Fan-out execution of the deferred-but-not-blocking pile from
prior status reports.
#000009 §7.2 — bench harness preflight_hash field:
- aborist/qa/query.py surfaces `preflight_hash` on result dict
(miss path, reject path, and cache-hit path via new helper
`_extract_preflight_hash_from_blob` that pulls the stage hash
out of persisted run_dag_blob).
- bench/qa_sweep.py adds 12-char preflight_hash prefix to bench
rows. Mirrors cache_key truncation pattern. Operators can
grep / SQL-filter bench JSONL by preflight policy state.
- 3 new tests in tests/test_dag.py for the extract helper.
#000009 §7.2 — `aborist providence --show-preflight CACHE_KEY_PREFIX`:
- New CLI flag pulls the preflight stage payload from a row's
run_dag_blob. Match by 12-char prefix. Renders preflight stage
hash + run-DAG stage list. Operator tool for inspecting which
policy state governed a cached row.
- Live verified on a real cache row (8a212fecb2a9 — current CEO
of OpenAI question, 10-stage CTI shape with preflight at idx 1).
- Legacy rows (predating #000009) report a clean fall-through
message: "run_dag has no preflight stage (predates #000009)".
#000010 §12.6 — reference-frame plumbing into QuestionState:
- Pre-retrieval preflight runs with reference_frames=()
(frame_detection needs source titles, not available yet).
Post-retrieval, query.py re-runs preflight_question() with
the detected frames so the result-dict + run-DAG QuestionState
carry frame-aware logical_statuses (specifically
`reference_frame_ambiguous` when 2+ frames match).
- Live verified on Orwell-style question; logical_statuses now
correctly includes `reference_frame_ambiguous` in the result.
Metacog-trigger bench fixture (#000010 §13.3):
- bench/qa_questions_metacog_subset.txt — 6 questions, one per
detector kind plus a well-formed control.
- Bench artifact 2026-05-04T02-18-42Z. Detector accuracy 6/6
on fixture; 2 of 5 trigger questions return STRICT on lattice
mode despite metacog warning (JSON STRICT on
George-Washington-stop-being-president-of-France false-premise
+ uploaded-contract out-of-corpus questions). Audit-line tails
correctly surface the warnings.
- qa-modes-bench.md Addendum 4 captures the per-question matrix
+ interpretation. #000010 §13.3 cross-references with bench
artifact stamp.
#000011 SOFT_PREFLIGHT_HINT design ticket opened:
- docs/tickets/ticket-000011-soft-preflight-hint-sidecar.md
captures the design proposal per #000010 §18 / source doc.
Implementation deferred — design only.
- Sidecar would add model-assisted preflight as a soft signal
(`SOFT_FALSE_PREMISE_SUSPECTED` etc.) that NEVER enters the
verifier hard path. Strict guardrail: cannot create
PREFLIGHT_OK or PREFLIGHT_BLOCKED without deterministic
support.
- Validated by §13.3 finding: deterministic detectors flag
correctly; corpus-accidental grounding produces 2/5 STRICT
on trigger questions; soft sidecar would add independent
semantic skepticism.
- TICKETS.md index row added; Next ID bumped to 000012.
996 tests passing (3 new for the extract helper).
Cross-doc consistency:
- qa-modes-bench.md Addenda 1+2+3+4 chronological
- #000010 §13.1 (broad subset) + §13.2 (full bench) + §13.3
(metacog trigger subset)
- #000011 design captured but not implemented
Closes ticket #000010 (Meta-Cognition Preflight Guard). Mechanism
complete; defaults preserve the dry-run discipline pattern from
#000008.
Phase 2 — wire preflight into query() and runner.ask():
- preflight_question() runs after policy resolution + quantifier
classification, before retrieval.
- QuestionState surfaces on miss path, cache-hit path, AND
reject-broad early-return path of query() — schema column-
aligned across all four returns.
- runner.ask() carries the same fields for `aborist ask` parity.
Phase 3 — policy fields + governance hash + CLI flags:
- 6 new policy fields, all default-on except
metacognition_block_on_contradiction (default False — label-
only by default; opt-in via --block-on-contradiction).
- All 6 folded into _VERIFIER_POLICY_FIELDS so flipping any
invalidates prior cache records on lookup.
- 2 new CLI flags on `aborist query`:
--no-preflight Level 2 master kill
--block-on-contradiction strict mode (hard-block on
lexical contradictions)
Phase 4 — audit-line labels + bench fields + tests:
- _render_warrant_tail extended with 5 metacog tail tokens:
· false premise
· contradictory
· stale risk
· out of corpus
· frame ambiguous
- Bench rows in qa_sweep.py gain 7 new bounded-size projection
fields (logical_statuses, question_shape, preflight_result,
temporal_sensitivity, has_false_premise, has_contradiction,
corpus_requirement). Full QuestionState stays on result dict
for CLI render only.
- tests/test_metacognition.py grew from 42 → 68 tests
(16 new: 6 governance + 6 audit-line tail + 4 default-policy
pinning).
Live verified end-to-end:
$ make query-dry Q="Who is the current CEO of OpenAI?" BURN=1
UNGROUNDED · via claim_lattice · stale risk
$ make query-dry Q="When did Mr. Burns become Homer's biological
father?" BURN=1
UNGROUNDED · via claim_lattice · false premise
978 tests passing; 36 skipped.
What's NOT shipped (deferred):
- Run-DAG node binding for metacognition_preflight stage —
joins ticket #000009 Phase 5 (same audit-replay gap; both
nodes can land together).
- Reference-frame plumbing — frame_detection runs post-retrieval,
preflight here is pre-retrieval; deferred until two-pass
or post-classification update lands.
- SOFT_PREFLIGHT_HINT (model-assisted sidecar) — source doc §18
reserves this label; hard rule preserved (no LLM in preflight
hard path).
- Bench A/B measuring preflight on vs off — quick to run once
stack settles.
Ticket #000010 status: closed · landed 2026-05-03.
Adds the §10.6 + §9.6 Phase-0.x bench-row fields. Pure additive —
no row-schema renames, no policy effects yet. Phase 1 classifier
will fill the quantifier_* slots; Phase 2 cap-table will fill
claim_cap_applied. Keeping the keys present here makes the JSONL
schema stable across the rollout so post-Phase-1 markdown can
re-render against pre-Phase-1 rows without column-misalignment.
New per-row fields:
- answer_pointer_count distinct E\d+ ids in raw_answer
- answer_chars_with_brackets chars inside [E\d+,...] regions
- raw_meaningful_line_count >20-char lines in raw_answer
(matches verifier FORMAT_COLLAPSED
denominator)
- quantifier_intensity slot for Phase 1 classifier
- quantifier_matched_token slot for Phase 1 classifier
- scope_bound_hint slot for Phase 1 classifier
- claim_cap_applied slot for Phase 2 cap-table
- model_profile_id configured model id verbatim
New helper _bracket_diagnostics() bundles the bracket/pointer/line
extraction in one place; module-level regexes (_BRACKET_RE,
_BRACKET_REGION_RE, _POINTER_ID_RE) avoid per-row recompilation.
5 new tests cover the helper: empty input, single pointer, multi-
pointer-in-one-bracket, separate brackets with shared id, format-
collapsed shape (5+ meaningful lines, 0 brackets — the
2026-05-02 winners-of-all-major-sports case).
Closes the bench-side gap surfaced in §5.2: JSONL was carrying summary
numbers only, blinding the harness to FORMAT_COLLAPSED rate and per-
violation-kind distributions. Without these, A/B/D bench measurements
on the broad-quantifier preflight guard would be guesses.
- query() result dict surfaces format_collapsed + raw_answer (lattice
modes only) so the bench can read them directly instead of re-deriving
from cache rows that --burn overwrites.
- Each bench row gains format_collapsed, violation_kinds (sorted unique
list — full payloads stay off the row to keep size bounded), and
answer_brackets (count of [E\d+] in raw_answer for lattice modes).
- _summarize aggregates per-mode FC count (only explicit True; None
means check didn't apply), per-kind tallies (each kind once per row),
and lattice-only bracket sum/n.
- Markdown renderer adds a `## format-collapse + violation kinds`
section with per-mode FC rate, mean raw brackets, and one column per
observed violation kind. Degrades gracefully when the sweep produces
no violations.
- 5 new bench-harness tests pin the aggregation rules.
Re-baseline (2026-05-02T20-58-57Z) sharpens §5.1 analysis dramatically:
NO_EVIDENCE_POINTER fires 3/3 in pointer mode and is the dominant gate,
not TITLE_MISMATCH (1/3) as §5.1 inferred from JSONL alone. FORMAT_
COLLAPSED actually fires 1/3 — not the rare corner the first baseline
called it. Implies Option B (prompt reminder) is the load-bearing fix
for the verdict; Option A (cap reduction) only moves secondary kinds.
§5.3 sub-investigation closed on first read — SCHEMA_INVALID:1 in
pointer mode is a legitimate kind emitted by verify_claim_lattice for
empty-claim-text (verify.py:1242) and bare-name-claim (verify.py:1270),
not a JSON-mode leak.
Two paired changes addressing fox's "learn this hyperparameter
from model use, not hard coding" + the 1M-context-window
caveat:
(1) Log-scale prompt-size buckets extend from 8KB through 1M+:
<8KB / 8-16KB / 16-32KB / 32-64KB / 64-128KB / 128-256KB /
256-512KB / 512K-1M / >=1M
The same bench harness now covers 8B-class models (Hermes 82K
context, max useful prompt ~32-64KB) through 1M-context models
(Gemini 1.5 Pro, Claude with extended context, Llama 4) without
code change. A model whose context window stops at 82K simply
never populates the giant buckets; a 1M-context model fills
them and finds its own sweet spot.
(2) New "recommended context budget (learned from this bench)"
section — per-mode peak-strict-rate bucket. Operator-driven
landing per the five-step algorithm step 5: surfaced, not
auto-applied. Minimum sample size of 5 runs per bucket so
statistical noise doesn't masquerade as signal. Tie-break on
smaller-bucket-wins so equivalent strict-rates favor the
cheaper choice.
The substrate is now self-tuning at the OBSERVATION layer: bench
records what budget actually grades best per model. Per-model
profile JSON (storing the recommended budget back into
~/.aborist/model_profiles/<model>.json) is the next beat once
this surface is observable in real bench runs.
3 new bench tests cover the recommended-budget section, the
sample-size floor, and the giant-context bucket coverage.
Full suite: 746 passed (was 738, +8).
Connects to D8 (automate after test-pinning): the bench tells us;
we don't guess.
Replaces the two-rung EVIDENCE-LINKED / EVIDENCE-LINKED-PARTIAL
display label for claim-lattice methods with a four-rung ladder
that names a strictly stronger property at each rung:
POINTER-LINKED pointer/source/chunk verified;
warrant either didn't apply or failed
ANCHOR-WARRANTED pointer-linked + warrant passed where it ran;
other soft demotes may apply
EVIDENCE-WARRANTED anchor-warranted + no soft demotes
UNGROUNDED no verified pairs
HYBRID gets a -PARTIAL suffix on whichever rung applies.
Implementation: _render_audit_label gains a violations parameter
(defaults to None for backward-compat). _ladder_rung_for_lattice
discriminates rungs from the existing violations list:
- WARRANT_MISSING in violations → POINTER-LINKED
- any of {LAZY_ANCHOR_DEMOTED, POINTER_OVERFLOW_TRIMMED,
TOO_MANY_CLAIMS, BARE_NAME_CLAIM} → ANCHOR-WARRANTED
- else → EVIDENCE-WARRANTED
Design simplification vs the ticket's §3 sketch: the proposed
verifier_steps_ran field on the verdict dict was NOT needed. The
existing violations list carries enough signal to discriminate
all rungs. Per the five-step algorithm step 2: don't add fields
you don't need.
Quote / span / entity / paraphrase methods stay unchanged (their
STRICT verifies pinned spans, not synthesis).
Schema column audit_mode enum stays {STRICT, HYBRID, UNGROUNDED}
— pure renderer transformation, no governance_policy_hash bump,
no cache invalidation, no mesh-wire-format change. Existing
providence records render under the new ladder on next read.
5 new renderer tests in tests/test_cli_render.py covering each
rung mapping. D7 anti-regression test in tests/test_directives.py
updated to gate on the ladder labels. Bench helper docstring
follows. Full suite: 711 passed.
Directive D7 stays at ✓; ticket #000005 closed.
Wires the seven-point program (docs/seven-point-program.md) into
the bench harness as a per-mode coverage table.
Per-row computation in _directive_compliance(answer_mode, result, err):
D2_pointer_clauses — answer_mode is lattice variant
D3_cti_substrate_ready — lattice mode + run_dag_root populated
(full coverage pending ticket #000002)
D4_evidence_map_bound — run_dag_root populated
(retrieval-plan binding pending #000001)
D6_warrant_fired — warrant ran (lattice mode today;
per-shape gating pending #000003)
D7_honest_label — audit_mode in canonical enum
(renderer transformation pinned in
test_cli_render.py)
D1 (no LLM in verifier), D5 (verifier_method enum), D8 (test-pinning
discipline) are global properties of the substrate and don't appear
per-row — they get tracked once in CLAUDE.md / the program doc.
_summarize aggregates per-row booleans into per-mode pass counts.
_render_markdown emits a 'directive coverage (seven-point program)'
section with per-mode per-directive coverage as 'count/N (pct%)'.
Discipline rationale per CLAUDE.md's five-step algorithm step 5:
the bench harness IS the automation substrate, so it must enforce
the directives before any feature gets layered on top. Bench scores
that climb without directive coverage climbing are graveyard-digging
(step 4: don't dig the grave faster).
5 new bench tests cover: per-mode pass aggregation, the markdown
section presence and content, the helper's behavior on quote mode
(D2 fail by design), lattice mode (all directives pass), and error
rows (empty dict — no signal). Full suite 669 passed.
Three layers, 14 new tests, full suite 611 passed (was 597):
UNIT — tests/test_query.py
test_query_returns_prompt_chars_breakdown
Asserts the result dict's prompt_chars carries exactly the five
expected keys and messages_total equals sum of message contents
the StubClient saw.
test_query_answer_chars_matches_answer_text
answer_chars == len(answer_text) — drift check.
test_query_cache_hit_also_returns_capacity_metrics
Cache-hit path populates prompt_chars + answer_chars (operators
inspecting cached records still want the breakdown).
test_query_evidence_chars_grows_with_topk
Sanity: more sources / larger budget → more evidence chars
(the metric tracks actual context build, not a stale constant).
INTEGRATION — tests/test_query.py (retrieval_keywords)
test_retrieval_keywords_does_not_alter_question_to_llm
Keywords don't appear in the LLM-facing question segment;
system prompt unchanged across runs. Pins the substrate
contract: keywords are FTS5/title-filter-only.
test_retrieval_keywords_changes_retrieved_sources
Different keyword sets surface different docs (the actual
user-visible behavior).
UNIT — tests/test_cli_render.py
test_render_shows_capacity_line_when_prompt_chars_present
Capacity line appears with messages_total + breakdown when
prompt_chars is in the result dict.
test_render_omits_capacity_line_on_legacy_results_without_prompt_chars
Backwards-compat: legacy results render cleanly without the
capacity line — no KeyError, no '0 chars' noise.
test_render_capacity_thousand_separators
61,550 not 61550 — operator legibility on daily renders.
UNIT/INTEGRATION — tests/test_bench_qa_sweep.py (NEW FILE)
Imports bench/qa_sweep.py via importlib.util so the module's
not in the Python path doesn't matter. Five tests:
- _summarize counts verdicts by mode
- deflections counted only on STRICT/HYBRID rows (not UNGROUNDED)
- rendered markdown has the headline summary + size buckets
- size buckets correctly stratify strict-rate by prompt_chars_total
- empty buckets are skipped (no '0 runs' noise)
FUNCTIONAL — live verification (no automated test, manual)
`make query Q="what is the capital of france?" BURN=1` confirmed
in commit f927298 to render the capacity one-liner under the
source list. Documented in that commit's body.
Also corrected the docstring on query()'s `retrieval_keywords` to
reflect that keywords don't enter cache_key DIRECTLY but do change
context_root + conversation_hash via source selection — so the same
question with different keywords lands under different cache_keys
(legitimately, since the LLM saw different contexts).
