Makes the CL(4,1) field geometry legible, honestly. The engine owns the field;
this surfaces ONLY the exact scalar invariants it computes — never the raw
multivector — so the workbench shows "this is the geometry, it's exact, it
can't fake coherence" without a decorative blob or any motion.
Persist-first (the C3 gating work was Python, not React): the honest scalars
were computed live per turn but discarded. Now captured per turn into the
journal so a read-only surface has real evidence.
Backend:
- workbench/field_evidence.py: FieldEvidence computed from the engine result —
exact versor_condition, field_valid (vs the 1e-6 ceiling), a content-
addressed field_digest (sha256 of the engine-canonical array bytes), and
cga_inner(before, after) as the exact transition value. Raw field bytes
never cross the boundary: only floats + digests. validate() is fail-closed —
field_valid can never disagree with versor_condition vs the ceiling (the
wrong=0 analogue for the geometry). No engine math re-implemented
(versor_condition / cga_inner imported from algebra; bytes via array_codec).
- workbench/schemas.py: FieldEvidence dataclass; field_evidence on
ChatTurnResult + TurnJournalEntrySchema. schema-snapshot.json regenerated.
- workbench/journal.py + api.py: persisted at from_chat_turn; first-class read
endpoint GET /trace/{turn_id}/field (trace facet, consistent with /pipeline).
- workbench/replay.py: field_evidence classified CRITICAL — replay now also
proves field determinism (digest + scalars must match on re-execution).
Frontend:
- types/api.ts FieldEvidence + field_evidence passthrough; client/query hook;
FieldInvariantCard (measured value vs ceiling, cga_inner transition, digests;
honest missing_evidence; no blob, no motion); Trace route Field tab.
Honest-empty for pre-widening journal rows (missing_evidence). Deferred:
cross-turn field-coherence trends, session-level field persistence.
Validation: 138 workbench/practice Python tests (incl. non-vacuous field guards
+ replay field-determinism); 465/465 frontend incl. schemaDrift; pnpm build
clean; git diff --check clean. No generate.derivation / reliability_gate /
stream / field.propagate / vault.store imports.
111 lines
4 KiB
Python
111 lines
4 KiB
Python
"""Sealed single-turn replay over the turn journal (Wave R3).
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``GET /replay/{turn_id}`` re-executes a journaled prompt in a sealed fresh
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runtime and compares the resulting envelope leaf-by-leaf against the
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recorded :class:`~workbench.journal.TurnJournalEntry`.
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The claim demonstrated is exactly the architectural one: same prompt, same
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genesis substrate -> bit-identical envelope. The original turn ran in its
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own fresh ``ChatRuntime()`` that may have loaded an engine-state checkpoint
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present at the time; the journal does not record whether one existed, so
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``origin_state`` is reported as ``"unrecorded"`` and a divergence means
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nondeterminism OR origin-state influence — never claimed to be one or the
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other.
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This module owns classification and comparison only. Execution is
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injected by the caller (``workbench.api`` passes its own chat-turn
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executor over a sealed runtime), which keeps the comparison pure and the
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no-fabricated-equivalence obligation testable: if the executor raises,
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no comparison object exists.
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"""
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from __future__ import annotations
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import time
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from dataclasses import fields, replace
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from typing import Callable
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from workbench.journal import TurnJournalEntry
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from workbench.schemas import (
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ChatTurnResult,
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TurnReplayComparison,
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TurnReplayDivergence,
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)
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# Every TurnJournalEntry field must appear in exactly one of these sets —
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# enforced by tests so a future journal field forces an explicit
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# classification decision instead of silently defaulting.
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CRITICAL_FIELDS = frozenset(
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{
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"turn_id",
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"prompt",
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"surface",
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"articulation_surface",
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"walk_surface",
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"trace_hash",
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"grounding_source",
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"epistemic_state",
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"normative_clearance",
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"verdicts",
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"refusal_emitted",
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"hedge_injected",
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"proposal_candidates",
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"leeway_evidence",
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"pipeline_record",
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"field_evidence",
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"checkpoint_emitted",
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"trace_integrity",
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}
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)
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# Wall-clock by nature, or derived over wall-clock bytes (journal_digest
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# hashes the timestamp): expected to differ on every replay and never
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# evidence against equivalence.
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INFORMATIONAL_FIELDS = frozenset({"timestamp", "turn_cost_ms", "journal_digest"})
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def replay_turn(
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entry: TurnJournalEntry,
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*,
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execute: Callable[[str], ChatTurnResult],
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) -> TurnReplayComparison:
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"""Re-execute ``entry.prompt`` via ``execute`` and compare envelopes.
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``execute`` must run the prompt through the same envelope-assembly path
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that produced the recorded entry, over a sealed runtime. This function
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never fabricates a comparison: if ``execute`` raises, the exception
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propagates and no ``TurnReplayComparison`` exists.
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"""
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started = time.perf_counter()
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result = execute(entry.prompt)
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elapsed_ms = max(0, int(round((time.perf_counter() - started) * 1000)))
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result = replace(result, turn_cost_ms=elapsed_ms, turn_id=entry.turn_id)
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replayed = TurnJournalEntry.from_chat_turn(result, turn_id=entry.turn_id)
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divergences: list[TurnReplayDivergence] = []
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for spec in fields(TurnJournalEntry):
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original_value = getattr(entry, spec.name)
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replay_value = getattr(replayed, spec.name)
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if original_value == replay_value:
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continue
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divergences.append(
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TurnReplayDivergence(
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path=spec.name,
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original=original_value,
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replay=replay_value,
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severity=(
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"critical" if spec.name in CRITICAL_FIELDS else "informational"
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),
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)
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)
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return TurnReplayComparison(
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turn_id=entry.turn_id,
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comparison_basis="sealed_fresh_runtime_single_turn",
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origin_state="unrecorded",
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original_trace_hash=entry.trace_hash,
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replay_trace_hash=replayed.trace_hash,
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equivalent=not any(d.severity == "critical" for d in divergences),
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replay_turn_cost_ms=elapsed_ms,
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divergences=divergences,
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leeway_evidence=entry.leeway_evidence,
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)
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