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"""Materialize a task-blind confidence-gated third-view temporal agent.
The third view is acquired on the same answer-disagreement cohort as the direct
third-view agent. Its proposal is committed only when its first-token margin
improves over the margin of the view that supplied the current answer by more
than one source-disjoint, globally frozen threshold. Runtime policy inputs do
not include task, benchmark identity, or gold labels.
"""
from __future__ import annotations
import argparse
import copy
import json
from pathlib import Path
from statistics import fmean
from typing import Any
from experiments.analyze_uncertainty_temporal_escalation import choice_margin
from experiments.materialize_third_view_disagreement_agent import (
IDENTITY_FIELDS,
_frame_indices,
_index,
)
def materialize(
recent: dict[str, Any],
event: dict[str, Any],
base_agent: dict[str, Any],
third: dict[str, Any],
threshold: dict[str, Any],
) -> dict[str, Any]:
for field in IDENTITY_FIELDS:
values = {value.get(field) for value in (recent, event, base_agent, third)}
if len(values) != 1:
raise ValueError(f"view identity mismatch: {field}")
if (
threshold.get("status") != "source_disjoint_internal_threshold_calibration"
or threshold.get("eligible_as_frozen_benchmark_controller_config") is not True
or threshold.get("model_manifest_sha256")
!= recent.get("model_manifest_sha256")
):
raise ValueError("threshold is not an identity-aligned internal calibration")
commit_threshold = float(threshold["policy"]["commit_threshold"])
recent_rows, event_rows, base_rows, third_rows = map(
_index, (recent, event, base_agent, third)
)
if set(base_rows) != set(recent_rows) or not set(event_rows) <= set(recent_rows):
raise ValueError("base/recent/event UID coverage diverged")
disagreements = {
uid
for uid, row in event_rows.items()
if str(row.get("prediction") or "")
!= str(recent_rows[uid].get("prediction") or "")
}
if not disagreements <= set(third_rows):
raise ValueError("third-view coverage must include the event disagreement cohort")
output_rows: list[dict[str, Any]] = []
committed_count = 0
for recent_row in recent.get("rows") or []:
uid = str(recent_row["uid"])
base = copy.deepcopy(base_rows[uid])
if uid not in disagreements:
base["third_view_policy"] = {
"triggered": False,
"reason": "no_acquired_event_disagreement",
"question_task_group_or_benchmark_router": False,
}
output_rows.append(base)
continue
event_row = event_rows[uid]
third_row = third_rows[uid]
if event_row.get("error") or third_row.get("error"):
raise ValueError(f"{uid}: acquired view contains an error")
if not bool((base.get("agent_policy") or {}).get("escalated")):
raise ValueError(f"{uid}: third view was triggered outside acquired cohort")
timestamps = (third_row.get("sampling") or {}).get("frame_timestamps") or []
if timestamps and max(map(float, timestamps)) > float(third_row["query_time"]) + 1e-3:
raise ValueError(f"{uid}: third view crosses the causal frontier")
recent_prediction = str(recent_row.get("prediction") or "")
event_prediction = str(event_row.get("prediction") or "")
current_prediction = str(base.get("prediction") or "")
third_prediction = str(third_row.get("prediction") or "")
if not all((recent_prediction, event_prediction, current_prediction, third_prediction)):
raise ValueError(f"{uid}: an agent proposal is empty")
if recent_prediction == event_prediction:
raise ValueError(f"{uid}: third view lacks a disagreement trigger")
if current_prediction == recent_prediction:
current_source = recent_row
current_view = "recent4"
elif current_prediction == event_prediction:
current_source = event_row
current_view = "causalbudget20"
else:
raise ValueError(f"{uid}: current answer is not supplied by recent or event view")
confidence_gain = choice_margin(third_row) - choice_margin(current_source)
committed = bool(
third_prediction != current_prediction
and confidence_gain > commit_threshold
)
if committed:
committed_count += 1
base["prediction"] = third_prediction
base["raw_response"] = third_row.get("raw_response")
unique_indices = _frame_indices(event_row) | _frame_indices(third_row)
base.update(
{
"current_prediction": current_prediction,
"third_view_prediction": third_prediction,
"third_view_policy": {
"triggered": True,
"trigger": "acquired_event_prediction_differs_from_recent_prediction",
"commit": "global_source_disjoint_confidence_gain",
"current_view": current_view,
"current_margin": choice_margin(current_source),
"third_margin": choice_margin(third_row),
"confidence_gain": confidence_gain,
"commit_threshold": commit_threshold,
"committed": committed,
"question_task_group_or_benchmark_router": False,
"gold_available_to_policy": False,
},
"media_calls": int(base.get("media_calls") or 2) + 1,
"num_turns": int(base.get("num_turns") or 2) + 1,
"model_processed_frames": int(base.get("model_processed_frames") or 24)
+ int(third_row.get("frames_observed") or 32),
"unique_frames": len(unique_indices) if unique_indices else 32,
"latency_seconds": float(base.get("latency_seconds") or 0.0)
+ float(third_row.get("latency_seconds") or 0.0),
"visual_tokens": int(recent_row.get("visual_tokens") or 0)
+ int(event_row.get("visual_tokens") or 0)
+ int(third_row.get("visual_tokens") or 0),
}
)
output_rows.append(base)
if any(not str(row.get("prediction") or "") for row in output_rows):
raise ValueError("materialized agent contains an empty prediction")
triggered = len(disagreements)
return {
**{
key: base_agent.get(key)
for key in (
"annotation",
"annotation_sha256",
"benchmark",
"evaluation_protocol",
"evaluation_protocol_sha256",
"model",
"model_manifest_sha256",
"processor",
)
},
"format": "streameva.confidence_gated_third_view_agent.v0",
"status": "benchmark_selected_router_free_development_agent",
"classification": "benchmark_selected_development_not_confirmatory",
"comparable_under_locked_recent4_protocol": False,
"policy": {
"first_view": "recent4",
"second_view": "frozen_uncertainty_triggered_causalbudget20",
"third_view": "causalbudget32r8_on_acquired_answer_disagreement",
"third_view_commit": "global_source_disjoint_confidence_gain",
"commit_threshold": commit_threshold,
"question_task_group_or_benchmark_router": False,
"benchmark_identity_available_to_policy": False,
"gold_available_to_policy": False,
},
"num_samples": len(output_rows),
"num_errors": 0,
"third_view_triggered": triggered,
"third_view_input_rows": len(third_rows),
"third_view_unused_precomputed_rows": len(set(third_rows) - disagreements),
"third_view_committed": committed_count,
"third_view_trigger_rate": triggered / len(output_rows),
"third_view_commit_rate": committed_count / triggered if triggered else 0.0,
"mean_model_calls": fmean(int(row.get("media_calls") or 1) for row in output_rows),
"mean_unique_frames": fmean(int(row.get("unique_frames") or 4) for row in output_rows),
"mean_model_processed_frames": fmean(
int(row.get("model_processed_frames") or 4) for row in output_rows
),
"rows": output_rows,
}
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--recent", type=Path, required=True)
parser.add_argument("--event", type=Path, required=True)
parser.add_argument("--base-agent", type=Path, required=True)
parser.add_argument("--third-view", type=Path, required=True)
parser.add_argument("--threshold-audit", type=Path, required=True)
parser.add_argument("--third-view-model-identity-sha256")
parser.add_argument("--output", type=Path, required=True)
args = parser.parse_args()
if args.output.exists():
raise FileExistsError(args.output)
values = [
json.loads(path.read_text(encoding="utf-8"))
for path in (
args.recent,
args.event,
args.base_agent,
args.third_view,
args.threshold_audit,
)
]
identity_override = None
if args.third_view_model_identity_sha256:
if len(args.third_view_model_identity_sha256) != 64 or any(
value not in "0123456789abcdef"
for value in args.third_view_model_identity_sha256
):
raise ValueError("third-view model identity is malformed")
third = values[3]
if third.get("model_manifest_sha256") not in {
None,
args.third_view_model_identity_sha256,
}:
raise ValueError("third-view model identity override diverges")
if third.get("model_manifest_sha256") is None:
third["model_manifest_sha256"] = args.third_view_model_identity_sha256
identity_override = {
"field": "model_manifest_sha256",
"source": "previously_sealed_checkpoint_identity",
"value": args.third_view_model_identity_sha256,
}
result = materialize(*values)
result["third_view_identity_override"] = identity_override
result["artifacts"] = {
"recent": str(args.recent.resolve()),
"event": str(args.event.resolve()),
"base_agent": str(args.base_agent.resolve()),
"third_view": str(args.third_view.resolve()),
"threshold_audit": str(args.threshold_audit.resolve()),
}
args.output.parent.mkdir(parents=True, exist_ok=True)
args.output.write_text(json.dumps(result, indent=2, sort_keys=True) + "\n")
print(
json.dumps(
{
"output": str(args.output),
"num_samples": result["num_samples"],
"triggered": result["third_view_triggered"],
"committed": result["third_view_committed"],
},
sort_keys=True,
)
)
if __name__ == "__main__":
main()
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