The #000025 §10.14 calibration showed _delta_5{s,t,f} mean over a
battery's 5 subs, so a single-sub gain weighs 1/5 of face value (the
5× dilution). #000047 ships the knob to pick the aggregation, default
unchanged.
WeightSet.delta_aggregator ∈ {"mean","max","sum"} (default "mean") —
a categorical field, validated in __post_init__ against
DELTA_AGGREGATORS; from_dict takes it as a string. Default unchanged →
ScoredFork output byte-identical → no fork_score.ESTIMATOR_VERSION
bump.
fork_score._aggregate(deltas, how): mean = arithmetic mean, max =
max(0.0, max_i Δ_i), sum = Σ Δ_i; empty → 0.0. _delta_5s/_delta_5t/
_delta_5f take an aggregator arg (default "mean"); the 5F efficiency
bonus is added after the aggregated base (aggregator-independent).
fork_score passes weights.delta_aggregator. The per-sub
HARD_REGRESSION_FLOOR flags are computed before aggregation, so a
single-sub regression still forces REJECT under max/sum. The chosen
aggregator is recorded in ScoredFork.weights["delta_aggregator"] (via
WeightSet.as_dict()); fork_score_branches traceability stays via the
opaque weights_id — no schema migration.
bench/scripts/fivef_threshold_calibration.py gained §5 — runs the
#000046 below-ceiling pack (5f/falsification at 0.333) and shows the
verdict / γ·Δ5f under each aggregator; bench/results/5f-threshold-
calibration-2026-05-11.md §5 is the captured record. Default stays
"mean" — the conservative, noise-robust, regression-symmetric choice
matching docs/bench-maxing.md's per-rate floor framing; v8 picks
max/sum per-deployment.
Tests: 8 new in tests/test_fork_score.py + 1 anchor in
tests/test_fivef_threshold_calibration.py; tests/test_weights.py
as_dict field-set test updated to include delta_aggregator;
test_fork_score.py AUTOCOUNT tags (#000012 §286, warrant-substrate-
cookbook.md ×2) bumped 23 → 31.
#000047 closed; #000012 §8 §3 + TICKETS.md row updated.
Full suite: 2330 passed, 28 skipped.
Closes the three open Phase-1b items of #000025; every §10 closure
criterion is now met, so the ticket flips to closed.
§10.14 — ForkScore threshold-calibration handoff to #000012.
bench/scripts/fivef_threshold_calibration.py (make bench-5f-threshold-
calibration) runs the canonical 5S/5T/5F packs + the 5F live packs and
reports baseline rates, observability granularity (1/n), and fork_score
verdicts on the parent vs synthetic child perturbations →
bench/results/5f-threshold-calibration-2026-05-11.md. Findings written
into ticket-000012 §8: keep SIGNAL_FLOOR / HARD_REGRESSION_FLOOR at
0.05; the small 5S packs (syntax n=10, semantics n=8) are coarser than
the floors so any regression there trips hard-reject (intended zero-
tolerance); the 5x averaging dilution in _delta_*; ceiling saturation
(every pack at 1.0 -> delta-rate terms <= 0). No constant change
shipped. 6 tests in tests/test_fivef_threshold_calibration.py.
§10.13 — feedback latency / efficiency on real workload.
run_feedback_loop now computes feedback_latency (listed in §5.5 since
Phase 1a, never implemented) — wall-clock seconds to apply a live
chain against its temp shard, surfaced per-task
(feedback_latency_seconds) + battery (feedback_latency_mean_seconds,
feedback_live_task_count). For live chains feedback_efficiency's cost
denominator switched from len(chain) (count of requested ops) to the
persisted footprint _persisted_cost = audit-event rows the chain
actually wrote + their body bytes / 1e6. Embedded chains keep
len(chain) and report feedback_latency_seconds = None. Latency is a
wall-clock field (run-to-run variable, like BatteryResult.timestamp)
and is not a fork_score input. 3 tests in tests/test_bench_batteries.py.
§10.11 — real selfmodel finetuning chains.
bench/scripts/selfmodel_chain_snapshot.py (make bench-5f-selfmodel-
snapshot) appends one chained SelfModel snapshot per run to a
persistent shard (~/.arborist/shards/selfmodel-chain.db, override via
ARBORIST_SELFMODEL_CHAIN_DB) with one CapabilityClaim per sub-battery
(metric = "5S-syntax" etc., measured_value = that pack's rate,
eval_digest = the pack's fixture digest, threshold = SIGNAL_FLOOR).
snapshot() auto-parents, so each snapshot is a distinct root and the
lineage grows by one per run. run_finetuning gains a third dispatch
mode — shard-chain (gated on a task's selfmodel_shard key) — via
_chain_finetuning_measure: reads the two most-recent snapshots
(latest() = child, its parent_selfmodel_root = parent) and measures
improvement on target_capability between them. This is the real
lineage replacing Phase-1a's synthetic parent->child pairs; the
chained delta reflects genuine cross-run drift (0.0 today — the
embedded packs are at ceiling). Operator pack
bench/fixtures/5f/finetuning-shardchain-v1.jsonl (6 tasks) + make
bench-5f-finetuning-shardchain; not in `make bench-5f`, `make test`,
or a fresh checkout (a missing/too-short chain fails honestly). The
real chain shard was bootstrapped 2-deep on 2026-05-11; make
chain-check-shards reports 0 breaks on it (and all other shards).
10 tests in tests/test_selfmodel_chain.py.
Full suite: 2311 passed, 28 skipped.
