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no nested functions.\",\r\n \"No function/argument/local annotations, decorators, defaults, imports, lambda, recursion, private names, or indirect calls.\",\r\n \"Calls only to advertised builtins or advertised methods.\",\r\n \"Numeric literals absolute value<=1000000; string literals<=2000 characters.\",\r\n \"No chained in/not-in comparisons; no augmented multiplication; augmented addition requires integer literal.\",\r\n \"Only operators represented by the AST allowlist are accepted.\"\r\n ],\r\n \"trace_budget_definition\": \"Per held-out invocation: at most400000 candidate-source line or call tracing events; counters reset between examples.\",\r\n \"timeout_definition\": \"Five seconds for the whole fresh evaluator subprocess, including startup, validation and all held-out examples; not5seconds per example.\"\r\n}\r\n", "cases/02_data/reference.py": "def allocate_units(total, weights):\r\n denominator = sum(weights)\r\n result = [total * weight // denominator for weight in weights]\r\n order = sorted([[-(total * weight % denominator), index] for index, weight in enumerate(weights)])\r\n for offset in range(total - sum(result)):\r\n result[order[offset][1]] += 1\r\n return result\r\n", "cases/03_validation/heldout.json": "[\r\n {\r\n \"args\": [\r\n []\r\n ],\r\n \"expected\": {\r\n \"invalid\": [],\r\n \"overlaps\": []\r\n }\r\n },\r\n {\r\n \"args\": [\r\n [\r\n [\r\n 0,\r\n 10\r\n ],\r\n [\r\n 10,\r\n 20\r\n ],\r\n [\r\n 4,\r\n 12\r\n ]\r\n ]\r\n ],\r\n \"expected\": {\r\n \"invalid\": [],\r\n \"overlaps\": [\r\n [\r\n 0,\r\n 2\r\n ],\r\n [\r\n 1,\r\n 2\r\n ]\r\n ]\r\n }\r\n },\r\n {\r\n \"args\": [\r\n [\r\n [\r\n false,\r\n 10\r\n ],\r\n [\r\n 0,\r\n true\r\n ],\r\n [\r\n 0,\r\n 1\r\n ],\r\n [\r\n 0,\r\n 1.0\r\n ]\r\n ]\r\n ],\r\n \"expected\": {\r\n \"invalid\": [\r\n 0,\r\n 1,\r\n 3\r\n ],\r\n \"overlaps\": []\r\n }\r\n },\r\n {\r\n \"args\": [\r\n [\r\n null,\r\n \"ab\",\r\n [],\r\n [\r\n 1\r\n ],\r\n [\r\n 0,\r\n 1,\r\n 2\r\n ],\r\n [\r\n -1,\r\n 1\r\n ],\r\n [\r\n 4,\r\n 4\r\n ],\r\n [\r\n 5,\r\n 2\r\n ],\r\n [\r\n 0,\r\n 1441\r\n ]\r\n ]\r\n ],\r\n \"expected\": {\r\n \"invalid\": [\r\n 0,\r\n 1,\r\n 2,\r\n 3,\r\n 4,\r\n 5,\r\n 6,\r\n 7,\r\n 8\r\n ],\r\n \"overlaps\": []\r\n }\r\n },\r\n {\r\n \"args\": [\r\n [\r\n [\r\n 30,\r\n 40\r\n ],\r\n [\r\n 0,\r\n 100\r\n ],\r\n [\r\n 10,\r\n 20\r\n ],\r\n [\r\n 15,\r\n 35\r\n ]\r\n ]\r\n ],\r\n \"expected\": {\r\n \"invalid\": [],\r\n \"overlaps\": [\r\n [\r\n 0,\r\n 1\r\n ],\r\n [\r\n 0,\r\n 3\r\n ],\r\n [\r\n 1,\r\n 2\r\n ],\r\n [\r\n 1,\r\n 3\r\n ],\r\n [\r\n 2,\r\n 3\r\n ]\r\n ]\r\n }\r\n },\r\n {\r\n \"args\": [\r\n [\r\n [\r\n 0,\r\n 1440\r\n ],\r\n [\r\n 0,\r\n 1440\r\n ]\r\n ]\r\n ],\r\n \"expected\": {\r\n \"invalid\": [],\r\n \"overlaps\": [\r\n [\r\n 0,\r\n 1\r\n ]\r\n ]\r\n }\r\n },\r\n {\r\n \"args\": [\r\n [\r\n [\r\n 0,\r\n 1\r\n ],\r\n [\r\n 1,\r\n 2\r\n ],\r\n [\r\n 2,\r\n 3\r\n ],\r\n [\r\n 3,\r\n 4\r\n ],\r\n [\r\n 4,\r\n 5\r\n ],\r\n [\r\n 5,\r\n 6\r\n ],\r\n [\r\n 6,\r\n 7\r\n ],\r\n [\r\n 7,\r\n 8\r\n ],\r\n [\r\n 8,\r\n 9\r\n ],\r\n [\r\n 9,\r\n 10\r\n ],\r\n [\r\n 10,\r\n 11\r\n ],\r\n [\r\n 11,\r\n 12\r\n ],\r\n [\r\n 12,\r\n 13\r\n ],\r\n [\r\n 13,\r\n 14\r\n ],\r\n [\r\n 14,\r\n 15\r\n ],\r\n [\r\n 15,\r\n 16\r\n ],\r\n [\r\n 16,\r\n 17\r\n ],\r\n [\r\n 17,\r\n 18\r\n ],\r\n [\r\n 18,\r\n 19\r\n ],\r\n [\r\n 19,\r\n 20\r\n ],\r\n [\r\n 20,\r\n 21\r\n ],\r\n [\r\n 21,\r\n 22\r\n ],\r\n [\r\n 22,\r\n 23\r\n ],\r\n [\r\n 23,\r\n 24\r\n ],\r\n [\r\n 24,\r\n 25\r\n ],\r\n [\r\n 25,\r\n 26\r\n ],\r\n [\r\n 26,\r\n 27\r\n ],\r\n [\r\n 27,\r\n 28\r\n ],\r\n [\r\n 28,\r\n 29\r\n ],\r\n [\r\n 29,\r\n 30\r\n ]\r\n ]\r\n ],\r\n \"expected\": {\r\n \"invalid\": [],\r\n \"overlaps\": []\r\n }\r\n }\r\n]\r\n", "cases/03_validation/public.json": "{\r\n \"case_id\": \"03_validation\",\r\n \"entrypoint\": \"validate_windows\",\r\n \"requirements\": \"windows is a list of JSON values. Valid window: list of exactly2 endpoints, each integer but not bool, with0<=start 50000:\n raise ValueError(\"range budget exceeded\")\n return result\n\n\nclass MembershipBudget(ast.NodeTransformer):\n def visit_Compare(self, node):\n self.generic_visit(node)\n if len(node.ops) == 1 and isinstance(node.ops[0], (ast.In, ast.NotIn)):\n result = ast.Call(func=ast.Name(id=\"_contains\", ctx=ast.Load()),\n args=[node.comparators[0], node.left], keywords=[])\n if isinstance(node.ops[0], ast.NotIn):\n result = ast.UnaryOp(op=ast.Not(), operand=result)\n return ast.copy_location(result, node)\n return node\n\n def visit_BinOp(self, node):\n self.generic_visit(node)\n if isinstance(node.op, (ast.Mult, ast.Add)):\n name = \"_multiply\" if isinstance(node.op, ast.Mult) else \"_add\"\n return ast.copy_location(ast.Call(func=ast.Name(id=name, ctx=ast.Load()),\n args=[node.left, node.right], keywords=[]), node)\n return node\n\n\ndef validate(code, entrypoint, node_limit):\n if not isinstance(code, str) or len(code) > 16000:\n raise ValueError(\"source must be a string no longer than 16000 characters\")\n tree = ast.parse(code)\n if len(tree.body) != 1 or not isinstance(tree.body[0], ast.FunctionDef):\n raise ValueError(\"exactly one top-level function required\")\n fn = tree.body[0]\n if fn.name != entrypoint or fn.decorator_list or fn.args.defaults or fn.args.kw_defaults:\n raise ValueError(\"wrong function name, decorators, or defaults\")\n if fn.returns is not None or any(arg.annotation is not None for arg in ast.walk(fn.args) if isinstance(arg, ast.arg)):\n raise ValueError(\"annotations unavailable\")\n nodes = list(ast.walk(tree))\n if len(nodes) > node_limit:\n raise ValueError(\"AST node budget exceeded\")\n if sum(isinstance(n, ast.FunctionDef) for n in nodes) != 1:\n raise ValueError(\"nested functions are unavailable\")\n for node in nodes:\n if type(node).__name__ not in ALLOWED:\n raise ValueError(\"unsupported syntax: \" + type(node).__name__)\n if isinstance(node, ast.Name) and node.id.startswith(\"_\"):\n raise ValueError(\"private names unavailable\")\n if isinstance(node, ast.arg) and node.arg.startswith(\"_\"):\n raise ValueError(\"private argument names unavailable\")\n if isinstance(node, ast.Attribute) and node.attr not in METHODS:\n raise ValueError(\"attribute unavailable\")\n if isinstance(node, ast.Constant):\n if isinstance(node.value, str) and len(node.value) > 2000:\n raise ValueError(\"string constant too long\")\n if isinstance(node.value, (int, float)) and abs(node.value) > 1000000:\n raise ValueError(\"numeric constant too large\")\n if isinstance(node, ast.Compare) and len(node.ops) > 1:\n if any(isinstance(op, (ast.In, ast.NotIn)) for op in node.ops):\n raise ValueError(\"membership comparison chains unavailable\")\n if isinstance(node, ast.AugAssign) and isinstance(node.op, ast.Mult):\n raise ValueError(\"augmented multiplication unavailable\")\n if isinstance(node, ast.AugAssign) and isinstance(node.op, ast.Add):\n if not isinstance(node.value, ast.Constant) or not isinstance(node.value.value, int):\n raise ValueError(\"augmented addition/multiplication requires an integer literal\")\n if isinstance(node, ast.Call):\n if isinstance(node.func, ast.Name):\n if node.func.id not in BUILTINS and node.func.id != \"range\":\n raise ValueError(\"call target unavailable\")\n elif not isinstance(node.func, ast.Attribute):\n raise ValueError(\"indirect calls unavailable\")\n return ast.fix_missing_locations(MembershipBudget().visit(tree)), len(nodes)\n\n\ndef child(payload):\n spec, tests = payload[\"spec\"], payload[\"tests\"]\n tree, node_count = validate(payload[\"code\"], spec[\"entrypoint\"], spec[\"ast_node_limit\"])\n state = {\"steps\": 0, \"membership\": 0}\n\n def contains(container, value):\n state[\"membership\"] += len(container) if isinstance(container, (list, tuple, str)) else 1\n if state[\"membership\"] > spec[\"membership_work_limit\"]:\n raise ValueError(\"membership work budget exceeded\")\n return value in container\n\n def multiply(left, right):\n if isinstance(left, (list, tuple, str)) and isinstance(right, int):\n if len(left) * max(0, right) > 50000:\n raise ValueError(\"sequence allocation budget exceeded\")\n elif isinstance(right, (list, tuple, str)) and isinstance(left, int):\n if len(right) * max(0, left) > 50000:\n raise ValueError(\"sequence allocation budget exceeded\")\n elif isinstance(left, (int, float)) and isinstance(right, (int, float)):\n if abs(left) > 1000000000000000 or abs(right) > 1000000000000000:\n raise ValueError(\"numeric budget exceeded\")\n return left * right\n\n def add(left, right):\n if isinstance(left, (list, tuple, str)) and isinstance(right, type(left)):\n if len(left) + len(right) > 50000:\n raise ValueError(\"sequence allocation budget exceeded\")\n return left + right\n\n env = {\"__builtins__\": {**BUILTINS, \"range\": checked_range},\n \"_contains\": contains, \"_multiply\": multiply, \"_add\": add}\n exec(compile(tree, \"\", \"exec\"), env)\n fn = env[spec[\"entrypoint\"]]\n\n def trace(frame, event, arg):\n if frame.f_code.co_filename == \"\" and event in {\"line\", \"call\"}:\n state[\"steps\"] += 1\n if state[\"steps\"] > spec[\"trace_step_limit\"]:\n raise ValueError(\"execution step budget exceeded\")\n return trace\n\n results = []\n for index, test in enumerate(tests):\n args = copy.deepcopy(test[\"args\"])\n before = copy.deepcopy(args)\n state.update(steps=0, membership=0)\n error = None\n actual = None\n try:\n sys.settrace(trace)\n actual = fn(*args)\n except Exception as exc:\n error = type(exc).__name__ + \": \" + str(exc)\n finally:\n sys.settrace(None)\n mutated = args != before or json.dumps(args, sort_keys=True) != json.dumps(before, sort_keys=True)\n match = error is None and actual == test[\"expected\"] and not mutated\n # JSON canonical comparison also distinguishes booleans from integer output.\n if match:\n match = json.dumps(actual, sort_keys=True) == json.dumps(test[\"expected\"], sort_keys=True)\n results.append({\"index\": index, \"passed\": match, \"error\": error,\n \"input_mutated\": mutated, **state})\n return {\"passed\": all(x[\"passed\"] for x in results), \"ast_nodes\": node_count,\n \"tests\": results}\n\n\ndef evaluate(case_dir, code):\n case_dir = Path(case_dir)\n spec = json.loads((case_dir / \"public.json\").read_text(encoding=\"utf-8\"))\n tests = json.loads((case_dir / \"heldout.json\").read_text(encoding=\"utf-8\"))\n payload = {\"spec\": spec, \"tests\": tests, \"code\": code}\n try:\n result = subprocess.run([sys.executable, \"-I\", \"-B\", __file__, \"--child\"],\n input=json.dumps(payload), capture_output=True, text=True,\n timeout=5, cwd=case_dir, env={\"SYSTEMROOT\": os.environ.get(\"SYSTEMROOT\", \"\")})\n except subprocess.TimeoutExpired:\n return {\"passed\": False, \"error\": \"subprocess timeout\"}\n try:\n return json.loads(result.stdout)\n except (ValueError, TypeError):\n return {\"passed\": False, \"error\": \"invalid evaluator result\", \"exit_code\": result.returncode}\n\n\ndef main():\n if sys.argv[1:] == [\"--child\"]:\n try:\n result = child(json.loads(sys.stdin.read()))\n except Exception as exc:\n result = {\"passed\": False, \"error\": type(exc).__name__ + \": \" + str(exc)}\n print(json.dumps(result))\n return\n submission = json.loads(Path(sys.argv[2]).read_text(encoding=\"utf-8\"))\n if not isinstance(submission, dict) or set(submission) != {\"code\"}:\n raise SystemExit('submission must be exactly {\"code\": string}')\n print(json.dumps(evaluate(Path(sys.argv[1]).resolve(), submission[\"code\"])))\n\n\nif __name__ == \"__main__\":\n main()\n", "cases/evaluator-negative-checks.json": "{\r\n \"import\": {\r\n \"passed\": false,\r\n \"error\": \"ValueError: unsupported syntax: Import\"\r\n },\r\n \"io\": {\r\n \"passed\": false,\r\n \"error\": \"ValueError: call target unavailable\"\r\n },\r\n \"reflection\": {\r\n \"passed\": false,\r\n \"error\": \"ValueError: attribute unavailable\"\r\n },\r\n \"indirect\": {\r\n \"passed\": false,\r\n \"error\": \"ValueError: call target unavailable\"\r\n },\r\n \"infinite\": {\r\n \"passed\": false,\r\n \"ast_nodes\": 7,\r\n \"tests\": [\r\n {\r\n \"index\": 0,\r\n \"passed\": false,\r\n \"error\": \"ValueError: execution step budget exceeded\",\r\n \"input_mutated\": false,\r\n \"steps\": 400001,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 1,\r\n \"passed\": false,\r\n \"error\": \"ValueError: execution step budget exceeded\",\r\n \"input_mutated\": false,\r\n \"steps\": 400001,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 2,\r\n \"passed\": false,\r\n \"error\": \"ValueError: execution step budget exceeded\",\r\n \"input_mutated\": false,\r\n \"steps\": 400001,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 3,\r\n \"passed\": false,\r\n \"error\": \"ValueError: execution step budget exceeded\",\r\n \"input_mutated\": false,\r\n \"steps\": 400001,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 4,\r\n \"passed\": false,\r\n \"error\": \"ValueError: execution step budget exceeded\",\r\n \"input_mutated\": false,\r\n \"steps\": 400001,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 5,\r\n \"passed\": false,\r\n \"error\": \"ValueError: execution step budget exceeded\",\r\n \"input_mutated\": false,\r\n \"steps\": 400001,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 6,\r\n \"passed\": false,\r\n \"error\": \"ValueError: execution step budget exceeded\",\r\n \"input_mutated\": false,\r\n \"steps\": 400001,\r\n \"membership\": 0\r\n }\r\n ]\r\n },\r\n \"allocation\": {\r\n \"passed\": false,\r\n \"ast_nodes\": 21,\r\n \"tests\": [\r\n {\r\n \"index\": 0,\r\n \"passed\": false,\r\n \"error\": \"ValueError: sequence allocation budget exceeded\",\r\n \"input_mutated\": false,\r\n \"steps\": 34,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 1,\r\n \"passed\": false,\r\n \"error\": \"ValueError: sequence allocation budget exceeded\",\r\n \"input_mutated\": false,\r\n \"steps\": 34,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 2,\r\n \"passed\": false,\r\n \"error\": \"ValueError: sequence allocation budget exceeded\",\r\n \"input_mutated\": false,\r\n \"steps\": 34,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 3,\r\n \"passed\": false,\r\n \"error\": \"ValueError: sequence allocation budget exceeded\",\r\n \"input_mutated\": false,\r\n \"steps\": 34,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 4,\r\n \"passed\": false,\r\n \"error\": \"ValueError: sequence allocation budget exceeded\",\r\n \"input_mutated\": false,\r\n \"steps\": 34,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 5,\r\n \"passed\": false,\r\n \"error\": \"ValueError: sequence allocation budget exceeded\",\r\n \"input_mutated\": false,\r\n \"steps\": 34,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 6,\r\n \"passed\": false,\r\n \"error\": \"ValueError: sequence allocation budget exceeded\",\r\n \"input_mutated\": false,\r\n \"steps\": 34,\r\n \"membership\": 0\r\n }\r\n ]\r\n },\r\n \"oversize\": {\r\n \"passed\": false,\r\n \"error\": \"ValueError: source must be a string no longer than 16000 characters\"\r\n },\r\n \"bool_mutation\": {\r\n \"passed\": false,\r\n \"ast_nodes\": 20,\r\n \"tests\": [\r\n {\r\n \"index\": 0,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 3,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 1,\r\n \"passed\": false,\r\n \"error\": null,\r\n \"input_mutated\": true,\r\n \"steps\": 4,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 2,\r\n \"passed\": false,\r\n \"error\": null,\r\n \"input_mutated\": true,\r\n \"steps\": 4,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 3,\r\n \"passed\": false,\r\n \"error\": null,\r\n \"input_mutated\": true,\r\n \"steps\": 4,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 4,\r\n \"passed\": false,\r\n \"error\": null,\r\n \"input_mutated\": true,\r\n \"steps\": 4,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 5,\r\n \"passed\": false,\r\n \"error\": null,\r\n \"input_mutated\": true,\r\n \"steps\": 4,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 6,\r\n \"passed\": false,\r\n \"error\": null,\r\n \"input_mutated\": true,\r\n \"steps\": 4,\r\n \"membership\": 0\r\n }\r\n ]\r\n },\r\n \"float_mutation\": {\r\n \"passed\": false,\r\n \"ast_nodes\": 20,\r\n \"tests\": [\r\n {\r\n \"index\": 0,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 3,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 1,\r\n \"passed\": false,\r\n \"error\": null,\r\n \"input_mutated\": true,\r\n \"steps\": 4,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 2,\r\n \"passed\": false,\r\n \"error\": null,\r\n \"input_mutated\": true,\r\n \"steps\": 4,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 3,\r\n \"passed\": false,\r\n \"error\": null,\r\n \"input_mutated\": true,\r\n \"steps\": 4,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 4,\r\n \"passed\": false,\r\n \"error\": null,\r\n \"input_mutated\": true,\r\n \"steps\": 4,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 5,\r\n \"passed\": false,\r\n \"error\": null,\r\n \"input_mutated\": true,\r\n \"steps\": 4,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 6,\r\n \"passed\": false,\r\n \"error\": null,\r\n \"input_mutated\": true,\r\n \"steps\": 4,\r\n \"membership\": 0\r\n }\r\n ]\r\n },\r\n \"equal_value_bool_mutation\": {\r\n \"passed\": false,\r\n \"ast_nodes\": 17,\r\n \"tests\": [\r\n {\r\n \"index\": 0,\r\n \"passed\": false,\r\n \"error\": null,\r\n \"input_mutated\": true,\r\n \"steps\": 3,\r\n \"membership\": 0\r\n }\r\n ]\r\n }\r\n}\r\n", "cases/manifest.json": "{\r\n \"version\": \"astra-fable-sanitized-pilot-20260904-v3-prefreeze\",\r\n \"cases\": 5,\r\n \"heldout_examples\": 37,\r\n \"model_calls_run_by_author\": 0,\r\n \"files\": {\r\n \"01_state/heldout.json\": \"2226d38371b361319a8907397fa41f796bc66a2143ff7fe50e89ba8a44b2f60a\",\r\n \"01_state/public.json\": \"4b179fe376f0f6b6b416e122825a1aaa6bd2def87e7c228e2a1fc7de42a4fd7e\",\r\n \"01_state/reference.py\": \"77b6a9784f2bbc7b4ef46aef7e3c03a2e5d811331b236fff8b1688518e32d829\",\r\n \"02_data/heldout.json\": \"7f81aeaf7e79bef3a878593189e5ee439cbfd9db44d68f11f61dc3b2794e4c1a\",\r\n \"02_data/public.json\": \"3b8947c423fa8fad0ad1ce78dce09a57e3ecd4f3c947c26c7cff832e87808278\",\r\n \"02_data/reference.py\": \"94652879b8fa97dd54fc71591823b769025aca0f5a231d94c21b05bccd1b9597\",\r\n \"03_validation/heldout.json\": \"f1aabde9d7ef140d156a69f5e0ffcb1b4bcbb31d979b43dbefc5378524aab740\",\r\n \"03_validation/public.json\": \"28eef92b6530de49dbb2bf0e81ac680d23f9ff51f95302af69218c185fd29b04\",\r\n \"03_validation/reference.py\": \"3cf66adf0d448a1164c8abc3c0bb3f15ce8e108e1bd56ce391d967b9c15a1268\",\r\n \"04_performance/heldout.json\": \"b45f127978644d24c94232e1d2b6e65a42c3c2cd12e23ce7c6b5e9b622026586\",\r\n \"04_performance/public.json\": \"66f2fed5c7e5fbba2eb16705f3571feb71ad04d85e42ac99aca6063898d4e739\",\r\n \"04_performance/reference.py\": \"128fcd547caf9d5a1c25f2f5b0dcc0bbd31cb7fbfa24cc3e7cac9b2511f9c40a\",\r\n \"05_maintainability/heldout.json\": \"583be4cec5413235a9d195f9c90d77502bc621cafb56944fb7816efa3c648a84\",\r\n \"05_maintainability/public.json\": \"c38dd7032aaed373f51fe394b54a747abbfc1611b560505f66ecf68e8ca160b4\",\r\n \"05_maintainability/reference.py\": \"d4ea4cb8773f4a5c63ce1b141c847d1c5e1107f463cc1d0b13d7e05cc3ea483e\",\r\n \"evaluate.py\": \"42f3d4f8439331753f328d47e1c0a55423340001c3ff0d551a7cb606e4ad101c\",\r\n \"evaluator-negative-checks.json\": \"05ff77ea2ac1e444d8baa2643624f09f0347e2ba844bdcfcae21dee6d8827d11\",\r\n \"README.md\": \"257d9f88bc818278a82a02f5dba3df1f55fc1fca114c006baec48c9d3e72a36a\",\r\n \"selfcheck.json\": \"b0a23dbced6d31e1b2544189c6370754eb6d4f758a096c0280fecae30c94b56f\"\r\n },\r\n \"content_sha256\": \"e3b18f7a9d0e8bec81498da7cca890efcd3958d6d94debd24ef083acdd500c29\"\r\n}\r\n", "cases/README.md": "# Astra / Fable collaboration pilot: frozen sanitized cases\r\n\r\nFive newly authored tasks model broad studio failure classes without copying production source, the four repaired regressions, prior answers, account data, or proprietary identifiers. They are publicly shareable. They are small synthetic verification exercises, not real product tasks or evidence of studio-wide speed, cost, or model superiority.\r\n\r\n## Participant boundary\r\n\r\nSend ONLY the selected case's public.json. It contains the complete requirements, buggy function, output contract, and declared resource/structure budgets. Do not include heldout.json, reference.py, selfcheck.json, negative checks, or evaluator internals in participant context. Participants return exactly {\"code\": \"complete replacement function source\"}, with source no longer than16000 characters. Reference implementations are author-only oracle checks, never participant examples.\r\n\r\nCases: 01_state selects deterministic latest state; 02_data allocates exact integer shares; 03_validation validates interval semantics; 04_performance uses an explicit membership-work budget; 05_maintainability compresses adjacent runs within an explicit flat-function AST budget. There are37 held-out examples total. Every reference passes and every supplied buggy implementation fails. Case05 fails the declared structural budget before behavior; its bug also omits a final run for longer inputs.\r\n\r\n## Matched six-arm design:30 calls\r\n\r\nFor each of5 cases, obtain two independent first passes A and F in fresh contexts. Freeze both complete responses. Then obtain four independent refinements, each in another fresh context: AA (A revises A), FF (F revises F), AF (F revises A), FA (A revises F). Thus each case needs2 first-pass and4 refinement calls; five cases require30 calls. A/F are routing labels retained privately, not participant-visible author labels.\r\n\r\nEach refinement receives exactly the same original public packet, the frozen precursor code, and the same neutral instruction: improve correctness and meet the stated verification contract; return replacement JSON only. Do not provide hidden grader failures, other arms, model identities, selfcheck results, or reference code. If a first pass is invalid JSON, preserve its failure; refinements receive its verbatim response uniformly. Do not silently repair a model response or retry a failed arm. Run all arms before grading/selection; pre-register call order and budget. Alternate first-pass order across cases and randomize refinement ordering with one recorded seed. Record actual requested/resolved model, effective runtime controls, latency, token/usage data when available, and failures; do not infer settings a provider does not expose.\r\n\r\nPrimary outcome: complete contract pass per case, reported as exact counts. Compare AF with AA and FA with FF, not only with one-pass controls. Also record new regressions from previously passing first passes, invalid output, resource rejection, and per-case behavior. Partial test counts are diagnostics, not a substitute for complete success. Case difficulty is deliberately modest;5 pairs have low statistical power. Do not claim significance or extrapolate to codebase work, production, routing promotion, spend savings, or worker-runtime value from this pilot.\r\n\r\n## Evaluator\r\n\r\nUsage: python evaluate.py . It emits one JSON result. Candidate code is never executed in the controller process: a fresh Python -I -B subprocess validates an AST allowlist, restricts builtins and callable methods, rejects imports/I/O/reflection/recursion, instruments membership and sequence concatenation/multiplication, applies an execution-step limit and5-second wall timeout, checks input immutability, and compares JSON output. AST node count is measured before instrumentation.\r\n\r\nThis is defense in depth for these bounded cooperative pure-function submissions, not a general hostile-code security boundary. Python builtins, interpreter resource use, hash complexity, and uninstrumented operations are not fully metered; there is no OS memory quota or filesystem/network sandbox here. A stronger OS sandbox is required before using it for adversarial/untrusted general code. Append, comprehensions, conversions and nested allocations are not fully metered; no total allocation bound is claimed. Before evaluating any generated candidate, the coordinator must inspect every exact source for resource hazards and record a resource-review receipt bound to all30 candidate hashes. Review identifiers omit arm/model labels. Each candidate is approved or rejected independently; rejected candidates count as resource failures without evaluator execution. Collection and grading are separate commands; no automatic grading immediately after collection. Do not run arbitrary extracted code directly in a host REPL. The membership metric is a declared operation proxy, not a wall-clock performance benchmark; the AST budget is a narrow structural check, not a universal maintainability score.\r\n\r\nSelfcheck confirms37 reference examples pass and all5 buggy baselines fail. Negative checks reject imports, I/O, reflection, forbidden indirect evaluation, infinite loops, excessive sequence concatenation, over-cap source and type-changing input mutations. These checks do not establish comprehensive sandbox security. No model calls or product edits were made by the case author.\r\n\r\n## Freeze\r\n\r\nmanifest.json hashes every public packet, held-out file, reference, evaluator, documentation and selfcheck artifact. Verify these hashes before dispatch and grading. Any correction after participant calls invalidates the affected matched case; do not quietly alter the grader or select favorable reruns. Published case contents will contaminate future reuse, so label this specific set/version.\r\n\r\nPrefreeze review corrections: public packets expose the exact AST node allowlist, metadata restrictions,16000-character source cap, line/call tracing semantics and whole-child5-second timeout. The maintainability baseline intentionally exceeds its declared structural budget; fixing that is part of the task. Native Fable conditions may include an allowed auxiliary Haiku model; the controller protocol and full per-model usage define that workflow rather than a pure-model comparison. All setup, authoring and review time is separate overhead.\r\n", "cases/selfcheck.json": "[\r\n {\r\n \"case_id\": \"01_state\",\r\n \"reference\": {\r\n \"passed\": true,\r\n \"ast_nodes\": 97,\r\n \"tests\": [\r\n {\r\n \"index\": 0,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 4,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 1,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 12,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 2,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 17,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 3,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 18,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 4,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 12,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 5,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 19,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 6,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 3006,\r\n \"membership\": 0\r\n }\r\n ]\r\n },\r\n \"buggy\": {\r\n \"passed\": false,\r\n \"ast_nodes\": 50,\r\n \"tests\": [\r\n {\r\n \"index\": 0,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 4,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 1,\r\n \"passed\": false,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 9,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 2,\r\n \"passed\": false,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 12,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 3,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 12,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 4,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 9,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 5,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 13,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 6,\r\n \"passed\": false,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 2005,\r\n \"membership\": 0\r\n }\r\n ]\r\n }\r\n },\r\n {\r\n \"case_id\": \"02_data\",\r\n \"reference\": {\r\n \"passed\": true,\r\n \"ast_nodes\": 101,\r\n \"tests\": [\r\n {\r\n \"index\": 0,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 10,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 1,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 8,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 2,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 16,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 3,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 14,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 4,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 12,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 5,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 14,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 6,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 16,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 7,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 16,\r\n \"membership\": 0\r\n }\r\n ]\r\n },\r\n \"buggy\": {\r\n \"passed\": false,\r\n \"ast_nodes\": 59,\r\n \"tests\": [\r\n {\r\n \"index\": 0,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 7,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 1,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 6,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 2,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 12,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 3,\r\n \"passed\": false,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 10,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 4,\r\n \"passed\": false,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 9,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 5,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 10,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 6,\r\n \"passed\": false,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 12,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 7,\r\n \"passed\": false,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 11,\r\n \"membership\": 0\r\n }\r\n ]\r\n }\r\n },\r\n {\r\n \"case_id\": \"03_validation\",\r\n \"reference\": {\r\n \"passed\": true,\r\n \"ast_nodes\": 195,\r\n \"tests\": [\r\n {\r\n \"index\": 0,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 7,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 1,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 48,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 2,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 37,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 3,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 58,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 4,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 68,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 5,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 32,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 6,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 1207,\r\n \"membership\": 0\r\n }\r\n ]\r\n },\r\n \"buggy\": {\r\n \"passed\": false,\r\n \"ast_nodes\": 193,\r\n \"tests\": [\r\n {\r\n \"index\": 0,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 7,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 1,\r\n \"passed\": false,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 49,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 2,\r\n \"passed\": false,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 56,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 3,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 58,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 4,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 68,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 5,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 32,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 6,\r\n \"passed\": false,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 1236,\r\n \"membership\": 0\r\n }\r\n ]\r\n }\r\n },\r\n {\r\n \"case_id\": \"04_performance\",\r\n \"reference\": {\r\n \"passed\": true,\r\n \"ast_nodes\": 63,\r\n \"tests\": [\r\n {\r\n \"index\": 0,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 6,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 1,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 8,\r\n \"membership\": 1\r\n },\r\n {\r\n \"index\": 2,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 20,\r\n \"membership\": 9\r\n },\r\n {\r\n \"index\": 3,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 18,\r\n \"membership\": 7\r\n },\r\n {\r\n \"index\": 4,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 10,\r\n \"membership\": 2\r\n },\r\n {\r\n \"index\": 5,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 30006,\r\n \"membership\": 15000\r\n },\r\n {\r\n \"index\": 6,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 20008,\r\n \"membership\": 20000\r\n }\r\n ]\r\n },\r\n \"buggy\": {\r\n \"passed\": false,\r\n \"ast_nodes\": 41,\r\n \"tests\": [\r\n {\r\n \"index\": 0,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 4,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 1,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 6,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 2,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 16,\r\n \"membership\": 20\r\n },\r\n {\r\n \"index\": 3,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 14,\r\n \"membership\": 11\r\n },\r\n {\r\n \"index\": 4,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 8,\r\n \"membership\": 2\r\n },\r\n {\r\n \"index\": 5,\r\n \"passed\": false,\r\n \"error\": \"ValueError: membership work budget exceeded\",\r\n \"input_mutated\": false,\r\n \"steps\": 24,\r\n \"membership\": 110000\r\n },\r\n {\r\n \"index\": 6,\r\n \"passed\": false,\r\n \"error\": \"ValueError: membership work budget exceeded\",\r\n \"input_mutated\": false,\r\n \"steps\": 23,\r\n \"membership\": 100008\r\n }\r\n ]\r\n }\r\n },\r\n {\r\n \"case_id\": \"05_maintainability\",\r\n \"reference\": {\r\n \"passed\": true,\r\n \"ast_nodes\": 60,\r\n \"tests\": [\r\n {\r\n \"index\": 0,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 4,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 1,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 7,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 2,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 10,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 3,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 10,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 4,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 16,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 5,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 16,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 6,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 6004,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 7,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 6004,\r\n \"membership\": 0\r\n }\r\n ]\r\n },\r\n \"buggy\": {\r\n \"passed\": false,\r\n \"error\": \"ValueError: AST node budget exceeded\"\r\n }\r\n }\r\n]\r\n", "execution-contract.json": "{\r\n \"freeze_sha256\": \"cd4f8a3e97e2ad6a8a1902b4ad45ebb58a5dbe9e03b0df37052f46fd5cbd147b\",\r\n \"seed\": 20260904,\r\n \"call_order\": [\r\n {\r\n \"index\": 1,\r\n \"case_id\": \"01_state\",\r\n \"arm\": \"A\",\r\n \"model\": \"A\",\r\n \"precursor\": null\r\n },\r\n {\r\n \"index\": 2,\r\n \"case_id\": \"01_state\",\r\n \"arm\": \"F\",\r\n \"model\": \"F\",\r\n \"precursor\": null\r\n },\r\n {\r\n \"index\": 3,\r\n \"case_id\": \"01_state\",\r\n \"arm\": \"AF\",\r\n \"model\": \"F\",\r\n \"precursor\": \"A\"\r\n },\r\n {\r\n \"index\": 4,\r\n \"case_id\": \"01_state\",\r\n \"arm\": \"FF\",\r\n \"model\": \"F\",\r\n \"precursor\": \"F\"\r\n },\r\n {\r\n \"index\": 5,\r\n \"case_id\": \"01_state\",\r\n \"arm\": \"FA\",\r\n \"model\": \"A\",\r\n \"precursor\": \"F\"\r\n },\r\n {\r\n \"index\": 6,\r\n \"case_id\": \"01_state\",\r\n \"arm\": \"AA\",\r\n \"model\": \"A\",\r\n \"precursor\": \"A\"\r\n },\r\n {\r\n \"index\": 7,\r\n \"case_id\": \"02_data\",\r\n \"arm\": \"F\",\r\n \"model\": \"F\",\r\n \"precursor\": null\r\n },\r\n {\r\n \"index\": 8,\r\n \"case_id\": \"02_data\",\r\n \"arm\": \"A\",\r\n \"model\": \"A\",\r\n \"precursor\": null\r\n },\r\n {\r\n \"index\": 9,\r\n \"case_id\": \"02_data\",\r\n \"arm\": \"FF\",\r\n \"model\": \"F\",\r\n \"precursor\": \"F\"\r\n },\r\n {\r\n \"index\": 10,\r\n \"case_id\": \"02_data\",\r\n \"arm\": \"AA\",\r\n \"model\": \"A\",\r\n \"precursor\": \"A\"\r\n },\r\n {\r\n \"index\": 11,\r\n \"case_id\": \"02_data\",\r\n \"arm\": \"FA\",\r\n \"model\": \"A\",\r\n \"precursor\": \"F\"\r\n },\r\n {\r\n \"index\": 12,\r\n \"case_id\": \"02_data\",\r\n \"arm\": \"AF\",\r\n \"model\": \"F\",\r\n \"precursor\": \"A\"\r\n },\r\n {\r\n \"index\": 13,\r\n \"case_id\": \"03_validation\",\r\n \"arm\": \"A\",\r\n \"model\": \"A\",\r\n \"precursor\": null\r\n },\r\n {\r\n \"index\": 14,\r\n \"case_id\": \"03_validation\",\r\n \"arm\": \"F\",\r\n \"model\": \"F\",\r\n \"precursor\": null\r\n },\r\n {\r\n \"index\": 15,\r\n \"case_id\": \"03_validation\",\r\n \"arm\": \"AF\",\r\n \"model\": \"F\",\r\n \"precursor\": \"A\"\r\n },\r\n {\r\n \"index\": 16,\r\n \"case_id\": \"03_validation\",\r\n \"arm\": \"AA\",\r\n \"model\": \"A\",\r\n \"precursor\": \"A\"\r\n },\r\n {\r\n \"index\": 17,\r\n \"case_id\": \"03_validation\",\r\n \"arm\": \"FA\",\r\n \"model\": \"A\",\r\n \"precursor\": \"F\"\r\n },\r\n {\r\n \"index\": 18,\r\n \"case_id\": \"03_validation\",\r\n \"arm\": \"FF\",\r\n \"model\": \"F\",\r\n \"precursor\": \"F\"\r\n },\r\n {\r\n \"index\": 19,\r\n \"case_id\": \"04_performance\",\r\n \"arm\": \"F\",\r\n \"model\": \"F\",\r\n \"precursor\": null\r\n },\r\n {\r\n \"index\": 20,\r\n \"case_id\": \"04_performance\",\r\n \"arm\": \"A\",\r\n \"model\": \"A\",\r\n \"precursor\": null\r\n },\r\n {\r\n \"index\": 21,\r\n \"case_id\": \"04_performance\",\r\n \"arm\": \"FA\",\r\n \"model\": \"A\",\r\n \"precursor\": \"F\"\r\n },\r\n {\r\n \"index\": 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See Protocol.md for native scaffold and identity limitations.\"\r\n}", "outputs/01_state/A.grade.json": "{\r\n \"index\": 1,\r\n \"case_id\": \"01_state\",\r\n \"arm\": \"A\",\r\n \"model\": \"A\",\r\n \"precursor\": null,\r\n \"call_status\": \"ok\",\r\n \"category\": \"response\",\r\n \"requested_model\": \"gpt-6-astra\",\r\n \"effort\": \"high\",\r\n \"provider_resolved_model\": null,\r\n \"auxiliary_models\": [],\r\n \"model_usage_summary\": null,\r\n \"primary_response_usage\": {\r\n \"input_tokens\": null,\r\n \"cache_creation_input_tokens\": null,\r\n \"cache_read_input_tokens\": null,\r\n \"output_tokens\": null\r\n },\r\n \"runtime_context\": {\r\n \"model\": \"gpt-6-astra\",\r\n \"effort\": \"high\"\r\n },\r\n \"grade\": {\r\n \"passed\": true,\r\n \"ast_nodes\": 88,\r\n \"tests\": [\r\n {\r\n \"index\": 0,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 4,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 1,\r\n \"passed\": true,\r\n 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best_seq[key] = sequence\\n best_id[key] = event_id\\n best_state[key] = state\\n elif sequence > best_seq[key] or (sequence == best_seq[key] and event_id > best_id[key]):\\n best_seq[key] = sequence\\n best_id[key] = event_id\\n best_state[key] = state\\n return [[key, best_state[key]] for key in sorted(best_seq)]\\n\"\r\n}\r\n", "outputs/01_state/F.grade.json": "{\r\n \"index\": 2,\r\n \"case_id\": \"01_state\",\r\n \"arm\": \"F\",\r\n \"model\": \"F\",\r\n \"precursor\": null,\r\n \"call_status\": \"ok\",\r\n \"category\": \"response\",\r\n \"requested_model\": \"claude-fable-5-1\",\r\n \"effort\": \"high\",\r\n \"provider_resolved_model\": \"claude-fable-5-1\",\r\n \"auxiliary_models\": [\r\n \"claude-haiku-4-5-20251001\"\r\n ],\r\n \"model_usage_summary\": {\r\n \"claude-haiku-4-5-20251001\": {\r\n \"input_tokens\": 1977,\r\n \"cache_creation_input_tokens\": 0,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 12\r\n },\r\n \"claude-fable-5-1\": {\r\n \"input_tokens\": 2,\r\n \"cache_creation_input_tokens\": 2198,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 199\r\n }\r\n },\r\n \"primary_response_usage\": {\r\n \"input_tokens\": 2,\r\n \"cache_creation_input_tokens\": 2198,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 199\r\n },\r\n \"runtime_context\": null,\r\n \"grade\": {\r\n \"passed\": true,\r\n \"ast_nodes\": 93,\r\n \"tests\": [\r\n {\r\n \"index\": 0,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 4,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 1,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 14,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 2,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 20,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 3,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 21,\r\n \"membership\": 0\r\n },\r\n {\r\n 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},\r\n \"claude-haiku-4-5-20251001\": {\r\n \"input_tokens\": 1977,\r\n \"cache_creation_input_tokens\": 0,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 12\r\n }\r\n }\r\n }\r\n }\r\n },\r\n \"call_cost\": {\r\n \"component_wall_seconds\": 5.89625370001886,\r\n \"provider_specific_usage\": {\r\n \"F\": {\r\n \"calls\": 1,\r\n \"components\": {\r\n \"output_tokens\": 211,\r\n \"cache_read_input_tokens\": 0,\r\n \"input_tokens\": 1979,\r\n \"cache_creation_input_tokens\": 2198\r\n },\r\n \"total\": 4388,\r\n \"cached_input_is_subset\": false,\r\n \"per_model_components\": {\r\n \"claude-fable-5-1\": {\r\n \"input_tokens\": 2,\r\n \"cache_creation_input_tokens\": 2198,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 199\r\n },\r\n \"claude-haiku-4-5-20251001\": {\r\n \"input_tokens\": 1977,\r\n \"cache_creation_input_tokens\": 0,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 12\r\n }\r\n }\r\n }\r\n }\r\n },\r\n \"freeze_sha256\": \"cd4f8a3e97e2ad6a8a1902b4ad45ebb58a5dbe9e03b0df37052f46fd5cbd147b\",\r\n \"prompt_sha256\": \"05e25f57dd5acdf6b1802dcc05a5d6367e90cf19cfeda9b3ff79a9bc516e20da\",\r\n \"resource_review_decision\": \"approved\",\r\n \"adopted\": false,\r\n \"origin_freeze_sha256\": \"cd4f8a3e97e2ad6a8a1902b4ad45ebb58a5dbe9e03b0df37052f46fd5cbd147b\",\r\n \"precursor_reused\": false\r\n}\r\n", "outputs/01_state/F.json": "{\r\n \"code\": \"def latest_states(events):\\n best = {}\\n for key, sequence, event_id, state in events:\\n marker = (sequence, event_id)\\n existing = best.get(key)\\n if existing is None or marker > existing[0]:\\n best[key] = (marker, state)\\n return [[key, best[key][1]] for key in sorted(best)]\\n\"\r\n}\r\n", "outputs/01_state/FA.grade.json": "{\r\n \"index\": 5,\r\n \"case_id\": \"01_state\",\r\n \"arm\": \"FA\",\r\n \"model\": \"A\",\r\n \"precursor\": \"F\",\r\n \"call_status\": \"ok\",\r\n \"category\": \"response\",\r\n \"requested_model\": \"gpt-6-astra\",\r\n \"effort\": 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\"steps\": 21,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 4,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 14,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 5,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 22,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 6,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 4006,\r\n \"membership\": 0\r\n }\r\n ]\r\n },\r\n \"workflow_cost\": {\r\n \"component_wall_seconds\": 22.768512499984354,\r\n \"provider_specific_usage\": {\r\n \"A\": {\r\n \"calls\": 1,\r\n \"components\": {\r\n \"cached_input_tokens\": 0,\r\n \"input_tokens\": 8591,\r\n \"output_tokens\": 276\r\n },\r\n \"total\": 8867,\r\n \"cached_input_is_subset\": true\r\n },\r\n \"F\": {\r\n \"calls\": 1,\r\n \"components\": {\r\n \"output_tokens\": 211,\r\n \"cache_read_input_tokens\": 0,\r\n \"input_tokens\": 1979,\r\n \"cache_creation_input_tokens\": 2198\r\n },\r\n \"total\": 4388,\r\n \"cached_input_is_subset\": false,\r\n \"per_model_components\": {\r\n \"claude-fable-5-1\": {\r\n \"input_tokens\": 2,\r\n \"cache_creation_input_tokens\": 2198,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 199\r\n },\r\n \"claude-haiku-4-5-20251001\": {\r\n \"input_tokens\": 1977,\r\n \"cache_creation_input_tokens\": 0,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 12\r\n }\r\n }\r\n }\r\n }\r\n },\r\n \"call_cost\": {\r\n \"component_wall_seconds\": 16.872258799965493,\r\n \"provider_specific_usage\": {\r\n \"A\": {\r\n \"calls\": 1,\r\n \"components\": {\r\n \"cached_input_tokens\": 0,\r\n \"input_tokens\": 8591,\r\n \"output_tokens\": 276\r\n },\r\n \"total\": 8867,\r\n \"cached_input_is_subset\": true\r\n }\r\n }\r\n },\r\n \"freeze_sha256\": \"cd4f8a3e97e2ad6a8a1902b4ad45ebb58a5dbe9e03b0df37052f46fd5cbd147b\",\r\n \"prompt_sha256\": \"af67998422e2df8b8ac7876b15f40297e65c5da8fa6f88a8570dfd3eedb51b34\",\r\n \"resource_review_decision\": \"approved\",\r\n \"adopted\": false,\r\n \"origin_freeze_sha256\": \"cd4f8a3e97e2ad6a8a1902b4ad45ebb58a5dbe9e03b0df37052f46fd5cbd147b\",\r\n \"precursor_reused\": true\r\n}\r\n", "outputs/01_state/FA.json": "{\r\n \"code\": \"def latest_states(events):\\n best = {}\\n for key, sequence, event_id, state in events:\\n rank = (sequence, event_id)\\n previous = best.get(key)\\n if previous is None or rank > previous[0]:\\n best[key] = (rank, state)\\n return [[key, best[key][1]] for key in sorted(best)]\\n\"\r\n}\r\n", "outputs/01_state/FF.grade.json": "{\r\n \"index\": 4,\r\n \"case_id\": \"01_state\",\r\n \"arm\": \"FF\",\r\n \"model\": \"F\",\r\n \"precursor\": \"F\",\r\n \"call_status\": \"ok\",\r\n \"category\": \"response\",\r\n \"requested_model\": \"claude-fable-5-1\",\r\n \"effort\": \"high\",\r\n \"provider_resolved_model\": \"claude-fable-5-1\",\r\n \"auxiliary_models\": [\r\n \"claude-haiku-4-5-20251001\"\r\n ],\r\n \"model_usage_summary\": {\r\n \"claude-haiku-4-5-20251001\": {\r\n \"input_tokens\": 2115,\r\n \"cache_creation_input_tokens\": 0,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 16\r\n },\r\n \"claude-fable-5-1\": {\r\n \"input_tokens\": 2,\r\n \"cache_creation_input_tokens\": 2391,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 625\r\n }\r\n },\r\n \"primary_response_usage\": {\r\n \"input_tokens\": 2,\r\n \"cache_creation_input_tokens\": 2391,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 625\r\n },\r\n \"runtime_context\": null,\r\n \"grade\": {\r\n \"passed\": true,\r\n \"ast_nodes\": 88,\r\n \"tests\": [\r\n {\r\n \"index\": 0,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 4,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 1,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 12,\r\n \"membership\": 2\r\n },\r\n {\r\n \"index\": 2,\r\n \"passed\": true,\r\n \"error\": null,\r\n 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\"cached_input_is_subset\": false,\r\n \"per_model_components\": {\r\n \"claude-fable-5-1\": {\r\n \"input_tokens\": 4,\r\n \"cache_creation_input_tokens\": 4589,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 824\r\n },\r\n \"claude-haiku-4-5-20251001\": {\r\n \"input_tokens\": 4092,\r\n \"cache_creation_input_tokens\": 0,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 28\r\n }\r\n }\r\n }\r\n }\r\n },\r\n \"call_cost\": {\r\n \"component_wall_seconds\": 13.541176599974278,\r\n \"provider_specific_usage\": {\r\n \"F\": {\r\n \"calls\": 1,\r\n \"components\": {\r\n \"output_tokens\": 641,\r\n \"cache_read_input_tokens\": 0,\r\n \"input_tokens\": 2117,\r\n \"cache_creation_input_tokens\": 2391\r\n },\r\n \"total\": 5149,\r\n \"cached_input_is_subset\": false,\r\n \"per_model_components\": {\r\n \"claude-fable-5-1\": {\r\n \"input_tokens\": 2,\r\n \"cache_creation_input_tokens\": 2391,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 625\r\n },\r\n \"claude-haiku-4-5-20251001\": {\r\n \"input_tokens\": 2115,\r\n \"cache_creation_input_tokens\": 0,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 16\r\n }\r\n }\r\n }\r\n }\r\n },\r\n \"freeze_sha256\": \"cd4f8a3e97e2ad6a8a1902b4ad45ebb58a5dbe9e03b0df37052f46fd5cbd147b\",\r\n \"prompt_sha256\": \"af67998422e2df8b8ac7876b15f40297e65c5da8fa6f88a8570dfd3eedb51b34\",\r\n \"resource_review_decision\": \"approved\",\r\n \"adopted\": false,\r\n \"origin_freeze_sha256\": \"cd4f8a3e97e2ad6a8a1902b4ad45ebb58a5dbe9e03b0df37052f46fd5cbd147b\",\r\n \"precursor_reused\": true\r\n}\r\n", "outputs/01_state/FF.json": "{\r\n \"code\": \"def latest_states(events):\\n best = {}\\n for key, sequence, event_id, state in events:\\n marker = (sequence, event_id)\\n if key not in best or marker > best[key][0]:\\n best[key] = (marker, state)\\n return [[key, best[key][1]] for key in sorted(best)]\\n\"\r\n}\r\n", "outputs/02_data/A.grade.json": "{\r\n \"index\": 8,\r\n \"case_id\": \"02_data\",\r\n \"arm\": \"A\",\r\n \"model\": \"A\",\r\n \"precursor\": null,\r\n \"call_status\": \"ok\",\r\n \"category\": \"response\",\r\n \"requested_model\": \"gpt-6-astra\",\r\n \"effort\": \"high\",\r\n \"provider_resolved_model\": null,\r\n \"auxiliary_models\": [],\r\n \"model_usage_summary\": null,\r\n \"primary_response_usage\": {\r\n \"input_tokens\": null,\r\n \"cache_creation_input_tokens\": null,\r\n \"cache_read_input_tokens\": null,\r\n \"output_tokens\": null\r\n },\r\n \"runtime_context\": {\r\n \"model\": \"gpt-6-astra\",\r\n \"effort\": \"high\"\r\n },\r\n \"grade\": {\r\n \"passed\": true,\r\n \"ast_nodes\": 101,\r\n \"tests\": [\r\n {\r\n \"index\": 0,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 10,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 1,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 8,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 2,\r\n \"passed\": true,\r\n \"error\": 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weight\\n result.append(product // denominator)\\n order.append((-(product % denominator), index))\\n order.sort()\\n remaining = total - sum(result)\\n for position in range(remaining):\\n result[order[position][1]] += 1\\n return result\\n\"\r\n}\r\n", "outputs/02_data/FF.grade.json": "{\r\n \"index\": 9,\r\n \"case_id\": \"02_data\",\r\n \"arm\": \"FF\",\r\n \"model\": \"F\",\r\n \"precursor\": \"F\",\r\n \"call_status\": \"ok\",\r\n \"category\": \"response\",\r\n \"requested_model\": \"claude-fable-5-1\",\r\n \"effort\": \"high\",\r\n \"provider_resolved_model\": \"claude-fable-5-1\",\r\n \"auxiliary_models\": [\r\n \"claude-haiku-4-5-20251001\"\r\n ],\r\n \"model_usage_summary\": {\r\n \"claude-haiku-4-5-20251001\": {\r\n \"input_tokens\": 2131,\r\n \"cache_creation_input_tokens\": 0,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 15\r\n },\r\n \"claude-fable-5-1\": {\r\n \"input_tokens\": 2,\r\n \"cache_creation_input_tokens\": 2415,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 561\r\n }\r\n },\r\n \"primary_response_usage\": {\r\n \"input_tokens\": 2,\r\n \"cache_creation_input_tokens\": 2415,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 561\r\n },\r\n \"runtime_context\": null,\r\n \"grade\": {\r\n \"passed\": true,\r\n \"ast_nodes\": 120,\r\n \"tests\": [\r\n {\r\n \"index\": 0,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 14,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 1,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 11,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 2,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 21,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 3,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 19,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 4,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": 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\"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 1140\r\n },\r\n \"claude-haiku-4-5-20251001\": {\r\n \"input_tokens\": 4118,\r\n \"cache_creation_input_tokens\": 0,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 30\r\n }\r\n }\r\n }\r\n }\r\n },\r\n \"call_cost\": {\r\n \"component_wall_seconds\": 13.130050699983258,\r\n \"provider_specific_usage\": {\r\n \"F\": {\r\n \"calls\": 1,\r\n \"components\": {\r\n \"output_tokens\": 576,\r\n \"cache_read_input_tokens\": 0,\r\n \"input_tokens\": 2133,\r\n \"cache_creation_input_tokens\": 2415\r\n },\r\n \"total\": 5124,\r\n \"cached_input_is_subset\": false,\r\n \"per_model_components\": {\r\n \"claude-fable-5-1\": {\r\n \"input_tokens\": 2,\r\n \"cache_creation_input_tokens\": 2415,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 561\r\n },\r\n \"claude-haiku-4-5-20251001\": {\r\n \"input_tokens\": 2131,\r\n \"cache_creation_input_tokens\": 0,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 15\r\n }\r\n }\r\n }\r\n }\r\n },\r\n \"freeze_sha256\": \"cd4f8a3e97e2ad6a8a1902b4ad45ebb58a5dbe9e03b0df37052f46fd5cbd147b\",\r\n \"prompt_sha256\": \"e724dbea9e2fa0acbfb896f529b449451a2218e4ae786ee133858bbb470de70e\",\r\n \"resource_review_decision\": \"approved\",\r\n \"adopted\": false,\r\n \"origin_freeze_sha256\": \"cd4f8a3e97e2ad6a8a1902b4ad45ebb58a5dbe9e03b0df37052f46fd5cbd147b\",\r\n \"precursor_reused\": true\r\n}\r\n", "outputs/02_data/FF.json": "{\r\n \"code\": \"def allocate_units(total, weights):\\n denominator = sum(weights)\\n result = [total * weight // denominator for weight in weights]\\n remainders = [total * weight % denominator for weight in weights]\\n order = sorted([(-remainders[index], index) for index in range(len(weights))])\\n remaining = total - sum(result)\\n for position in range(remaining):\\n result[order[position][1]] += 1\\n return result\\n\"\r\n}\r\n", "outputs/03_validation/A.grade.json": "{\r\n \"index\": 13,\r\n \"case_id\": \"03_validation\",\r\n \"arm\": \"A\",\r\n \"model\": \"A\",\r\n \"precursor\": null,\r\n \"call_status\": \"ok\",\r\n \"category\": \"response\",\r\n \"requested_model\": \"gpt-6-astra\",\r\n \"effort\": \"high\",\r\n \"provider_resolved_model\": null,\r\n \"auxiliary_models\": [],\r\n \"model_usage_summary\": null,\r\n \"primary_response_usage\": {\r\n \"input_tokens\": null,\r\n \"cache_creation_input_tokens\": null,\r\n \"cache_read_input_tokens\": null,\r\n \"output_tokens\": null\r\n },\r\n \"runtime_context\": {\r\n \"model\": \"gpt-6-astra\",\r\n \"effort\": \"high\"\r\n },\r\n \"grade\": {\r\n \"passed\": true,\r\n \"ast_nodes\": 211,\r\n \"tests\": [\r\n {\r\n \"index\": 0,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 7,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 1,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 33,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 2,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 21,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 3,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 34,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 4,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 50,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 5,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 21,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 6,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 1462,\r\n \"membership\": 0\r\n }\r\n ]\r\n },\r\n \"workflow_cost\": {\r\n \"component_wall_seconds\": 12.9992973000044,\r\n \"provider_specific_usage\": {\r\n \"A\": {\r\n \"calls\": 1,\r\n \"components\": {\r\n \"cached_input_tokens\": 0,\r\n \"input_tokens\": 8630,\r\n \"output_tokens\": 297\r\n },\r\n \"total\": 8927,\r\n \"cached_input_is_subset\": true\r\n }\r\n }\r\n },\r\n \"call_cost\": {\r\n \"component_wall_seconds\": 12.9992973000044,\r\n \"provider_specific_usage\": {\r\n \"A\": {\r\n \"calls\": 1,\r\n \"components\": {\r\n \"cached_input_tokens\": 0,\r\n \"input_tokens\": 8630,\r\n \"output_tokens\": 297\r\n },\r\n \"total\": 8927,\r\n \"cached_input_is_subset\": true\r\n }\r\n }\r\n },\r\n \"freeze_sha256\": \"cd4f8a3e97e2ad6a8a1902b4ad45ebb58a5dbe9e03b0df37052f46fd5cbd147b\",\r\n \"prompt_sha256\": \"fa527c756e16491d61445c9b2ec97907ccb46d42c3c4874fdc283f702f760d7e\",\r\n \"resource_review_decision\": \"approved\",\r\n \"adopted\": false,\r\n \"origin_freeze_sha256\": \"cd4f8a3e97e2ad6a8a1902b4ad45ebb58a5dbe9e03b0df37052f46fd5cbd147b\",\r\n \"precursor_reused\": false\r\n}\r\n", "outputs/03_validation/A.json": "{\r\n \"code\": \"def validate_windows(windows):\\n invalid = []\\n valid = []\\n for index, item in enumerate(windows):\\n if not isinstance(item, list) or len(item) != 2 or type(item[0]) is not int or type(item[1]) is not int or not 0 <= item[0] < item[1] <= 1440:\\n invalid.append(index)\\n else:\\n valid.append(index)\\n overlaps = []\\n for offset, i in enumerate(valid):\\n for position in range(offset + 1, len(valid)):\\n j = valid[position]\\n if max(windows[i][0], windows[j][0]) < min(windows[i][1], windows[j][1]):\\n overlaps.append([i, j])\\n return {'invalid': invalid, 'overlaps': overlaps}\\n\"\r\n}\r\n", "outputs/03_validation/AA.grade.json": "{\r\n \"index\": 16,\r\n \"case_id\": \"03_validation\",\r\n \"arm\": \"AA\",\r\n \"model\": \"A\",\r\n \"precursor\": \"A\",\r\n \"call_status\": \"ok\",\r\n \"category\": \"response\",\r\n \"requested_model\": \"gpt-6-astra\",\r\n \"effort\": \"high\",\r\n \"provider_resolved_model\": null,\r\n \"auxiliary_models\": [],\r\n \"model_usage_summary\": null,\r\n \"primary_response_usage\": {\r\n \"input_tokens\": null,\r\n \"cache_creation_input_tokens\": null,\r\n \"cache_read_input_tokens\": null,\r\n \"output_tokens\": null\r\n },\r\n \"runtime_context\": {\r\n \"model\": \"gpt-6-astra\",\r\n \"effort\": \"high\"\r\n },\r\n \"grade\": {\r\n \"passed\": true,\r\n \"ast_nodes\": 209,\r\n \"tests\": [\r\n {\r\n \"index\": 0,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 7,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 1,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 33,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 2,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 21,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 3,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 34,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 4,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 50,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 5,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 21,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 6,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 1462,\r\n \"membership\": 0\r\n }\r\n ]\r\n },\r\n \"workflow_cost\": {\r\n \"component_wall_seconds\": 26.206996600027196,\r\n \"provider_specific_usage\": {\r\n \"A\": {\r\n \"calls\": 2,\r\n \"components\": {\r\n \"cached_input_tokens\": 0,\r\n \"input_tokens\": 17487,\r\n \"output_tokens\": 581\r\n },\r\n \"total\": 18068,\r\n \"cached_input_is_subset\": true\r\n }\r\n }\r\n },\r\n \"call_cost\": {\r\n \"component_wall_seconds\": 13.207699300022796,\r\n \"provider_specific_usage\": {\r\n \"A\": {\r\n \"calls\": 1,\r\n \"components\": {\r\n \"cached_input_tokens\": 0,\r\n \"input_tokens\": 8857,\r\n \"output_tokens\": 284\r\n },\r\n \"total\": 9141,\r\n \"cached_input_is_subset\": true\r\n }\r\n }\r\n },\r\n \"freeze_sha256\": \"cd4f8a3e97e2ad6a8a1902b4ad45ebb58a5dbe9e03b0df37052f46fd5cbd147b\",\r\n \"prompt_sha256\": \"fd0ee7ba36ac1309cc3ab1978157f42106e07da17ac4f91a3be1904f05ca4e11\",\r\n \"resource_review_decision\": \"approved\",\r\n \"adopted\": false,\r\n \"origin_freeze_sha256\": \"cd4f8a3e97e2ad6a8a1902b4ad45ebb58a5dbe9e03b0df37052f46fd5cbd147b\",\r\n \"precursor_reused\": true\r\n}\r\n", "outputs/03_validation/AA.json": "{\r\n \"code\": \"def validate_windows(windows):\\n invalid = []\\n valid = []\\n for index, item in enumerate(windows):\\n if not isinstance(item, list) or len(item) != 2 or type(item[0]) is not int or type(item[1]) is not int or not 0 <= item[0] < item[1] <= 1440:\\n invalid.append(index)\\n else:\\n valid.append(index)\\n overlaps = []\\n for offset, i in enumerate(valid):\\n for position in range(offset + 1, len(valid)):\\n j = valid[position]\\n if windows[i][0] < windows[j][1] and windows[j][0] < windows[i][1]:\\n overlaps.append([i, j])\\n return {'invalid': invalid, 'overlaps': overlaps}\\n\"\r\n}\r\n", "outputs/03_validation/AF.grade.json": "{\r\n \"index\": 15,\r\n \"case_id\": \"03_validation\",\r\n \"arm\": \"AF\",\r\n \"model\": \"F\",\r\n \"precursor\": \"A\",\r\n \"call_status\": \"ok\",\r\n \"category\": \"response\",\r\n \"requested_model\": \"claude-fable-5-1\",\r\n \"effort\": \"high\",\r\n \"provider_resolved_model\": \"claude-fable-5-1\",\r\n \"auxiliary_models\": [\r\n \"claude-haiku-4-5-20251001\"\r\n ],\r\n \"model_usage_summary\": {\r\n \"claude-haiku-4-5-20251001\": {\r\n \"input_tokens\": 2405,\r\n \"cache_creation_input_tokens\": 0,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 13\r\n },\r\n \"claude-fable-5-1\": {\r\n \"input_tokens\": 2,\r\n \"cache_creation_input_tokens\": 2784,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 978\r\n }\r\n },\r\n \"primary_response_usage\": {\r\n \"input_tokens\": 2,\r\n \"cache_creation_input_tokens\": 2784,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 978\r\n },\r\n \"runtime_context\": null,\r\n \"grade\": {\r\n \"passed\": true,\r\n \"ast_nodes\": 289,\r\n \"tests\": [\r\n {\r\n \"index\": 0,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 8,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 1,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 76,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 2,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 53,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 3,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 85,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 4,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 115,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 5,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 46,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 6,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 3503,\r\n \"membership\": 0\r\n }\r\n ]\r\n },\r\n \"workflow_cost\": {\r\n \"component_wall_seconds\": 29.254707600048278,\r\n \"provider_specific_usage\": {\r\n \"A\": {\r\n \"calls\": 1,\r\n \"components\": {\r\n \"cached_input_tokens\": 0,\r\n \"input_tokens\": 8630,\r\n \"output_tokens\": 297\r\n },\r\n \"total\": 8927,\r\n \"cached_input_is_subset\": true\r\n },\r\n \"F\": {\r\n \"calls\": 1,\r\n \"components\": {\r\n \"output_tokens\": 991,\r\n \"cache_read_input_tokens\": 0,\r\n \"input_tokens\": 2407,\r\n \"cache_creation_input_tokens\": 2784\r\n },\r\n \"total\": 6182,\r\n \"cached_input_is_subset\": false,\r\n \"per_model_components\": {\r\n \"claude-fable-5-1\": {\r\n \"input_tokens\": 2,\r\n \"cache_creation_input_tokens\": 2784,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 978\r\n },\r\n \"claude-haiku-4-5-20251001\": {\r\n \"input_tokens\": 2405,\r\n \"cache_creation_input_tokens\": 0,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 13\r\n }\r\n }\r\n }\r\n }\r\n },\r\n \"call_cost\": {\r\n \"component_wall_seconds\": 16.255410300043877,\r\n \"provider_specific_usage\": {\r\n \"F\": {\r\n \"calls\": 1,\r\n \"components\": {\r\n \"output_tokens\": 991,\r\n \"cache_read_input_tokens\": 0,\r\n \"input_tokens\": 2407,\r\n \"cache_creation_input_tokens\": 2784\r\n },\r\n \"total\": 6182,\r\n \"cached_input_is_subset\": false,\r\n \"per_model_components\": {\r\n \"claude-fable-5-1\": {\r\n \"input_tokens\": 2,\r\n \"cache_creation_input_tokens\": 2784,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 978\r\n },\r\n \"claude-haiku-4-5-20251001\": {\r\n \"input_tokens\": 2405,\r\n \"cache_creation_input_tokens\": 0,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 13\r\n }\r\n }\r\n }\r\n }\r\n },\r\n \"freeze_sha256\": \"cd4f8a3e97e2ad6a8a1902b4ad45ebb58a5dbe9e03b0df37052f46fd5cbd147b\",\r\n \"prompt_sha256\": \"fd0ee7ba36ac1309cc3ab1978157f42106e07da17ac4f91a3be1904f05ca4e11\",\r\n 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else j_start\\n high = i_end if i_end < j_end else j_end\\n if low < high:\\n overlaps.append([i, j])\\n return {'invalid': invalid, 'overlaps': overlaps}\\n\"\r\n}\r\n", "outputs/03_validation/F.grade.json": "{\r\n \"index\": 14,\r\n \"case_id\": \"03_validation\",\r\n \"arm\": \"F\",\r\n \"model\": \"F\",\r\n \"precursor\": null,\r\n \"call_status\": \"ok\",\r\n \"category\": \"response\",\r\n \"requested_model\": \"claude-fable-5-1\",\r\n \"effort\": \"high\",\r\n \"provider_resolved_model\": \"claude-fable-5-1\",\r\n \"auxiliary_models\": [\r\n \"claude-haiku-4-5-20251001\"\r\n ],\r\n \"model_usage_summary\": {\r\n \"claude-haiku-4-5-20251001\": {\r\n \"input_tokens\": 2147,\r\n \"cache_creation_input_tokens\": 0,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 13\r\n },\r\n \"claude-fable-5-1\": {\r\n \"input_tokens\": 2,\r\n \"cache_creation_input_tokens\": 2439,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 631\r\n }\r\n },\r\n \"primary_response_usage\": 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'overlaps': overlaps}\\n\"\r\n}\r\n", "outputs/03_validation/FA.grade.json": "{\r\n \"index\": 17,\r\n \"case_id\": \"03_validation\",\r\n \"arm\": \"FA\",\r\n \"model\": \"A\",\r\n \"precursor\": \"F\",\r\n \"call_status\": \"ok\",\r\n \"category\": \"response\",\r\n \"requested_model\": \"gpt-6-astra\",\r\n \"effort\": \"high\",\r\n \"provider_resolved_model\": null,\r\n \"auxiliary_models\": [],\r\n \"model_usage_summary\": null,\r\n \"primary_response_usage\": {\r\n \"input_tokens\": null,\r\n \"cache_creation_input_tokens\": null,\r\n \"cache_read_input_tokens\": null,\r\n \"output_tokens\": null\r\n },\r\n \"runtime_context\": {\r\n \"model\": \"gpt-6-astra\",\r\n \"effort\": \"high\"\r\n },\r\n \"grade\": {\r\n \"passed\": true,\r\n \"ast_nodes\": 217,\r\n \"tests\": [\r\n {\r\n \"index\": 0,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 7,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 1,\r\n \"passed\": true,\r\n \"error\": 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0,\r\n \"input_tokens\": 8935,\r\n \"output_tokens\": 297\r\n },\r\n \"total\": 9232,\r\n \"cached_input_is_subset\": true\r\n }\r\n }\r\n },\r\n \"freeze_sha256\": \"cd4f8a3e97e2ad6a8a1902b4ad45ebb58a5dbe9e03b0df37052f46fd5cbd147b\",\r\n \"prompt_sha256\": \"abd30a4601fb2c592729521445fe34902c260be567aae59129357344dced70e2\",\r\n \"resource_review_decision\": \"approved\",\r\n \"adopted\": false,\r\n \"origin_freeze_sha256\": \"cd4f8a3e97e2ad6a8a1902b4ad45ebb58a5dbe9e03b0df37052f46fd5cbd147b\",\r\n \"precursor_reused\": true\r\n}\r\n", "outputs/03_validation/FA.json": "{\r\n \"code\": \"def validate_windows(windows):\\n invalid = []\\n valid = []\\n for index, item in enumerate(windows):\\n if (not isinstance(item, list) or len(item) != 2\\n or type(item[0]) is not int or type(item[1]) is not int\\n or not 0 <= item[0] < item[1] <= 1440):\\n invalid.append(index)\\n else:\\n valid.append(index)\\n overlaps = []\\n for a in range(len(valid)):\\n i = valid[a]\\n for b in range(a + 1, len(valid)):\\n j = valid[b]\\n if windows[i][0] < windows[j][1] and windows[j][0] < windows[i][1]:\\n overlaps.append([i, j])\\n return {'invalid': invalid, 'overlaps': overlaps}\\n\"\r\n}\r\n", "outputs/03_validation/FF.grade.json": "{\r\n \"index\": 18,\r\n \"case_id\": \"03_validation\",\r\n \"arm\": \"FF\",\r\n \"model\": \"F\",\r\n \"precursor\": \"F\",\r\n \"call_status\": \"ok\",\r\n \"category\": \"response\",\r\n \"requested_model\": \"claude-fable-5-1\",\r\n \"effort\": \"high\",\r\n \"provider_resolved_model\": \"claude-fable-5-1\",\r\n \"auxiliary_models\": [\r\n \"claude-haiku-4-5-20251001\"\r\n ],\r\n \"model_usage_summary\": {\r\n \"claude-haiku-4-5-20251001\": {\r\n \"input_tokens\": 2490,\r\n \"cache_creation_input_tokens\": 0,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 13\r\n },\r\n \"claude-fable-5-1\": {\r\n \"input_tokens\": 2,\r\n \"cache_creation_input_tokens\": 2885,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 881\r\n }\r\n },\r\n 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},\r\n {\r\n \"index\": 5,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 48,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 6,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 2693,\r\n \"membership\": 0\r\n }\r\n ]\r\n },\r\n \"workflow_cost\": {\r\n \"component_wall_seconds\": 21.37014770001406,\r\n \"provider_specific_usage\": {\r\n \"F\": {\r\n \"calls\": 2,\r\n \"components\": {\r\n \"cache_creation_input_tokens\": 5324,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 1538,\r\n \"input_tokens\": 4641\r\n },\r\n \"total\": 11503,\r\n \"cached_input_is_subset\": false,\r\n \"per_model_components\": {\r\n \"claude-fable-5-1\": {\r\n \"input_tokens\": 4,\r\n \"cache_creation_input_tokens\": 5324,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 1512\r\n },\r\n \"claude-haiku-4-5-20251001\": {\r\n \"input_tokens\": 4637,\r\n \"cache_creation_input_tokens\": 0,\r\n 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windows[j][1])\\n if lo < hi:\\n overlaps.append([i, j])\\n return {'invalid': invalid, 'overlaps': overlaps}\\n\"\r\n}\r\n", "outputs/04_performance/A.grade.json": "{\r\n \"index\": 20,\r\n \"case_id\": \"04_performance\",\r\n \"arm\": \"A\",\r\n \"model\": \"A\",\r\n \"precursor\": null,\r\n \"call_status\": \"ok\",\r\n \"category\": \"response\",\r\n \"requested_model\": \"gpt-6-astra\",\r\n \"effort\": \"high\",\r\n \"provider_resolved_model\": null,\r\n \"auxiliary_models\": [],\r\n \"model_usage_summary\": null,\r\n \"primary_response_usage\": {\r\n \"input_tokens\": null,\r\n \"cache_creation_input_tokens\": null,\r\n \"cache_read_input_tokens\": null,\r\n \"output_tokens\": null\r\n },\r\n \"runtime_context\": {\r\n \"model\": \"gpt-6-astra\",\r\n \"effort\": \"high\"\r\n },\r\n \"grade\": {\r\n \"passed\": true,\r\n \"ast_nodes\": 63,\r\n \"tests\": [\r\n {\r\n \"index\": 0,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 6,\r\n 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\"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 6004,\r\n \"membership\": 0\r\n }\r\n ]\r\n },\r\n \"workflow_cost\": {\r\n \"component_wall_seconds\": 12.080823399999645,\r\n \"provider_specific_usage\": {\r\n \"F\": {\r\n \"calls\": 1,\r\n \"components\": {\r\n \"output_tokens\": 925,\r\n \"cache_read_input_tokens\": 0,\r\n \"input_tokens\": 2129,\r\n \"cache_creation_input_tokens\": 2363\r\n },\r\n \"total\": 5417,\r\n \"cached_input_is_subset\": false,\r\n \"per_model_components\": {\r\n \"claude-fable-5-1\": {\r\n \"input_tokens\": 2,\r\n \"cache_creation_input_tokens\": 2363,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 912\r\n },\r\n \"claude-haiku-4-5-20251001\": {\r\n \"input_tokens\": 2127,\r\n \"cache_creation_input_tokens\": 0,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 13\r\n }\r\n }\r\n }\r\n }\r\n },\r\n \"call_cost\": {\r\n \"component_wall_seconds\": 12.080823399999645,\r\n \"provider_specific_usage\": {\r\n \"F\": {\r\n \"calls\": 1,\r\n \"components\": {\r\n \"output_tokens\": 925,\r\n \"cache_read_input_tokens\": 0,\r\n \"input_tokens\": 2129,\r\n \"cache_creation_input_tokens\": 2363\r\n },\r\n \"total\": 5417,\r\n \"cached_input_is_subset\": false,\r\n \"per_model_components\": {\r\n \"claude-fable-5-1\": {\r\n \"input_tokens\": 2,\r\n \"cache_creation_input_tokens\": 2363,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 912\r\n },\r\n \"claude-haiku-4-5-20251001\": {\r\n \"input_tokens\": 2127,\r\n \"cache_creation_input_tokens\": 0,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 13\r\n }\r\n }\r\n }\r\n }\r\n },\r\n \"freeze_sha256\": \"cd4f8a3e97e2ad6a8a1902b4ad45ebb58a5dbe9e03b0df37052f46fd5cbd147b\",\r\n \"prompt_sha256\": \"8fab153837bae20cdbfe1e2b60bf70fc14b6236fe143fd64d678c17ee06e5518\",\r\n \"resource_review_decision\": \"approved\",\r\n \"adopted\": false,\r\n \"origin_freeze_sha256\": \"cd4f8a3e97e2ad6a8a1902b4ad45ebb58a5dbe9e03b0df37052f46fd5cbd147b\",\r\n \"precursor_reused\": false\r\n}\r\n", "outputs/05_maintainability/F.json": "{\r\n \"code\": \"def compress_runs(values):\\n result = []\\n for item in values:\\n if result and result[-1][0] == item:\\n result[-1][1] += 1\\n else:\\n result.append([item, 1])\\n return result\\n\"\r\n}\r\n", "outputs/05_maintainability/FA.grade.json": "{\r\n \"index\": 30,\r\n \"case_id\": \"05_maintainability\",\r\n \"arm\": \"FA\",\r\n \"model\": \"A\",\r\n \"precursor\": \"F\",\r\n \"call_status\": \"ok\",\r\n \"category\": \"response\",\r\n \"requested_model\": \"gpt-6-astra\",\r\n \"effort\": \"high\",\r\n \"provider_resolved_model\": null,\r\n \"auxiliary_models\": [],\r\n \"model_usage_summary\": null,\r\n \"primary_response_usage\": {\r\n \"input_tokens\": null,\r\n \"cache_creation_input_tokens\": null,\r\n \"cache_read_input_tokens\": null,\r\n \"output_tokens\": null\r\n },\r\n \"runtime_context\": {\r\n \"model\": \"gpt-6-astra\",\r\n \"effort\": \"high\"\r\n },\r\n \"grade\": {\r\n \"passed\": true,\r\n \"ast_nodes\": 60,\r\n \"tests\": [\r\n {\r\n \"index\": 0,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 4,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 1,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 7,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 2,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 10,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 3,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 10,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 4,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 16,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 5,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 16,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 6,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 6004,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 7,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 6004,\r\n \"membership\": 0\r\n }\r\n ]\r\n },\r\n \"workflow_cost\": {\r\n \"component_wall_seconds\": 20.605264699959662,\r\n \"provider_specific_usage\": {\r\n \"A\": {\r\n \"calls\": 1,\r\n \"components\": {\r\n \"cached_input_tokens\": 0,\r\n \"input_tokens\": 8710,\r\n \"output_tokens\": 143\r\n },\r\n \"total\": 8853,\r\n \"cached_input_is_subset\": true\r\n },\r\n \"F\": {\r\n \"calls\": 1,\r\n \"components\": {\r\n \"output_tokens\": 925,\r\n \"cache_read_input_tokens\": 0,\r\n \"input_tokens\": 2129,\r\n \"cache_creation_input_tokens\": 2363\r\n },\r\n \"total\": 5417,\r\n \"cached_input_is_subset\": false,\r\n \"per_model_components\": {\r\n \"claude-fable-5-1\": {\r\n \"input_tokens\": 2,\r\n \"cache_creation_input_tokens\": 2363,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 912\r\n },\r\n \"claude-haiku-4-5-20251001\": {\r\n \"input_tokens\": 2127,\r\n \"cache_creation_input_tokens\": 0,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 13\r\n }\r\n }\r\n }\r\n }\r\n },\r\n \"call_cost\": {\r\n \"component_wall_seconds\": 8.524441299960017,\r\n \"provider_specific_usage\": {\r\n \"A\": {\r\n \"calls\": 1,\r\n \"components\": {\r\n \"cached_input_tokens\": 0,\r\n \"input_tokens\": 8710,\r\n \"output_tokens\": 143\r\n },\r\n \"total\": 8853,\r\n \"cached_input_is_subset\": true\r\n }\r\n }\r\n },\r\n \"freeze_sha256\": \"cd4f8a3e97e2ad6a8a1902b4ad45ebb58a5dbe9e03b0df37052f46fd5cbd147b\",\r\n \"prompt_sha256\": \"3ecc865387b86cd1b8f94b9aeaa5a3d2f8a5ae2d14fca9dc4721007fcde1ec0e\",\r\n \"resource_review_decision\": \"approved\",\r\n \"adopted\": false,\r\n \"origin_freeze_sha256\": \"cd4f8a3e97e2ad6a8a1902b4ad45ebb58a5dbe9e03b0df37052f46fd5cbd147b\",\r\n \"precursor_reused\": true\r\n}\r\n", "outputs/05_maintainability/FA.json": "{\r\n \"code\": \"def compress_runs(values):\\n result = []\\n for item in values:\\n if result and result[-1][0] == item:\\n result[-1][1] += 1\\n else:\\n result.append([item, 1])\\n return result\\n\"\r\n}\r\n", "outputs/05_maintainability/FF.grade.json": "{\r\n \"index\": 28,\r\n \"case_id\": \"05_maintainability\",\r\n \"arm\": \"FF\",\r\n \"model\": \"F\",\r\n \"precursor\": \"F\",\r\n \"call_status\": \"ok\",\r\n \"category\": \"response\",\r\n \"requested_model\": \"claude-fable-5-1\",\r\n \"effort\": \"high\",\r\n \"provider_resolved_model\": \"claude-fable-5-1\",\r\n \"auxiliary_models\": [\r\n \"claude-haiku-4-5-20251001\"\r\n ],\r\n \"model_usage_summary\": {\r\n \"claude-haiku-4-5-20251001\": {\r\n \"input_tokens\": 2237,\r\n \"cache_creation_input_tokens\": 0,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 12\r\n },\r\n \"claude-fable-5-1\": {\r\n \"input_tokens\": 2,\r\n \"cache_creation_input_tokens\": 2513,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 951\r\n }\r\n },\r\n \"primary_response_usage\": {\r\n \"input_tokens\": 2,\r\n \"cache_creation_input_tokens\": 2513,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 951\r\n },\r\n \"runtime_context\": null,\r\n \"grade\": {\r\n \"passed\": true,\r\n \"ast_nodes\": 60,\r\n \"tests\": [\r\n {\r\n \"index\": 0,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 4,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 1,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 7,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 2,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 10,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 3,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 10,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 4,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 16,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 5,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 16,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 6,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 6004,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 7,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 6004,\r\n \"membership\": 0\r\n }\r\n ]\r\n },\r\n \"workflow_cost\": {\r\n \"component_wall_seconds\": 25.182654100004584,\r\n \"provider_specific_usage\": {\r\n \"F\": {\r\n \"calls\": 2,\r\n \"components\": {\r\n \"cache_creation_input_tokens\": 4876,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 1888,\r\n \"input_tokens\": 4368\r\n },\r\n \"total\": 11132,\r\n \"cached_input_is_subset\": false,\r\n \"per_model_components\": {\r\n \"claude-fable-5-1\": {\r\n \"input_tokens\": 4,\r\n \"cache_creation_input_tokens\": 4876,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 1863\r\n },\r\n \"claude-haiku-4-5-20251001\": {\r\n \"input_tokens\": 4364,\r\n \"cache_creation_input_tokens\": 0,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 25\r\n }\r\n }\r\n }\r\n }\r\n },\r\n \"call_cost\": {\r\n \"component_wall_seconds\": 13.101830700004939,\r\n \"provider_specific_usage\": {\r\n \"F\": {\r\n \"calls\": 1,\r\n \"components\": {\r\n \"output_tokens\": 963,\r\n \"cache_read_input_tokens\": 0,\r\n \"input_tokens\": 2239,\r\n \"cache_creation_input_tokens\": 2513\r\n },\r\n \"total\": 5715,\r\n \"cached_input_is_subset\": false,\r\n \"per_model_components\": {\r\n \"claude-fable-5-1\": {\r\n \"input_tokens\": 2,\r\n \"cache_creation_input_tokens\": 2513,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 951\r\n },\r\n \"claude-haiku-4-5-20251001\": {\r\n \"input_tokens\": 2237,\r\n \"cache_creation_input_tokens\": 0,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 12\r\n }\r\n }\r\n }\r\n }\r\n },\r\n \"freeze_sha256\": \"cd4f8a3e97e2ad6a8a1902b4ad45ebb58a5dbe9e03b0df37052f46fd5cbd147b\",\r\n \"prompt_sha256\": \"3ecc865387b86cd1b8f94b9aeaa5a3d2f8a5ae2d14fca9dc4721007fcde1ec0e\",\r\n \"resource_review_decision\": \"approved\",\r\n \"adopted\": false,\r\n \"origin_freeze_sha256\": \"cd4f8a3e97e2ad6a8a1902b4ad45ebb58a5dbe9e03b0df37052f46fd5cbd147b\",\r\n \"precursor_reused\": true\r\n}\r\n", "outputs/05_maintainability/FF.json": "{\r\n \"code\": \"def compress_runs(values):\\n result = []\\n for item in values:\\n if result and result[-1][0] == item:\\n result[-1][1] += 1\\n else:\\n result.append([item, 1])\\n return result\\n\"\r\n}\r\n", "Protocol.md": "# Nymrel Astra / Fable pilot\r\n\r\nQuestion: on five fixed, sanitized Python repair exercises, does cross-model refinement improve complete contract success over either one pass or same-model refinement? This is a small offline code-repair pilot, not a test of whole agent workflows or general model rankings.\r\n\r\nUse the case manifest and participant boundary in cases/README.md. Six outputs per case: A, F, AA, FF, AF, FA. Each first pass is reused by both corresponding refinements; outputs are correlated, not independent samples. Every call starts a fresh context. Refiners receive the original packet and the unlabeled precursor response, without hidden-test feedback. All outputs are collected before grading. No retries, dropped cases, or result-driven task replacement. If infrastructure prevents two calls, stop and report the incomplete pilot. Code-envelope or correctness failures count as failures, not infrastructure failures.\r\n\r\nAlternate first-pass order by case; shuffle four refinement arms using one Python Random(20260904) instance across all five cases. Freeze the explicit order, prompt recipe, case files, model harness and runner hashes before the first scored call. Five cases require 30 calls. Maximum 180 seconds per call, 50,000 prompt characters, 16,000 characters in the returned code string. A timeout is a failed scored call and counts toward the two-infrastructure-failure stop; its descendants still receive the original task and literal no-response marker if no answer exists. Invalid available answers propagate verbatim, never repaired. The adapter extracts Codex last-message JSON and Claude structured_output (fallback: strict JSON parse of result); exactly one code key is required. Refinement prompts differ only in task and precursor. Self-refiners may recognize their own writing style despite unlabeled answers. Both native routes request high effort; providers do not define equivalent effort or expose a verified common output-token cap. Do not claim equal compute.\r\n\r\nNative subscription authentication only; no API-key billing. Calls are sequential. System/task instructions match, but CLI scaffolding differs: Astra smoke input was 8,041 tokens and Fable input including cache creation was 786. This overhead is part of the measured native surface, not intrinsic model efficiency. Codex runtime context verifies gpt-6-astra/high; provider-resolved model is unavailable. Fable modelUsage reports claude-fable-5-1. Tool use is forbidden. Claude tools are disabled; Codex disables shell, unified exec, plugins and multi-agent and rejects observed tool events, but identical tool availability is not established.\r\n\r\nRuntime disclosure before freeze: the method review exposed a Claude Haiku 4.5 background call alongside Fable 5.1. F therefore denotes the native Fable workflow, potentially including this auxiliary model (auxiliary effort unknown), not pure isolated Fable. Accept only Fable5.1 with optional claude-haiku-4-5-20251001; unexpected model identities fail the protocol. Preserve actual model sets per call. Do not remap background models just to hide native overhead. Primary foreground requests remain high. Reference: https://code.claude.com/docs/en/env-vars (ANTHROPIC_DEFAULT_HAIKU_MODEL background functionality).\r\n\r\nPrimary outcome: full contract passes out of five per arm, plus per-case results and regressions from successful first passes. Compare AF with AA and FA with FF. Report summed component wall latency for each one/two-call workflow and total experiment latency separately. Sum Codex input/output with cached input identified as a subset; for Claude sum input, cache creation, cache reads and output across ALL modelUsage entries, retaining its separate top-level primary-response usage. Token totals are provider-specific accounting, not interchangeable work units. Report missing usage as unknown. Do not equate Claude estimated API cost with subscription cash spend or infer monetary savings.\r\n\r\nNo significance, production performance, universal maintainability, global superiority or routing-promotion claim. Membership operations and AST limits are declared proxies. Tasks were authored by an Astra-side agent and reviewed by Fable before execution; modest difficulty and author selection limit external validity. Report ties, ceiling effects, negative outcomes and all failures. Setup smoke/review overhead is separate from scored calls. Publish sanitized tasks, outputs, grader and results; retain account identifiers, native session logs and raw stderr privately.\r\n\r\nExecution review must approve the method before dispatch. After collecting all outputs, a human-readable resource review covers every exact candidate hash, including function annotations and definition-time expressions, before any grader execution. Reject unsafe resource use as a resource failure; do not edit it. The restricted evaluator does not provide an OS memory or security sandbox. Review covers only safe execution, not correctness coaching or hidden-test feedback. Any later necessary correction gets a separately labeled version and invalidates affected comparisons; never silently repair scored evidence.\r\n", "RESULTS.md": "# Nymrel: Astra / Fable native workflow pilot\r\n\r\nAll six workflows passed all five tasks. This pilot hit a ceiling: cross-model refinement showed no quality gain over either one pass or same-model refinement. Two-pass workflows took more component time. The result does not establish how pairing performs on harder studio work.\r\n\r\nFive small, sanitized Python repair tasks; 37 held-out examples across the five tasks per workflow (222 executed example checks across all six workflows); 30 native CLI calls. This tests bounded offline repairs, not production development or isolated base-model ability.\r\n\r\n| Workflow | Complete passes | Total component time, five tasks |\r\n|---|---:|---:|\r\n| Astra | 5/5 | 49.64 s |\r\n| Fable native workflow | 5/5 | 43.34 s |\r\n| Astra → Astra | 5/5 | 107.52 s |\r\n| Fable → Fable | 5/5 | 103.35 s |\r\n| Astra → Fable | 5/5 | 110.82 s |\r\n| Fable → Astra | 5/5 | 106.55 s |\r\n\r\nTwo-pass times include both calls. Initial answers feed two refinements, so the outcomes are correlated. These totals represent each workflow separately; adding all six rows would double-count reused first passes.\r\n\r\n| Task | A | F | AA | FF | AF | FA |\r\n|---|---|---|---|---|---|---|\r\n| 01_state | PASS | PASS | PASS | PASS | PASS | PASS |\r\n| 02_data | PASS | PASS | PASS | PASS | PASS | PASS |\r\n| 03_validation | PASS | PASS | PASS | PASS | PASS | PASS |\r\n| 04_performance | PASS | PASS | PASS | PASS | PASS | PASS |\r\n| 05_maintainability | PASS | PASS | PASS | PASS | PASS | PASS |\r\n\r\n## Experiment overhead\r\n\r\nThe 30 scored calls totaled 335.3 seconds. Claim-to-collection wall time was 33.7 minutes, including 28.1 minutes outside scored calls. That includes setup, method reviews, fixes and orchestration, and excludes earlier startup and later reporting/publication. This was not an end-to-end efficiency win on five small tasks. The setup is reusable, but future savings remain unmeasured. See setup-overhead.json.\r\n\r\n## Scope and limits\r\n\r\n- Same tasks, fresh contexts, unlabeled refinements, hidden checks, no grader feedback or performance-based retries.\r\n- Same-model second passes control for additional work. Five cases provide descriptive results, not statistical significance.\r\n- Fable native calls can include Haiku background work; all reported model usage is included. Astra identity is verified in runtime context, with provider-resolved identity unavailable.\r\n- Both primary routes request high effort. CLI context overhead and provider token definitions differ; compute is not equalized.\r\n- Membership and AST budgets are narrow proxies. Neither establishes production speed or universal code quality.\r\n- Setup, protocol reviews, and safety review are additional overhead. Main-session and subagent tokens are not fully attributable; scored-call timing is not end-to-end studio savings. Subscription use does not establish cash cost.\r\n- The original first-call receipt check rejected Windows CRLF serialization. A documented amendment normalizes text validation, preserves the original evidence, and reuses the call once. Tasks, order, answers and grader are unchanged.\r\n\r\n## Offline reproduction\r\n\r\nThe evidence bundle includes every task, held-out check, oracle, emitted candidate and grade. On the public gist, download artifact-data.json and extract_evidence.py into the same directory, then run `python extract_evidence.py` to extract and verify the files in a new nymrel-pilot-evidence folder. Change into that folder. After reviewing candidate safety, rerun a grade with:\r\n\r\n```text\r\npython cases/evaluate.py cases/01_state outputs/01_state/A.json\r\n```\r\n\r\nThe evaluator uses restricted Python and time limits, not an OS sandbox for hostile code. Only run these inspected cooperative submissions. Published cases become contaminated for future model evaluations.\r\n\r\nSee summary.json for provider-specific usage, per-call metadata, failures, and component durations. Protocol.md records the native-call method. The artifact manifest verifies the exported files.\r\n", "setup-overhead.json": "{\r\n \"native_cli_versions\": {\r\n \"Codex\": \"0.153.3\",\r\n \"Claude Code\": \"2.1.258\"\r\n },\r\n \"claim_started_at_utc\": \"2026-09-04T22:22:42Z\",\r\n \"main_session_and_subagent_tokens\": \"unavailable; not included in scored token totals\",\r\n \"cash_cost\": \"not measured; native subscription authentication used\",\r\n \"smokes\": [\r\n {\r\n \"model_condition\": \"A\",\r\n \"status\": \"ok\",\r\n \"elapsed_seconds\": 4.904122099978849,\r\n \"usage\": {\r\n \"schema_version\": \"codex_exec_usage.v1\",\r\n \"source\": \"codex_exec_jsonl.turn.completed\",\r\n \"accounting_complete\": true,\r\n \"input_tokens\": 8041,\r\n \"cached_input_tokens\": 0,\r\n \"output_tokens\": 24,\r\n \"reasoning_output_tokens\": 0,\r\n \"total_tokens\": 8065,\r\n \"incomplete_reason\": null\r\n },\r\n \"model_usage\": null\r\n },\r\n {\r\n \"model_condition\": \"F\",\r\n \"status\": \"ok\",\r\n \"elapsed_seconds\": 4.685582000005525,\r\n \"usage\": {\r\n \"input_tokens\": 2,\r\n \"cache_creation_input_tokens\": 784,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 60,\r\n \"output_tokens_details\": {\r\n \"thinking_tokens\": 0\r\n },\r\n \"server_tool_use\": {\r\n \"web_search_requests\": 0,\r\n \"web_fetch_requests\": 0\r\n },\r\n \"service_tier\": \"standard\",\r\n \"cache_creation\": {\r\n \"ephemeral_1h_input_tokens\": 784,\r\n \"ephemeral_5m_input_tokens\": 0\r\n },\r\n \"inference_geo\": \"not_available\",\r\n \"iterations\": [\r\n {\r\n \"input_tokens\": 2,\r\n \"output_tokens\": 60,\r\n \"cache_read_input_tokens\": 0,\r\n \"cache_creation_input_tokens\": 784,\r\n \"cache_creation\": {\r\n \"ephemeral_5m_input_tokens\": 0,\r\n \"ephemeral_1h_input_tokens\": 784\r\n },\r\n \"type\": \"message\"\r\n }\r\n ],\r\n \"speed\": \"standard\"\r\n },\r\n \"model_usage\": {\r\n \"claude-haiku-4-5-20251001\": {\r\n \"inputTokens\": 910,\r\n \"outputTokens\": 11,\r\n \"cacheCreationInputTokens\": 0,\r\n \"cacheReadInputTokens\": 0\r\n },\r\n \"claude-fable-5-1\": {\r\n \"inputTokens\": 2,\r\n \"outputTokens\": 60,\r\n \"cacheCreationInputTokens\": 784,\r\n \"cacheReadInputTokens\": 0\r\n }\r\n }\r\n }\r\n ],\r\n \"method_reviews\": [\r\n {\r\n \"name\": \"initial xhigh adapter\",\r\n \"status\": \"timed out; no usable review\",\r\n \"timeout_seconds\": 180,\r\n \"usage\": \"unknown\"\r\n },\r\n {\r\n \"name\": \"method-review-v2\",\r\n \"status\": \"changes_requested\",\r\n \"duration_ms\": 102935,\r\n \"model_usage\": {\r\n \"claude-haiku-4-5-20251001\": {\r\n \"inputTokens\": 4395,\r\n \"outputTokens\": 21,\r\n \"cacheReadInputTokens\": 0,\r\n \"cacheCreationInputTokens\": 0\r\n },\r\n \"claude-fable-5-1\": {\r\n \"inputTokens\": 2,\r\n \"outputTokens\": 7255,\r\n \"cacheReadInputTokens\": 0,\r\n \"cacheCreationInputTokens\": 5603\r\n }\r\n }\r\n },\r\n {\r\n \"name\": \"method-review-final\",\r\n \"status\": \"approved\",\r\n \"duration_ms\": 32550,\r\n \"model_usage\": {\r\n \"claude-haiku-4-5-20251001\": {\r\n \"inputTokens\": 3659,\r\n \"outputTokens\": 21,\r\n \"cacheReadInputTokens\": 0,\r\n \"cacheCreationInputTokens\": 0\r\n },\r\n \"claude-fable-5-1\": {\r\n \"inputTokens\": 2,\r\n \"outputTokens\": 2319,\r\n \"cacheReadInputTokens\": 0,\r\n \"cacheCreationInputTokens\": 4646\r\n }\r\n }\r\n }\r\n ],\r\n \"serialization_incident\": {\r\n \"count\": 1,\r\n \"first_call_rerun\": false,\r\n \"task_or_grader_changed\": false,\r\n \"disclosure\": \"Windows CRLF made raw artifact bytes differ from intended text hash. Exact normalized text verified; original call adopted once under documented amendment.\"\r\n },\r\n \"all_calls_collected_at_utc\": \"2026-09-04T22:56:22.502212+00:00\",\r\n \"claim_to_collection_wall_seconds\": 2020.502212,\r\n \"scored_call_component_seconds\": 335.26908169983653,\r\n \"unattributed_noncall_wall_seconds\": 1685.2331303001636,\r\n \"overhead_scope_note\": \"Claim-to-collection only, excludes earlier startup and later resource review/report/publication; unattributed wall time includes authoring, model reviews, code fixes, tooling and orchestration.\"\r\n}", "summary.json": "{\r\n \"freeze_sha256\": \"cd4f8a3e97e2ad6a8a1902b4ad45ebb58a5dbe9e03b0df37052f46fd5cbd147b\",\r\n \"complete\": true,\r\n \"calls\": 30,\r\n \"experiment\": {\r\n \"current_invocation_started_at\": \"2026-09-04T22:50:55.703132+00:00\",\r\n \"current_invocation_wall_seconds\": 326.7916600999888,\r\n \"resumption_boundary\": \"Wall duration covers this invocation; actual call-cost sum covers all sealed calls.\"\r\n },\r\n \"first_pass_reuse\": \"Each initial output feeds two refinement arms; outcomes are correlated.\",\r\n \"actual_experiment_cost\": {\r\n \"component_wall_seconds\": 335.26908169983653,\r\n \"provider_specific_usage\": {\r\n \"A\": {\r\n \"calls\": 15,\r\n \"components\": {\r\n \"cached_input_tokens\": 19456,\r\n \"input_tokens\": 129404,\r\n \"output_tokens\": 3149\r\n },\r\n \"total\": 132553,\r\n \"cached_input_is_subset\": true\r\n },\r\n \"F\": {\r\n \"calls\": 15,\r\n \"components\": {\r\n \"cache_creation_input_tokens\": 36439,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 9811,\r\n \"input_tokens\": 32299\r\n },\r\n \"total\": 78549,\r\n \"cached_input_is_subset\": false,\r\n \"per_model_components\": {\r\n \"claude-fable-5-1\": {\r\n \"input_tokens\": 30,\r\n \"cache_creation_input_tokens\": 36439,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 9610\r\n },\r\n \"claude-haiku-4-5-20251001\": {\r\n \"input_tokens\": 32269,\r\n \"cache_creation_input_tokens\": 0,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 201\r\n }\r\n }\r\n }\r\n }\r\n },\r\n \"arms\": {\r\n \"A\": {\r\n \"full_contract_passes\": 5,\r\n \"cases\": 5,\r\n \"sum_component_wall_seconds\": 49.63861769990763,\r\n \"regressions_from_passing_precursor\": 0\r\n },\r\n \"F\": {\r\n \"full_contract_passes\": 5,\r\n \"cases\": 5,\r\n \"sum_component_wall_seconds\": 43.33526650001295,\r\n \"regressions_from_passing_precursor\": 0\r\n },\r\n \"AA\": {\r\n \"full_contract_passes\": 5,\r\n \"cases\": 5,\r\n \"sum_component_wall_seconds\": 107.51824159990065,\r\n \"regressions_from_passing_precursor\": 0\r\n },\r\n \"FF\": {\r\n \"full_contract_passes\": 5,\r\n \"cases\": 5,\r\n \"sum_component_wall_seconds\": 103.35299410001608,\r\n \"regressions_from_passing_precursor\": 0\r\n },\r\n \"AF\": {\r\n \"full_contract_passes\": 5,\r\n \"cases\": 5,\r\n \"sum_component_wall_seconds\": 110.8228978998959,\r\n \"regressions_from_passing_precursor\": 0\r\n },\r\n \"FA\": {\r\n \"full_contract_passes\": 5,\r\n \"cases\": 5,\r\n \"sum_component_wall_seconds\": 106.54883229994448,\r\n \"regressions_from_passing_precursor\": 0\r\n }\r\n },\r\n \"comparisons\": {\r\n \"AF_vs_AA\": {\r\n \"cross_passes\": 5,\r\n \"same_passes\": 5\r\n },\r\n \"FA_vs_FF\": {\r\n \"cross_passes\": 5,\r\n \"same_passes\": 5\r\n }\r\n },\r\n \"results\": [\r\n {\r\n \"index\": 1,\r\n \"case_id\": \"01_state\",\r\n \"arm\": \"A\",\r\n \"model\": \"A\",\r\n \"precursor\": null,\r\n \"call_status\": \"ok\",\r\n \"category\": \"response\",\r\n \"requested_model\": \"gpt-6-astra\",\r\n \"effort\": \"high\",\r\n \"provider_resolved_model\": null,\r\n \"auxiliary_models\": [],\r\n \"model_usage_summary\": null,\r\n \"primary_response_usage\": {\r\n \"input_tokens\": null,\r\n \"cache_creation_input_tokens\": null,\r\n \"cache_read_input_tokens\": null,\r\n \"output_tokens\": null\r\n },\r\n \"runtime_context\": {\r\n \"model\": \"gpt-6-astra\",\r\n \"effort\": \"high\"\r\n },\r\n \"grade\": {\r\n \"passed\": true,\r\n \"ast_nodes\": 88,\r\n \"tests\": [\r\n {\r\n \"index\": 0,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 4,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 1,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 12,\r\n \"membership\": 2\r\n },\r\n {\r\n \"index\": 2,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 17,\r\n \"membership\": 3\r\n },\r\n {\r\n \"index\": 3,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 18,\r\n \"membership\": 3\r\n },\r\n {\r\n \"index\": 4,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 12,\r\n \"membership\": 2\r\n },\r\n {\r\n \"index\": 5,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 19,\r\n \"membership\": 3\r\n },\r\n {\r\n \"index\": 6,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 3006,\r\n \"membership\": 1000\r\n }\r\n ]\r\n },\r\n \"workflow_cost\": {\r\n \"component_wall_seconds\": 11.344931199972052,\r\n \"provider_specific_usage\": {\r\n \"A\": {\r\n \"calls\": 1,\r\n \"components\": {\r\n \"cached_input_tokens\": 0,\r\n \"input_tokens\": 8470,\r\n \"output_tokens\": 155\r\n },\r\n \"total\": 8625,\r\n \"cached_input_is_subset\": true\r\n }\r\n }\r\n },\r\n \"call_cost\": {\r\n \"component_wall_seconds\": 11.344931199972052,\r\n \"provider_specific_usage\": {\r\n \"A\": {\r\n \"calls\": 1,\r\n \"components\": {\r\n \"cached_input_tokens\": 0,\r\n \"input_tokens\": 8470,\r\n \"output_tokens\": 155\r\n },\r\n \"total\": 8625,\r\n \"cached_input_is_subset\": true\r\n }\r\n }\r\n },\r\n \"freeze_sha256\": \"cd4f8a3e97e2ad6a8a1902b4ad45ebb58a5dbe9e03b0df37052f46fd5cbd147b\",\r\n \"prompt_sha256\": \"05e25f57dd5acdf6b1802dcc05a5d6367e90cf19cfeda9b3ff79a9bc516e20da\",\r\n \"resource_review_decision\": \"approved\",\r\n \"adopted\": true,\r\n \"origin_freeze_sha256\": \"d90c9ecaa122c91212087f9460cb245de99eea2c761d264524350a497c0445e2\",\r\n \"precursor_reused\": false\r\n },\r\n {\r\n \"index\": 2,\r\n \"case_id\": \"01_state\",\r\n \"arm\": \"F\",\r\n \"model\": \"F\",\r\n \"precursor\": null,\r\n \"call_status\": \"ok\",\r\n \"category\": \"response\",\r\n \"requested_model\": \"claude-fable-5-1\",\r\n \"effort\": \"high\",\r\n \"provider_resolved_model\": \"claude-fable-5-1\",\r\n \"auxiliary_models\": [\r\n \"claude-haiku-4-5-20251001\"\r\n ],\r\n \"model_usage_summary\": {\r\n \"claude-haiku-4-5-20251001\": {\r\n \"input_tokens\": 1977,\r\n \"cache_creation_input_tokens\": 0,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 12\r\n },\r\n \"claude-fable-5-1\": {\r\n \"input_tokens\": 2,\r\n \"cache_creation_input_tokens\": 2198,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 199\r\n }\r\n },\r\n \"primary_response_usage\": {\r\n \"input_tokens\": 2,\r\n \"cache_creation_input_tokens\": 2198,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 199\r\n },\r\n \"runtime_context\": null,\r\n \"grade\": {\r\n \"passed\": true,\r\n \"ast_nodes\": 93,\r\n \"tests\": [\r\n {\r\n \"index\": 0,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 4,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 1,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 14,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 2,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 20,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 3,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 21,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 4,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 14,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 5,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 22,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 6,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 4006,\r\n \"membership\": 0\r\n }\r\n ]\r\n },\r\n \"workflow_cost\": {\r\n \"component_wall_seconds\": 5.89625370001886,\r\n \"provider_specific_usage\": {\r\n \"F\": {\r\n \"calls\": 1,\r\n \"components\": {\r\n \"output_tokens\": 211,\r\n \"cache_read_input_tokens\": 0,\r\n \"input_tokens\": 1979,\r\n \"cache_creation_input_tokens\": 2198\r\n },\r\n \"total\": 4388,\r\n \"cached_input_is_subset\": false,\r\n \"per_model_components\": {\r\n \"claude-fable-5-1\": {\r\n \"input_tokens\": 2,\r\n \"cache_creation_input_tokens\": 2198,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 199\r\n },\r\n \"claude-haiku-4-5-20251001\": {\r\n \"input_tokens\": 1977,\r\n 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\"05e25f57dd5acdf6b1802dcc05a5d6367e90cf19cfeda9b3ff79a9bc516e20da\",\r\n \"resource_review_decision\": \"approved\",\r\n \"adopted\": false,\r\n \"origin_freeze_sha256\": \"cd4f8a3e97e2ad6a8a1902b4ad45ebb58a5dbe9e03b0df37052f46fd5cbd147b\",\r\n \"precursor_reused\": false\r\n },\r\n {\r\n \"index\": 3,\r\n \"case_id\": \"01_state\",\r\n \"arm\": \"AF\",\r\n \"model\": \"F\",\r\n \"precursor\": \"A\",\r\n \"call_status\": \"ok\",\r\n \"category\": \"response\",\r\n \"requested_model\": \"claude-fable-5-1\",\r\n \"effort\": \"high\",\r\n \"provider_resolved_model\": \"claude-fable-5-1\",\r\n \"auxiliary_models\": [\r\n \"claude-haiku-4-5-20251001\"\r\n ],\r\n \"model_usage_summary\": {\r\n \"claude-haiku-4-5-20251001\": {\r\n \"input_tokens\": 2108,\r\n \"cache_creation_input_tokens\": 0,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 14\r\n },\r\n \"claude-fable-5-1\": {\r\n \"input_tokens\": 2,\r\n \"cache_creation_input_tokens\": 2380,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 901\r\n }\r\n },\r\n \"primary_response_usage\": {\r\n \"input_tokens\": 2,\r\n \"cache_creation_input_tokens\": 2380,\r\n \"cache_read_input_tokens\": 0,\r\n \"output_tokens\": 901\r\n },\r\n \"runtime_context\": null,\r\n \"grade\": {\r\n \"passed\": true,\r\n \"ast_nodes\": 169,\r\n \"tests\": [\r\n {\r\n \"index\": 0,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 6,\r\n \"membership\": 0\r\n },\r\n {\r\n \"index\": 1,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 23,\r\n \"membership\": 2\r\n },\r\n {\r\n \"index\": 2,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 33,\r\n \"membership\": 3\r\n },\r\n {\r\n \"index\": 3,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 36,\r\n \"membership\": 3\r\n },\r\n {\r\n \"index\": 4,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 23,\r\n \"membership\": 2\r\n },\r\n {\r\n \"index\": 5,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 36,\r\n \"membership\": 3\r\n },\r\n {\r\n \"index\": 6,\r\n \"passed\": true,\r\n \"error\": null,\r\n \"input_mutated\": false,\r\n \"steps\": 7009,\r\n \"membership\": 1000\r\n }\r\n ]\r\n },\r\n \"workflow_cost\": {\r\n \"component_wall_seconds\": 24.07642479997594,\r\n \"provider_specific_usage\": {\r\n \"A\": {\r\n \"calls\": 1,\r\n \"components\": {\r\n \"cached_input_tokens\": 0,\r\n \"input_tokens\": 8470,\r\n \"output_tokens\": 155\r\n },\r\n \"total\": 8625,\r\n \"cached_input_is_subset\": true\r\n },\r\n \"F\": {\r\n \"calls\": 1,\r\n \"components\": {\r\n \"output_tokens\": 915,\r\n \"cache_read_input_tokens\": 0,\r\n \"input_tokens\": 2110,\r\n \"cache_creation_input_tokens\": 2380\r\n },\r\n \"total\": 5405,\r\n \"cached_input_is_subset\": false,\r\n \"per_model_components\": {\r\n \"claude-fable-5-1\": 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