| |
| """Derive the graph-free direct GPT-OSS baseline from saved model choices. |
| |
| This script performs no inference and loads no benchmark labels. It releases a |
| case iff the frozen primary GPT-OSS choice is NONE; otherwise it reviews only |
| the selected component. Governance actions use the benchmark's public, |
| deterministic authority/event router shared by the open-weight reference arms. |
| """ |
|
|
| from __future__ import annotations |
|
|
| import argparse |
| import json |
| from pathlib import Path |
|
|
|
|
| ROOT = Path(__file__).resolve().parents[1] |
|
|
|
|
| def load_jsonl(path: Path) -> list[dict]: |
| return [json.loads(line) for line in path.read_text().splitlines() if line.strip()] |
|
|
|
|
| def default_measurements() -> Path: |
| released = ROOT / "results/open_weight/gptoss20b_direct_measurements.jsonl" |
| if released.exists(): |
| return released |
| return ROOT / "results/open_weight_full_archive/gptoss20b_open_weight_token_sseg_measurements.jsonl" |
|
|
|
|
| def input_rows() -> list[dict]: |
| base = ROOT / "closed_loop_inputs" if (ROOT / "closed_loop_inputs").exists() else ROOT / "hf/closed_loop_inputs" |
| return sorted( |
| (row for split in ("development", "validation", "test") for row in load_jsonl(base / f"{split}.jsonl")), |
| key=lambda row: row["case_id"], |
| ) |
|
|
|
|
| def action_for(row: dict, release: bool) -> str: |
| if release: |
| return "release" |
| trigger = row["event"]["trigger_category"] |
| tier = row["workflow_context"]["authority_tier"] |
| if any(c.get("status") == "failed" and not c.get("compensable", True) for c in row.get("red_line_controls", [])): |
| return "suspend" |
| if trigger == "tool_permission": |
| return "restrict" if tier.startswith("C") else "scoped_revalidation" |
| if trigger == "incident_near_miss": |
| return "withhold" |
| if trigger in {"performance_behaviour", "model_vendor"}: |
| return "escalate" |
| if trigger == "prompt_policy_orchestration": |
| return "withhold" |
| if trigger == "purpose_population": |
| return "scoped_revalidation" |
| if trigger == "memory_data": |
| return "rollback" if tier.startswith("A") else ("withhold" if row["domain"] == "market_data" else "scoped_revalidation") |
| return "escalate" |
|
|
|
|
| def main() -> None: |
| parser = argparse.ArgumentParser() |
| parser.add_argument("--measurements", type=Path, default=default_measurements()) |
| parser.add_argument("--output", type=Path, default=ROOT / "results/open_weight/direct_llm.jsonl") |
| args = parser.parse_args() |
| measurements = {row["case_id"]: row for row in load_jsonl(args.measurements)} |
| predictions = [] |
| for row in input_rows(): |
| measurement = measurements[row["case_id"]] |
| primary = measurement["observed_primary"] |
| selected = primary.get("selected_node") or "NONE" |
| release = selected == "NONE" |
| scope = [] if release else [selected] |
| predictions.append({ |
| "case_id": row["case_id"], |
| "method": "direct_llm", |
| "model": measurement.get("model_requested", "openai/gpt-oss-20b"), |
| "measurement_channel": "frozen primary model choice; no probability gate or graph propagation", |
| "iceberg_detected": not release, |
| "governance_action": action_for(row, release), |
| "affected_components": scope, |
| "revalidation_components": scope, |
| "reviewed_components": scope, |
| "release_permitted": release, |
| "diagnostic": {"selected_code": primary.get("selected_code"), "selected_node": selected}, |
| }) |
| args.output.parent.mkdir(parents=True, exist_ok=True) |
| args.output.write_text("".join(json.dumps(row, sort_keys=True) + "\n" for row in predictions)) |
| print(json.dumps({ |
| "method": "direct_llm", |
| "predictions": len(predictions), |
| "model_calls": 0, |
| "uses_probability_gate": False, |
| "uses_graph_propagation": False, |
| "output": str(args.output), |
| }, indent=2)) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
