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Author SHA1 Message Date
Garry TanandClaude Opus 4.6 a4cdb41b07 feat(eval): Day 8 — Cat 8 skill compliance + Cat 9 end-to-end workflows 
**eval/runner/cat8-skill-compliance.ts** — Deterministic, judge-free Cat 8
scoring. Replays inbound signals through the agent adapter (Day 5) and
extracts four iron-law metrics directly from the tool-bridge state:

  - brain_first_compliance: agent called search/get_page BEFORE producing
    its final answer. Non-compliance = hallucinating from general knowledge.
  - back_link_compliance: every dry_run_put_page intent has at least one
    markdown [Name](slug) back-link in its compiled_truth.
  - citation_format: timeline entries use canonical `- **YYYY-MM-DD** |
    Source — Summary`; long final answers cite at least one slug.
  - tier_escalation: simple probes use light tooling (≥1 brain call);
    complex probes require ≥2 brain calls or a dry_run write when
    expects_dry_run_write is set.

No judge call required — everything is computable from
`tool_bridge_state.made_dry_run_writes` + `count_by_tool` + final_answer
regex. Fast, deterministic, reproducible.

Bounded concurrency (p-limit style) worker pool at default 4 to keep
Sonnet rate limits comfortable across 100-probe batches.

**eval/runner/cat9-workflows.ts** — Rubric-graded Cat 9. 5 canonical
workflows (meeting_ingestion, email_to_brain, daily_task_prep, briefing,
sync) × ~10 scenarios each. Each scenario runs through the agent adapter,
then judge.ts scores the answer against a per-scenario rubric.

`buildEvidence(scenario, agentResult, pagesBySlug)` composes the
JudgeEvidence contract: resolves ground_truth_slugs to full
GroundTruthPage[] from a slug-map, pulls tool_call_summary directly from
tool_bridge_state (no raw tool_result content — Section-3 defense),
attaches rubric from the scenario.

Per-workflow rollup: each workflow gets its own pass_rate so the verdict
can fail one workflow without failing the whole Cat. Overall verdict
requires every populated workflow's pass_rate ≥ threshold (default 0.80)
when enableThreshold=true.

Both Cats default to verdict=baseline_only in v1 per codex fix #9: real
thresholds return after 10-probe Haiku-vs-hand-score calibration (κ > 0.7)
runs against the Day 3b amara-life-v1 corpus.

**Tests (23):** Cat 8 per-metric scorer unit tests covering every branch
(brain_first ordering, back-link compliance on mixed writes, long vs
short answer citation requirement, tier escalation for simple/complex/
writey probes, finalAnswerCiteCount dedups across syntaxes). Cat 9
buildEvidence contract shape — evidence_refs flow from agent, missing
slugs skip gracefully, no raw_transcript/tool_result leakage to judge.
Cat 9 runCat9 integration with stubbed agent + mixed-verdict judge
produces fractional pass rates correctly.

Total eval suite now: 273 pass, 0 fail.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-04-20 22:05:14 +08:00
Garry TanandClaude Opus 4.6 33e2e73940 feat(eval): Day 7 — Cat 5 provenance runner + structured classify_claim judge 
**eval/runner/cat5-provenance.ts** — BrainBench Cat 5 scoring. Samples
claims from gbrain brain-export and classifies each against its source
material via a dedicated Haiku judge (classify_claim tool with a
three-label enum: supported | unsupported | over-generalized).

Separate from judge.ts by design: Cat 5 is a single three-way
classification per claim, not a weighted rubric. Rather than overload
judge.ts with a mode switch, Cat 5 has its own tool definition
(CLASSIFY_CLAIM_TOOL) and prompt. The retry-once pattern, $20 cost gate
semantics, and structured parsing are mirrored from judge.ts so failures
look the same across Cats.

Metric: `citation_accuracy` = fraction where predicted label equals
gold expected_label. Threshold (informational): >0.90 per design-doc
METRICS.md. v1 ships with `enableThreshold: false` so the verdict is
always baseline_only — we don't have hand-authored gold claims yet, and
codex flagged that threshold gating should wait until the amara-life-v1
corpus + gold file authoring lands in Day 3b.

runCat5 uses a bounded-concurrency worker pool (default 4) to respect
Haiku rate limits across 100+ claim batches. Evidence pages are looked
up by slug from a caller-provided pagesBySlug map — missing pages don't
crash, they just pass an empty source list to the judge (correct
behavior for genuinely unsupported claims).

**Tests (23):** classifyClaim happy/retry/fallback paths with stubbed
Haiku, aggregate accuracy math, threshold gating (pass/fail vs
baseline_only), runCat5 concurrency + missing-page handling,
renderClaimPrompt embeds claim + sources correctly, parseClassification
rejects invalid enum values + plain-text responses.

Total eval suite now: 250 pass, 0 fail.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-04-20 22:01:58 +08:00
Garry TanandClaude Opus 4.6 839258dc42 feat(eval): Day 6 — adversarial-injections + Cat 6 prose-scale + Cat 11 multi-modal 
Three modules that together cover BrainBench v1 Cat 6 (prose-scale
extraction fidelity) and Cat 11 (multi-modal ingest fidelity).

**eval/runner/adversarial-injections.ts** — 6 deterministic content
transforms shared by Cat 10 (adversarial.ts, 22 hand-crafted cases) and
Cat 6 (prose-scale variants). Each injection produces a modified content
string + a structured GoldDelta describing what the extractor MUST and
MUST NOT produce. Kinds:
  - code_fence_leak — fake [X](people/fake) inside ``` fence, must NOT extract
  - inline_code_slug — `people/fake` in backticks, must NOT extract
  - substring_collision — "SamAI" near real `people/sam`, exactly one link
  - ambiguous_role — "works with" vs "works at", downgrade type to mentions
  - prose_only_mention — strip markdown link syntax, bare name → mentions only
  - multi_entity_sentence — pack 4+ entities into one clause, extract all

Mulberry32 PRNG keeps variant generation deterministic under fixed seed.
Codex flagged the original plan's wording ("extract injection engine from
adversarial.ts") as overstated — adversarial.ts is a static case list,
not a reusable engine. This module is NEW code.

**eval/runner/cat6-prose-scale.ts** — Runner. Loads world-v1, applies all
6 injection kinds to sampled base pages (default 50 variants per kind ×
6 kinds = 300 variants), runs extractPageLinks on each, compares to gold
delta. Emits per-kind + overall metrics (precision, recall, F1,
code_fence_leak_rate, substring_fp_rate, pages_with_links_coverage,
mean_links_per_page). **v1 verdict is always "baseline_only"** — no
gating threshold per codex fix #9 (current extractor residuals make
>0.80 unreachable; v1 records a baseline, regression guard triggers on
drop below it).

**eval/runner/cat11-multimodal.ts** — PDF + HTML + audio runners.
Fixtures load from eval/data/multimodal/<modality>/fixtures.json
manifests; each modality skips gracefully when manifest missing or
(audio) when neither GROQ_API_KEY nor OPENAI_API_KEY is set. Metrics:
  - PDF: char-level similarity via Levenshtein + optional entity_recall
  - HTML: word-recall over normalized tokens (multiset semantics)
  - Audio: WER (word error rate) via Levenshtein on word sequences
Fixtures are NOT committed; a future eval:fetch-multimodal script will
download them hash-verified from public sources (arXiv CC-licensed
papers, Wikipedia CC-BY-SA, Common Voice CC0).

Injectable audio transcriber (`opts.transcribe`) means tests don't need
GROQ/OpenAI keys — stubbed transcriptions exercise the WER math path
directly.

**Tests (60 new):** adversarial-injections (19) — per-kind assertions +
dispatcher coverage + slug regex conformance; cat6 (12) — variant
determinism, scoreVariant shape, aggregate per-kind + overall metrics,
corpus resolver slug rules; cat11 (29) — charSimilarity / wordRecall /
wer math, htmlToText strips scripts + decodes entities, HTML modality
with real fixtures, audio modality gracefully skips without key + uses
stub transcriber correctly.

All 60 tests pass in 48ms + 41ms.
Total eval suite now: 227 pass, 0 fail.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-04-20 21:59:30 +08:00
Garry TanandClaude Opus 4.6 05aec3feb8 feat(eval): Day 5 — agent adapter + judge with structured evidence contract 
Two modules that together wire Cat 8 / Cat 9 / Cat 5 end-to-end scoring.

**eval/runner/judge.ts** — Haiku 4.5 via tool-use `score_answer`. Input is
the structured JudgeEvidence contract (fix #16 from the plan's codex
review): probe + final_answer_text + evidence_refs + tool_call_summary +
ground_truth_pages + rubric. Raw tool output NEVER reaches the judge —
that's the Section-3 defense against paraphrased prompt-injection payloads
in gold/poison.json.

Retry policy: one retry on malformed tool_use response. If the second
attempt is still malformed, score the probe as `judge_failed` (all scores
0, verdict=fail) so the run still completes.

Aggregation: weighted mean across rubric criteria. Canonical thresholds
(pass ≥3.5, partial 2.5-3.5, fail <2.5) — judge can propose a verdict but
the computed verdict from the weighted mean is what the scorecard records.
This prevents the model from inflating or deflating its own verdict.

Score values are clamped to 0-5 on parse even if the model returns out of
range. `assertNoRawToolOutput(evidence)` is a regression guard that
returns the list of forbidden fields (tool_result, raw_transcript, etc.)
if any leak into the evidence contract.

**eval/runner/adapters/claude-sonnet-with-tools.ts** — The agent adapter.
Implements `Adapter` interface minimally: `init()` spins up PGLite and
seeds it, `query()` throws because the adapter is Cat 8/9-only and emits
a final-answer text, not a RankedDoc[]. Retrieval scorecard stays at 4
adapters.

`runAgentLoop(probeId, text, state, config)` drives the multi-turn loop:
Sonnet → tool_use → tool-bridge.executeTool → tool_result → back to
Sonnet. Turn cap 10. max_tokens 1024. System prompt (brain-first iron
law, citation format, amara context) is cached via cache_control.
Exponential backoff on rate-limit errors (1s, 2s, 4s).

Emits a `Transcript` per eval/schemas/transcript.schema.json — consumed
directly by recorder.ts for the flight-recorder bundle.

`brain_first_ordering` classifies Cat 8's flagship metric: did the agent
call search/get_page BEFORE producing the final answer? The `no_brain_calls`
case (agent answers from general knowledge without ever hitting the brain)
is the compliance failure to surface.

ForbiddenOpError + UnknownToolError from the bridge are caught in the
agent loop and surfaced as tool_result with is_error=true — keeps the
loop going and preserves full audit trail for the judge.

**Tests (35 new):** judge (23) — happy path, retry, fallback, evidence
contract sanitization, rendered prompt does not contain raw tool_result
text, verdict thresholds, score clamping, weighted mean with mixed
weights, parseToolUse rejects malformed input. agent-adapter (12) —
Adapter.query() throws, init() seeds PGLite, end-to-end tool loop with
stubbed Sonnet, turn cap exhaustion, mutating-op rejection surfaces as
tool_result error, extractSlugs regex.

All 12 agent tests take ~23s because PGLite runs 13 schema migrations per
test; the alternative of shared-engine-across-tests was rejected so each
test is isolated.

Total eval suite now: 167 pass, 0 fail.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-04-20 21:52:56 +08:00
Garry TanandClaude Opus 4.6 934d7ea4b6 feat(eval): Day 4 — pdf-parse + flight-recorder + tool-bridge (dry_run + expand:false) 
Three infrastructure modules for BrainBench v1 Complete Cats 5/8/9/11.

**eval/runner/loaders/pdf.ts** — Thin pdf-parse wrapper. Lazy import keeps
pdf-parse out of the module-load path (avoids library debug-mode side
effects). Size cap (50MB default), encryption detection, structured error
classes (PdfEncryptedError, PdfTooLargeError, PdfParseError). Only Cat 11
multimodal will import this; production bundle never sees pdf-parse.

**eval/runner/tool-bridge.ts** — Maps 12 read-only operations from
src/core/operations.ts to Anthropic tool definitions + adds 3 dry_run write
tools. Three structural invariants enforced:

  1. No hidden LLM calls. `operations.query` defaults expand=true which
     routes through expansion.ts → Haiku. Bridge strips `expand` from the
     query tool's input schema AND executor hard-sets expand:false. Zero
     nested Haiku calls in any agent trace.

  2. Mutating ops throw ForbiddenOpError. put_page, add_link, delete_page,
     etc. are rejected by name. Agents record intent via dry_run_put_page /
     dry_run_add_link / dry_run_add_timeline_entry which persist to the
     flight-recorder without mutating the engine. This is how Cat 8's
     back_link_compliance + citation_format metrics measure anything with
     a read-only tool surface.

  3. Poison tagged by the bridge, not the judge. Every tool result is
     scanned for slugs matching gold/poison.json fixtures. Matched
     fixture_ids flow into tool_call_summary.saw_poison_items for the
     structured-evidence judge contract. Judge never reads raw tool
     output — Section-3 defense against paraphrased prompt injections
     (poison payloads never reach the judge model at all).

32K-token cap (~128K chars) with "…[truncated]" suffix.

**eval/runner/recorder.ts** — Per-run flight-recorder bundle emitter. Full
6-artifact bundle (transcript.md, brain-export.json, entity-graph.json,
citations.json, scorecard.json, judge-notes.md) when the adapter provides
an AdapterExport; 3-artifact fallback (transcript + scorecard +
judge-notes) otherwise. Atomic writes via tmp+rename. Collision-safe:
duplicate directory names get incremental -2, -3 suffix. `safeStringify`
handles circular references without throwing and JSON-serializes
Float32Array embeddings.

**package.json:** adds pdf-parse@2.4.5 as a devDependency. Scoped to eval/
use only; production gbrain binary unaffected.

**Tests:** 63 new — 30 tool-bridge, 21 recorder, 12 pdf-loader. All pass.
Fake engine uses a Proxy with `__default__` fallback so poison-matching
tests don't have to mock the exact engine method name that each operation
calls (some route via searchKeyword, others via getPage — proxy handles
both uniformly).

Total eval suite now: 132 pass, 0 fail, 923 expect() calls.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-04-20 21:46:55 +08:00
Garry Tan 1f38c4f96b feat(eval): amara-life-gen.ts with structured cache key + $20 cost gate 
Opus prose expansion of the amara-life-v1 skeleton. Per-item structured
cache key = sha256({schema_version, template_id, template_hash, model_id,
model_params, seed, item_spec_hash}). Prompt-template tweak changes
template_hash; only those items regenerate. Schema bump changes
schema_version; everything invalidates cleanly. Interrupted runs resume
from the last cached item; zero re-spend.

Cost-gated at $20 hard-stop with Anthropic input/output pricing tracking.
Dry-run mode (--dry-run) executes the full pipeline with stub bodies for
smoke-testing the I/O layout without LLM spend. --max N caps items per
type for debugging. --force ignores cache.

Writes per-format outputs under eval/data/amara-life-v1/:
  inbox/emails.jsonl (one email per line with body_text appended)
  slack/messages.jsonl (one message per line with text appended)
  calendar.ics (RFC-5545 VEVENT format, templated — no LLM)
  meetings/<id>.md (transcript with YAML frontmatter)
  notes/<YYYY-MM-DD-topic>.md (first-person journal)
  docs/*.md (6 reference docs, templated — no LLM)
  corpus-manifest.json (per eval/schemas/corpus-manifest.schema.json,
    including per-item content_sha256 and generator_cache_key)

Perturbation hints (contradiction, stale-fact, poison, implicit-
preference) flow through the prompt so Opus weaves the specific claim
into each item's body. Poison items are hand-crafted to include
paraphrased prompt-injection attempts (not literal 'IGNORE ALL
PREVIOUS' — defense is the structured-evidence judge contract at
Day 5, not regex redaction).

New package.json scripts:
  eval:generate-amara-life       # real run (~$12 Opus estimated)
  eval:generate-amara-life:dry   # smoke test, zero spend

test:eval extended to include test/eval/. 10 cache-key tests cover
determinism, invalidation across every field of the key, canonical JSON
stability under object-key reorder, and per-skeleton-item spec-hash
uniqueness (50 distinct hashes for 50 distinct emails).
 2026-04-20 21:27:22 +08:00
Garry Tan 53e32ee427 feat(eval): amara-life-v1 skeleton + Page.type enum for email/slack/cal/note 
Deterministic procedural generator for the twin-amara-lite fictional-life
corpus (BrainBench v1 Cat 5/8/9/11 target). 15 contacts picked from
world-v1, 50 emails + 300 Slack messages across 4 channels + 20 calendar
events + 8 meeting transcripts + 40 first-person notes. Mulberry32 PRNG
gives byte-identical output under reseed.

Plants 10 contradictions + 5 stale facts + 5 poison items + 3 implicit
preferences at deterministic positions. Fixture_ids are unique across the
corpus so gold/contradictions.json + gold/poison.json + gold/implicit-
preferences.json can cross-reference by stable ID.

PageType extended in both src/core/types.ts and eval/runner/types.ts to
include email | slack | calendar-event | note (+ meeting on the production
side). src/core/markdown.ts inferType() heuristics updated for the new
one-slash slug prefixes (emails/em-NNNN, slack/sl-NNNN, cal/evt-NNNN,
notes/YYYY-MM-DD-topic, meeting/mtg-NNNN).

17 tests cover counts (50/300/20/8/40), perturbation counts (exact
10/5/5/3), seed determinism + divergence, slug regex conformance (matches
eval/runner/queries/validator.ts:131 one-slash rule), unique fixture_ids,
amara-in-every-email invariant, calendar dtstart < dtend, and Amara-is-
attendee on every meeting.
 2026-04-20 21:27:02 +08:00
Garry Tan 18baca6629 feat(eval): BrainBench v1 portable JSON schemas + gold templates 
Adds the v1→v2 contract boundary for BrainBench. 6 JSON schemas at
eval/schemas/ pin the shape of every artifact a stack must emit to be
scorable: corpus-manifest, public-probe (PublicQuery with gold stripped),
tool-schema (12 read + 3 dry_run tools, 32K tool-output cap), transcript,
scorecard (N ∈ {1, 5, 10}), evidence-contract (structured judge input).

8 gold file templates at eval/data/gold/ scaffold the sealed qrels,
contradictions, poison items, and citation labels. Empty-but-valid
skeletons; Day 3b fills them with real content once the amara-life-v1
corpus generates.

48 tests validate schema syntax, $schema/$id/title/type headers,
round-trip stability, and cross-schema coherence (new Page types in
manifest enum, tool counts, token cap, N enum).

When v2 ports to Python + Inspect AI + Docker, these schemas are the
boundary. Same fixtures, same tool contracts, zero rework.
 2026-04-20 21:26:47 +08:00