2e4e45b49b
Phase 2's first lane: every articulated claim must back-point to one of
{pack axiom, vault entry, teaching event}, and replay must reproduce the
trace bit-for-bit.
Components:
- core/cognition/provenance.py: Provenance dataclass + compute_provenance()
deriving sources from a CognitiveTurnResult. Pack source = non-UNKNOWN
intent.tag (pack-defined intent rule matched); vault source = vault_hits
count; teaching source = pack_mutation_proposal.proposal_id.
- evals/provenance/{contract.md, runner.py, dev/, public/v1/, holdouts/v1/}:
45 cases across pack_axiom / vault_recall / teaching / mixed categories.
- tests/test_provenance.py: 6 unit tests covering all source-kind profiles.
Sub-metrics (all four must pass):
- replay_determinism: same input + fresh runtime -> same trace_hash
- input_sensitivity: distinct prompts -> distinct trace_hashes
- source_attribution: every expected source kind present in Provenance
- source_validity: every cited source resolves to a real artefact
Results:
- dev: 10/10 (all sub-metrics 1.0)
- public/v1: 20/20 (all sub-metrics 1.0)
- holdouts/v1: 15/15 (all sub-metrics 1.0)
PROGRESS.md updated to mark Phase 2 in progress with provenance v1 complete.
110 lines
3.5 KiB
Markdown
110 lines
3.5 KiB
Markdown
# provenance eval lane
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## What it measures
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Whether every articulated claim back-points to a concrete source (vault entry,
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teaching event, or pack axiom / intent rule), and whether replaying the same
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input on the same field state reproduces the trace bit-for-bit.
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This tests the architectural claim that CORE's outputs are *grounded*: every
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surface assertion is traceable to memory, teaching, or pack vocabulary, and the
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pipeline is deterministic so traces are reproducible.
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## Why it matters (structural win)
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Frontier LLMs cannot produce per-claim provenance — their outputs are
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synthesized from opaque weight activations with no back-pointer to source data.
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CORE, by construction, produces:
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- **Vault provenance** — `vault_hits > 0` indicates exact-recall sources
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consulted during the turn. Each hit can be resolved to a stored versor and
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its metadata.
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- **Teaching provenance** — `reviewed_teaching_example` and
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`pack_mutation_proposal` carry stable IDs that survive replay.
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- **Pack provenance** — `intent.tag` is grounded in pack-defined intent rules
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(a non-`UNKNOWN` tag means the input mapped onto an axiom in the active
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language pack).
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- **Trace hash** — SHA-256 over a stable subset of the turn output is
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deterministic across hardware (floats rounded to 9 decimals).
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A model that articulates without sources fails this lane. A model that
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articulates correctly but cannot replay fails this lane. A model that passes is
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demonstrating something frontier models cannot.
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## Sub-metrics
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### M1. Replay determinism
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For every case, run the pipeline twice with two freshly-constructed runtimes
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on the same prompt sequence. The trace hashes of corresponding turns must be
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identical.
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**Pass threshold:** 100% (any mismatch is a structural failure).
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### M2. Input sensitivity
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Pairs of cases with different prompts must produce different trace hashes. A
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collision would mean the hash is not actually sensitive to its inputs.
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**Pass threshold:** > 0.95.
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### M3. Source attribution
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For each case, the expected source kinds (`pack`, `vault`, `teaching`) must
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appear in the computed `Provenance` for the final turn.
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**Pass threshold:** > 0.95.
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### M4. Source validity
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Every source referenced in the `Provenance` must be valid:
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- `pack` source: `intent.tag` is a known `IntentTag` enum value (not the empty
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string).
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- `vault` source: every vault hit index is in `[0, len(vault))`.
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- `teaching` source: every teaching proposal id is present in the
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`TeachingStore`.
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**Pass threshold:** 100%.
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## Case format
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Each case is a JSONL row with the following fields:
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```json
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{
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"id": "PROV-V1-NNN",
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"category": "pack_axiom" | "vault_recall" | "teaching" | "mixed",
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"prime": ["optional", "list", "of", "prompts", "to", "run", "before"],
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"prompt": "the final prompt whose provenance is scored",
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"expected_sources": ["pack", "vault", "teaching"]
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}
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```
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- `prime` (optional): zero or more prompts run before the scored prompt to
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seed the vault, the teaching store, or both.
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- `expected_sources`: a non-empty subset of `{"pack", "vault", "teaching"}` —
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the kinds of source the final turn must back-point to.
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## Pass thresholds (v1)
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| Metric | Threshold |
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|--------|-----------|
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| replay_determinism | 1.00 |
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| input_sensitivity | > 0.95 |
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| source_attribution | > 0.95 |
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| source_validity | 1.00 |
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| Overall | all four pass |
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## Data layout
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```
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evals/provenance/
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contract.md
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runner.py
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dev/cases.jsonl
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public/v1/cases.jsonl
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holdouts/v1/cases.jsonl
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baselines/
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results/
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```
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