Phase 5 (ADR-0067 follow-up):
teaching/cross_pack_supersede.py — supersede_cross_pack_chain()
CLI: core teaching supersede ... --cross-pack
--subject-pack-id ... --object-pack-id ...
Strict per-chain residency, anti-leakage, byte-identical rollback
on any post-append re-load failure. 9 new tests.
Articulation benchmark suite (Phase 4 capability proof):
benchmarks/articulation.py — 5 sub-benches
[1] breadth — every intent shape (9 + OOV + cross-pack)
[2] determinism — N reruns / unique-surface count
[3] footprint — psutil RSS profile across T turns
[4] cross-topic — thread context across mixed subjects
[5] ollama-compare — opt-in side-by-side with local Ollama
CLI: core bench --suite articulation
--runs N (det rerun count)
--turns N (footprint sample window)
--ollama-model MODEL --ollama-reruns N
Full operator preamble + JSON report path.
10 new tests cover the bench shape (psutil import-skipped).
Documentation:
benchmarks/README.md — full operator manual: catalogue of every
bench suite, how to read good/neutral/bad results for each sub-
bench, why CORE vs Ollama comparisons are valid on the
determinism axis and not on linguistic quality, workflow guide.
README.md — articulation bench listed in the live-demo grid and
quick-start examples.
Reference run (llama3:8b, 100 turns, 5 reruns):
determinism_all_identical=True
per-turn ΔRSS ≈ 23 KiB
CORE byte_identical_on_every_prompt=True
Ollama unique_surfaces≥2 on every prompt
Verification:
18 new tests pass
Full lane: 2116 passed, 2 skipped, 0 failed in 2:38
`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.
benchmarks/cost.py measures CORE per-turn cost honestly:
Measured (no estimation):
- turns, wall_seconds_total, cpu_seconds_total
- latency stats: min / median / p95 / max in ms
- throughput in turns per second
Derived with disclosed assumptions:
- USD per 1000 turns at AWS t3.medium on-demand
($0.0416/hr, source cited in CloudReference.source_note)
- Frontier pricing comparison: Anthropic Claude Sonnet 4.5 /
Haiku 4.5 and OpenAI GPT-4o, public per-token rates with
source notes, derived using a conservative 20-in / 40-out
tokens-per-turn assumption.
Explicitly NOT reported:
- Joules per turn. Honest energy measurement requires RAPL
(Linux) or IOKit/powermetrics (macOS) with privileged access
that a plain Python process cannot get. Reporting a fabricated
figure from a hand-waved TDP would violate "speculation is not
evidence." cpu_seconds_total is the available proxy.
CLI:
core bench --suite cost --runs 100
Measured numbers (100 turns, "What is truth?", warmup 5):
median latency: 444.88 ms
p95 latency: 447.10 ms
throughput: 2.61 turns/s
$/1000 turns: $0.0044
vs frontier: 48–149× cheaper depending on provider
CLAIMS.md Tier 4 cost/latency rows updated with real numbers
replacing TBDs. evals/reports/cost_latest.json committed as the
captured baseline.
Verified: smoke (67), bench --suite cost CLI works.
Audit of the one-mutation-path invariant (ADR-0021 §3) found three leaks
where pack authority or session-state writes could substitute for coherence
judgment. All three landed fixes or partial closures in this push.
Leaks closed:
- Leak A: pack vocab defaulted to COHERENT — flipped to SPECULATIVE in
language_packs/{compiler,schema}.py; docstring corrected to align with
ADR-0021 (it was rationalizing the leak).
- Leak B: vault.recall was epistemic-blind — VaultStore.store() now stamps
every entry with EpistemicStatus (default SPECULATIVE); recall(min_status=)
filters to admissible-as-evidence tier. All 4 vault-write sites updated.
- Leak C (write-side): generate/proposition.py:198 stored articulated
propositions unmarked — now stamps SPECULATIVE, breaking the
fabrication-feedback loop in principle. Read-side audit of 5 call sites
is the residual.
New architectural invariants (tests/test_architectural_invariants.py):
- INV-21: one-mutation-path allowlist (caught Leak C on first run)
- INV-22: pack lexicon default is SPECULATIVE (Leak A guard)
- INV-23: vault recall epistemic-aware (Leak B guard)
New eval lanes:
- teaching_injection_resistance — ships GREEN at 1.00/1.00/0 (the
structural anti-injection claim is real and measurable)
- refusal_calibration — honest gap: 0% refusal, 0% fabrication
- contradiction_detection — honest gap: 50% flag via versor-delta heuristic,
100% false-positive; motivates the proper coherence-checker
- articulation_of_status — honest gap: 0% speculative articulation, 60%
false certainty; output-side leak surface
New benchmarks:
- benchmarks/footprint.py — total deployed runtime is 7.06 MiB
(109,358x smaller than Llama 3.1 405B, runs offline, no GPU)
- benchmarks/learning_curve.py — monotonic + replay-deterministic curve
per lane
Documentation:
- docs/truth_seeking_schema.md — foundational architectural commitment,
five rules, mapped to human failure modes, leaks published openly
- evals/CLAIMS.md — five-tier public claims doc; Tier 4.5 publishes
known gaps with named fixes; verification contract at top
- README.md — new pillar between algebraic substrate and language pillar
Includes in-flight formation pipeline scaffolding (formation/, tests/formation/,
docs/formation_pipeline_plan.md) and minor CLI/contracts/gitignore edits
that were already in the working tree at session start.
Verification: 798 passed, 2 skipped, 1 deselected (pre-existing pack-count
test drift unrelated to schema changes).
Closes the user-flagged scope gap: every previous fluency lane (Phase
5.1 + 5.4-5.7 + grammatical_coverage) operates on 3-word SVO probes.
These three pieces stress paragraph-scale generation, give per-stage
latency visibility, and expose the realizer's word-choice geometry —
all on top of the existing deterministic infrastructure.
# discourse_paragraph lane (paragraph-scale fluency)
Forces the realizer to emit multi-sentence paragraphs from a
multi-step ArticulationTarget with rhetorical moves (ASSERT, SEQUENCE,
ELABORATE, CONTRAST). Same realizer, much richer input — every case
is 3-5 sentences with deterministic discourse markers.
Public 12 cases / holdouts 5 / dev 1 across 12 + 5 topic chains
(epistemic, scientific method, creation arc, logical dependency,
ethical grounding, linguistic layers, mathematical chain, narrative,
biology, physics, two contrast-shaped, musical, social, computational,
psychological, economic).
Sub-metrics per case:
- sentence count (within min..max window)
- subject coverage rate
- discourse marker presence (next / furthermore / in contrast)
- sentence-initial capitalization
- replay determinism (run twice, surfaces match)
Result: 12/12 public + 5/5 holdouts at 100%, replay rate 100%, mean
sentence count 4.
# Realizer capitalization (G4, addresses user-flagged concern)
generate/realizer.py gains `_capitalize_sentence` + `_join_as_paragraph`
helpers. Sentence-initial alphabetic characters are now uppercased
(skipping leading whitespace/punctuation). Surfaces went from
"wisdom grounds knowledge. next, knowledge requires evidence."
to
"Wisdom grounds knowledge. Next, knowledge requires evidence."
The discourse_paragraph runner ships a strict per-sentence
capitalization check so future regressions get caught.
# Pipeline-stage profiler (benchmarks/pipeline_profiler.py)
External monkey-patch wrapper around CognitiveTurnPipeline.run() that
records per-stage ns budgets without editing any pipeline source.
Stages: intent, graph_planner, realize_semantic, runtime_chat,
maybe_transitive_walk, fold_walk_into_surface, run_teaching,
trace_hash.
API: `profile_turn(pipeline, text) -> ProfileReport` with
`.stages: dict`, `.total_ns: int`, `.as_dict()`.
Empirical: runtime_chat dominates >99% on the runtime hot path (which
is correct — that's where ingest + propagate + recall + articulate
all happen). Future optimisation work has a clear per-stage signal.
# Word-selection tracer (benchmarks/word_selection_tracer.py)
External wrapper around generate.articulation._resolve_slot that
records every nearest-neighbor lookup as a WordSelectionStep:
- slot (subject/predicate/object)
- input versor (32-d copy)
- top-K candidate words by CGA inner product
- chosen word + morphology
- output language
Top-K scoring uses the diagonal Cl(4,1) metric kernel from
algebra.backend (same vectorised path vault_recall uses), not a
per-word Python loop over cga_inner. No approximation, exact
deterministic ranking, bit-identical to a scalar scan.
API: `trace_realization(pipeline, text) -> RealizationTrace` with
`.steps`, `.realization_steps`, `.surface`, `.as_dict()`.
# CLI lane registration
Cognition suite now sweeps the benchmark profiler/tracer tests
(test_benchmarks_profiler.py) so any future regression in the
instrumentation surfaces immediately.
# Constraints honoured
- Zero edits to core/, chat/, vault/, teaching/, language_packs/, or
the algebra hot path. All instrumentation is external monkey-patch
with originals restored in finally.
- discourse_paragraph runner bypasses ChatRuntime grounding (named v2
gap) so paragraph capability is isolated to the realizer.
- No semantic changes; no hidden normalisation; no approximate
recall.
# Lane health
smoke 55, runtime 19, teaching 17, packs 6, cognition 105 (was 103),
algebra 132. All Phase 5 fluency lanes still 100% with the
capitalised surfaces (rubric is case-insensitive). discourse_paragraph
100%.
# What ships next (named v2)
- Round-trip: discourse_paragraph through ChatRuntime end-to-end,
not just realize_target.
- Per-sentence grammatical_coverage rubric on each emitted sentence.
- Longer chains (10/20/50 sentences) with per-sentence determinism
scaling curves.
- compose_relations operator to lift compositionality recall from
68.8% toward 100%.
Shay