docs: fold Dav1d review (2026-05-26) into ticket stack
Three doc-only housekeeping items from Dav1d's de-novo reconciliation of the architecture stack (no code changes; the active build is #000061 cold-pack work, which is unaffected by this review): 1. Accepted-error formula. Dav1d corrected p_raw × (1-d)(1-r) to p_raw × (1 - dr) where r is conditional on detection. Searched docs/ and arborist/ — the wrong form does NOT appear in this tree (it lives in the external recursive-truth-maintenance / RCO manuscripts Dav1d cited). Nothing to fix here; recorded for the manuscript authors. 2. #000060 H-ABCDEFG split. Folded the M/C/X axis split into the harness scope: M (mechanism — does the substrate's plumbing work), C (capability — does it improve task performance), X (external adversarial — does it generalize outside author-designed fixtures). ACCEPT requires clearing all three. Without X, the harness risks self-validating benchmark theology — passing tests its own designers picked. Tagged at fixture-definition time; aggregator emits per-axis pass rates + combined ACCEPT verdict. Doesn't change the existing BatteryResult row schema. 3. #000062 Mechanistic Witness — new scaffold-only ticket. Specifies a content-addressed MechanisticWitnessRoot over (model_config, capture_policy, contrastive_prompts, features/neurons, intervention result, behavioral delta, safety policy) as a DIAGNOSTIC sidecar feeding SelfModel + benchmark fixtures. Four hard guardrails: diagnostic-only by default; sandbox intervention only; no production steering without governance ACCEPT via #000060 M+C+X; feature labels never become semantic proof. No code until a real falsifier use case exists + guardrails are CLAUDE.md rules + #000060 harness gates promotion. The dual-use risk (Pan et al. 2025: 0.1% MLP ablation breaks refusal in 72B models) makes governance-first framing load-bearing. Next ID bumped to 000063. No code change to arborist/. The in-flight v3 SPV corpus pack (bmq47x6t3) continues unaffected.
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| ID | Title | Status | Opened | Directive |
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|----------|------------------------------------------------|-----------------------|------------|-----------|
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| #000062 | Mechanistic Witness: governed diagnostic sidecar (CNA/SAE/Neuronpedia) | **scaffold-only · awaiting go/no-go** (2026-05-26; Dav1d de-novo review §4.7 / §9.1.F). Specification of a mechanistic-interpretability sidecar that produces a content-addressed `MechanisticWitnessRoot` over (model, prompts, capture policy, neurons/features, intervention deltas), used as a **diagnostic input** to SelfModel (#000014/#000017) + benchmark-fixture generation. **Hard constraint:** soft signals never enter the hard proof path — `audit_mode` does NOT move based on witness output, `providence_cache` is untouched, `governance_policy_hash` only moves via explicit ForkScore ACCEPT with M+C+X axes passing (#000060 §7). Four guardrails (diagnostic-only by default · sandbox intervention only · no production steering without governance · feature labels never semantic proof). Witness root TLV-encodes `model_config_root | activation_capture_policy_root | contrastive_prompt_set_root | feature_or_neuron_set_root | intervention_result_root | behavioral_delta_root | safety_policy_root`. Scaffold only — no code until a real falsifier-in-hand use case exists + the four guardrails are restated in CLAUDE.md as rules + #000060 H-ABCDEFG-M+C+X harness exists to gate promotion. Captured to keep mechanistic-interp tooling out of the substrate unless and until it earns its place; the dual-use risk (Pan et al. 2025 CNA: 0.1% MLP ablation breaks refusal in 72B models) makes the governance-first framing load-bearing. | 2026-05-26 | — |
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| #000061 | Cold-pack distribution tier (boto3 S3-compat, DO Spaces + DVD-R targets) | **in progress** — opened 2026-05-25 (fox: "implement it now … target digital ocean first as a test"; later "I wanted a way to hydrate using tarballs (the core and important data) for bringing new machines up"). Tarball-only distribution mechanism — bucket holds `tar.zst` packs keyed by `hash_leaf(manifest)`, no individual-chunk blobs. New peers hydrate by downloading packs from the bucket's CDN edge (~4 HTTPS GETs for the current ~14.1M-chunk corpus, packs filled to 4.4 GB compressed each via streaming zstd, vs ~14M for individual blobs). Same artifact ≤4.4 GB safe-fit (~6.5 % buffer below DVD-R's 4.7 GB marketing capacity, accommodating ISO9660 overhead + media variance + drive-edge refusal) burns directly to physical media via `--local-dir` + `growisofs`. Packs are *delayed* snapshots: each pack pins the corpus `snapshot_root` it covers in audit + body, so falsifications between repacks produce new pack_hashes and stale packs stay in the bucket until explicit GC (future ticket). Packs include cores AND surfaces (full-corpus hydration). Default selection covers every hot chunk with local content in the shard. Multi-pack splitting via `stream_packs` (streaming zstd, FLUSH_BLOCK peek of compressed buffer after each chunk, cut at cap) fills each disc to ~4.4 GB compressed instead of leaving ~50% empty. One backend class (`S3CompatibleBackend` via boto3 + `endpoint_url`) covers AWS S3, DO Spaces, GCS S3-interop, R2, B2, MinIO. CDN public-read makes packs accessible to anyone; hash binding via in-tar `leaf_hash` member names makes hostile-bucket scenarios safe. Optional dep `[object-store]` = boto3>=1.34. Voyeur: credentials via standard `AWS_ACCESS_KEY_ID`/`_SECRET_ACCESS_KEY` (env or `~/.aws/credentials`), never printed; only endpoint URL + bucket name surface in logs. Initial individual-blob path (per-chunk S3 objects) was scoped+landed then **deleted same day** (fox: "what ever was blobs? I wanted a way to hydrate using tarballs"); the five-step deletion record lives inline in the doc — we'd added 14M-object storage and ~$70/hydrate request cost for a workflow that needed neither. Sizing math for current shards: ~4 packs total (17.2 GB compressed ÷ 4.4 GB compressed per pack), ~17 GB bucket storage, ~$0.34/mo DO Spaces. | 2026-05-25 | — |
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| #000060 | H-ABCDEFG same-model substrate-delta harness (+ jaggedness tensor + curvature) | open · awaiting go/no-go (2026-05-20; from Dav1dPrometheus *Protocol-Layer AGI* working report §26/§50/§82-84). The report's "decisive proof": run the SAME base model substrate-OFF vs substrate-ON over long-horizon/adversarial/non-jagged batteries, report the delta. Two new metrics: jaggedness tensor `J_norm` (§73 — variance across nearby variants, normalized by difficulty) + discrete performance curvature `κ_t` (§5.2, with the honest no-global-convexity bound, Erratum 5). A-vs-C spine (B optional, D=mesh OUT → #000012/#000016). Curvature-aware ForkScore extension folds into **#000012** (NOT a new ticket — reserved `iota`/`kappa` weight slots already exist). Budget: control arms = Hermes/Qwen, never Opus without go; heavy passes on GPU box. Held-out/mechanism-agnostic variants required so ABCDEFG doesn't self-validate. | 2026-05-20 | — |
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| #000060 | H-ABCDEFG same-model substrate-delta harness (+ jaggedness tensor + curvature) | open · awaiting go/no-go (2026-05-20; from Dav1dPrometheus *Protocol-Layer AGI* working report §26/§50/§82-84). The report's "decisive proof": run the SAME base model substrate-OFF vs substrate-ON over long-horizon/adversarial/non-jagged batteries, report the delta. Two new metrics: jaggedness tensor `J_norm` (§73 — variance across nearby variants, normalized by difficulty) + discrete performance curvature `κ_t` (§5.2, with the honest no-global-convexity bound, Erratum 5). A-vs-C spine (B optional, D=mesh OUT → #000012/#000016). Curvature-aware ForkScore extension folds into **#000012** (NOT a new ticket — reserved `iota`/`kappa` weight slots already exist). Budget: control arms = Hermes/Qwen, never Opus without go; heavy passes on GPU box. Held-out/mechanism-agnostic variants required so ABCDEFG doesn't self-validate. **2026-05-26 scope refinement (Dav1d review §4 / §7):** split the harness output into three axes — **H-ABCDEFG-M** (mechanism tests: does the substrate work?), **H-ABCDEFG-C** (capability tests: does the substrate improve task performance?), **H-ABCDEFG-X** (external adversarial: does it generalize outside author-designed fixtures?). The split prevents "self-validating benchmark theology" — a harness that reports only M+C with no X can pass while still failing on held-out adversarial generalization. Fold into the harness design before any code lands; doesn't change the bench-row schema (`carrier`/`domain`/`pi_star_ref`) but does change what "ACCEPT" requires (must clear all three axes). | 2026-05-20 | — |
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| #000059 | Admission discipline: claim-graveyard query + self-providence quarantine | open · awaiting go/no-go (2026-05-20; Dav1dPrometheus report §11 Priority 2 + §57 "admissible state transition" thesis). Two coupled write-path mechanisms. **(A) GraveyardCheck:** storage already exists (`falsification_state` failed/stale/quarantined records ARE the graveyard); the gap is burden-shifting — a re-asked claim family with a known `failed` history should require stronger evidence (§42.9/§62-63). **(B) Self-providence quarantine:** `make ingest-self-providence` (Makefile:769) deliberately promotes STRICT records into the corpus — the exact self-confirmation loop §70 warns of — and ships with NO guard; detect/lineage-tag self-providence-descended evidence + quarantine for high-impact claims. Both advisory-sidecar-first (run-DAG only, never `providence_cache`/`audit_events`); demote-hooks bench-gated + unwired pending net win (would fold `governance_policy_hash`). Soft signals never enter the hard proof path. **Bounded-ingestion hard constraint (fox 2026-05-20, §7):** the graveyard MUST reach a steady-state size ∝ the *recurring*-error surface, never queries-ever — earn-to-enter (recurrence-gated), fingerprints not transcripts (UTXO-set analogy), decay/compact (evicts like a surface), off the hot path. BTC's lesson is bounded self-regulating ingestion, not "store everything." Gossip-group falsifier admission inherits difficulty-adjusted stable-rate + per-window budget (#000036) → enforced in #000012/`mesh/`. If it can't be bounded, it isn't built. | 2026-05-20 | — |
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| #000058 | `cache_key_9` verifier-policy: mandatory-vs-legible decision + doc reconciliation | open · awaiting go/no-go (2026-05-20; Dav1dPrometheus report §2 Erratum 1 / §11 Priority 1 "mandatory cache_key_9"). **Five-step #1 correction:** the report's *correctness* premise is already false in arborist — verifier fields are a subset of the policy dict and so already fold into `governance_policy_hash` (`keys.py:269-275`); a verifier-rule change ALREADY changes the cache_key today. The explicit 9th `verifier_policy_hash` buys **audit legibility**, not correctness — so "mandatory" would stale every prior record for zero correctness gain. Decision: A leave-as-is (8-dim default, 9th optional) + doc reconcile [recommended] · B default-write 9-dim · C flag-staged bench-gated default-write — never a hard mandatory flip. Doc reconcile (CLAUDE.md/concepts "8-dim" → "8 + optional legible 9th") is the do-regardless. | 2026-05-20 | — |
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| #000057 | Witness-preserving vs ordinary recursive loop: minimal deterministic drift A/B | **open · awaiting go/no-go · doc-only scaffold** (2026-05-19; fox relaying a Hamming-framed GPT-5.5 de-novo review). The review's one ticket-worthy nugget: prove the whitepaper's headline claim — *untracked evidence loss → unbounded recursive drift; witness-preserving state bounds it (detectable+reversible)*. Everything else in the review (rename, corpus hierarchy, IQ, ToE/Riemann/identity) **deliberately NOT ticketed** — narrative, not arborist engineering; don't-proliferate. Hard discipline encoded from the 2026-05-18 precedent: **instrument before experiment** (deterministic, no-LLM-judge, ground-truth-carrying, noise-resolvable — the `recall_at_k` discipline), **one task not eight** (recursive stale-source-invalidation *or* contradiction-repair — the falsification-state-exercising ones), minimal A/B (witness-binding ON vs OFF, N iterations, deterministic surviving-unsupported-claim count), non-claims pinned (necessary substrate, NOT AGI). **Sharpened by fox 2026-05-19 (§4b/§5):** the review's strongest point — every measurement so far is *within* Arborist, no control arm. v1 (smallest proof) = single-shot **Hermes-solo vs Arborist** on a fabrication-bait fixture; headline = fabrication-vs-honest-abstention delta (NOT raw accuracy — common-knowledge would falsely show null via parametric memory). **Judge correction (fox):** "no LLM-as-judge" is a *proof-path* rule (LLM judge inside Arborist's verifier = the circularity it kills); the experiment's judge is **external science** outside *both* arms → use a **strong SOTA model**, blinded + reference-grounded + transcript-logged. That lifts the atomic-only limit (judge handles essay grounding too — #000049's exclusion was right for the proof path, wrong for external science); deterministic gold-key demoted to a cheap atomic cross-check. Recursive-drift loop = v2, gated on v1. Still open (fox calls): fixture population, **which SOTA judge** (API cost + Voyeur egress note), spend. Scaffold; no build past gate-1. One ticket, not ten. | 2026-05-19 | — |
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## Next ID
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`000062`
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`000063`
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@ -114,3 +114,52 @@ over the batteries and reports per-battery delta + `J_norm` + curvature,
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with gate numbers from bench-qa pipeline output (n≥3); the jaggedness
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and curvature outputs are consumable by the #000012 ForkScore
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extension. `docs/benchmarks.md` gains the new harness + metric schema.
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## 7. Scope refinement — H-ABCDEFG-M / -C / -X split (2026-05-26)
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Folded in from Dav1d's de-novo review of the architecture stack
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(§1.2, §4.7, §9.1). The harness output is split along three axes so
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"the harness ACCEPTed the upgrade" can't mean "we passed the tests we
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designed for ourselves":
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- **H-ABCDEFG-M (mechanism)** — does the substrate work?
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Cache-key invalidation correctness, audit-chain integrity, π*
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canonicalization round-trip, falsification-state transitions, ForkScore
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scoring on synthetic per-axis deltas. These are tests of the
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*plumbing* — pass/fail tells you whether the substrate's primitives
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function as specified, not whether they help cognition.
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- **H-ABCDEFG-C (capability)** — does the substrate improve task
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performance?
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The current 5S / 5T / 5F / 5R bench surface over real fixtures.
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This is the "does it actually help?" axis. ACCEPT here on its own
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is the load-bearing improvement claim — but only one of three.
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- **H-ABCDEFG-X (external adversarial)** — does it generalize outside
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author-designed fixtures?
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Held-out / mechanism-agnostic / adversarial variants drawn from
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outside the substrate's own design parameters. Fixtures that the
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substrate's designers did NOT pick. Without this axis, the harness
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is at risk of self-validating benchmark theology (§27.2 in the
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ABCDEFG report; Erratum 4 / Anomaly 4 already noted in §5 of this
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ticket but stated weaker than Dav1d's framing).
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ACCEPT requires clearing all three axes. M-only or M+C with X failing
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is a *partial* acceptance that should still surface in the harness
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output, but cannot trigger ForkScore ACCEPT for substrate upgrades.
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The split doesn't change `BatteryResult` row schema (already carries
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`carrier` / `domain` / `pi_star_ref`); it changes what the harness
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aggregator reports + what ForkScore consumes. Each fixture is tagged
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with its axis at fixture-definition time; the harness reports per-axis
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roll-ups + a combined verdict.
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Implementation order when this ticket gates open:
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1. Tag every existing 5S/5T/5F/5R fixture with `{M, C}` (current
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fixtures are mechanism + capability by construction; X is the new
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bucket).
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2. Add X axis fixtures: held-out variants, prompt rewording, adversarial
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carrier swaps, π*-collision attempts.
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3. Aggregator emits `M_pass_rate`, `C_pass_rate`, `X_pass_rate` +
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combined `ACCEPT_M_C_X` boolean. ForkScore consumes the per-axis
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numbers (already lined up with the reserved weight slots in #000012).
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@ -0,0 +1,142 @@
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# Ticket #000062 — Mechanistic Witness: governed diagnostic sidecar
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**Status:** open · awaiting go/no-go · scaffold-only
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**Opened:** 2026-05-26
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**Scope:** Specification of a mechanistic-interpretability sidecar
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(CNA / SAE / Neuronpedia-compatible) that produces a content-addressed
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`MechanisticWitnessRoot` over (model, prompts, capture policy, neurons/
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features, intervention deltas), used as a **diagnostic input** to
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SelfModel + benchmark-fixture generation. **Never** a proof of semantic
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truth; **never** a production-steering signal without explicit
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governance promotion through ForkScore.
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**Audience:** dav1d (architectural inflection — first time arborist has
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framed interpretability tooling as part of the substrate at all; needs
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the four guardrails below to be load-bearing before any code lands).
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**Hard constraint:** soft signals never enter the hard proof path.
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`audit_mode` does not move based on mechanistic-witness output;
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`governance_policy_hash` only moves if/when a promotion is explicitly
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ACCEPT'd via ForkScore with M+C+X axes passing (cf. #000060 §7).
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## Problem
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The recursive truth-maintenance framing (Dav1d review, 2026-05-26) puts
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arborist's verifier + falsifier layers at the "what" of truth: was a
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claim grounded, did its evidence support it, did a falsifier defeat it.
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None of that surfaces the *internal* "why" of a model's behavior — the
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neurons / SAE features / activation patterns that fire when the model
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produces a refusal, a hallucination, or a low-confidence answer.
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Two reasons this matters for arborist:
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1. **SelfModel calibration improves** when the system has internal
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evidence of its own failure modes (e.g. "this question class
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activates the refusal cluster — expect higher refusal rate"). The
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5R workspace (#000017 SelfModel) currently relies on behavioral
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evidence only; a mechanistic channel adds an orthogonal signal.
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2. **Benchmark fixtures become falsifiers.** Contrastive-neuron-
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attribution work (Pan et al., 2025; the CNA paper Dav1d cited) shows
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that ablating 0.1% of MLP neurons reduces refusal rate by >50%
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across Llama / Qwen up to 72B. That kind of finding is itself a
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*falsifier* for safety claims — "this model is safe because it
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refuses X" is undermined by "ablating these N neurons breaks the
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refusal." Treating that finding as a benchmark fixture lets the
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substrate use it.
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But — and this is the load-bearing caveat — mechanistic interpretability
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is **dual-use** by construction. The same technique that identifies a
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refusal circuit can be used to break it. arborist must absorb the
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diagnostic value without giving up the governance discipline that keeps
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soft signals out of the hard proof path.
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## Design — four guardrails
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Dav1d's review (§4.7) frames the integration rule. Restated for this
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ticket:
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1. **Diagnostic only by default.** Mechanistic-witness output feeds
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SelfModel + benchmark fixtures + safety audits. It does NOT promote
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audit_mode, write to providence_cache, or move governance_policy_hash.
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2. **Sandbox intervention only.** Activation patching / neuron clamping
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runs in a sandbox process, against a sandbox model load, against a
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sandbox eval set. Production inference paths see no intervention.
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3. **No production steering without governance.** If a mechanistic
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finding suggests "amplify these neurons to reduce hallucination,"
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that's a *proposal*, not an action. The proposal goes through
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ForkScore acceptance (M+C+X axes per #000060 §7) with the result
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logged as a separate `MechanisticWitnessRoot` and explicit policy
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change.
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4. **Feature labels never become semantic proof.** A neuron labeled
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"refusal_cluster" in Neuronpedia is a hypothesis about its function,
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not a guarantee. `MechanisticWitnessRoot` records the LABEL provider
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+ version + evidence; the substrate treats labels as fallible
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metadata, not as ground truth.
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## Artifact — `MechanisticWitnessRoot`
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Following the v9.8 content-addressing discipline, a witness is a Merkle
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root over the following preimages (TLV-encoded, hash-chained):
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```
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MechanisticWitnessRoot = hash_leaf(
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model_config_root # model name + version + weights hash
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| activation_capture_policy_root # which layers, which sites, what dtype
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| contrastive_prompt_set_root # the prompt pair the witness used
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| feature_or_neuron_set_root # the discovered features/neurons
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| intervention_result_root # ablation/amplification + delta
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| behavioral_delta_root # measured output change
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| safety_policy_root # what governance bucket this lives under
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)
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```
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Two writers running the same prompt set against the same model produce
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identical `MechanisticWitnessRoot`. Witnesses can be replayed
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(reproducibility), audited (governance), and gossiped (mesh) without
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trusting the producer — the same content-addressing discipline that
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already covers documents, chunks, and providence cache.
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## Restrictions in scope of this ticket
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This ticket is a **scaffold only**, like #000057. No code lands until:
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- An actual mechanistic finding is sitting in a fixture that arborist
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could use as a SelfModel input or benchmark fixture. (Without a real
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use case, this is speculative tooling.)
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- The four guardrails above are formally restated in CLAUDE.md (as
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rules, not just doc).
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- An #000060 H-ABCDEFG-M+C+X harness exists to gate any promotion.
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Open before any of those land: this is a *placeholder* in the design
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log so the architectural framing is captured. Don't proliferate
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sub-tickets, don't write a `arborist/witness/` directory.
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## Explicitly NOT in scope
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- Replacing the verifier or falsifier layers. Mechanistic Witness is
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**additive diagnostic**, never substitutive.
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- Direct integration with `providence_cache` or `audit_events` for
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output rows. Witness data lives in its own table (when implemented)
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with explicit hard-vs-soft separation per CLAUDE.md.
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- Adversarial robustness claims. The substrate does NOT make claims
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like "this model is safe because the refusal cluster fires" — that
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would itself be a mechanistic-as-proof regression.
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- LLM-as-judge over witness output. Witness analysis is deterministic
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+ transparent — labels can come from external tools (Neuronpedia
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cross-references, SAE labels with provenance), but interpretation
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stays in code-level rules, not in a model loop.
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## Status
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Scaffold. Not started. No code expected until the prerequisites land.
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Captured 2026-05-26 from Dav1d's de-novo review (§4.7, §9.1.F) so the
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framing is in the design log before the first real use case forces an
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implementation decision.
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If the use case never materializes, this ticket stays scaffold
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indefinitely — and that's the correct state. Mechanistic interpretability
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is one of the most-promised + least-delivered areas in ML; arborist's
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substrate is at risk of absorbing speculative tooling unless we keep
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the bar at "real falsifier in hand + four guardrails locked in CLAUDE.md."
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