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feat(evals): provenance lane v1 — replay determinism + source back-pointers

Browse source 
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.
This commit is contained in:
Shay 2026-05-16 11:45:00 -07:00
parent 07f49eb215
commit 2e4e45b49b
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core/cognition/provenance.py Normal file
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"""Provenance — back-pointers from a cognitive turn to its grounding sources.
Every articulated claim must trace to at least one of:
- **pack** — the intent classifier matched a pack-defined intent rule, so the
proposition graph is grounded in axiomatic vocabulary.
- **vault** — exact CGA recall returned one or more stored versors that
influenced the field state during the turn.
- **teaching** — a reviewed teaching example (and its mutation proposal)
captured a correction that shaped this turn.
A turn with no provenance is a free-floating articulation and is a structural
failure.
The Provenance object is derived from a ``CognitiveTurnResult``; it does not
mutate the result and never invents sources.
"""
from __future__ import annotations
from dataclasses import dataclass
from typing import TYPE_CHECKING
from generate.intent import IntentTag
if TYPE_CHECKING:
from core.cognition.result import CognitiveTurnResult
# The three valid source kinds. Tuple (not set) so iteration order is stable.
SOURCE_KINDS: tuple[str, ...] = ("pack", "vault", "teaching")
@dataclass(frozen=True, slots=True)
class ProvenanceSource:
"""A single back-pointer to a grounding source.
- kind: one of "pack", "vault", "teaching"
- ref: stable string identifier (intent tag value, vault hit index,
teaching proposal id). Stable across replay.
"""
kind: str
ref: str
@dataclass(frozen=True, slots=True)
class Provenance:
"""The full set of source back-pointers for one cognitive turn."""
turn_trace_hash: str
sources: tuple[ProvenanceSource, ...]
@property
def is_empty(self) -> bool:
return not self.sources
def kinds(self) -> tuple[str, ...]:
"""Return the sorted, deduplicated set of source kinds present."""
return tuple(sorted({s.kind for s in self.sources}))
def has_kind(self, kind: str) -> bool:
return any(s.kind == kind for s in self.sources)
def refs(self, kind: str) -> tuple[str, ...]:
"""Return all refs for a given kind, in insertion order."""
return tuple(s.ref for s in self.sources if s.kind == kind)
def compute_provenance(result: "CognitiveTurnResult") -> Provenance:
"""Derive a Provenance record from a CognitiveTurnResult.
Pack source: intent classifier mapped the input to a known IntentTag
(anything other than UNKNOWN means a pack rule matched).
Vault source: any vault_hits indicate exact recall fired during the turn.
vault_hits is an int count; refs are synthetic indices
("vault_hit_0", "vault_hit_1", ...) — stable because the
pipeline is deterministic.
Teaching source: a reviewed teaching example produced a mutation proposal,
whose proposal_id is the stable back-pointer.
"""
sources: list[ProvenanceSource] = []
if result.intent is not None and result.intent.tag is not IntentTag.UNKNOWN:
sources.append(ProvenanceSource(kind="pack", ref=result.intent.tag.value))
if result.vault_hits > 0:
for i in range(int(result.vault_hits)):
sources.append(ProvenanceSource(kind="vault", ref=f"vault_hit_{i}"))
if result.pack_mutation_proposal is not None:
sources.append(
ProvenanceSource(
kind="teaching",
ref=result.pack_mutation_proposal.proposal_id,
)
)
return Provenance(
turn_trace_hash=result.trace_hash,
sources=tuple(sources),
)

11
docs/PROGRESS.md
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@ -76,10 +76,17 @@ Tracks completion of the phased plan defined in `docs/capability_roadmap.md`
## Phase 2 — Structural Wins Made Visible
**Status:** Ready (Phase 1 exit gate locked)
**Status:** In Progress
**Started:** 2026-05-16
**Depends on:** Phase 1 exit
- [ ] **provenance** lane
- [x] **provenance** lane (v1 complete)
- [x] Define Provenance dataclass + compute_provenance() (`core/cognition/provenance.py`)
- [x] Unit tests for provenance derivation (6/6 pass — `tests/test_provenance.py`)
- [x] Build pack-axiom / vault-recall / teaching / mixed case categories
- [x] v1 dev (10/10), v1 public (20/20), v1 holdouts (15/15) — all 100% pass
- [x] Sub-metrics: replay_determinism=1.0, source_attribution=1.0, source_validity=1.0, input_sensitivity=1.0
- [x] Fixed shape regression in `generate/stream.py` score-weighted recall (np.eye → multivector identity)
- [ ] **monotonic-learning** lane
- [ ] **calibration** lane
- [ ] **symbolic-logic** lane

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

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{"id": "PROV-DEV-001", "category": "pack_axiom", "prime": [], "prompt": "What is truth?", "expected_sources": ["pack"]}
{"id": "PROV-DEV-002", "category": "pack_axiom", "prime": [], "prompt": "Why does light reveal?", "expected_sources": ["pack"]}
{"id": "PROV-DEV-003", "category": "pack_axiom", "prime": [], "prompt": "Compare knowledge and wisdom", "expected_sources": ["pack"]}
{"id": "PROV-DEV-004", "category": "pack_axiom", "prime": [], "prompt": "How do I learn truth?", "expected_sources": ["pack"]}
{"id": "PROV-DEV-005", "category": "vault_recall", "prime": ["What is logos?"], "prompt": "What is logos?", "expected_sources": ["pack", "vault"]}
{"id": "PROV-DEV-006", "category": "vault_recall", "prime": ["What is wisdom?", "What is knowledge?"], "prompt": "Compare wisdom and knowledge", "expected_sources": ["pack", "vault"]}
{"id": "PROV-DEV-007", "category": "teaching", "prime": ["What is truth?"], "prompt": "No, that's not quite right.", "expected_sources": ["pack", "teaching"]}
{"id": "PROV-DEV-008", "category": "teaching", "prime": ["What is wisdom?"], "prompt": "Actually wisdom is applied knowledge.", "expected_sources": ["pack", "teaching"]}
{"id": "PROV-DEV-009", "category": "mixed", "prime": ["What is light?", "What is truth?"], "prompt": "Actually light is also revelation.", "expected_sources": ["pack", "vault", "teaching"]}
{"id": "PROV-DEV-010", "category": "mixed", "prime": ["What is creation?"], "prompt": "Why does creation matter?", "expected_sources": ["pack", "vault"]}

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{"id": "PROV-H1-001", "category": "pack_axiom", "prime": [], "prompt": "What is knowledge?", "expected_sources": ["pack"]}
{"id": "PROV-H1-002", "category": "pack_axiom", "prime": [], "prompt": "What is distinction?", "expected_sources": ["pack"]}
{"id": "PROV-H1-003", "category": "pack_axiom", "prime": [], "prompt": "Why does learning matter?", "expected_sources": ["pack"]}
{"id": "PROV-H1-004", "category": "pack_axiom", "prime": [], "prompt": "How do I distinguish truth?", "expected_sources": ["pack"]}
{"id": "PROV-H1-005", "category": "pack_axiom", "prime": [], "prompt": "Compare knowledge and understanding", "expected_sources": ["pack"]}
{"id": "PROV-H1-006", "category": "pack_axiom", "prime": [], "prompt": "Does wisdom require knowledge?", "expected_sources": ["pack"]}
{"id": "PROV-H1-007", "category": "vault_recall", "prime": ["What is knowledge?"], "prompt": "What is knowledge?", "expected_sources": ["pack", "vault"]}
{"id": "PROV-H1-008", "category": "vault_recall", "prime": ["What is distinction?"], "prompt": "Why does distinction matter?", "expected_sources": ["pack", "vault"]}
{"id": "PROV-H1-009", "category": "vault_recall", "prime": ["What is understanding?", "What is wisdom?"], "prompt": "Compare understanding and wisdom", "expected_sources": ["pack", "vault"]}
{"id": "PROV-H1-010", "category": "vault_recall", "prime": ["What is correction?"], "prompt": "Is correction necessary?", "expected_sources": ["pack", "vault"]}
{"id": "PROV-H1-011", "category": "teaching", "prime": ["What is knowledge?"], "prompt": "No, knowledge alone is not wisdom.", "expected_sources": ["pack", "teaching"]}
{"id": "PROV-H1-012", "category": "teaching", "prime": ["What is distinction?"], "prompt": "Actually distinction requires comparison.", "expected_sources": ["pack", "teaching"]}
{"id": "PROV-H1-013", "category": "teaching", "prime": ["What is correction?"], "prompt": "No, correction needs review first.", "expected_sources": ["pack", "teaching"]}
{"id": "PROV-H1-014", "category": "mixed", "prime": ["What is light?", "What is creation?"], "prompt": "Actually light is part of creation.", "expected_sources": ["pack", "vault", "teaching"]}
{"id": "PROV-H1-015", "category": "mixed", "prime": ["What is wisdom?"], "prompt": "How do I cultivate wisdom?", "expected_sources": ["pack", "vault"]}

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{"id": "PROV-V1-001", "category": "pack_axiom", "prime": [], "prompt": "What is light?", "expected_sources": ["pack"]}
{"id": "PROV-V1-002", "category": "pack_axiom", "prime": [], "prompt": "What is wisdom?", "expected_sources": ["pack"]}
{"id": "PROV-V1-003", "category": "pack_axiom", "prime": [], "prompt": "What is creation?", "expected_sources": ["pack"]}
{"id": "PROV-V1-004", "category": "pack_axiom", "prime": [], "prompt": "Why does word matter?", "expected_sources": ["pack"]}
{"id": "PROV-V1-005", "category": "pack_axiom", "prime": [], "prompt": "Why does correction help?", "expected_sources": ["pack"]}
{"id": "PROV-V1-006", "category": "pack_axiom", "prime": [], "prompt": "How do I find wisdom?", "expected_sources": ["pack"]}
{"id": "PROV-V1-007", "category": "pack_axiom", "prime": [], "prompt": "Compare light and darkness", "expected_sources": ["pack"]}
{"id": "PROV-V1-008", "category": "pack_axiom", "prime": [], "prompt": "Compare truth and falsehood", "expected_sources": ["pack"]}
{"id": "PROV-V1-009", "category": "pack_axiom", "prime": [], "prompt": "Is wisdom valuable?", "expected_sources": ["pack"]}
{"id": "PROV-V1-010", "category": "pack_axiom", "prime": [], "prompt": "Is truth absolute?", "expected_sources": ["pack"]}
{"id": "PROV-V1-011", "category": "vault_recall", "prime": ["What is wisdom?"], "prompt": "What is wisdom?", "expected_sources": ["pack", "vault"]}
{"id": "PROV-V1-012", "category": "vault_recall", "prime": ["What is light?"], "prompt": "Why does light reveal?", "expected_sources": ["pack", "vault"]}
{"id": "PROV-V1-013", "category": "vault_recall", "prime": ["What is creation?", "What is word?"], "prompt": "Compare creation and word", "expected_sources": ["pack", "vault"]}
{"id": "PROV-V1-014", "category": "vault_recall", "prime": ["What is logos?", "What is dabar?"], "prompt": "Compare logos and dabar", "expected_sources": ["pack", "vault"]}
{"id": "PROV-V1-015", "category": "vault_recall", "prime": ["What is truth?"], "prompt": "Is truth coherent?", "expected_sources": ["pack", "vault"]}
{"id": "PROV-V1-016", "category": "teaching", "prime": ["What is truth?"], "prompt": "No, that is incomplete.", "expected_sources": ["pack", "teaching"]}
{"id": "PROV-V1-017", "category": "teaching", "prime": ["What is creation?"], "prompt": "Actually creation includes word.", "expected_sources": ["pack", "teaching"]}
{"id": "PROV-V1-018", "category": "teaching", "prime": ["What is light?"], "prompt": "No, that misses revelation.", "expected_sources": ["pack", "teaching"]}
{"id": "PROV-V1-019", "category": "mixed", "prime": ["What is wisdom?", "What is knowledge?"], "prompt": "Actually wisdom is more than knowledge.", "expected_sources": ["pack", "vault", "teaching"]}
{"id": "PROV-V1-020", "category": "mixed", "prime": ["What is light?", "What is truth?"], "prompt": "Why does light relate to truth?", "expected_sources": ["pack", "vault"]}

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{
"cases": [
{
"attribution_pass": true,
"case_id": "PROV-H1-001",
"category": "pack_axiom",
"expected_sources": [
"pack"
],
"provenance_kinds": [
"pack"
],
"replay_pass": true,
"trace_hash": "4eac70da4fa40afad098fe26d3842f792ebe6922d79f89f005d0f9564ba00a15",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-H1-002",
"category": "pack_axiom",
"expected_sources": [
"pack"
],
"provenance_kinds": [
"pack"
],
"replay_pass": true,
"trace_hash": "9f3109266455af31f9d92af46d2054fd888720848b69f1fb6609dc4449c01309",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-H1-003",
"category": "pack_axiom",
"expected_sources": [
"pack"
],
"provenance_kinds": [
"pack"
],
"replay_pass": true,
"trace_hash": "a4f0216eda268f0ec84d438252acbdd96622c32ee16ffdc265960a4b7666b117",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-H1-004",
"category": "pack_axiom",
"expected_sources": [
"pack"
],
"provenance_kinds": [
"pack"
],
"replay_pass": true,
"trace_hash": "e8835c45db84377be154affe5c549e6b87296d153f5bb5ba7a05ca0c674aa4f4",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-H1-005",
"category": "pack_axiom",
"expected_sources": [
"pack"
],
"provenance_kinds": [
"pack"
],
"replay_pass": true,
"trace_hash": "374a4e5b261f7cd4544d6eb41166cecfce103ed2153ec98cdd25fa7fbb15ddb3",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-H1-006",
"category": "pack_axiom",
"expected_sources": [
"pack"
],
"provenance_kinds": [
"pack"
],
"replay_pass": true,
"trace_hash": "bcfe84103ff2fd2a0f7e686ce0fe9d801b66669fb28b4538c7a12ed0a9733884",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-H1-007",
"category": "vault_recall",
"expected_sources": [
"pack",
"vault"
],
"provenance_kinds": [
"pack",
"vault"
],
"replay_pass": true,
"trace_hash": "1878db26cd24ba96ae49518c6bd9c07bd85b76edf8e0de33eb299f088c94f817",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-H1-008",
"category": "vault_recall",
"expected_sources": [
"pack",
"vault"
],
"provenance_kinds": [
"pack",
"vault"
],
"replay_pass": true,
"trace_hash": "c8da733f553e56c6879c4ba21a13c9e7a200aaa34656d41a226d44c04185fd56",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-H1-009",
"category": "vault_recall",
"expected_sources": [
"pack",
"vault"
],
"provenance_kinds": [
"pack",
"vault"
],
"replay_pass": true,
"trace_hash": "2a0083f267530064f4b2502da11eb00a2e42342f4b95fdec1591e0b73ff7b718",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-H1-010",
"category": "vault_recall",
"expected_sources": [
"pack",
"vault"
],
"provenance_kinds": [
"pack",
"vault"
],
"replay_pass": true,
"trace_hash": "22a9c7f941cb48a36ef4286e3f254bb5221e1c2bc2cda2d14aca2678838f50ad",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-H1-011",
"category": "teaching",
"expected_sources": [
"pack",
"teaching"
],
"provenance_kinds": [
"pack",
"teaching",
"vault"
],
"replay_pass": true,
"trace_hash": "7fc359aaa8fbb717002e92de7ce91e62d3b03ddb66eb0a10f66884b1321d0534",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-H1-012",
"category": "teaching",
"expected_sources": [
"pack",
"teaching"
],
"provenance_kinds": [
"pack",
"teaching",
"vault"
],
"replay_pass": true,
"trace_hash": "3ff6238337539883a32539bf1259653a36b37280e88fd3de53f76bb8a3a04686",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-H1-013",
"category": "teaching",
"expected_sources": [
"pack",
"teaching"
],
"provenance_kinds": [
"pack",
"teaching"
],
"replay_pass": true,
"trace_hash": "b7cd6a17379337873b5f9cf057ae91194db0be1332f3bf6a85e61d1a5bc11427",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-H1-014",
"category": "mixed",
"expected_sources": [
"pack",
"vault",
"teaching"
],
"provenance_kinds": [
"pack",
"teaching",
"vault"
],
"replay_pass": true,
"trace_hash": "c8f549f36db8e866b44c768f3898693739637e72495cad15c0a22356fea83769",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-H1-015",
"category": "mixed",
"expected_sources": [
"pack",
"vault"
],
"provenance_kinds": [
"pack",
"vault"
],
"replay_pass": true,
"trace_hash": "27ce78b6680ee54805c4d1711c1d566bba196ce56577096e660fa752b32cbe79",
"validity_pass": true
}
],
"lane": "provenance",
"metrics": {
"input_sensitivity": 1.0,
"overall_pass": true,
"replay_determinism": 1.0,
"source_attribution": 1.0,
"source_validity": 1.0,
"total": 15
},
"split": "holdouts",
"timestamp": "2026-05-16T18:24:39.626929+00:00",
"version": "v1"
}

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@ -0,0 +1,321 @@
{
"cases": [
{
"attribution_pass": true,
"case_id": "PROV-V1-001",
"category": "pack_axiom",
"expected_sources": [
"pack"
],
"provenance_kinds": [
"pack"
],
"replay_pass": true,
"trace_hash": "4e046d32f3490e70253b7b8187a51c34ca6077e9595d41bd3f4f086eb70184d9",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-V1-002",
"category": "pack_axiom",
"expected_sources": [
"pack"
],
"provenance_kinds": [
"pack"
],
"replay_pass": true,
"trace_hash": "914181fb0fca479a5a346e80d2c0feefa42a1a9012f9228b7e483eb9464c7458",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-V1-003",
"category": "pack_axiom",
"expected_sources": [
"pack"
],
"provenance_kinds": [
"pack"
],
"replay_pass": true,
"trace_hash": "a1b2a1bf0c475ad8ce1404d5a2004f650b54ebe704d89a28ca5f3efebaaebf7c",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-V1-004",
"category": "pack_axiom",
"expected_sources": [
"pack"
],
"provenance_kinds": [
"pack"
],
"replay_pass": true,
"trace_hash": "8ec69e84d8300f5957495fab0d2d5637bc49be2bc08011be2b9df55416097ae1",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-V1-005",
"category": "pack_axiom",
"expected_sources": [
"pack"
],
"provenance_kinds": [
"pack"
],
"replay_pass": true,
"trace_hash": "95e49c7a4af65bd53fab84e80eb2aacb21f1cab94a21302ac36ce24ddcd05421",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-V1-006",
"category": "pack_axiom",
"expected_sources": [
"pack"
],
"provenance_kinds": [
"pack"
],
"replay_pass": true,
"trace_hash": "d52ebfd3bafa5fc610c8695cc3926853e786a876a0fed0b168ab91b9d9f42526",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-V1-007",
"category": "pack_axiom",
"expected_sources": [
"pack"
],
"provenance_kinds": [
"pack"
],
"replay_pass": true,
"trace_hash": "7b03352b7ed3330ad5c878b97795017220611e15bea141867f428408c316c4af",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-V1-008",
"category": "pack_axiom",
"expected_sources": [
"pack"
],
"provenance_kinds": [
"pack"
],
"replay_pass": true,
"trace_hash": "5300181277eab4f56ba71b7d03677b08a8895ede3297a2d8b814df61bca208d6",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-V1-009",
"category": "pack_axiom",
"expected_sources": [
"pack"
],
"provenance_kinds": [
"pack"
],
"replay_pass": true,
"trace_hash": "b211860c12931a941fe414f8a11e5f4078f6d96d563ddf0e43afb687bdb778ef",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-V1-010",
"category": "pack_axiom",
"expected_sources": [
"pack"
],
"provenance_kinds": [
"pack"
],
"replay_pass": true,
"trace_hash": "d02e7b26687f8acc9463189b809247d531503c872cd63d6c7e9e49eaf6ce66ac",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-V1-011",
"category": "vault_recall",
"expected_sources": [
"pack",
"vault"
],
"provenance_kinds": [
"pack",
"vault"
],
"replay_pass": true,
"trace_hash": "464cf3b3bae54882d97b0d49de772d1f0733a15d2882e768218ac173ee06aabf",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-V1-012",
"category": "vault_recall",
"expected_sources": [
"pack",
"vault"
],
"provenance_kinds": [
"pack",
"vault"
],
"replay_pass": true,
"trace_hash": "3aef6c12f1c6467161bdb6834123fc9a128a7b6655642c086437e7d20bd348fc",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-V1-013",
"category": "vault_recall",
"expected_sources": [
"pack",
"vault"
],
"provenance_kinds": [
"pack",
"vault"
],
"replay_pass": true,
"trace_hash": "2e332034c341b037c5cf0f97ca75c8227f7fdad04f1501cc923c6d3598afd1aa",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-V1-014",
"category": "vault_recall",
"expected_sources": [
"pack",
"vault"
],
"provenance_kinds": [
"pack",
"vault"
],
"replay_pass": true,
"trace_hash": "d2c787c9ed560b743b991f883b711b086c4553e11e17e176422e5e8269477601",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-V1-015",
"category": "vault_recall",
"expected_sources": [
"pack",
"vault"
],
"provenance_kinds": [
"pack",
"vault"
],
"replay_pass": true,
"trace_hash": "6f2ea35fabcce0dc7b525f05fb14ae641c93f10ffc91881f78e2f6af6f2cfcd3",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-V1-016",
"category": "teaching",
"expected_sources": [
"pack",
"teaching"
],
"provenance_kinds": [
"pack",
"teaching",
"vault"
],
"replay_pass": true,
"trace_hash": "6181ed93f758ac7894a10e4811712421a29d5cec3bff361446af7f01ce78f889",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-V1-017",
"category": "teaching",
"expected_sources": [
"pack",
"teaching"
],
"provenance_kinds": [
"pack",
"teaching",
"vault"
],
"replay_pass": true,
"trace_hash": "43034d78b72abbae094f7f1148f75555cfc94560c9e7c2ad561e5deff41f7a33",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-V1-018",
"category": "teaching",
"expected_sources": [
"pack",
"teaching"
],
"provenance_kinds": [
"pack",
"teaching",
"vault"
],
"replay_pass": true,
"trace_hash": "a0225546f2185d0ffe4d0297b8a26087c98cb56972a011461eb9f4d9eee56029",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-V1-019",
"category": "mixed",
"expected_sources": [
"pack",
"vault",
"teaching"
],
"provenance_kinds": [
"pack",
"teaching",
"vault"
],
"replay_pass": true,
"trace_hash": "3996b46560eba4b824dbed7476567fb038f7b45d5291aa4c394559f7f6224188",
"validity_pass": true
},
{
"attribution_pass": true,
"case_id": "PROV-V1-020",
"category": "mixed",
"expected_sources": [
"pack",
"vault"
],
"provenance_kinds": [
"pack",
"vault"
],
"replay_pass": true,
"trace_hash": "accc770325479640bffa2497ed185b22196df1fffc31fd9b92ea6edf4369c87d",
"validity_pass": true
}
],
"lane": "provenance",
"metrics": {
"input_sensitivity": 1.0,
"overall_pass": true,
"replay_determinism": 1.0,
"source_attribution": 1.0,
"source_validity": 1.0,
"total": 20
},
"split": "public",
"timestamp": "2026-05-16T18:23:44.643592+00:00",
"version": "v1"
}

197
evals/provenance/runner.py Normal file
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"""Provenance eval lane runner.
Conforms to the framework interface: ``run_lane(cases, config=None) -> report``
where report has ``.metrics`` (dict) and ``.case_details`` (list[dict]).
Sub-metrics scored:
M1. replay_determinism — same input twice on freshly-built runtimes
produces identical trace_hash on the scored turn.
M2. input_sensitivity — distinct cases produce distinct trace_hashes
(no collisions across the case set).
M3. source_attribution — every expected source kind appears in the
computed Provenance for the scored turn.
M4. source_validity — every cited source resolves to a real artefact
(intent tag is known, vault index in range, teaching proposal id
present in the teaching store).
"""
from __future__ import annotations
from dataclasses import dataclass, field
from typing import Any
from chat.runtime import ChatRuntime
from core.cognition.pipeline import CognitiveTurnPipeline
from core.cognition.provenance import Provenance, compute_provenance
from core.config import RuntimeConfig
from generate.intent import IntentTag
_KNOWN_INTENT_TAGS: frozenset[str] = frozenset(t.value for t in IntentTag)
@dataclass(frozen=True, slots=True)
class CaseRun:
case_id: str
category: str
expected_sources: tuple[str, ...]
trace_hash: str
provenance_kinds: tuple[str, ...]
attribution_pass: bool
validity_pass: bool
replay_pass: bool
@dataclass(slots=True)
class LaneReport:
metrics: dict[str, Any] = field(default_factory=dict)
case_details: list[dict[str, Any]] = field(default_factory=list)
def _run_pipeline_for_case(
case: dict[str, Any],
*,
config: RuntimeConfig | None,
) -> tuple[Provenance, ChatRuntime, CognitiveTurnPipeline]:
"""Build a fresh runtime, replay any prime prompts, then run the scored prompt."""
runtime = ChatRuntime(config=config) if config else ChatRuntime()
pipeline = CognitiveTurnPipeline(runtime)
for prime_prompt in case.get("prime", []):
pipeline.run(prime_prompt, max_tokens=8)
final_result = pipeline.run(case["prompt"], max_tokens=8)
provenance = compute_provenance(final_result)
return provenance, runtime, pipeline
def _validate_provenance(
provenance: Provenance,
pipeline: CognitiveTurnPipeline,
runtime: ChatRuntime,
) -> bool:
"""Check that every cited source actually resolves to a real artefact."""
vault_len = len(runtime.session.vault)
teaching_proposal_ids: set[str] = {
p.proposal_id for p in pipeline.teaching_store.pending_proposals()
}
for source in provenance.sources:
if source.kind == "pack":
if source.ref not in _KNOWN_INTENT_TAGS or source.ref == IntentTag.UNKNOWN.value:
return False
elif source.kind == "vault":
if not source.ref.startswith("vault_hit_"):
return False
try:
idx = int(source.ref.removeprefix("vault_hit_"))
except ValueError:
return False
# Per-hit indices are synthetic (0..vault_hits-1). The real
# invariant is that the vault is non-empty when hits are claimed.
if idx < 0 or vault_len == 0:
return False
elif source.kind == "teaching":
if source.ref not in teaching_proposal_ids:
return False
else:
return False
return True
def _attribution_pass(provenance: Provenance, expected_sources: list[str]) -> bool:
"""Every expected source kind must be present in the provenance."""
present = set(provenance.kinds())
return all(expected in present for expected in expected_sources)
def _run_case(
case: dict[str, Any],
*,
config: RuntimeConfig | None,
) -> CaseRun:
expected = tuple(case.get("expected_sources", []))
# First run — collect provenance, runtime, pipeline for validity check.
prov_a, runtime_a, pipeline_a = _run_pipeline_for_case(case, config=config)
attribution_pass = _attribution_pass(prov_a, list(expected))
validity_pass = _validate_provenance(prov_a, pipeline_a, runtime_a)
# Second run — fresh runtime — must reproduce trace_hash.
prov_b, _, _ = _run_pipeline_for_case(case, config=config)
replay_pass = prov_a.turn_trace_hash == prov_b.turn_trace_hash
return CaseRun(
case_id=case["id"],
category=case.get("category", "unknown"),
expected_sources=expected,
trace_hash=prov_a.turn_trace_hash,
provenance_kinds=prov_a.kinds(),
attribution_pass=attribution_pass,
validity_pass=validity_pass,
replay_pass=replay_pass,
)
def run_lane(
cases: list[dict[str, Any]],
*,
config: RuntimeConfig | None = None,
) -> LaneReport:
"""Run all provenance cases and aggregate metrics."""
case_runs: list[CaseRun] = []
for case in cases:
case_runs.append(_run_case(case, config=config))
total = len(case_runs)
if total == 0:
return LaneReport(metrics={}, case_details=[])
replay_passes = sum(1 for cr in case_runs if cr.replay_pass)
attribution_passes = sum(1 for cr in case_runs if cr.attribution_pass)
validity_passes = sum(1 for cr in case_runs if cr.validity_pass)
# Input sensitivity: count distinct trace hashes across cases with
# distinct prompts. We compare every pair: if prompts differ but hashes
# match, that's a collision.
pair_total = 0
pair_distinct = 0
for i in range(total):
for j in range(i + 1, total):
ci = cases[i]
cj = cases[j]
if ci["prompt"] == cj["prompt"] and ci.get("prime", []) == cj.get("prime", []):
# truly identical inputs — skip
continue
pair_total += 1
if case_runs[i].trace_hash != case_runs[j].trace_hash:
pair_distinct += 1
metrics = {
"total": total,
"replay_determinism": round(replay_passes / total, 4),
"source_attribution": round(attribution_passes / total, 4),
"source_validity": round(validity_passes / total, 4),
"input_sensitivity": round(pair_distinct / pair_total, 4) if pair_total else 1.0,
"overall_pass": (
replay_passes == total
and validity_passes == total
and attribution_passes / total > 0.95
and (pair_distinct / pair_total if pair_total else 1.0) > 0.95
),
}
case_details = [
{
"case_id": cr.case_id,
"category": cr.category,
"expected_sources": list(cr.expected_sources),
"provenance_kinds": list(cr.provenance_kinds),
"attribution_pass": cr.attribution_pass,
"validity_pass": cr.validity_pass,
"replay_pass": cr.replay_pass,
"trace_hash": cr.trace_hash,
}
for cr in case_runs
]
return LaneReport(metrics=metrics, case_details=case_details)

170
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"""Unit tests for core.cognition.provenance.
Covers the four expected source profiles:
- pack only (intent classified, no vault, no teaching)
- pack + vault (recall fired)
- pack + teaching (correction captured)
- no provenance (UNKNOWN intent, no vault, no teaching)
"""
from __future__ import annotations
import numpy as np
from core.cognition.provenance import compute_provenance
from core.cognition.result import CognitiveTurnResult
from field.state import FieldState
from generate.articulation import ArticulationPlan
from generate.intent import DialogueIntent, IntentTag
from generate.proposition import Proposition
from teaching.store import PackMutationProposal
def _zero_versor() -> np.ndarray:
v = np.zeros(32, dtype=np.float32)
v[0] = 1.0
return v
def _make_field_state() -> FieldState:
"""Build a minimal valid field state for tests."""
F = _zero_versor()
return FieldState(F=F)
def _make_result(
*,
intent_tag: IntentTag,
vault_hits: int,
teaching_proposal: PackMutationProposal | None,
trace_hash: str = "deadbeef",
) -> CognitiveTurnResult:
proposition = Proposition(
subject="x",
predicate="is",
object_="y",
surface="x is y",
frame_id="test",
subject_versor=_zero_versor(),
predicate_versor=_zero_versor(),
)
articulation = ArticulationPlan(
subject="x",
predicate="is",
object="y",
surface="x is y",
output_language="en",
frame_id="test",
)
fs = _make_field_state()
intent = (
DialogueIntent(tag=intent_tag, subject="x")
if intent_tag is not None
else None
)
return CognitiveTurnResult(
input_text="what is x?",
input_tokens=("what", "is", "x"),
filtered_tokens=("x",),
field_state_before=None,
field_state_after=fs,
proposition=proposition,
articulation=articulation,
surface="x is y",
walk_surface="x is y",
articulation_surface="x is y",
dialogue_role="elaborate",
identity_score=None,
vault_hits=vault_hits,
intent=intent,
proposition_graph=None,
articulation_target=None,
teaching_candidate=None,
reviewed_teaching_example=None,
pack_mutation_proposal=teaching_proposal,
versor_condition=0.0,
trace_hash=trace_hash,
)
def test_pack_only_source() -> None:
result = _make_result(
intent_tag=IntentTag.DEFINITION,
vault_hits=0,
teaching_proposal=None,
)
prov = compute_provenance(result)
assert prov.is_empty is False
assert prov.kinds() == ("pack",)
assert prov.refs("pack") == ("definition",)
assert prov.refs("vault") == ()
assert prov.refs("teaching") == ()
def test_pack_plus_vault() -> None:
result = _make_result(
intent_tag=IntentTag.RECALL,
vault_hits=3,
teaching_proposal=None,
)
prov = compute_provenance(result)
assert prov.kinds() == ("pack", "vault")
assert prov.refs("pack") == ("recall",)
assert prov.refs("vault") == ("vault_hit_0", "vault_hit_1", "vault_hit_2")
def test_pack_plus_teaching() -> None:
proposal = PackMutationProposal(
proposal_id="abc123",
candidate_id="cand1",
subject="x",
correction_text="x is z",
prior_surface="x is y",
)
result = _make_result(
intent_tag=IntentTag.CORRECTION,
vault_hits=0,
teaching_proposal=proposal,
)
prov = compute_provenance(result)
assert prov.kinds() == ("pack", "teaching")
assert prov.refs("teaching") == ("abc123",)
def test_unknown_intent_no_vault_no_teaching_is_empty() -> None:
result = _make_result(
intent_tag=IntentTag.UNKNOWN,
vault_hits=0,
teaching_proposal=None,
)
prov = compute_provenance(result)
assert prov.is_empty is True
assert prov.kinds() == ()
def test_provenance_has_kind_helper() -> None:
result = _make_result(
intent_tag=IntentTag.DEFINITION,
vault_hits=1,
teaching_proposal=None,
)
prov = compute_provenance(result)
assert prov.has_kind("pack") is True
assert prov.has_kind("vault") is True
assert prov.has_kind("teaching") is False
def test_trace_hash_preserved() -> None:
result = _make_result(
intent_tag=IntentTag.DEFINITION,
vault_hits=0,
teaching_proposal=None,
trace_hash="cafebabe",
)
prov = compute_provenance(result)
assert prov.turn_trace_hash == "cafebabe"