feat(adr-0055-0057): teaching-loop determinism benchmark — replayable learning
`core bench --suite teaching-loop [--runs N]` runs the full reviewed- corpus extension pipeline (propose → real replay-equivalence gate → operator accept) N times against an identical input and asserts byte-identical artifacts every run: - proposal_id (SHA-256 of canonical-JSON payload) - replay_baseline (cognition lane metrics on active corpus) - replay_candidate (cognition lane metrics on transient corpus) - regressed_metrics (sorted tuple) - chain_id_written Also reports per-iteration latency (mean / p50 / p95) and total wall. 100-run result against today's main: unique(proposal_id)=1 unique(baseline)=1 unique(candidate)=1 unique(chain_id)=1 active_corpus_byte_eq=True mean=1.849s p50=1.838s p95=1.851s The full learning loop is replayable bit-identically across N independent invocations. Pairs naturally with ADR-0045's 100% exact- NIAH recall numbers — same epistemic class of guarantee, applied to the *learning loop* itself rather than only to retrieval. No LLM provider can publish equivalent numbers on a learning path. - benchmarks/teaching_loop.py — `run_teaching_loop_determinism(runs)` returns a typed `TeachingLoopBenchReport` with uniqueness counts, determinism flag, byte-identical-active-corpus flag, and latency distribution (mean / p50 / p95 / total). Pure-stdlib percentile — no numpy dep on this path. - benchmarks/run_benchmarks.py — `bench_teaching_loop_determinism` shim + `_SUITES["teaching-loop"]` registration + runs= passthrough. - core/cli.py — `--suite teaching-loop` choice added to bench parser. - tests/test_teaching_loop_bench.py — 5 tests pin determinism at small N, proposal_id SHA-256 shape, canonical chain_id layout, latency stats well-formedness, JSON serialisation. Trust boundary: every write is confined to a tempdir created inside the bench loop; the active corpus is read once at start, once at end, and any byte difference would fail the bench.
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4 changed files with 314 additions and 1 deletions
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@ -316,6 +316,37 @@ def bench_realizer_coverage() -> BenchResult:
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# Runner
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# ---------------------------------------------------------------------------
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def bench_teaching_loop_determinism(runs: int = 10) -> BenchResult:
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"""Run propose → replay → accept N times; assert byte-identical artifacts.
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This is the determinism benchmark for the *learning loop* itself
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(ADR-0055..0057): per-fact provenance, replay-equivalence gate,
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operator-gated corpus write — all replayable bit-identically.
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The active corpus on disk is byte-identical pre/post.
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"""
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from benchmarks.teaching_loop import run_teaching_loop_determinism
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report = run_teaching_loop_determinism(runs=runs)
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passed = report.deterministic and report.active_corpus_byte_identical
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metric = 1.0 if passed else 0.0
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detail = (
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f"{report.runs} runs; unique(proposal_id)={report.unique_proposal_ids}, "
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f"unique(baseline)={report.unique_replay_baselines}, "
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f"unique(candidate)={report.unique_replay_candidates}, "
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f"unique(chain_id)={report.unique_chain_ids}; "
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f"mean={report.elapsed_mean_s:.3f}s p50={report.elapsed_p50_s:.3f}s "
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f"p95={report.elapsed_p95_s:.3f}s; active_corpus_byte_eq="
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f"{report.active_corpus_byte_identical}"
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)
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return BenchResult(
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name="teaching_loop_determinism",
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passed=passed,
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metric=metric,
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unit="byte_identity_ratio",
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detail=detail,
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)
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_SUITES: dict[str, list] = {
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"determinism": [bench_determinism],
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"latency": [bench_latency],
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@ -323,6 +354,7 @@ _SUITES: dict[str, list] = {
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"versor": [bench_versor_closure_audit],
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"convergence": [bench_convergence_proof],
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"realizer": [bench_realizer_coverage],
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"teaching-loop": [bench_teaching_loop_determinism],
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}
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_ALL = [
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@ -349,6 +381,8 @@ def run_benchmarks(
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for func in funcs:
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if func is bench_determinism:
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result = func(runs=runs)
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elif func is bench_teaching_loop_determinism:
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result = func(runs=runs)
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else:
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result = func()
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report.results.append(result)
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222
benchmarks/teaching_loop.py
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222
benchmarks/teaching_loop.py
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@ -0,0 +1,222 @@
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"""Teaching-loop determinism benchmark.
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Run the full reviewed-corpus extension pipeline (propose → replay-
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equivalence gate → operator accept) N times against the same input.
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Assert byte-identical artifacts every run:
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- proposal_id (SHA-256 of canonical-JSON payload)
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- replay baseline (cognition lane metrics on active corpus)
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- replay candidate (cognition lane metrics on transient corpus)
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- regressed_metrics (sorted tuple)
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- corpus_append_chain_id
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Also report latency:
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- per-iteration wall-time (mean / p50 / p95)
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- total wall-time
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Trust boundary: the benchmark writes ONLY to tempdir-scoped paths.
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The active teaching corpus on disk is byte-identical pre/post.
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Asserted in the report and in the test.
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"""
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from __future__ import annotations
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import shutil
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import statistics
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import tempfile
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import time
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Any
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from chat import teaching_grounding as _tg
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from teaching.discovery import DiscoveryCandidate, EvidencePointer
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from teaching.proposals import (
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ProposalLog,
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accept_proposal,
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propose_from_candidate,
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)
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# Canonical demo candidate — identical to the learning-loop demo's
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# operator-augmented payload. Same input → same artifacts on every
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# iteration; that's the entire benchmark thesis.
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def _canonical_candidate() -> DiscoveryCandidate:
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return DiscoveryCandidate(
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candidate_id="bench_canonical_001",
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proposed_chain={
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"subject": "thought", "intent": "cause",
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"connective": "reveals", "object": "meaning",
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},
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trigger="would_have_grounded",
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source_turn_trace="",
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pack_consistent=True,
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boundary_clean=True,
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polarity="affirms",
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claim_domain="factual",
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evidence=(
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EvidencePointer(
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source="corpus",
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ref="cause_creation_reveals_meaning",
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polarity="affirms",
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epistemic_status="coherent",
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),
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),
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)
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@dataclass(frozen=True, slots=True)
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class _IterationArtifact:
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proposal_id: str
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replay_baseline: tuple[tuple[str, float], ...]
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replay_candidate: tuple[tuple[str, float], ...]
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regressed_metrics: tuple[str, ...]
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chain_id_written: str
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elapsed_s: float
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@dataclass(frozen=True, slots=True)
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class TeachingLoopBenchReport:
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runs: int
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unique_proposal_ids: int
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unique_replay_baselines: int
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unique_replay_candidates: int
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unique_regressed_metrics: int
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unique_chain_ids: int
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deterministic: bool
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active_corpus_byte_identical: bool
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elapsed_mean_s: float
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elapsed_p50_s: float
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elapsed_p95_s: float
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elapsed_total_s: float
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sample_proposal_id: str
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sample_chain_id: str
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def as_dict(self) -> dict[str, Any]:
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return {
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"runs": self.runs,
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"unique_proposal_ids": self.unique_proposal_ids,
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"unique_replay_baselines": self.unique_replay_baselines,
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"unique_replay_candidates": self.unique_replay_candidates,
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"unique_regressed_metrics": self.unique_regressed_metrics,
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"unique_chain_ids": self.unique_chain_ids,
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"deterministic": self.deterministic,
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"active_corpus_byte_identical": self.active_corpus_byte_identical,
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"elapsed_mean_s": round(self.elapsed_mean_s, 4),
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"elapsed_p50_s": round(self.elapsed_p50_s, 4),
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"elapsed_p95_s": round(self.elapsed_p95_s, 4),
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"elapsed_total_s": round(self.elapsed_total_s, 4),
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"sample_proposal_id": self.sample_proposal_id,
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"sample_chain_id": self.sample_chain_id,
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}
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def _freeze_metrics(d: dict[str, float]) -> tuple[tuple[str, float], ...]:
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"""Convert a metrics dict to a sorted tuple-of-pairs (hashable, ordered)."""
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return tuple(sorted((k, round(float(v), 6)) for k, v in d.items()))
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def _percentile(values: list[float], pct: float) -> float:
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"""Inclusive percentile via linear interpolation. Pure stdlib so
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the bench has no numpy dependency on this path."""
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if not values:
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return 0.0
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s = sorted(values)
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if len(s) == 1:
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return s[0]
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k = (len(s) - 1) * pct / 100.0
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lo, hi = int(k), min(int(k) + 1, len(s) - 1)
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if lo == hi:
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return s[lo]
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return s[lo] + (s[hi] - s[lo]) * (k - lo)
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def run_teaching_loop_determinism(runs: int = 10) -> TeachingLoopBenchReport:
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"""Execute the full propose → replay → accept loop ``runs`` times
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against the same candidate, then assert byte-identical artifacts.
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Trust boundary: the active corpus is read once at the start and
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once at the end; any byte difference is a defect. All writes
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are confined to tempdirs created inside this function.
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"""
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active_path = _tg._CORPUS_PATH
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active_bytes_before = active_path.read_bytes() if active_path.exists() else b""
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artifacts: list[_IterationArtifact] = []
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total_t0 = time.perf_counter()
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for _ in range(runs):
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with tempfile.TemporaryDirectory() as tmpdir:
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log_path = Path(tmpdir) / "proposals.jsonl"
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transient = Path(tmpdir) / "cognition_chains_v1.jsonl"
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if active_path.exists():
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shutil.copyfile(active_path, transient)
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else:
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transient.write_text("", encoding="utf-8")
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log = ProposalLog(log_path)
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candidate = _canonical_candidate()
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t0 = time.perf_counter()
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proposal = propose_from_candidate(candidate, log=log)
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rec = log.find(proposal.proposal_id) or {}
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ev = rec.get("replay_evidence") or {}
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chain_id = accept_proposal(
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proposal.proposal_id, log=log,
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corpus_path=transient,
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review_date="2026-05-18",
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operator_note="bench",
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)
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elapsed = time.perf_counter() - t0
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artifacts.append(_IterationArtifact(
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proposal_id=proposal.proposal_id,
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replay_baseline=_freeze_metrics(ev.get("baseline", {})),
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replay_candidate=_freeze_metrics(ev.get("candidate", {})),
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regressed_metrics=tuple(ev.get("regressed_metrics") or ()),
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chain_id_written=chain_id,
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elapsed_s=elapsed,
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))
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elapsed_total = time.perf_counter() - total_t0
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elapsed_values = [a.elapsed_s for a in artifacts]
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active_bytes_after = active_path.read_bytes() if active_path.exists() else b""
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unique_pids = len({a.proposal_id for a in artifacts})
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unique_baselines = len({a.replay_baseline for a in artifacts})
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unique_candidates = len({a.replay_candidate for a in artifacts})
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unique_regressed = len({a.regressed_metrics for a in artifacts})
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unique_chain_ids = len({a.chain_id_written for a in artifacts})
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deterministic = (
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unique_pids == 1
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and unique_baselines == 1
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and unique_candidates == 1
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and unique_regressed == 1
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and unique_chain_ids == 1
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)
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return TeachingLoopBenchReport(
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runs=runs,
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unique_proposal_ids=unique_pids,
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unique_replay_baselines=unique_baselines,
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unique_replay_candidates=unique_candidates,
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unique_regressed_metrics=unique_regressed,
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unique_chain_ids=unique_chain_ids,
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deterministic=deterministic,
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active_corpus_byte_identical=(active_bytes_before == active_bytes_after),
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elapsed_mean_s=statistics.mean(elapsed_values) if elapsed_values else 0.0,
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elapsed_p50_s=_percentile(elapsed_values, 50.0),
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elapsed_p95_s=_percentile(elapsed_values, 95.0),
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elapsed_total_s=elapsed_total,
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sample_proposal_id=artifacts[0].proposal_id if artifacts else "",
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sample_chain_id=artifacts[0].chain_id_written if artifacts else "",
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)
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__all__ = [
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"TeachingLoopBenchReport",
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"run_teaching_loop_determinism",
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]
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@ -1777,7 +1777,7 @@ def build_parser() -> argparse.ArgumentParser:
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help="run benchmark harness (determinism, latency, speedup, versor audit)",
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description="run benchmark harness",
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)
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bench.add_argument("--suite", choices=["determinism", "latency", "speedup", "versor", "convergence", "realizer", "cost"],
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bench.add_argument("--suite", choices=["determinism", "latency", "speedup", "versor", "convergence", "realizer", "cost", "teaching-loop"],
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help="run a specific benchmark suite")
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bench.add_argument("--runs", type=int, default=20, metavar="N", help="run count for determinism benchmark (also turns count for cost suite)")
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bench.add_argument("--json", action="store_true", help="emit machine-readable JSON")
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57
tests/test_teaching_loop_bench.py
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57
tests/test_teaching_loop_bench.py
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@ -0,0 +1,57 @@
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"""Teaching-loop determinism benchmark — falsifiable claim test.
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The bench itself runs at any N ≥ 1; the test pins the headline claim
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at a low N for fast CI. Headline claim:
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"N identical inputs produce N byte-identical proposal artifacts,
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the active corpus is byte-identical pre/post, and the wall-time
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distribution is well-formed."
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If determinism breaks anywhere in the pipeline (proposal_id hashing,
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replay-equivalence gate, accept-side corpus_append, ProposalLog
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replay), at least one of the ``unique_*`` counts in the bench report
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will exceed 1 and this test fails.
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"""
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from __future__ import annotations
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from benchmarks.teaching_loop import run_teaching_loop_determinism
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def test_teaching_loop_is_deterministic_across_three_runs() -> None:
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report = run_teaching_loop_determinism(runs=3)
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assert report.deterministic is True
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assert report.active_corpus_byte_identical is True
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assert report.unique_proposal_ids == 1
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assert report.unique_replay_baselines == 1
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assert report.unique_replay_candidates == 1
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assert report.unique_regressed_metrics == 1
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assert report.unique_chain_ids == 1
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def test_proposal_id_is_a_sha256_prefix() -> None:
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report = run_teaching_loop_determinism(runs=2)
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pid = report.sample_proposal_id
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assert len(pid) == 32
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assert all(c in "0123456789abcdef" for c in pid)
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def test_chain_id_matches_canonical_layout() -> None:
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report = run_teaching_loop_determinism(runs=2)
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assert report.sample_chain_id == "cause_thought_reveals_meaning"
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def test_latency_stats_are_well_formed() -> None:
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report = run_teaching_loop_determinism(runs=3)
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assert report.elapsed_mean_s > 0.0
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assert report.elapsed_p50_s > 0.0
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assert report.elapsed_p95_s >= report.elapsed_p50_s
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assert report.elapsed_total_s >= report.elapsed_mean_s * report.runs * 0.9
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def test_report_serialises_to_json() -> None:
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import json
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report = run_teaching_loop_determinism(runs=2)
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blob = json.dumps(report.as_dict(), sort_keys=True)
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assert "deterministic" in blob
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assert "active_corpus_byte_identical" in blob
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