expansion.
+
+## Verification
+
+Read active F1/F2/F3 and E0/E1/E2/E5 criteria, device construction design,
+current worker/search call paths, installed scheduler verifier and sprint evidence.
+No new test run claimed; latest regression is Sprint 061's 923 passes. Strict
+MkDocs and whitespace checks passed. Sealed held-out tasks were not inspected.
+
+## Failed Attempts
+
+No experimental attempt in this review. Historical missing-objective/stopping
+claims were superseded using current evidence rather than copied forward.
+
+## Risks and Follow-ups
+
+No full CPU gate is declared. MSLR/source obligations, real searches, joint A9
+selection, current evidence reconciliation and formal author cohorts remain.
+Starting GPU early requires an explicit sequencing amendment, not silently
+forgetting these obligations. No push or publication.
+
+## Commits
+
+- This review slice; parent a2a03e4.
diff --git a/learnings/2026-09-06-v1-current-ranking-worker.md b/learnings/2026-09-06-v1-current-ranking-worker.md
new file mode 100644
index 0000000..1a52368
--- /dev/null
+++ b/learnings/2026-09-06-v1-current-ranking-worker.md
@@ -0,0 +1,43 @@
+# 2026-09-06: Preserve query weights and source-row ties in current ranking
+
+## Context
+
+Parent ca73a2d. The public CPU ranking recipe and baseline query validator existed,
+but the current worker had no A4 path. Real MSLR access/binding remains unresolved.
+
+## Decision or Result
+
+Reuse strict contiguous/disjoint query validation. Map groups to integer public
+structure roles and repeat explicit per-query weights across their rows. Preserve
+integer source row IDs for NDCG tie behavior; reject ordinary row weights and
+ambiguous/overlapping identities. Both pairwise and lambda modes are exposed.
+
+## Changes
+
+- Current worker and inference support A4 raw scores and query-weighted selection.
+- Source manifests include the imported query validator.
+- Four direct/fresh cases plus seven invalid-input cases; no core changes.
+- [Sprint 061](../v1-sprints/061-ranking-worker.md).
+
+## Verification
+
+- Current worker suite: 83 passed, including four new fresh-process replays.
+- Full CPU regression: 923 passed. Ruff, strict MkDocs and whitespace pass.
+  Commands use uv run --no-sync and UV_CACHE_DIR=/tmp/openboost-research-uv-cache;
+  macOS/Python 3.12.12/NumPy 2.3.5.
+- Configured build/v1-data has no MSLR/ranking-fold files. No real-data fit claimed.
+
+## Failed Attempts
+
+No data substitution or modified query population was used. The missing real-data
+prerequisite remains explicit, rather than counting synthetic parity as A4 quality.
+
+## Risks and Follow-ups
+
+Per-query pair enumeration is quadratic. Real source agreement, data binding,
+quality/search, D5 and CPU phase acceptance are open. Next review those finite
+prerequisites and the GPU transition; CUDA remains unimplemented.
+
+## Commits
+
+- This current A4 adapter slice; parent ca73a2d. Local only, no push.
diff --git a/learnings/2026-09-06-v1-histogram-gather.md b/learnings/2026-09-06-v1-histogram-gather.md
new file mode 100644
index 0000000..b7e5929
--- /dev/null
+++ b/learnings/2026-09-06-v1-histogram-gather.md
@@ -0,0 +1,54 @@
+# 2026-09-06: Reuse selected statistics across histogram features
+
+## Context
+
+Parent 215d85f. Sprint 050 identified histogram aggregation as a major full-input
+CPU cost. The existing loop repeatedly selected identical statistic rows for each
+feature. Sprint 051 removed a separate repeated row-hashing cost.
+
+## Decision or Result
+
+Gather selected rows once, preserve the original C-order parent sum, then retain
+contiguous statistic columns for all feature bincount calls. Each bin receives
+identical weights in identical row order. This is local scratch, not a persistent
+cache, new public abstraction or shared-run fusion.
+
+## Changes
+
+- `ops.histogram`: reuse selected columns without changing statistic semantics.
+- Exact legacy-formula tests cover mixed/missing features, vector weighted fields,
+  independent fields, cancellation, and full/reordered/empty row selections.
+- Frozen-packet replay harness verifies input hashes and fresh model inference.
+- [Sprint 052](../v1-sprints/052-histogram-gather.md) records full-input outcomes.
+
+## Verification
+
+Use `UV_CACHE_DIR=/tmp/openboost-research-uv-cache`.
+
+- All three new cases failed the buffer-reuse assertion before the change.
+- `uv run --no-sync pytest tests/ -m 'not gpu and not benchmark' -q --tb=short`:
+  851 passed, including independent candidate/routing/tree and recipe checks.
+- Fold zero has byte-identical model persistence and exact prediction arrays
+  against Sprint 051. Source hashes match the evaluated implementation.
+- Full frozen-fold evidence: [histogram-052](../benchmarks/v1/evidence/histogram-052/README.md).
+
+## Failed Attempts
+
+No mathematical failure was reproduced. Repeated gathering was redundant work.
+Preserving the parent reduction layout avoids introducing floating-point changes
+while optimizing the per-feature aggregation path.
+
+## Risks and Follow-ups
+
+The contiguous buffer retains approximately rows times fields times eight bytes;
+transpose construction can transiently retain two such arrays. Peak process
+memory was not measured. No memory improvement or stable speed ratio is claimed.
+Full quality searches, remaining adapters, author probes and CUDA remain open.
+
+## Commits
+
+- This histogram-gather slice; parent 215d85f.
+
+Final verification: all five folds pass (67.8–70.3 seconds), exact fresh replay on
+each, and all source/output hashes match. Focused tests pass again (3), Ruff and
+strict MkDocs pass. These outcomes close the bounded integration failure only.
diff --git a/learnings/2026-09-06-v1-paid-event-binding.md b/learnings/2026-09-06-v1-paid-event-binding.md
new file mode 100644
index 0000000..6a88c8b
--- /dev/null
+++ b/learnings/2026-09-06-v1-paid-event-binding.md
@@ -0,0 +1,44 @@
+# 2026-09-06: Match composition frequency to eligible severity events
+
+## Context
+
+Parent a041f1b. Direct A9 aggregate integration passed, but the public composition
+helper accepts declared aggregates and cannot prove payment eligibility or joins.
+A7 raw claim counts must not substitute for matched positive-payment counts.
+
+## Decision or Result
+
+Independently reconstruct count/amount aggregates from retained claim rows and
+require exact equality with frozen source arrays. Bind to hashed A9 packets and
+training IDs; require exact exposure weights and annualized target agreement.
+Keep source population/preprocessing unchanged and emit no test labels.
+
+## Changes
+
+- paid_event_data.py: checked source-to-composition binding and five-fold CLI.
+- Eleven tests exercise semantic mismatches and wrong partition/unit inputs.
+- [Sprint 057](../v1-sprints/057-paid-event-binding.md).
+
+## Verification
+
+- Focused tests: 11 passed. Full CPU regression: 892 passed.
+- All five real bindings pass; output/source hashes match the retained
+  [manifest](../benchmarks/v1/evidence/paid-events-057/README.md).
+- Ruff, strict MkDocs and whitespace pass. Commands use uv run --no-sync and
+  UV_CACHE_DIR=/tmp/openboost-research-uv-cache; macOS/Python 3.12.12/NumPy 2.3.5.
+
+## Failed Attempts
+
+Lint found an import ordering issue, corrected before closure. No source data,
+rows, thresholds or eligibility criteria were changed to obtain passing results.
+
+## Risks and Follow-ups
+
+The verifier is structurally independent aggregation over retained claims, not
+an independently implemented raw ARFF parser. Frozen hashes bind the upstream
+reader. Hash checks are not OS isolation. Next fit and replay composition on all
+five packets; joint selection, full A9 quality, other adapters, D5 and CUDA stay open.
+
+## Commits
+
+- This input-binding slice; parent a041f1b. Local only, no push.
diff --git a/learnings/2026-09-06-v1-quantile-worker.md b/learnings/2026-09-06-v1-quantile-worker.md
new file mode 100644
index 0000000..0493276
--- /dev/null
+++ b/learnings/2026-09-06-v1-quantile-worker.md
@@ -0,0 +1,46 @@
+# 2026-09-06: Compose A5 quantiles through public scalar recipes
+
+## Context
+
+Parent f9bf01f. The frozen Bike exporter existed, but the current OpenBoost worker
+only accepted A1/A2/A3/A6/A11. Historical baseline integration was not evidence
+that the redesigned foundation supported the real A5 workflow.
+
+## Decision or Result
+
+Compose three independent public quantile recipes at frozen levels 0.1/0.5/0.9.
+Persist explicit ordered models and level metadata, separate stopping and best
+selection, and raw predictions. Report crossings without post-hoc sorting.
+No new public/core primitive was necessary.
+
+## Changes
+
+- Current worker, inference bundle and smoke harness now support A5.
+- Tests cover weighted direct parity, stopping selection, fresh replay, malformed
+  schemas and preservation of crossing predictions.
+- [Sprint 053](../v1-sprints/053-quantile-worker.md) records scope and reflection.
+
+## Verification
+
+- Six initial tests failed on unsupported A5 before implementation.
+- All five frozen Bike origins pass with exact fresh inference and source IDs.
+- Independent NumPy pinball recomputation matches each selected score.
+- Source/output hashes match [raw evidence](../benchmarks/v1/evidence/quantile-053/README.md).
+- `uv run --no-sync pytest tests/ -m "not gpu and not benchmark" -q --tb=short`: 858 passed.
+- Ruff, strict MkDocs and diff whitespace passed; macOS/Python 3.12.12/NumPy 2.3.5.
+  Commands use UV_CACHE_DIR=/tmp/openboost-research-uv-cache.
+
+## Failed Attempts
+
+The missing adapter was reproduced without changing the frozen data split or
+objective. No quality failure was corrected or hidden by this integration.
+
+## Risks and Follow-ups
+
+Independent levels may cross. No observed crossings here is not a guarantee.
+Real searches, calibrated quality, remaining adapters, D5 and CUDA stay open.
+Next address A7 count/exposure binding; offsets must not be silently ignored.
+
+## Commits
+
+- This current A5 worker slice; parent f9bf01f. Local only, no push.
diff --git a/learnings/2026-09-06-v1-severity-worker.md b/learnings/2026-09-06-v1-severity-worker.md
new file mode 100644
index 0000000..5126f1c
--- /dev/null
+++ b/learnings/2026-09-06-v1-severity-worker.md
@@ -0,0 +1,47 @@
+# 2026-09-06: Preserve claim-level units in the current Gamma worker
+
+## Context
+
+Parent 606487c. The frozen A8 exporter retains positive joined claim records,
+policy-grouped splits and unit claim weights. The current worker lacked A8
+support despite an existing public Gamma recipe.
+
+## Decision or Result
+
+Use the scalar Gamma recipe and original weights, retaining a log-mean model
+and explicit positive-claim output. Do not introduce exposure or average claims
+by policy. The selection score is the Gamma objective y/mean+log(mean), not a
+fitted-dispersion likelihood or deviance statistic.
+
+## Changes
+
+- Current worker, inference and smoke harness add A8 without core changes.
+- Weighted direct parity covers final/best selection and fresh persistence.
+- Nonpositive targets, exposure and extra offsets fail explicitly.
+- [Sprint 055](../v1-sprints/055-severity-worker.md).
+
+## Verification
+
+- Both new direct A8 cases failed on unsupported application before implementation.
+- Current worker suite: 53 passed, including independent selected objective checks.
+- Full CPU regression: 872 passed. Ruff, strict MkDocs and whitespace passed.
+- All five frozen folds pass with exact fresh replay; independent Gamma objectives
+  agree at rtol=1e-12/atol=1e-14. Source/output hashes match the
+  [raw evidence](../benchmarks/v1/evidence/severity-055/README.md).
+- Commands use UV_CACHE_DIR=/tmp/openboost-research-uv-cache and uv run --no-sync;
+  macOS/Python 3.12.12/NumPy 2.3.5.
+
+## Failed Attempts
+
+The missing adapter was reproduced without changing the foundation or frozen
+population. No averaging or exposure weighting approximation was introduced.
+
+## Risks and Follow-ups
+
+The next required row is A9, with distinct annualized target/exposure-weight
+semantics and composition requirements. Real searches, quality, D5 and GPU
+remain open. Short integration runs do not establish calibrated severity models.
+
+## Commits
+
+- This A8 worker slice; parent 606487c. Local only, no push.
diff --git a/learnings/2026-09-06-v1-structured-worker.md b/learnings/2026-09-06-v1-structured-worker.md
new file mode 100644
index 0000000..981d42f
--- /dev/null
+++ b/learnings/2026-09-06-v1-structured-worker.md
@@ -0,0 +1,45 @@
+# 2026-09-06: Keep Formula structure outside parameter-tree inputs
+
+## Context
+
+Parent 3ff97c3. The A12 exporter provided ordinary GBDT inputs with appended age,
+but the current Formula recipe requires age as a separate structural argument.
+
+## Decision or Result
+
+Emit a separate formula-input packet retaining the identical encoded composition
+features and normalized age/28. Bind two raw parameters and public saturation
+prediction, with explicit structured inference. Preserve the ordinary packet.
+
+## Changes
+
+- Exporter/current worker/inference/smoke add structured A12 integration.
+- Tests cover weighted recipe parity, fresh replay, missing/invalid inference age
+  and exact exclusion of appended age from tree predictors.
+- [Sprint 060](../v1-sprints/060-structured-worker.md).
+
+## Verification
+
+- Full CPU regression: 912 passed. Ruff, strict MkDocs and whitespace pass.
+- Five frozen folds pass with exact replay; independent formula/score and exact
+  feature/age/source-ID checks pass. Source/output hashes match
+  [evidence](../benchmarks/v1/evidence/structured-060/README.md).
+- Commands use uv run --no-sync and UV_CACHE_DIR=/tmp/openboost-research-uv-cache;
+  real export additionally uses --offline --with xlrd. macOS/Python 3.12.12/NumPy 2.3.5.
+
+## Failed Attempts
+
+The initial binding test ignored preprocessing missing-indicator columns; changed
+it to assert removal of exactly the appended age column. Initial real export
+failed due to missing xlrd, before fitting. Cached ephemeral dependency retry
+passed; no project environment mutation or source change.
+
+## Risks and Follow-ups
+
+No real extrapolation/global-control/quality acceptance. A4, searches/D5 and CPU
+phase gates remain; GPU is unimplemented. The adapter cannot prove the units of
+arbitrary standalone packets; frozen exporter hashes bind those semantics.
+
+## Commits
+
+- This structured A12 slice; parent 3ff97c3. Local only, no push.
diff --git a/learnings/2026-09-06-v1-survival-worker.md b/learnings/2026-09-06-v1-survival-worker.md
new file mode 100644
index 0000000..686e032
--- /dev/null
+++ b/learnings/2026-09-06-v1-survival-worker.md
@@ -0,0 +1,46 @@
+# 2026-09-06: Bind event/right censoring in current A10 evaluation
+
+## Context
+
+Parent 20d9b7c. The public fixed-scale AFT recipe existed, while the current
+real-data worker did not accept the frozen time/event packets.
+
+## Decision or Result
+
+Convert exact events to equal positive bounds and right censoring to positive
+lower/infinite upper bounds. Fix sigma=1 explicitly and persist its metadata.
+Emit [log-time location, sigma], matching the evaluation schema. Validation
+selection uses censored likelihood, not squared error or treating censoring as death.
+
+## Changes
+
+- Current worker and inference add A10 with strict time/event/scale validation.
+- Weighted final/best direct parity and fresh replay tests.
+- Independent erfc-based censored NLL checks and malformed input rejection.
+- [Sprint 059](../v1-sprints/059-survival-worker.md).
+
+## Verification
+
+- Both direct A10 tests failed on unsupported application before implementation.
+- Current worker suite: 70 passed.
+- Full CPU regression: 909 passed. All five real folds pass exact replay and
+  independent censored NLL checks; source/output hashes match
+  [evidence](../benchmarks/v1/evidence/survival-059/README.md).
+- Ruff, strict MkDocs and whitespace pass. Commands use uv run --no-sync with
+  UV_CACHE_DIR=/tmp/openboost-research-uv-cache; macOS/Python 3.12.12/NumPy 2.3.5.
+
+## Failed Attempts
+
+A target_kind keyword was initially placed before a positional argument; lint
+and test collection caught the syntax error. Corrected before running workloads.
+No mathematical support, source split or evaluation threshold changed.
+
+## Risks and Follow-ups
+
+Fixed scale and event/right censoring only. Left/interval censoring, calibration,
+IPCW quality, source licensing closure and complete A10 search remain open.
+CPU/GPU transition prerequisites are recorded in the sprint; CUDA is unimplemented.
+
+## Commits
+
+- This A10 worker slice; parent 20d9b7c. Local only, no push.
diff --git a/src/openboost/ops.py b/src/openboost/ops.py
index 5754815..f2ec3c8 100644
--- a/src/openboost/ops.py
+++ b/src/openboost/ops.py
@@ -48,6 +48,11 @@ def histogram(data, fields, rows=None):
     if "unweighted" in fields.roles:
         raise ValueError("apply objective training weights before aggregation")
     selected = _rows(rows, len(data.data.values))
+    selected_values = fields.values[selected]
+    # Preserve the original C-order reduction before laying out contiguous columns.
+    total = _owned(selected_values.sum(axis=0), ndim=1)
+    columns = np.ascontiguousarray(selected_values.T)
+    del selected_values
     sums, counts = [], []
     for feature, bins in enumerate(data.binning.bin_counts):
         codes = np.where(data.missing[feature, selected], bins, data.codes[feature, selected])
@@ -55,8 +60,8 @@ def histogram(data, fields, rows=None):
             _owned(
                 np.column_stack(
                     [
-                        np.bincount(codes, weights=column[selected], minlength=bins + 1)
-                        for column in fields.values.T
+                        np.bincount(codes, weights=column, minlength=bins + 1)
+                        for column in columns
                     ]
                 ),
                 ndim=2,
@@ -69,7 +74,7 @@ def histogram(data, fields, rows=None):
         fields,
         tuple(sums),
         tuple(counts),
-        _owned(fields.values[selected].sum(axis=0), ndim=1),
+        total,
     )
 
 
@@ -97,6 +102,7 @@ def key(self):
 def candidates(hist):
     """Prefix/suffix sums; enumerate both missing routes, including missing-only splits."""
     result = []
+    rows_identity = _identity(hist.rows)
     for feature, (sums, counts) in enumerate(zip(hist.sums, hist.counts, strict=True)):
         prefix = np.cumsum(sums[:-1], axis=0)
         suffix = np.cumsum(sums[:-1][::-1], axis=0)[::-1]
@@ -130,7 +136,7 @@ def candidates(hist):
                         nleft,
                         len(hist.rows) - nleft,
                         hist.data.identity,
-                        _identity(hist.rows),
+                        rows_identity,
                         "categorical" if categorical else "numeric",
                     )
                 )
diff --git a/tests/v1/test_candidate_hash.py b/tests/v1/test_candidate_hash.py
new file mode 100644
index 0000000..59e6b27
--- /dev/null
+++ b/tests/v1/test_candidate_hash.py
@@ -0,0 +1,40 @@
+"""Candidate row identity is invariant across split enumeration."""
+
+import numpy as np
+import pytest
+
+from openboost import MixedData, Problem, ops
+from openboost.binning import Binning
+from openboost.stats import newton
+
+
+@pytest.mark.parametrize("rows", [None, [0, 2, 3, 5]])
+def test_candidate_digest_computed_once_with_exact_identity(monkeypatch, rows):
+    data = MixedData(
+        [[0, "a"], [1, "b"], [2, None], [3, "a"], [None, "b"], [5, "c"]],
+        np.arange(6),
+        ("numeric", "category"),
+        ("numeric", "categorical"),
+    )
+    p = Problem(data, np.zeros((6, 1)), data.row_ids, weight=[1, 0, 2, 1, 3, 1])
+    hist = ops.histogram(
+        Binning.fit(data, bins=6).transform(data), newton(p, np.arange(6.0) - 2, np.ones(6)), rows
+    )
+    original = ops._identity
+    expected = original(hist.rows)
+    calls = []
+
+    def counted(*parts):
+        calls.append(parts)
+        return original(*parts)
+
+    monkeypatch.setattr(ops, "_identity", counted)
+    candidates = ops.candidates(hist)
+    assert len(candidates) > 2
+    assert all(c.rows_identity == expected for c in candidates)
+    assert all(c.data_identity == hist.data.identity for c in candidates)
+    for candidate in candidates:
+        np.testing.assert_allclose(candidate.left + candidate.right, hist.total, atol=1e-14)
+        assert candidate.left_count + candidate.right_count == len(hist.rows)
+    assert len(calls) == 1
+    assert calls[0][0] is hist.rows
diff --git a/tests/v1/test_composition_worker.py b/tests/v1/test_composition_worker.py
new file mode 100644
index 0000000..1b7fd1e
--- /dev/null
+++ b/tests/v1/test_composition_worker.py
@@ -0,0 +1,106 @@
+"""Composition adapters preserve public recipes and named inference roles."""
+
+import subprocess
+import sys
+from pathlib import Path
+
+import numpy as np
+import pytest
+from benchmarks.v1.composition_worker import fit
+
+from openboost import NumericData, RunContext
+from openboost.composition import paid_loss_problems
+from openboost.recipes import gamma, poisson
+
+
+def fixture():
+    arrays = {}
+    for p, ids in [("train", np.arange(6)), ("validation", np.arange(10, 16))]:
+        arrays.update(
+            {
+                f"x_{p}": np.arange(6.0)[:, None],
+                f"row_ids_{p}": ids,
+                f"paid_count_{p}": np.array([0, 2, 1, 0, 3, 1]),
+                f"paid_total_{p}": np.array([0.0, 6, 4, 0, 9, 2]),
+                f"exposure_{p}": np.array([0.1, 0.5, 1, 2, 1, 0.2]),
+            }
+        )
+    job = dict(
+        seed=7,
+        patience=None,
+        config=dict(rounds=3, learning_rate=0.1, bins=8, max_depth=2, reg_lambda=1),
+    )
+    return job, arrays
+
+
+@pytest.mark.parametrize("patience", [None, 1])
+def test_direct_components_and_fresh_replay(patience, tmp_path):
+    job, a = fixture()
+    job["patience"] = patience
+    model, outputs, training = fit(job, a)
+    pairs = []
+    for p in ["train", "validation"]:
+        d = NumericData(a["x_" + p], a["row_ids_" + p], ("x0",))
+        pairs.append(
+            paid_loss_problems(d, a["paid_count_" + p], a["paid_total_" + p], a["exposure_" + p])
+        )
+    for i, (name, recipe) in enumerate([("frequency", poisson), ("severity", gamma)]):
+        r = recipe(
+            pairs[0][i],
+            pairs[1][i],
+            context=RunContext(name, 7),
+            patience=patience,
+            **job["config"],
+        )
+        expected = r.state.model if patience is None else r.state.best_model
+        assert getattr(model, name).identity == expected.identity
+        assert training["components"][name]["stop"]["completed_rounds"] == r.stop.completed_rounds
+    np.testing.assert_allclose(
+        outputs["annualized_mean"], outputs["paid_count_rate"] * outputs["severity_mean"]
+    )
+    np.testing.assert_allclose(
+        outputs["period_mean"], outputs["annualized_mean"] * a["exposure_validation"]
+    )
+    model.save(tmp_path / "model.bin")
+    np.savez(
+        tmp_path / "features.npz",
+        x=a["x_validation"],
+        row_ids=a["row_ids_validation"],
+        exposure=a["exposure_validation"],
+    )
+    script = Path(__file__).resolve().parents[2] / "benchmarks/v1/composition_worker.py"
+    subprocess.run(
+        [
+            sys.executable,
+            str(script),
+            "predict",
+            str(tmp_path / "model.bin"),
+            str(tmp_path / "features.npz"),
+            str(tmp_path / "replay.npz"),
+        ],
+        cwd=tmp_path,
+        check=True,
+    )
+    with np.load(tmp_path / "replay.npz") as b:
+        for k, v in outputs.items():
+            np.testing.assert_array_equal(v, b[k])
+        np.testing.assert_array_equal(b["row_ids"], a["row_ids_validation"])
+
+
+@pytest.mark.parametrize(
+    "bad", ["test", "offset", "weight", "overlap", "count", "zero_exposure", "config"]
+)
+def test_invalid_composition_contract(bad):
+    job, a = fixture()
+    if bad in ["test", "offset", "weight"]:
+        a[bad] = np.ones(6)
+    elif bad == "overlap":
+        a["row_ids_validation"] = a["row_ids_train"]
+    elif bad == "count":
+        a["paid_count_train"][0] = 1
+    elif bad == "zero_exposure":
+        a["exposure_train"][0] = 0
+    else:
+        job["config"]["sampling"] = 0.5
+    with pytest.raises(ValueError):
+        fit(job, a)
diff --git a/tests/v1/test_current_worker.py b/tests/v1/test_current_worker.py
index e558735..b5df04b 100644
--- a/tests/v1/test_current_worker.py
+++ b/tests/v1/test_current_worker.py
@@ -13,7 +13,7 @@
 
 from openboost import NumericData, Problem, RunContext
 from openboost.objectives import Normal
-from openboost.recipes import normal, squared
+from openboost.recipes import gamma, normal, squared, tweedie
 
 
 def test_current_worker_is_available():
@@ -42,11 +42,17 @@ def fixture(app="A1"):
     return job, arrays
 
 
-@pytest.mark.parametrize("app", ["A1", "A11"])
+@pytest.mark.parametrize("app", ["A1", "A11", "A8", "A9"])
 @pytest.mark.parametrize("patience", [None, 1])
 def test_direct_recipe_parity_and_fresh_prediction(app, patience, tmp_path):
     job, arrays = fixture(app)
     job["early_stopping_rounds"] = patience
+    if app in {"A8", "A9"}:
+        for part in ("train", "validation"):
+            arrays["y_" + part] = np.exp(arrays["y_" + part])
+    if app == "A9":
+        arrays["weight_train"] += 0.1
+        arrays["y_train"][0] = 0
     prediction, saved, training = fit(job, arrays)
     problems = []
     for part in ("train", "validation"):
@@ -58,15 +64,30 @@ def test_direct_recipe_parity_and_fresh_prediction(app, patience, tmp_path):
                 y[:, None],
                 data.row_ids,
                 weight=arrays["weight_" + part],
-                raw_width=1 if app == "A1" else 2,
+                raw_width=1 if app in {"A1", "A8", "A9"} else 2,
             )
         )
-    direct = (squared if app == "A1" else normal)(
+    direct = {"A1": squared, "A11": normal, "A8": gamma, "A9": tweedie}[app](
         *problems, context=RunContext("evaluation", 7), patience=patience, **job["config"]
     )
     model = direct.state.model if patience is None else direct.state.best_model
     raw = model.predict(problems[1].data)
-    expected = raw[:, 0] if app == "A1" else Normal.parameters(raw)
+    expected = raw[:, 0] if app in {"A1", "A8", "A9"} else Normal.parameters(raw)
+    if app in {"A8", "A9"}:
+        expected = np.exp(raw[:, 0])
+        loss = np.average(
+            arrays["y_validation"] / expected + np.log(expected),
+            weights=arrays["weight_validation"],
+        )
+        if app == "A8":
+            assert training["selected_validation_gamma_objective"] == pytest.approx(loss)
+        else:
+            loss = np.average(
+                2 * arrays["y_validation"] / np.sqrt(expected) + 2 * np.sqrt(expected),
+                weights=arrays["weight_validation"],
+            )
+            assert training["selected_validation_tweedie_objective"] == pytest.approx(loss)
+            assert saved["power"] == 1.5
     np.testing.assert_array_equal(prediction, expected)
     assert training["selected_model_identity"] == model.identity
     assert training["stop"]["completed_rounds"] == direct.stop.completed_rounds
@@ -114,7 +135,7 @@ def test_unsupported_or_contaminated_inputs_fail(bad):
     elif bad == "threads":
         job["threads"] = 2
     else:
-        job["application"] = "A5"
+        job["application"] = "unsupported"
     with pytest.raises(ValueError):
         fit(job, arrays)
 
@@ -285,3 +306,463 @@ def test_classification_invalid_contracts_fail(bad):
         job["config"]["mode"] = "natural"
     with pytest.raises(ValueError):
         fit(job, arrays)
+
+
+@pytest.mark.parametrize("patience", [None, 1])
+def test_quantile_weighted_direct_and_fresh_replay(patience, tmp_path):
+    from openboost.recipes import quantile
+
+    job, arrays = fixture("A5")
+    job["early_stopping_rounds"] = patience
+    prediction, saved, training = fit(job, arrays)
+    assert saved["quantiles"] == [0.1, 0.5, 0.9]
+    assert prediction.shape == (4, 3)
+    problems = []
+    for part in ("train", "validation"):
+        data = NumericData(arrays["x_" + part], np.arange(len(arrays["x_" + part])), ("x0",))
+        problems.append(
+            Problem(
+                data, arrays["y_" + part][:, None], data.row_ids, weight=arrays["weight_" + part]
+            )
+        )
+    for i, q in enumerate(saved["quantiles"]):
+        direct = quantile(
+            *problems, context=RunContext("evaluation", 7), q=q, patience=patience, **job["config"]
+        )
+        model = direct.state.model if patience is None else direct.state.best_model
+        np.testing.assert_array_equal(prediction[:, i], model.predict(problems[1].data)[:, 0])
+        assert (
+            training["quantile_runs"][i]["stop"]["completed_rounds"] == direct.stop.completed_rounds
+        )
+        residual = arrays["y_validation"] - prediction[:, i]
+        score = np.average(
+            np.maximum(q * residual, (q - 1) * residual), weights=arrays["weight_validation"]
+        )
+        assert training["quantile_runs"][i]["selected_validation_pinball"] == pytest.approx(score)
+    path = tmp_path / "model.bin"
+    path.write_text(json.dumps(saved))
+    packet = tmp_path / "features.npz"
+    np.savez(packet, x=arrays["x_validation"], row_ids=arrays["validation_row_ids"])
+    script = Path(__file__).resolve().parents[2] / "benchmarks/v1/openboost_predict.py"
+    subprocess.run(
+        [sys.executable, str(script), str(path), str(packet), str(tmp_path / "replay.npz")],
+        cwd=tmp_path,
+        check=True,
+    )
+    with np.load(tmp_path / "replay.npz") as replay:
+        np.testing.assert_array_equal(replay["prediction"], prediction)
+        np.testing.assert_array_equal(replay["row_ids"], arrays["validation_row_ids"])
+
+
+@pytest.mark.parametrize("bad", ["order", "count", "width", "features"])
+def test_quantile_saved_schema_rejected(bad):
+    job, arrays = fixture("A5")
+    _, saved, _ = fit(job, arrays)
+    if bad == "order":
+        saved["quantiles"].reverse()
+    elif bad == "count":
+        saved["models"].pop()
+    elif bad == "width":
+        saved["models"][0]["base"] = [0, 0]
+    else:
+        saved["models"][0]["feature_names"] = ["foreign"]
+    with pytest.raises(ValueError):
+        predict_saved(saved, arrays["x_validation"])
+
+
+def test_quantile_crossings_are_not_sorted():
+    from openboost.artifacts import Model
+
+    saved = dict(
+        format="openboost-evaluation-v1",
+        application="A5",
+        output="quantiles",
+        quantiles=[0.1, 0.5, 0.9],
+        models=[Model(("x0",), [v]).record() for v in [3, 2, 1]],
+    )
+    np.testing.assert_array_equal(predict_saved(saved, [[0], [1]]), [[3, 2, 1], [3, 2, 1]])
+
+
+@pytest.mark.parametrize("patience", [None, 1])
+def test_count_exposure_direct_and_fresh(patience, tmp_path):
+    import math
+
+    from openboost.outputs import poisson_mean
+    from openboost.recipes import poisson
+
+    job, arrays = fixture("A7")
+    job["early_stopping_rounds"] = patience
+    problems = []
+    for part in ("train", "validation"):
+        x = arrays["x_" + part]
+        arrays["y_" + part] = np.arange(len(x)) % 3
+        arrays["exposure_" + part] = np.linspace(0.1, 2, len(x))
+        data = NumericData(x, np.arange(len(x)), ("x0",))
+        problems.append(
+            Problem(
+                data,
+                arrays["y_" + part][:, None],
+                data.row_ids,
+                weight=arrays["weight_" + part],
+                structure={"exposure": arrays["exposure_" + part][:, None]},
+            )
+        )
+    direct = poisson(
+        *problems, context=RunContext("evaluation", 7), patience=patience, **job["config"]
+    )
+    prediction, saved, training = fit(job, arrays)
+    model = direct.state.model if patience is None else direct.state.best_model
+    np.testing.assert_array_equal(
+        prediction,
+        poisson_mean(model.predict(problems[1].data), arrays["exposure_validation"])["count_mean"],
+    )
+    assert training["selected_model_identity"] == model.identity
+    y = arrays["y_validation"]
+    nll = np.average(
+        prediction - y * np.log(prediction) + [math.lgamma(v + 1) for v in y],
+        weights=arrays["weight_validation"],
+    )
+    assert training["selected_validation_nll"] == pytest.approx(nll)
+    np.testing.assert_allclose(
+        predict_saved(saved, arrays["x_validation"], exposure=arrays["exposure_validation"] * 2),
+        prediction * 2,
+    )
+    path = tmp_path / "model.bin"
+    path.write_text(json.dumps(saved))
+    packet = tmp_path / "features.npz"
+    np.savez(
+        packet,
+        x=arrays["x_validation"],
+        row_ids=arrays["validation_row_ids"],
+        exposure=arrays["exposure_validation"],
+    )
+    script = Path(__file__).resolve().parents[2] / "benchmarks/v1/openboost_predict.py"
+    subprocess.run(
+        [sys.executable, str(script), str(path), str(packet), str(tmp_path / "replay.npz")],
+        cwd=tmp_path,
+        check=True,
+    )
+    with np.load(tmp_path / "replay.npz") as replay:
+        np.testing.assert_array_equal(replay["prediction"], prediction)
+        np.testing.assert_array_equal(replay["row_ids"], arrays["validation_row_ids"])
+    for exposure in [None, np.zeros(4), np.ones((4, 1)), np.full(4, np.nan)]:
+        with pytest.raises(ValueError):
+            predict_saved(saved, arrays["x_validation"], exposure=exposure)
+
+
+@pytest.mark.parametrize("bad", ["missing", "zero", "shape", "fractional", "offset", "foreign"])
+def test_count_worker_rejects_invalid_exposure_contract(bad):
+    job, arrays = fixture("A7")
+    for part in ["train", "validation"]:
+        arrays["y_" + part] = np.zeros(len(arrays["y_" + part]))
+        arrays["exposure_" + part] = np.ones(len(arrays["y_" + part]))
+    if bad == "missing":
+        arrays.pop("exposure_train")
+    elif bad == "zero":
+        arrays["exposure_validation"][0] = 0
+    elif bad == "shape":
+        arrays["exposure_train"] = arrays["exposure_train"][:, None]
+    elif bad == "fractional":
+        arrays["y_train"][0] = 0.5
+    elif bad == "offset":
+        arrays["offset_train"] = np.zeros(8)
+    else:
+        job["application"] = "A1"
+    with pytest.raises(ValueError):
+        fit(job, arrays)
+
+
+@pytest.mark.parametrize("bad", ["zero", "negative", "exposure", "offset"])
+def test_severity_invalid_contracts(bad):
+    job, arrays = fixture("A8")
+    for part in ("train", "validation"):
+        arrays["y_" + part] = np.exp(arrays["y_" + part])
+    if bad == "zero":
+        arrays["y_train"][0] = 0
+    elif bad == "negative":
+        arrays["y_validation"][0] = -1
+    else:
+        arrays[bad + "_train"] = np.ones(8)
+    with pytest.raises(ValueError):
+        fit(job, arrays)
+
+
+@pytest.mark.parametrize(
+    "bad", ["missing", "zero", "negative_target", "exposure", "offset", "power"]
+)
+def test_aggregate_contract_rejects_invalid_inputs(bad):
+    job, arrays = fixture("A9")
+    arrays["y_train"] = np.abs(arrays["y_train"])
+    arrays["y_validation"] = np.abs(arrays["y_validation"])
+    arrays["weight_train"] += 0.1
+    if bad == "missing":
+        arrays.pop("weight_train")
+    elif bad == "zero":
+        arrays["weight_validation"][0] = 0
+    elif bad == "negative_target":
+        arrays["y_train"][0] = -1
+    elif bad == "power":
+        job["config"]["power"] = 1.9
+    else:
+        arrays[bad + "_train"] = np.ones(8)
+    with pytest.raises(ValueError):
+        fit(job, arrays)
+
+
+def test_aggregate_bundle_power_is_validated():
+    job, arrays = fixture("A9")
+    for part in ("train", "validation"):
+        arrays["y_" + part] = np.abs(arrays["y_" + part])
+        arrays["weight_" + part] += 0.1
+    _, saved, _ = fit(job, arrays)
+    saved["power"] = 1.9
+    with pytest.raises(ValueError):
+        predict_saved(saved, arrays["x_validation"])
+
+
+@pytest.mark.parametrize("patience", [None, 1])
+def test_aft_direct_and_fresh(patience, tmp_path):
+    import math
+
+    from openboost.recipes import aft
+
+    job, arrays = fixture("A10")
+    job["early_stopping_rounds"] = patience
+    problems = []
+    for part in ("train", "validation"):
+        x = arrays["x_" + part]
+        y = arrays["y_" + part] = np.exp(arrays["y_" + part])
+        event = arrays["event_" + part] = np.arange(len(x)) % 2
+        d = NumericData(x, np.arange(len(x)), ("x0",))
+        problems.append(
+            Problem(
+                d,
+                np.column_stack([y, np.where(event, y, np.inf)]),
+                d.row_ids,
+                target_kind="event_right",
+                weight=arrays["weight_" + part],
+            )
+        )
+    direct = aft(
+        *problems, context=RunContext("evaluation", 7), sigma=1, patience=patience, **job["config"]
+    )
+    prediction, saved, training = fit(job, arrays)
+    model = direct.state.model if patience is None else direct.state.best_model
+    np.testing.assert_array_equal(prediction[:, 0], model.predict(problems[1].data)[:, 0])
+    np.testing.assert_array_equal(prediction[:, 1], np.ones(4))
+    z = np.log(arrays["y_validation"]) - prediction[:, 0]
+    losses = [
+        math.log(t) + v * v / 2 + math.log(2 * math.pi) / 2
+        if e
+        else -math.log(math.erfc(v / math.sqrt(2)) / 2)
+        for t, v, e in zip(arrays["y_validation"], z, arrays["event_validation"], strict=True)
+    ]
+    assert training["selected_validation_censored_nll"] == pytest.approx(
+        np.average(losses, weights=arrays["weight_validation"])
+    )
+    path = tmp_path / "model.bin"
+    path.write_text(json.dumps(saved))
+    np.savez(
+        tmp_path / "features.npz", x=arrays["x_validation"], row_ids=arrays["validation_row_ids"]
+    )
+    script = Path(__file__).resolve().parents[2] / "benchmarks/v1/openboost_predict.py"
+    subprocess.run(
+        [
+            sys.executable,
+            str(script),
+            str(path),
+            str(tmp_path / "features.npz"),
+            str(tmp_path / "replay.npz"),
+        ],
+        cwd=tmp_path,
+        check=True,
+    )
+    with np.load(tmp_path / "replay.npz") as replay:
+        np.testing.assert_array_equal(prediction, replay["prediction"])
+    saved["sigma"] = 2
+    with pytest.raises(ValueError):
+        predict_saved(saved, arrays["x_validation"])
+
+
+@pytest.mark.parametrize("bad", ["missing", "fractional", "range", "time", "offset", "scale"])
+def test_survival_invalid_contract(bad):
+    job, arrays = fixture("A10")
+    for part in ("train", "validation"):
+        arrays["y_" + part] = np.exp(arrays["y_" + part])
+        arrays["event_" + part] = np.ones(len(arrays["y_" + part]))
+    if bad == "missing":
+        arrays.pop("event_train")
+    elif bad == "fractional":
+        arrays["event_validation"][0] = 0.5
+    elif bad == "range":
+        arrays["event_train"][0] = 2
+    elif bad == "time":
+        arrays["y_validation"][0] = 0
+    elif bad == "offset":
+        arrays["offset_train"] = np.ones(8)
+    else:
+        job["config"]["sigma"] = 2
+    with pytest.raises(ValueError):
+        fit(job, arrays)
+
+
+@pytest.mark.parametrize("patience", [None, 1])
+def test_formula_direct_structure_and_fresh(patience, tmp_path):
+    from openboost.recipes import formula
+
+    job, arrays = fixture("A12")
+    job["early_stopping_rounds"] = patience
+    problems = []
+    for part in ("train", "validation"):
+        x = arrays["x_" + part]
+        arrays["age_" + part] = np.linspace(0.1, 3, len(x))
+        arrays["y_" + part] = 10 * -np.expm1(-arrays["age_" + part])
+        d = NumericData(x, np.arange(len(x)), ("x0",))
+        problems.append(
+            Problem(
+                d,
+                arrays["y_" + part][:, None],
+                d.row_ids,
+                raw_width=2,
+                weight=arrays["weight_" + part],
+                structure={"x": arrays["age_" + part][:, None]},
+            )
+        )
+    direct = formula(
+        *problems, context=RunContext("evaluation", 7), patience=patience, **job["config"]
+    )
+    prediction, saved, training = fit(job, arrays)
+    model = direct.state.model if patience is None else direct.state.best_model
+    a, b = np.logaddexp(0, model.predict(problems[1].data)).T
+    np.testing.assert_allclose(prediction, a * -np.expm1(-b * arrays["age_validation"]), rtol=1e-14)
+    assert training["selected_model_identity"] == model.identity
+    assert training["selected_validation_half_squared_error"] == pytest.approx(
+        np.average(
+            (prediction - arrays["y_validation"]) ** 2 / 2, weights=arrays["weight_validation"]
+        )
+    )
+    path = tmp_path / "model.bin"
+    path.write_text(json.dumps(saved))
+    np.savez(
+        tmp_path / "features.npz",
+        x=arrays["x_validation"],
+        row_ids=arrays["validation_row_ids"],
+        age=arrays["age_validation"],
+    )
+    script = Path(__file__).resolve().parents[2] / "benchmarks/v1/openboost_predict.py"
+    subprocess.run(
+        [
+            sys.executable,
+            str(script),
+            str(path),
+            str(tmp_path / "features.npz"),
+            str(tmp_path / "replay.npz"),
+        ],
+        cwd=tmp_path,
+        check=True,
+    )
+    with np.load(tmp_path / "replay.npz") as r:
+        np.testing.assert_array_equal(prediction, r["prediction"])
+    for age in [None, np.zeros(4), np.ones((4, 1))]:
+        with pytest.raises(ValueError):
+            predict_saved(saved, arrays["x_validation"], age=age)
+
+
+def ranking_fixture():
+    job, arrays = fixture("A4")
+    arrays.pop("weight_train")
+    arrays.pop("weight_validation")
+    arrays.update(
+        train_row_ids=np.array([7, 6, 5, 4, 3, 2, 1, 0]),
+        validation_row_ids=np.array([11, 10, 9, 8]),
+        query_train=np.repeat(["a", "b"], 4),
+        query_validation=np.repeat(["c", "d"], 2),
+        query_weight_train=np.array([2.0, 0.5]),
+        query_weight_validation=np.array([0.25, 3.0]),
+        y_train=np.array([0, 4, 1, 2, 2, 1, 3, 0]),
+        y_validation=np.array([0, 3, 2, 0]),
+    )
+    return job, arrays
+
+
+@pytest.mark.parametrize("lambdas", [False, True])
+@pytest.mark.parametrize("patience", [None, 1])
+def test_ranking_direct_and_fresh(lambdas, patience, tmp_path):
+    from openboost.recipes import ranking
+
+    job, a = ranking_fixture()
+    job["config"]["lambdas"] = lambdas
+    job["early_stopping_rounds"] = patience
+    prediction, saved, training = fit(job, a)
+    problems = []
+    for part, size in [("train", 4), ("validation", 2)]:
+        ids = a["train_row_ids" if part == "train" else "validation_row_ids"]
+        d = NumericData(a["x_" + part], ids, ("x0",))
+        problems.append(
+            Problem(
+                d,
+                a["y_" + part][:, None],
+                ids,
+                structure={
+                    "query": np.repeat([0, 1], size)[:, None],
+                    "query_weight": np.repeat(a["query_weight_" + part], size)[:, None],
+                },
+            )
+        )
+    direct = ranking(
+        *problems, context=RunContext("evaluation", 7), patience=patience, **job["config"]
+    )
+    model = direct.state.model if patience is None else direct.state.best_model
+    np.testing.assert_array_equal(prediction, model.predict(problems[1].data)[:, 0])
+    assert training["selected_model_identity"] == model.identity
+    assert training["best_validation_score"] == direct.state.best_score
+    path = tmp_path / "model.bin"
+    path.write_text(json.dumps(saved))
+    np.savez(tmp_path / "features.npz", x=a["x_validation"], row_ids=a["validation_row_ids"])
+    script = Path(__file__).resolve().parents[2] / "benchmarks/v1/openboost_predict.py"
+    subprocess.run(
+        [
+            sys.executable,
+            str(script),
+            str(path),
+            str(tmp_path / "features.npz"),
+            str(tmp_path / "replay.npz"),
+        ],
+        cwd=tmp_path,
+        check=True,
+    )
+    with np.load(tmp_path / "replay.npz") as r:
+        np.testing.assert_array_equal(prediction, r["prediction"])
+        np.testing.assert_array_equal(a["validation_row_ids"], r["row_ids"])
+
+
+@pytest.mark.parametrize(
+    "bad",
+    [
+        "fragment",
+        "query_overlap",
+        "row_weight",
+        "row_overlap",
+        "string_rows",
+        "relevance",
+        "query_weight",
+    ],
+)
+def test_ranking_invalid_contract(bad):
+    job, a = ranking_fixture()
+    if bad == "fragment":
+        a["query_train"] = np.array(["a", "b"] * 4)
+    elif bad == "query_overlap":
+        a["query_validation"][0:2] = "a"
+    elif bad == "row_weight":
+        a["weight_train"] = np.ones(8)
+    elif bad == "row_overlap":
+        a["validation_row_ids"][0] = 0
+    elif bad == "string_rows":
+        a["train_row_ids"] = a["train_row_ids"].astype(str)
+    elif bad == "relevance":
+        a["y_validation"][0] = 5
+    else:
+        a["query_weight_train"] = np.ones(8)
+    with pytest.raises(ValueError):
+        fit(job, a)
diff --git a/tests/v1/test_histogram_gather.py b/tests/v1/test_histogram_gather.py
new file mode 100644
index 0000000..c727b31
--- /dev/null
+++ b/tests/v1/test_histogram_gather.py
@@ -0,0 +1,54 @@
+"""Exact histogram semantics while reusing feature-independent row gathers."""
+
+import numpy as np
+import pytest
+
+from openboost import MixedData, Problem
+from openboost.binning import Binning
+from openboost.ops import histogram
+from openboost.stats import vector_newton
+
+
+@pytest.mark.parametrize("rows", [None, [5, 2, 0, 3], []])
+def test_histogram_reuses_contiguous_columns_with_exact_statistics(monkeypatch, rows):
+    data = MixedData(
+        [[0, "a"], [1, "b"], [2, None], [3, "a"], [None, "b"], [5, "c"]],
+        np.arange(6),
+        ("number", "category"),
+        ("numeric", "categorical"),
+    )
+    problem = Problem(data, np.zeros((6, 3)), data.row_ids, raw_width=3, weight=[1, 0, 2, 1, 3, 1])
+    gradient = np.array(
+        [[1e10, -1, 3], [2, 0, 4], [-1e10, 4, -2], [1e-8, -2, 8], [5, 8, 0], [-7, 3, 1.0]]
+    )
+    fields = vector_newton(problem, gradient, np.ones((6, 3))).add_independent(
+        "cohort", np.arange(6.0) % 2
+    )
+    binned = Binning.fit(data, bins=6).transform(data)
+    selected = np.arange(6) if rows is None else np.asarray(rows, dtype=int)
+    original = np.bincount
+    buffers = []
+
+    def counted(codes, weights=None, **kwargs):
+        if weights is not None:
+            buffers.append(weights)
+        return original(codes, weights=weights, **kwargs)
+
+    monkeypatch.setattr(np, "bincount", counted)
+    actual = histogram(binned, fields, rows)
+    for f, bins in enumerate(binned.binning.bin_counts):
+        codes = np.where(binned.missing[f, selected], bins, binned.codes[f, selected])
+        expected = np.column_stack(
+            [original(codes, weights=c[selected], minlength=bins + 1) for c in fields.values.T]
+        )
+        np.testing.assert_array_equal(actual.sums[f], expected)
+        np.testing.assert_array_equal(actual.counts[f], original(codes, minlength=bins + 1))
+    np.testing.assert_array_equal(actual.total, fields.values[selected].sum(axis=0))
+    assert all(a.flags.c_contiguous for a in buffers)
+    width = fields.values.shape[1]
+    assert len(buffers) == 2 * width
+    # Every feature reuses the same memory for each selected statistic column.
+    assert all(
+        a.ctypes.data == b.ctypes.data
+        for a, b in zip(buffers[:width], buffers[width:], strict=True)
+    )
diff --git a/tests/v1/test_paid_event_data.py b/tests/v1/test_paid_event_data.py
new file mode 100644
index 0000000..669189f
--- /dev/null
+++ b/tests/v1/test_paid_event_data.py
@@ -0,0 +1,78 @@
+"""Matched payment binding must not substitute raw claim counts or wrong rows."""
+
+import numpy as np
+import pytest
+from benchmarks.v1.paid_event_data import bind
+
+
+def fixture():
+    raw = dict(
+        group=np.array([10, 20, 30, 40]),
+        exposure=np.array([0.5, 1.0, 2.0, 1.0]),
+        paid_count=np.array([2, 0, 1, 1]),
+        paid_total=np.array([8.0, 0, 3, 7]),
+        aggregate_eligible=np.ones(4, dtype=bool),
+        severity_policy_row=np.array([0, 2, 0, 3]),
+        severity_y=np.array([2.0, 3, 6, 7]),
+        y=np.array([9, 0, 2, 1]),
+    )
+    arrays = dict(
+        x_train=np.array([[1.0], [0.0]]),
+        y_train=np.array([0.0, 16.0]),
+        weight_train=np.array([1.0, 0.5]),
+        x_validation=np.array([[3.0], [2.0]]),
+        y_validation=np.array([7.0, 1.5]),
+        weight_validation=np.array([1.0, 2.0]),
+        validation_row_ids=np.array([40, 30]),
+    )
+    return raw, arrays, np.array([20, 10])
+
+
+def test_matched_payments_follow_frozen_order_not_raw_claim_counts():
+    raw, arrays, ids = fixture()
+    result = bind(raw, arrays, ids)
+    np.testing.assert_array_equal(result["paid_count_train"], [0, 2])
+    np.testing.assert_array_equal(result["paid_total_train"], [0, 8])
+    np.testing.assert_array_equal(result["paid_count_validation"], [1, 1])
+    assert not any("test" in k or "weight" in k for k in result)
+
+
+@pytest.mark.parametrize(
+    "bad",
+    [
+        "count",
+        "total",
+        "payment",
+        "mapping",
+        "eligibility",
+        "weight",
+        "target",
+        "foreign",
+        "overlap",
+        "test",
+    ],
+)
+def test_mismatched_binding_fails(bad):
+    raw, arrays, ids = fixture()
+    if bad == "count":
+        raw["paid_count"] = raw["y"]
+    elif bad == "total":
+        raw["paid_total"][0] += 1
+    elif bad == "payment":
+        raw["severity_y"][0] = 0
+    elif bad == "mapping":
+        raw["severity_policy_row"][0] = 10
+    elif bad == "eligibility":
+        raw["aggregate_eligible"][0] = False
+    elif bad == "weight":
+        arrays["weight_train"][0] = 0.5
+    elif bad == "target":
+        arrays["y_train"][0] = 1
+    elif bad == "foreign":
+        ids[0] = 999
+    elif bad == "overlap":
+        ids[0] = 40
+    else:
+        arrays["y_test"] = np.ones(2)
+    with pytest.raises(ValueError):
+        bind(raw, arrays, ids)
diff --git a/tests/v1/test_profile_worker.py b/tests/v1/test_profile_worker.py
new file mode 100644
index 0000000..dd41c16
--- /dev/null
+++ b/tests/v1/test_profile_worker.py
@@ -0,0 +1,25 @@
+"""Diagnostic profiles survive a bounded soft interruption."""
+
+import json
+import signal
+import time
+
+import pytest
+from benchmarks.v1.profile_worker import ProfileDeadline, profile_call
+
+
+def test_profile_preserves_return_and_timer(tmp_path):
+    before = signal.getsignal(signal.SIGALRM)
+    assert profile_call(lambda: sum(range(100)), tmp_path, 1) == 4950
+    assert signal.getsignal(signal.SIGALRM) == before
+    assert signal.getitimer(signal.ITIMER_REAL) == (0.0, 0.0)
+    assert json.loads((tmp_path / "profile.json").read_text())["status"] == "complete"
+
+
+def test_deadline_retains_profile(tmp_path):
+    with pytest.raises(ProfileDeadline):
+        profile_call(lambda: time.sleep(1), tmp_path, 0.02)
+    report = json.loads((tmp_path / "profile.json").read_text())
+    assert report["status"] == "deadline"
+    assert report["functions"] and (tmp_path / "profile.pstats").stat().st_size
+    assert signal.getitimer(signal.ITIMER_REAL) == (0.0, 0.0)
diff --git a/tests/v1/test_worker_data.py b/tests/v1/test_worker_data.py
index 70a42c0..c19db75 100644
--- a/tests/v1/test_worker_data.py
+++ b/tests/v1/test_worker_data.py
@@ -180,3 +180,22 @@ def test_survival_events_and_training_support_are_not_recomputed_on_validation()
     frozen["censoring_support"]["survival"][0] = 0
     with pytest.raises(ValueError, match="censoring support"):
         bind("A10", data, parts, frozen)
+
+
+def test_formula_packet_keeps_age_out_of_tree_features():
+    data = dict(
+        x=np.arange(18.0).reshape(9, 2),
+        y=np.arange(9.0) + 1,
+        structure=np.arange(9.0) / 28 + 0.1,
+        group=np.arange(9),
+    )
+    parts = dict(train=np.arange(3), validation=np.arange(3, 6), test=np.arange(6, 9))
+    frozen = prepare("concrete", data, [parts])[0]
+    packets, _ = bind("A12", data, parts, frozen)
+    formula = packets["formula-input"]
+    ordinary = packets["worker-input"]
+    for p in ["train", "validation"]:
+        np.testing.assert_array_equal(formula["x_" + p], ordinary["x_" + p][:, :-1])
+        np.testing.assert_array_equal(formula["age_" + p], ordinary["x_" + p][:, -1])
+        assert formula["x_" + p].shape[1] == ordinary["x_" + p].shape[1] - 1
+    assert not any("test" in k for k in formula)
diff --git a/v1-sprints/038-goal-progress-and-plan.md b/v1-sprints/038-goal-progress-and-plan.md
index c2aec4e..366994a 100644
--- a/v1-sprints/038-goal-progress-and-plan.md
+++ b/v1-sprints/038-goal-progress-and-plan.md
@@ -13,7 +13,28 @@ adds scale-bound selection and a synthetic current search. [Sprint 047](047-mult
 adds standardized A6 quality reporting. [Sprint 048](048-classification-workers.md)
 adds classification adapters and Adult validation. [Sprint 049](049-covertype-worker.md)
 records five Covertype worker timeouts: profile this full-data CPU path next,
-before additional adapters. Remaining M2 and M3–M6 are open.
+before additional adapters. [Sprint 050](050-covertype-profile.md) identifies
+histogram aggregation and repeated candidate row hashing; next remove invariant
+rehashing with exact conformance checks. [Sprint 051](051-candidate-row-hash.md)
+implements that change and completes fold zero within the cap; histogram cost
+and the remaining full folds are addressed in [Sprint 052](052-histogram-gather.md).
+All five bounded replays pass after histogram gather reuse.
+[Sprint 053](053-quantile-worker.md) adds current A5 independent quantile
+integration on all five Bike origins. [Sprint 054](054-count-worker.md) adds
+A7 count/exposure integration on all five frequency folds.
+[Sprint 055](055-severity-worker.md) adds A8 grouped claim severity.
+[Sprint 056](056-aggregate-worker.md) adds direct A9 annualized aggregate means.
+[Sprint 057](057-paid-event-binding.md) binds matched paid-event inputs.
+[Sprint 058](058-composition-worker.md) trains/replays all five compositions.
+[Sprint 059](059-survival-worker.md) adds fixed-scale A10 survival and the CPU-to-GPU
+checkpoint. [Sprint 060](060-structured-worker.md) adds A12 structured Formula.
+[Sprint 061](061-ranking-worker.md) verifies the current A4 adapter synthetically;
+real MSLR binding remains open. [Sprint 062](062-cpu-exit-and-gpu-entry.md)
+records the CPU exit/GPU entry review. Next: installed D5 run-ID RNG and stale
+preparation probes, then source/workflow and gate reconciliation. Remaining
+searches, B11, joint A9 selection and formal E5 stay open. F3 has not started;
+the review defines its initial vertical path without changing phase ordering.
+Remaining M2 and M3–M6 are open.
 This updates execution priorities after Sprints 036–037. It preserves the
 [main plan](../planning/agent-boosting-foundation-plan.md),
 [construction design](../planning/foundation-construction-design.md), all
diff --git a/v1-sprints/050-covertype-profile.md b/v1-sprints/050-covertype-profile.md
new file mode 100644
index 0000000..244dbde
--- /dev/null
+++ b/v1-sprints/050-covertype-profile.md
@@ -0,0 +1,47 @@
+# Sprint 050: Bounded full-data CPU profile
+
+Parent: 078ca61 (merged PR 23). Status: profiling slice complete; full A3 validation still incomplete.
+
+## Plan and acceptance
+
+1. Add a diagnostic wrapper that retains cProfile statistics and periodic Python
+   stacks when a worker reaches a soft deadline. Keep an outer process kill cap.
+2. Test successful and interrupted callbacks; profile the unchanged Sprint 049
+   fold-zero packet/configuration on the full input with a 60-second soft deadline
+   and 90-second hard limit, one thread. No core/recipe edits or budget increase.
+3. Identify measured call paths, record overhead/limitations and exact artifacts,
+   update next implementation step and commit locally without pushing.
+
+An interrupted profile is not a passing fit or comparable timing benchmark.
+
+## Results and reflection
+
+The diagnostic ended intentionally at its 60-second soft deadline (exit 124);
+the outer process reports error, not a successful fit. Raw cProfile statistics
+and 20-second stacks were retained. In this interrupted instrumented window:
+
+- histogram: 20 calls, 29.48 seconds self / 29.97 cumulative;
+- hashlib update: 11,228 calls, 22.32 seconds self;
+- candidates: six calls, 23.55 seconds cumulative;
+- depthwise: two calls, 54.54 seconds cumulative;
+- binning transform: seven calls, 2.28 seconds cumulative.
+
+Cumulative times overlap and must not be added. The profile is incomplete and
+instrumented; it is not an end-to-end speed result. Local regression tests also
+ran during the beginning of the diagnostic window. Stacks at 20/40 seconds show
+histogram and candidate identity hashing respectively. One round reached update
+transactions before the deadline; a second tree-growth call was active.
+
+Inspection confirms candidates recomputes `_identity(hist.rows)` for every
+candidate even though the immutable row set is constant for that invocation.
+This falsifies the earlier priority hypothesis that prediction-time binning was
+the main cause in this window. Next hoist this invariant hash, preserving exact
+candidate identities and statistics, then measure the same bounded workload.
+Histogram optimization remains a separate measured opportunity, not a speculative
+rewrite bundled into the first change.
+
+Two diagnostic tests verify success/deadline artifacts and timer restoration.
+Full CPU regression: 846 passed; Ruff and strict docs pass. Source/raw artifact
+hashes match [profile-050](../benchmarks/v1/evidence/profile-050/README.md).
+No core code changed and A3 remains incomplete. See
+[learning](../learnings/2026-09-06-v1-covertype-profile.md).
diff --git a/v1-sprints/051-candidate-row-hash.md b/v1-sprints/051-candidate-row-hash.md
new file mode 100644
index 0000000..b1f41b2
--- /dev/null
+++ b/v1-sprints/051-candidate-row-hash.md
@@ -0,0 +1,36 @@
+# Sprint 051: Hoist invariant candidate row hashing
+
+Parent: d1dede6. Status: invariant-hash optimization complete; one full fold passes.
+
+## Plan and acceptance
+
+1. Demonstrate repeated identical row hashing within one candidate enumeration.
+2. Compute that digest once per invocation without changing any candidate field,
+   routing, statistics or immutable identity semantics.
+3. Run focused and full correctness tests; rerun the unchanged full Covertype
+   fold-zero worker at its original 90-second cap, retain outcomes and replay.
+4. Record evidence and next step, commit locally. No histogram or cache rewrite.
+
+One fold is a bounded diagnostic rerun, not full A3 acceptance or a speed claim.
+
+## Results and reflection
+
+The focused mixed-feature/full/subset tests reproduced sixteen identical row
+hash calls per enumeration. The implementation now computes the same digest once
+per call. Candidate order, immutable identities and all statistics are unchanged;
+independent histogram/tree and recipe tests pass. Full CPU regression: 848 passed.
+Ruff and strict docs pass.
+
+The unchanged full Covertype fold-zero job completes in 87.4 seconds under its
+original 90-second cap, with four rounds and a saved seven-class model. Fresh
+inference reproduces probabilities and source row IDs exactly, and probability
+rows normalize to one. Source/input/output hashes and raw artifacts are retained
+in [row-hash-051](../benchmarks/v1/evidence/row-hash-051/README.md).
+
+The previous uninstrumented fold-zero run timed out at 90 seconds. This single
+successful rerun demonstrates completion under the cap, not a stable speed ratio;
+there is little margin and the other four folds were not rerun. Full A3 validation
+remains incomplete. Next address the separately profiled histogram cost, retaining
+exact weighted/missing/categorical/vector statistics, before repeating full-fold
+validation. No general cache redesign or GPU claim is justified by this slice.
+See [learning](../learnings/2026-09-06-v1-candidate-row-hash.md).
diff --git a/v1-sprints/052-histogram-gather.md b/v1-sprints/052-histogram-gather.md
new file mode 100644
index 0000000..decefeb
--- /dev/null
+++ b/v1-sprints/052-histogram-gather.md
@@ -0,0 +1,48 @@
+# Sprint 052: Reuse selected histogram statistics
+
+Parent: 215d85f. Status: complete; all five bounded full-data replays pass.
+
+## Plan and acceptance
+
+1. Reproduce feature-by-feature allocation of identical selected statistic columns.
+2. Gather selected row statistics once, retain contiguous columns for bincount,
+   and preserve both original accumulation order and C-order parent reduction.
+3. Check exact legacy-formula sums/counts/parent totals across numeric, missing,
+   categorical, weighted/vector/independent fields and full/reordered/empty rows.
+4. Run regression and the five existing full Covertype packets under unchanged
+   90-second fit/30-second replay caps. Preserve failures and compare fold zero
+   exactly with Sprint 051. Record memory tradeoff, reflect and commit locally.
+
+No dtype, row order, scoring, objective or binning changes. A histogram-local
+scratch buffer is not a persistent cache or shared-run fusion. Full A3 quality
+and comparative speed gates remain open regardless of smoke outcome.
+
+## Results and reflection
+
+All three new tests failed the redundant-buffer check before the change and pass
+afterward, with exact legacy-formula statistics. Full CPU regression: 851 passed,
+including independent tree/routing and recipe checks. The parent total keeps its
+original reduction layout; no numerical tolerance was introduced for conformance.
+
+All five frozen full Covertype jobs now complete within the unchanged cap, in
+70.3, 69.9, 68.1, 67.8 and 67.9 seconds for folds 0–4. Seven-class probability
+shape, normalization, source IDs and exact fresh inference pass for every fold.
+Fold zero's model bytes and all prediction arrays exactly match Sprint 051.
+All recorded source and output hashes match. See the committed
+[raw evidence](../benchmarks/v1/evidence/histogram-052/README.md) and
+[learning](../learnings/2026-09-06-v1-histogram-gather.md).
+
+The full-data timeout counterexample is resolved for this bounded configuration.
+Three slices (profiling, row hashing, histogram gathering) support two local
+implementation fixes without changing public contracts. This is evidence for
+keeping the abstraction boundary, not evidence that all CPU cost work is done.
+Scratch memory increases during transpose construction and peak RSS is unmeasured.
+Repeated comparative timings and matched-quality searches were not performed.
+
+Return to M3's missing real-data adapters, starting with A5 temporal quantile
+integration, then remaining application rows and real selection searches; remaining
+D5 author checks also stay open. Full A1–A13 quality, formal author evaluation,
+CUDA parity/cost and adoption remain required. No push or publication.
+
+Closure checks: focused tests 3 passed; Ruff, strict MkDocs and diff whitespace
+checks passed. The full 851-test regression completed before the real-data run.
diff --git a/v1-sprints/053-quantile-worker.md b/v1-sprints/053-quantile-worker.md
new file mode 100644
index 0000000..6b90557
--- /dev/null
+++ b/v1-sprints/053-quantile-worker.md
@@ -0,0 +1,46 @@
+# Sprint 053: Current A5 temporal quantile worker
+
+Parent: f9bf01f. Status: complete; all five bounded real-data origins pass.
+
+## Plan and acceptance
+
+1. Reproduce the missing A5 worker contract with weighted direct-recipe parity.
+2. Compose independent public scalar quantile recipes at frozen levels 0.1/0.5/0.9,
+   retaining per-level stopping and final/best selection, explicit persistence
+   metadata and ordered raw output. Reject malformed level/model schemas.
+3. Verify fresh-process persistence, weighted pinball scores, unsupported input
+   rejection and all five frozen calendar-only Bike rolling origins at unchanged
+   90-second fit/30-second replay caps. Retain failures and raw artifacts.
+4. Run regression/lint/docs, record reflection and learning, commit locally.
+
+No quantile sorting, joint stopping, test scoring or quality acceptance claim.
+Calendar availability, source identity and whole-date split checks remain those
+of the frozen exporter. A5 quality/search and all other required rows remain open.
+
+## Results and reflection
+
+Six new A5 tests initially failed because the current worker rejected A5. The
+adapter now composes the existing public scalar recipes, without core changes.
+Weighted direct-recipe parity covers final and best-validation selection; fresh
+processes reproduce all columns and source IDs exactly. Malformed level order,
+model count, output width and feature schema are rejected. A crossing fixture
+confirms raw predictions are never sorted.
+
+All five frozen calendar-only Bike origins pass bounded integration and replay.
+Independent recomputation matches each selected validation pinball score; no
+crossings occurred in these four-round runs. Source/output hashes match the
+[raw evidence](../benchmarks/v1/evidence/quantile-053/README.md).
+
+This adds evidence that routed scalar leaves compose into a real multi-quantile
+workflow without a new foundation primitive. Independent stopping is explicit;
+a future coupled/noncrossing algorithm must declare different semantics rather
+than silently changing this recipe. The result does not establish A5 quality,
+calibration, search acceptance, CUDA or reduced external author effort.
+
+Next connect remaining real-data adapters, starting with A7 count/exposure
+semantics, then A8/A9/A10/A12 and unresolved A4 integration, while retaining real
+search and D5 checks in the execution map. All required applications remain in
+scope. See [learning](../learnings/2026-09-06-v1-quantile-worker.md).
+
+Closure: 858 CPU tests passed; Ruff, strict MkDocs and diff whitespace checks
+passed. No foundation production files changed. Nothing pushed.
diff --git a/v1-sprints/054-count-worker.md b/v1-sprints/054-count-worker.md
new file mode 100644
index 0000000..273e9c6
--- /dev/null
+++ b/v1-sprints/054-count-worker.md
@@ -0,0 +1,44 @@
+# Sprint 054: Current A7 count/exposure worker
+
+Parent: 003fdac. Status: complete; all five bounded real-data folds pass.
+
+## Plan and acceptance
+
+1. Reproduce missing A7 support with weighted direct public Poisson parity.
+2. Bind strictly positive train/validation exposure as a structure role once,
+   retain raw log-rate models, and require new exposure for period-count inference.
+   Reject missing/invalid exposure, foreign-task exposure and extra offsets.
+3. Verify weighted likelihood, final/best selection, fresh exposure-aware replay
+   and count scaling. Run all five frozen real folds under unchanged 90/30-second
+   caps and preserve failures without relaxing the workload.
+4. Run regression/lint/docs, record evidence/reflection and commit locally.
+
+No exposure-as-weight substitution, quality/search claim or CUDA claim. All
+remaining application adapters and formal gates remain required.
+
+## Results and reflection
+
+The two new direct-parity tests initially failed on missing A7 support. The
+adapter now binds exposure through public Poisson structure and retains explicit
+period-count inference semantics. Weighted final/best recipe parity, fresh replay,
+exposure scaling and invalid/missing/foreign inputs pass without core changes.
+
+All five frozen frequency folds pass and replay exactly with positive means and
+source IDs. Independent mean NLL checks differ by at most approximately 2.4e-14.
+An initial relative tolerance of 1e-13 failed on fold two; the raw discrepancy and
+math.fsum recomputation are retained. A 1e-12 relative/1e-14 absolute numerical
+check passes; no prediction equality or quality threshold was relaxed.
+See [raw evidence](../benchmarks/v1/evidence/count-054/README.md).
+
+This supports the existing separation between raw state, exposure and original
+sample weights. The frozen exporter calls exposure an offset; the public API
+represents it separately and adds its logarithm once. No additional log offset
+or exposure weighting is used. Five successful short runs do not establish A7
+quality, calibration, full search acceptance or GPU readiness.
+
+Next: A8 severity integration, followed by A9/A10/A12 and unresolved A4, real
+search integration and D5 checks. No required application is dropped. See
+[learning](../learnings/2026-09-06-v1-count-worker.md).
+
+Closure: 866 CPU tests passed; Ruff, strict MkDocs and whitespace checks passed.
+No foundation production files changed. No push or publication.
diff --git a/v1-sprints/055-severity-worker.md b/v1-sprints/055-severity-worker.md
new file mode 100644
index 0000000..c837b0b
--- /dev/null
+++ b/v1-sprints/055-severity-worker.md
@@ -0,0 +1,43 @@
+# Sprint 055: Current A8 claim severity worker
+
+Parent: 606487c. Status: complete; all five bounded real-data folds pass.
+
+## Plan and acceptance
+
+1. Reproduce missing A8 support with weighted direct Gamma recipe parity.
+2. Bind positive claim targets and original sample weights, preserve scalar
+   log-mean models and explicit positive severity output. Reject nonpositive
+   targets and unsupported exposure/offset inputs.
+3. Verify final/best selection and fresh replay, then run all five frozen
+   policy-grouped claim folds at unchanged 90/30-second caps. Independently
+   recompute selected Gamma objective and retain raw outcomes/failures.
+4. Run regression/lint/docs, reflect across Sprints 053–055 and commit locally.
+
+No policy-average substitution, dispersion fitting, quality/search or CUDA claim.
+All required remaining applications, real searches and D5 checks remain open.
+
+## Results and reflection
+
+Both new A8 direct cases initially failed on unsupported application. The worker
+now composes the existing Gamma recipe and positive_mean transform. Weighted
+final/best selection, fresh inference and nonpositive/foreign-input rejection
+pass. All five frozen policy-grouped claim folds pass bounded execution and exact
+replay. Independent selected Gamma objective recomputation agrees at the declared
+rtol=1e-12/atol=1e-14; source/output hashes match the
+[raw evidence](../benchmarks/v1/evidence/severity-055/README.md).
+
+Across Sprints 053–055, multi-quantile, period-count/exposure and positive-claim
+workflows required evaluation adapters but no foundation production changes.
+This supports keeping the present composition boundary for these consumers;
+it does not establish that external authors need less work. Explicit row units,
+output transforms and stopping semantics remain necessary at the adapter layer.
+Do not replace outstanding D5 or quality searches with more objective counts.
+
+Next connect A9 annualized aggregate targets and exposure weights, retaining the
+separate frequency-severity composition requirement; then A10/A12 and unresolved
+A4, real searches and D5. CUDA implementation and formal acceptance stay open.
+No required use case is removed. See
+[learning](../learnings/2026-09-06-v1-severity-worker.md).
+
+Closure: 872 CPU tests passed; Ruff, strict MkDocs and whitespace checks passed.
+No foundation production changes. No push or publication.
diff --git a/v1-sprints/056-aggregate-worker.md b/v1-sprints/056-aggregate-worker.md
new file mode 100644
index 0000000..50f01db
--- /dev/null
+++ b/v1-sprints/056-aggregate-worker.md
@@ -0,0 +1,43 @@
+# Sprint 056: Current A9 annualized aggregate worker
+
+Parent: d7622f4. Status: complete for direct aggregate integration.
+
+## Plan and acceptance
+
+1. Reproduce missing aggregate support with weighted public Tweedie parity.
+2. Require explicit positive exposure weights for annualized targets, fixed
+   evaluation power 1.5 and persisted annualized output metadata. Reject negative
+   targets, missing weights, exposure inputs and second offsets.
+3. Verify final/best selection, fresh replay and independent weighted objective;
+   run five frozen aggregate folds at unchanged 90/30-second caps with raw evidence.
+4. Regression/lint/docs, reflection and local commit.
+
+This slice covers the direct aggregate path. Frequency-severity composition must
+bind counts of eligible positive payments, not A7 raw claim counts, and remains
+an explicit next slice. Neither path substitutes for full A9 quality/search.
+
+## Results and reflection
+
+Both direct A9 tests initially failed on missing support. The current worker now
+uses public Tweedie at explicit power 1.5 with required positive exposure weights
+and no second exposure offset. Final/best selection and fresh inference match
+direct weighted recipes; malformed weights/targets and persisted power fail.
+No foundation production code changed.
+
+All five frozen direct aggregate folds pass with exact fresh replay. Independent
+weighted objectives agree at rtol=1e-12/atol=1e-14. Validation weights exactly match
+hashed period exposures; annualized targets times exposure reproduce paid totals.
+Period predictions are retained alongside annualized outputs in the
+[raw evidence](../benchmarks/v1/evidence/aggregate-056/README.md).
+
+The direct aggregate consumer fits existing scalar geometry, but its units differ
+from A7. Keeping that distinction explicit prevents silent double exposure.
+This success does not validate a compound distribution, full A9 quality/search,
+or the separate frequency-severity algorithm. Next bind positive-payment counts
+to the same eligible totals and build the composition evaluation; then continue
+A10/A12, unresolved A4, real searches and D5. All required scope remains open
+until its own evidence passes. See
+[learning](../learnings/2026-09-06-v1-aggregate-worker.md).
+
+Closure: 881 CPU tests passed; Ruff, strict MkDocs and whitespace passed.
+No foundation changes, push or publication.
diff --git a/v1-sprints/057-paid-event-binding.md b/v1-sprints/057-paid-event-binding.md
new file mode 100644
index 0000000..fc48b0d
--- /dev/null
+++ b/v1-sprints/057-paid-event-binding.md
@@ -0,0 +1,41 @@
+# Sprint 057: Bind matched paid events to frozen A9 rows
+
+Parent: a041f1b. Status: complete for input binding; composition fit is next.
+
+## Plan and acceptance
+
+1. Independently reconstruct positive-payment counts/totals from retained claim
+   rows and compare them with source aggregates, rejecting mismatches.
+2. Map those aggregates to the exact frozen A9 training/validation policy IDs,
+   verifying packet hashes, eligibility, target units and exposure weights.
+3. Emit composition-only training/validation packets for all five folds; test
+   mismatched counts, row identities, eligibility and weights before accepting.
+4. Run regression/lint/docs, record raw manifests/reflection and commit locally.
+
+This prerequisite slice proves input binding, not fitted composition quality.
+The next slice trains and replays the public two-model composition on these
+packets. The preparer reads the full source but opens no test-truth packet and
+selects/scores no test partition. No frozen splits change.
+
+## Results and reflection
+
+All five frozen A9 training/validation bindings pass. Independent claim iteration
+reproduces paid counts/totals exactly, while the fixture deliberately has different
+raw claim counts. Policy order, eligibility, source exposure and annualized target
+units agree with the frozen packets. Output zero-count/zero-total consistency,
+positive exposure and at least one paid policy per partition were also verified.
+The [manifest](../benchmarks/v1/evidence/paid-events-057/README.md) retains hashes
+and source audit; large feature packets remain reproducibly generated artifacts.
+
+Eleven focused tests pass, including altered aggregates, invalid claim joins,
+eligibility, weights, targets, foreign/overlapping rows and test-label injection.
+Full CPU regression: 892 passed. Ruff, strict MkDocs and whitespace checks pass.
+
+The composition helper alone could not prove raw payment provenance. This binder
+closes that input gap without changing the source freeze, population or foundation.
+The summary and packet hashes establish reproducibility, not hostile-process
+isolation. No model training occurred in this slice. Next train Poisson paid-event
+frequency and count-weighted Gamma severity through paid_loss_problems, persist
+FrequencySeverity, and replay annualized/period outputs on all five packets.
+Component selection must be labeled separately from joint aggregate selection.
+See [learning](../learnings/2026-09-06-v1-paid-event-binding.md).
diff --git a/v1-sprints/058-composition-worker.md b/v1-sprints/058-composition-worker.md
new file mode 100644
index 0000000..af182d9
--- /dev/null
+++ b/v1-sprints/058-composition-worker.md
@@ -0,0 +1,35 @@
+# Sprint 058: Fit and replay matched frequency-severity composition
+
+Parent: e603f1f. Status: complete for component-selected execution/replay.
+
+## Plan and acceptance
+
+1. Test public component parity and strict composition packet/job contracts.
+2. Train paid-event Poisson and count-weighted Gamma through paid_loss_problems;
+   retain separate stopping and final/best selection, persist FrequencySeverity.
+3. Run all five hash-bound Sprint 057 packets within 90-second combined fit and
+   30-second fresh replay caps. Verify all named outputs, products and units.
+4. Regression/lint/docs, evidence, reflection and local commit.
+
+Component selection is not joint aggregate selection. No test labels, source
+population changes, compound distribution, quality/search or CUDA claims.
+
+## Results and reflection
+
+Nine focused tests pass, including direct public component identities, stopping,
+fresh persistence, output products and rejected ambiguous/invalid packets.
+Full CPU regression: 901 passed. All five hash-bound real packets pass combined
+fit and fresh replay. Every named array is exactly reproduced; rate times severity
+matches annualized mean and exposure conversion matches period mean. Source,
+input and output hashes match the retained
+[raw evidence](../benchmarks/v1/evidence/composition-058/README.md).
+
+No foundation changes were needed. The source-binding prerequisite mattered:
+this executes paid-event frequency, not A7 raw claim frequency. The result verifies
+composition mechanics and persistence on real inputs; separate component selection
+is recorded honestly and cannot substitute for joint A9 quality/search acceptance.
+Next connect A10 survival evaluation, then A12/unresolved A4 and real searches/D5.
+Joint composition selection remains in A9's unfinished quality workflow.
+See [learning](../learnings/2026-09-06-v1-composition-worker.md).
+
+Closure: Ruff, strict MkDocs and whitespace checks pass. No push or publication.
diff --git a/v1-sprints/059-survival-worker.md b/v1-sprints/059-survival-worker.md
new file mode 100644
index 0000000..ed7908a
--- /dev/null
+++ b/v1-sprints/059-survival-worker.md
@@ -0,0 +1,43 @@
+# Sprint 059: Current A10 fixed-scale survival worker
+
+Parent: 20d9b7c. Status: complete for fixed-scale integration.
+
+## Plan and acceptance
+
+1. Verify weighted direct AFT parity with event/right-censored targets and reject
+   malformed events/nonpositive times/unsupported scale or offset options.
+2. Persist fixed sigma=1 and emit [log-time location, sigma] for evaluation;
+   verify fresh replay and independently recompute censored likelihood.
+3. Run all five frozen Veteran folds at unchanged 90/30-second caps, retain raw
+   outcomes and source hashes. No licensing or quality acceptance inferred.
+4. Regression/lint/docs, record remaining CPU-to-GPU prerequisites and commit.
+
+No left/interval censoring, learned scale, test scoring, calibration or GPU claim.
+
+## Results and reflection
+
+Both direct A10 cases initially failed on missing support. The worker now binds
+explicit event/right targets, fixed scale and weighted censored validation loss.
+Final/best direct parity, fresh replay and invalid event/time/scale rejection pass.
+All five frozen Veteran folds pass; independent erfc-based censored likelihood
+agrees at rtol=1e-12/atol=1e-14. Source/output hashes match the
+[raw evidence](../benchmarks/v1/evidence/survival-059/README.md).
+Full CPU regression: 909 passed. No foundation production changes were needed.
+
+## CPU-to-GPU checkpoint
+
+The remaining current application adapter gaps are A12 structured Formula and A4
+ranking; implement those next without adding new built-in objective families.
+A13 real search integration, joint A9 selection and outstanding D5/independent
+author checks remain unfinished CPU workflow/evaluation work. Apply the declared
+B11/F1–F2 acceptance gates before reporting phase exit; test counts are not exits.
+Then B12 implements device operations and recipe parity, and B13 verifies batched
+train-many execution/cost. Full real quality and adoption remain required, but
+must not become an invented requirement to polish every CPU performance path
+before attempting CUDA. Any phase overlap requires an explicit plan amendment.
+
+This slice validates fixed-scale survival integration only. Licensing closure,
+IPCW/calibration and full A10 quality remain open. The next implementation is A12.
+See [learning](../learnings/2026-09-06-v1-survival-worker.md).
+
+Closure: Ruff, strict MkDocs and whitespace pass. No push or publication.
diff --git a/v1-sprints/060-structured-worker.md b/v1-sprints/060-structured-worker.md
new file mode 100644
index 0000000..b3feee6
--- /dev/null
+++ b/v1-sprints/060-structured-worker.md
@@ -0,0 +1,40 @@
+# Sprint 060: Current A12 structured Formula integration
+
+Parent: 3ff97c3. Status: complete for structured integration.
+
+## Plan and acceptance
+
+1. Emit a separate frozen Formula packet with age/28 as structure and only
+   composition features as tree inputs; preserve the ordinary GBDT packet.
+2. Bind the public two-parameter saturation recipe with explicit structured
+   inference, weighted direct parity and age/feature separation tests.
+3. Run five frozen concrete folds under unchanged 90/30-second caps; retain
+   exact replay, independent formula/score checks and raw artifacts.
+4. Regression/lint/docs, reflection and local commit. A4 and CPU phase gates next.
+
+No extrapolation/quality acceptance, global-formula parity or GPU claim.
+
+## Results and reflection
+
+The exporter now emits a separate formula-input packet without changing ordinary
+GBDT inputs or the preprocessing freeze. Age/28 is structure, not a tree predictor.
+Weighted final/best direct Formula parity and exact fresh replay pass. All five
+real concrete folds pass, with independently recomputed saturation predictions
+and scores, exact age separation and source IDs. See
+[raw evidence](../benchmarks/v1/evidence/structured-060/README.md).
+
+The first binding test incorrectly assumed raw feature width equaled encoded
+width; train-fitted missing indicators make that false. Corrected the assertion
+to verify removal of exactly the appended age column. The initial real export
+lacked xlrd and failed before fitting; retry with cached ephemeral uv dependency
+passed. Both failures are documented rather than changing data/recipe semantics.
+
+Full CPU regression: 912 passed. No foundation production changes. A12 remains
+open for interpolation/extrapolation quality, controls and complete searches.
+A4 ranking is now the remaining current application adapter gap. Complete that,
+then assess remaining A13/joint-search and D5/author requirements against B11;
+do not infer CUDA readiness from recipe count. The Sprint 059 transition
+checkpoint remains active. See
+[learning](../learnings/2026-09-06-v1-structured-worker.md).
+
+Closure: Ruff, strict MkDocs and whitespace pass. No push or publication.
diff --git a/v1-sprints/061-ranking-worker.md b/v1-sprints/061-ranking-worker.md
new file mode 100644
index 0000000..dbafdc9
--- /dev/null
+++ b/v1-sprints/061-ranking-worker.md
@@ -0,0 +1,37 @@
+# Sprint 061: Current query-aware A4 adapter
+
+Parent: ca73a2d. Status: adapter verified; real A4 binding remains open.
+
+## Plan and acceptance
+
+1. Bind contiguous disjoint query groups and explicit per-query weights through
+   the public ranking recipe; preserve integer source row IDs for tie behavior.
+2. Verify pairwise/lambda modes, final/best selection and fresh score replay;
+   reject row weights, fragmented/overlapping groups and ambiguous row identities.
+3. Run regression/lint/docs and record the missing MSLR real-data prerequisite.
+   Synthetic tests do not close the required real A4 gate.
+4. Commit locally and schedule CPU workflow/phase-gap review before more expansion.
+
+## Results and reflection
+
+Pairwise and lambda modes pass weighted direct parity with final/best selection
+and exact fresh score/row-ID replay. The fixture uses unequal query weights and
+reversed source row IDs to exercise stable tie identities. Seven counterexamples
+reject fragmented/overlapping queries, ordinary row weights, overlapping/string
+source rows, out-of-contract relevance and mis-shaped query weights.
+
+No foundation production changes were needed. The configured build/v1-data
+inventory has no MSLR files and no committed MSLR preprocessing freeze exists.
+Existing source-access/agreement gaps remain unresolved; synthetic verification
+does not close real A4 integration or establish ranking scalability. The current
+public ranking implementation still enumerates pairs quadratically per query.
+
+This completes the missing adapter implementation, not every application workflow.
+Next audit the finite CPU exit list: A4 source/binding, A13 real searches and joint
+A9 selection, remaining D5/independent author gates, and B11 acceptance. Then
+record the exact B12 GPU starting slice or an explicit phase-overlap amendment.
+Do not use more objective adapters or test counts as substitutes for that decision.
+See [learning](../learnings/2026-09-06-v1-current-ranking-worker.md).
+
+Closure: 923 CPU tests passed; Ruff, strict MkDocs and whitespace pass.
+Nothing pushed.
diff --git a/v1-sprints/062-cpu-exit-and-gpu-entry.md b/v1-sprints/062-cpu-exit-and-gpu-entry.md
new file mode 100644
index 0000000..64473b6
--- /dev/null
+++ b/v1-sprints/062-cpu-exit-and-gpu-entry.md
@@ -0,0 +1,126 @@
+# Sprint 062: CPU exit audit and GPU entry decision
+
+Reviewed revision: a2a03e4. Status: review complete; no phase exit declared.
+
+## Purpose and method
+
+Reconcile implementation with F0–F3/B11–B13, instead of treating adapter count as
+completion. Read the current worker, installed scheduler verifier, search harness,
+construction design and E0–E7 criteria. Retain earlier raw evidence and sealed
+held-out boundaries. This is a source/evidence review, not a new test or benchmark.
+Latest completed CPU regression remains Sprint 061: 923 tests.
+
+## What is built
+
+| Layer | Implemented evidence | Remaining acceptance boundary |
+|---|---|---|
+| C1 data/targets | Numeric/mixed inputs, training preparation, class/weight/offset/structure and event-right roles | Aggregate current R/C/A evidence into the declared gate ledger; source identity is not access isolation |
+| C2/C3 ops/trees/leaves | Named statistics, candidate/feasibility/scoring/routing, three growers, scalar/vector/residual leaves; installed D2/D3 probes | Current coverage reconciliation and formal author evidence; no GPU ops |
+| C4 runtime | Immutable transactions, deterministic keyed RNG, validation stopping, sequential M=1/8/32 and external result contract | Complete explicit installed D5 mutation probes; no device workspace/stream/transfer implementation |
+| C5 persistence | Raw models and specialized output/scale/composition inference; current workers replay in fresh processes | Complete current workflow/installed delivery evidence; no training-resume or GPU parity claim |
+| R1–R8 recipes | Twelve public CPU recipes plus frequency-severity composition; recent applications needed no core edits | Scope limits remain (fixed AFT scale/event-right, quadratic query pairs, no calibrated compound distribution) |
+| R9/C7 workflows | Shared preparation, sequential scheduling, strict selection/release infrastructure | Current A13 evidence is synthetic A6 search; real searches and joint composition selection unfinished |
+| C6 author evaluation | Installed D1–D4 exploratory packages and partial D5 evidence | Formal E5 cohort, independent attempts, cost accounting and held-out results absent |
+| CUDA | Construction design only; RunContext rejects non-CPU | B12 device operations/recipe parity and B13 batching/cost are unimplemented |
+
+These are implemented capabilities, not a claim that CPU E0/E1/E2 pass in full.
+The historical Sprint 035 gap table is superseded by this review where newer
+sprints supply evidence; its old missing-objective/stopping statements are not
+current blockers.
+
+## Application position
+
+| Applications | Current evidence | Still open |
+|---|---|---|
+| A1/A2/A3/A5/A6/A7/A8/A10/A11 | Five frozen current validation fits and fresh inference per application | Full baseline-matched quality/search, applicable domain metrics and source obligations |
+| A9 | Direct annualized aggregate and matched paid-event composition pass five folds | Joint aggregate selection, complete searches, quality and calibration claims |
+| A12 | Five Formula folds with explicit age separation and fresh inference | Control comparisons, interpolation/extrapolation and full quality/search |
+| A4 | Current pairwise/lambda adapter; weighted synthetic direct/fresh checks | MSLR data/agreement, preprocessing freeze, official-query-fold binding and real fits |
+| A13 | Current synthetic A6 16-trial selection and sealed release; M1/8/32 semantics | Real-data complete search/release workflows and full-set device cost |
+
+All A1–A13 remain required. No source was substituted, and no application was
+removed. The Veteran source-license gap is not closed by its passing fits.
+Short four-round validation jobs are not fair quality searches. No full E3 or E4
+claim can be derived from these artifacts.
+
+## Finite remaining work, in order
+
+1. **Complete explicit installed D5 development probes.** Extend the existing
+   examples/v1_extensions/scheduler_checks.py workflow, not a new trainer:
+   - Same seed/different stable run IDs must produce distinct keyed streams;
+     each stream must remain exact under permutation and retry.
+   - Changed feature content with unchanged row IDs must invalidate old prepared
+     data; altered row identity must also be rejected. Fresh preparation must
+     recover and match independent direct execution.
+   - Retain mixed K=1/2, M=1/8/32, different stopping and failure artifacts already
+     present. No private imports/core edits; build and exercise installed wheels.
+   These explicit cases are absent from the current installed verifier; this is
+   not a claim that the underlying runtime behavior is absent or incorrect.
+2. **Complete source/workflow prerequisites.** Resolve MSLR access/agreement and
+   freeze official folds before real A4 execution. Preserve source terms. Finish
+   a real A13 selection/release run from existing supported frozen data, then
+   route remaining output schemas, including composition's joint selection,
+   through the declared workflow. Retain failed trials; no budget retuning.
+3. **Reconcile CPU E0/E1/E2 and F0.3 readiness.** Produce current item-to-artifact
+   records for R1–R9/C1–C7/A1–A13, independent verifiers and fair comparator paths.
+   Mark missing records as unverified. Do not relabel historical or synthetic
+   results as real/current. Resolve judges, source and execution-budget gaps.
+4. **Freeze and execute F2/E5.** First verify accounting with the required smoke.
+   Freeze version, model/reasoning/tools/docs/budgets and independent judge access.
+   Run all five development types and two held-outs with three attempts per arm;
+   30 minutes/20k generated tokens per attempt, failures retained. Apply the
+   existing correctness/cost thresholds unchanged. Do not inspect sealed tasks
+   for implementation planning. Existing exploratory scripts are not a cohort.
+5. **Enter B12/F3 after its prerequisites, or adopt an explicit amendment.**
+   No more built-in objectives or unmeasured CPU optimization are scheduled.
+   Correctness failures and demonstrated consumer blockers still take priority.
+
+Full baseline quality E3 and external adoption E7 remain v1 obligations, but
+must not be invented as prerequisites for every initial GPU experiment. The
+actual current ordering is F0/F1/F2 before F3, including the existing approved
+CPU overlap; this review does not silently widen that authorization.
+
+## GPU decision and first slice
+
+**Decision under the current plan: F3 is not started or accepted.** The first
+next implementation is the installed D5 slice above. Starting CUDA before the
+formal CPU/author prerequisites would be a sequencing change; this review does
+not claim such a change has already been approved. If the user chooses that
+route, record a bounded B12 feasibility overlap, with all CPU/E5 obligations
+retained and no interface-freeze or performance claim.
+
+The first B12 deliverable is one resident squared-error vertical path, followed
+immediately by a two-parameter Normal path. Use the design's CuPy-owned arrays
+and streams with numba-cuda kernels initially; change the kernel technology only
+from measured evidence. Host metadata remains separate from device targets,
+codes, raw predictions, statistics and row routing. Initial CPU preparation plus
+one upload is permitted with its complete cost recorded.
+
+Acceptance for that first slice:
+
+- Real CUDA hardware; no skipped job counted as success and no CPU fallback.
+- At least two rounds with declared precision/tie policy: compare gradients,
+  curvature, histogram totals, candidate decisions, routing, leaves, raw updates,
+  final predictions and task metrics against CPU/independent fixtures.
+- Preserve transaction/stop/RNG semantics; expose synchronization and failures.
+- Record preparation/upload, compilation cold/warm policy, transfer bytes,
+  synchronization, workspace peak and complete fit/predict cost.
+- Persist output for CPU fresh inference. Single-recipe parity does not establish
+  multiparameter, nondefault extension, train-many or E4 acceptance.
+
+Only then expand required device compositions, a nondefault public component and
+actual backtracking. B13 groups compatible M=1/8/32 runs after sequential device
+parity; no fusion, multi-GPU or Ray claim precedes its evidence.
+
+## Reflection and verification
+
+Sprints 053–061 mostly added adapters and evidence rather than core primitives.
+That supports the boundary for these internal consumers, but is not evidence of
+lower external authoring cost. The next work changes from application plumbing
+to explicit scheduling probes and phase acceptance. Count growth must not become
+a substitute for the foundation's central hypothesis.
+
+At review start, the local branch was clean and twelve commits ahead of origin/main. No push,
+network retrieval, new model fits, held-out inspection or threshold changes were
+part of this review. Documentation and whitespace checks passed. See
+[learning](../learnings/2026-09-06-v1-cpu-exit-review.md).
diff --git a/v1-sprints/README.md b/v1-sprints/README.md
index 2b260d7..f4628b6 100644
--- a/v1-sprints/README.md
+++ b/v1-sprints/README.md
@@ -13,7 +13,7 @@ folder manages execution, not a competing roadmap. R1–R9/C1–C7/A1–A13 all
 
 Current review and remaining plan: [Sprint 038](038-goal-progress-and-plan.md).
 Public CPU components now cover twelve recipes, multi-output target scaling and
-shared training preparation. The latest full regression records 844 passes;
+shared training preparation. The latest full regression records 923 passes;
 this includes references/evaluation infrastructure and is not a phase gate.
 Independent stopping is delivered in [Sprint 039](039-independent-stopping.md).
 Installed D2/D3 development wheels pass in [Sprint 040](040-installed-extensions.md).
@@ -29,8 +29,28 @@ quality reporting while retaining every per-target gate. Next: remaining
 application adapters, real search integration and D5 checks.
 [Sprint 048](048-classification-workers.md) adds A2/A3 probability adapters and
 five-fold Adult integration. [Sprint 049](049-covertype-worker.md) records all
-five full Covertype folds timing out at 90 seconds. A3 validation remains
-incomplete; next profile this CPU path before further adapter expansion.
+five full Covertype folds timing out at 90 seconds. [Sprint 050](050-covertype-profile.md) profiles the full input:
+histogram aggregation and repeated candidate row hashing dominate the sampled
+window. [Sprint 051](051-candidate-row-hash.md) hoists the invariant digest;
+fold zero completes within 90 seconds with exact fresh replay.
+[Sprint 052](052-histogram-gather.md) reuses selected histogram statistics;
+all five full folds pass within the unchanged cap with exact fresh replay.
+[Sprint 053](053-quantile-worker.md) adds current A5 independent quantiles on
+all five Bike origins. [Sprint 054](054-count-worker.md) adds A7 explicit
+count/exposure integration on all five frequency folds.
+[Sprint 055](055-severity-worker.md) adds A8 severity on all five grouped claim
+folds. [Sprint 056](056-aggregate-worker.md) adds direct A9 aggregate integration
+on all five folds. [Sprint 057](057-paid-event-binding.md) verifies matched paid
+event input packets. [Sprint 058](058-composition-worker.md) trains and replays
+all five compositions with independent component selection.
+[Sprint 059](059-survival-worker.md) adds A10 fixed-scale survival on all five folds
+and records the CPU-to-GPU checkpoint. [Sprint 060](060-structured-worker.md)
+adds A12 structured Formula. [Sprint 061](061-ranking-worker.md) adds the A4
+query-aware adapter; real MSLR binding remains open.
+[Sprint 062](062-cpu-exit-and-gpu-entry.md) reviews CPU exit and GPU entry.
+Next: installed D5 run-ID RNG and stale-preparation probes, then source/workflow
+and gate reconciliation. Real searches and joint A9 selection remain open.
+F3 has not started; the review preserves current phase ordering.
 CUDA, formal author comparisons, real application
 acceptance and independent adoption remain open.