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from dataclasses import dataclass
import hashlib
import json
@dataclass(frozen=True)
class CausalDiagnostic:
metrics: dict[str, float]
failures: tuple[str, ...]
failure_locus: str | None
earliest_failure_time_ms: float | None = None
belief_event_id: str | None = None
belief_time_ms: float | None = None
decision_event_id: str | None = None
decision_time_ms: float | None = None
def _time(event: dict, field: str) -> float:
value = event.get(field)
return float(event["event_time_ms"] if value is None else value)
def canonical_state_hash(event: dict) -> str:
payload = {k: v for k, v in event.items() if k != "state_hash"}
return hashlib.sha256(json.dumps(payload, sort_keys=True, separators=(",", ":")).encode()).hexdigest()
def evaluate_causal_ledger(ledger: dict) -> CausalDiagnostic:
events = ledger.get("events", [])
by_id: dict[str, dict] = {}
failures: set[str] = set()
failure_points: list[tuple[float, str, str]] = []
decision_candidates: list[tuple[float, str]] = []
for event in events:
eid = event["event_id"]
if eid in by_id:
failure_points.append((_time(event, "event_time_ms"), "provenance", "duplicate_event_id"))
else:
by_id[eid] = event
def ancestors(event: dict) -> set[str]:
seen: set[str] = set()
stack = list(event.get("parent_event_ids", []))
while stack:
eid = stack.pop()
if eid in seen:
continue
seen.add(eid)
parent = by_id.get(eid)
if parent:
stack.extend(parent.get("parent_event_ids", []))
return seen
checked_edges = invalid_edges = 0
last_append_sequence: int | None = None
consequential_kinds = {"commitment", "shared_state_write", "tool_action", "spoken_consequence"}
for event in events:
eid = event["event_id"]
et = _time(event, "event_time_ms")
append_sequence = event.get("append_sequence")
if append_sequence is not None:
sequence = int(append_sequence)
if last_append_sequence is not None and sequence <= last_append_sequence:
failure_points.append((et, "provenance", "non_append_only_history"))
last_append_sequence = sequence
if event.get("state_hash") and event["state_hash"] != canonical_state_hash(event):
failure_points.append((et, "provenance", "state_hash_mismatch"))
for parent_id in event.get("parent_event_ids", []):
checked_edges += 1
parent = by_id.get(parent_id)
if parent is None:
invalid_edges += 1
failure_points.append((et, "provenance", "invalid_causal_parent"))
elif float(parent["evidence_available_time_ms"]) > et:
invalid_edges += 1
failure_points.append((et, "context/prior_contamination", "future_knowledge_contamination"))
if event.get("kind") in consequential_kinds:
decision_candidates.append((et, eid))
for old_id in event.get("supersedes_event_ids", []):
old = by_id.get(old_id)
if old is None:
failure_points.append((et, "provenance", "invalid_supersession_target"))
elif event.get("kind") != "revision" or et <= _time(old, "event_time_ms"):
failure_points.append((et, "provenance", "invalid_supersession_order"))
fabricated_evidence = [
event
for event in events
if event.get("kind") == "acoustic_evidence" and event.get("fabricated") is True
]
fabricated_ids = {event["event_id"] for event in fabricated_evidence}
consequential = [event for event in events if event["kind"] in consequential_kinds]
fabrication_consequences = 0
for event in consequential:
if ancestors(event) & fabricated_ids:
fabrication_consequences += 1
et = _time(event, "event_time_ms")
failure_points.append((et, "perception", "fabricated_acoustic_evidence"))
decision_candidates.append((et, event["event_id"]))
commitments = [event for event in events if event["kind"] == "commitment"]
revisions = [event for event in events if event["kind"] == "revision"]
confidences = [event for event in events if event["kind"] == "confidence"]
actions = [event for event in events if event["kind"] == "tool_action"]
evidence_to_commit: list[float] = []
confidence_to_commit: list[float] = []
commit_to_action: list[float] = []
revision_costs: list[float] = []
premature_ids: set[str] = set()
for commitment in commitments:
cid = commitment["event_id"]
ct = _time(commitment, "commitment_time_ms")
evidence_to_commit.append(ct - float(commitment["evidence_available_time_ms"]))
prior_conf = [event for event in confidences if float(event["event_time_ms"]) <= ct]
if prior_conf:
latest = max(prior_conf, key=lambda event: float(event["event_time_ms"]))
confidence_to_commit.append(ct - _time(latest, "confidence_time_ms"))
if latest.get("confidence") is not None and float(latest["confidence"]) < 0.5:
premature_ids.add(cid)
superseding = [
event
for event in revisions
if cid in event.get("supersedes_event_ids", []) and float(event["event_time_ms"]) > ct
]
if superseding:
first = min(superseding, key=lambda event: float(event["event_time_ms"]))
premature_ids.add(cid)
revision_costs.append(_time(first, "revision_time_ms") - ct)
later = [event for event in actions if float(event["event_time_ms"]) >= ct]
if later:
first = min(later, key=lambda event: float(event["event_time_ms"]))
commit_to_action.append(_time(first, "action_authorized_time_ms") - ct)
for cid in premature_ids:
ct = _time(by_id[cid], "commitment_time_ms")
failure_points.append((ct, "commitment_routing", "premature_commitment"))
decision_candidates.append((ct, cid))
for _, _, failure in failure_points:
failures.add(failure)
earliest = min(failure_points, key=lambda point: point[0]) if failure_points else None
decision_event_id = decision_time_ms = None
belief_event_id = belief_time_ms = None
if decision_candidates:
decision_time_ms, decision_event_id = min(decision_candidates, key=lambda item: item[0])
decision_event = by_id.get(decision_event_id)
if decision_event is not None:
belief_candidates = [
by_id[eid]
for eid in ancestors(decision_event)
if eid in by_id
and by_id[eid].get("kind") in {"hypothesis", "confidence"}
and _time(by_id[eid], "event_time_ms") <= decision_time_ms
]
if belief_candidates:
belief = max(belief_candidates, key=lambda event: _time(event, "event_time_ms"))
belief_event_id = belief["event_id"]
belief_time_ms = _time(belief, "event_time_ms")
invalid_rate = invalid_edges / checked_edges if checked_edges else 0.0
parent_validity = 1.0 - invalid_rate
historical_failures = {
"future_knowledge_contamination",
"non_append_only_history",
"state_hash_mismatch",
"invalid_supersession_target",
"invalid_supersession_order",
"duplicate_event_id",
}
historical_integrity = 0.0 if failures & historical_failures else 1.0
premature_rate = len(premature_ids) / max(1, len(commitments))
acoustic_evidence_count = len([event for event in events if event["kind"] == "acoustic_evidence"])
aefr = len(fabricated_evidence) / max(1, acoustic_evidence_count)
fabrication_integrity = 0.0 if fabrication_consequences else 1.0
tbrf = min(parent_validity, historical_integrity)
cci = min(
historical_integrity,
parent_validity,
fabrication_integrity,
1.0 if not premature_ids else 0.0,
)
def mean(values: list[float]) -> float:
return round(sum(values) / len(values), 6) if values else 0.0
metrics = {
"evidence_to_commit_delay_ms": mean(evidence_to_commit),
"confidence_to_commit_delay_ms": mean(confidence_to_commit),
"commit_to_action_delay_ms": mean(commit_to_action),
"premature_commitment_rate": round(premature_rate, 6),
"revision_cost_ms": mean(revision_costs),
"acoustic_evidence_fabrication_rate": round(aefr, 6),
"fabrication_consequence_rate": round(fabrication_consequences / max(1, len(consequential)), 6),
"future_knowledge_contamination_rate": round(
sum(1 for _, _, failure in failure_points if failure == "future_knowledge_contamination")
/ max(1, checked_edges),
6,
),
"causal_parent_validity": round(parent_validity, 6),
"historical_integrity": historical_integrity,
"time_bounded_reconstruction_fidelity": round(tbrf, 6),
"causal_cognition_integrity": round(cci, 6),
"earliest_divergence_time_ms": earliest[0] if earliest else -1.0,
}
return CausalDiagnostic(
metrics=metrics,
failures=tuple(sorted(failures)),
failure_locus=earliest[1] if earliest else None,
earliest_failure_time_ms=earliest[0] if earliest else None,
belief_event_id=belief_event_id,
belief_time_ms=belief_time_ms,
decision_event_id=decision_event_id,
decision_time_ms=decision_time_ms,
)
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