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"""Dependency-free structural, integrity, and leakage checks for the release."""
from __future__ import annotations
import hashlib
import json
import re
import sys
import urllib.parse
from collections import Counter
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
EXPECTED_IDS = [
"erdos_001",
"erdos_003",
"erdos_025",
"erdos_075",
"erdos_149",
*[f"aim_ag_{index:03d}" for index in range(1, 11)],
"counterexample_114",
"counterexample_128",
"counterexample_131",
"counterexample_134",
"counterexample_135",
]
EXPECTED_STREAMS = {
"erdos_variant": 5,
"aim_ag_rl": 10,
"counterexample_variant": 5,
}
EXPECTED_AIM_MILESTONE_COUNTS = [5, 4, 4, 4, 5, 4, 4, 4, 4, 4]
EXPECTED_AIM_REFERENCE_COUNTS = [5, 7, 5, 5, 6, 6, 5, 6, 4, 5]
EXPECTED_JSONL_COUNTS = {
"data/showcase.jsonl": 20,
"data/erdos_variants.jsonl": 5,
"data/aim_ag_tasks.jsonl": 10,
"data/counterexample_variants.jsonl": 5,
"rl/data/public_tasks.jsonl": 10,
"rl/data/curriculum_episodes.jsonl": 53,
"rl/data/curriculum_episodes_hf.jsonl": 53,
"rl/data/curriculum_train_hf.jsonl": 18,
"rl/data/curriculum_validation_hf.jsonl": 14,
"rl/data/curriculum_test_hf.jsonl": 21,
"rl/data/exact_benchmark_public.jsonl": 33,
"rl/data/exact_benchmark_train.jsonl": 11,
"rl/data/exact_benchmark_validation.jsonl": 11,
"rl/data/exact_benchmark_test.jsonl": 11,
"rl/data/frontier_eval_public.jsonl": 10,
}
EXPECTED_CONFIG_PATHS = set(EXPECTED_JSONL_COUNTS) - {
"rl/data/curriculum_episodes.jsonl",
"rl/data/curriculum_episodes_hf.jsonl",
"rl/data/exact_benchmark_public.jsonl",
}
def fail(message: str) -> None:
raise AssertionError(message)
def sha256(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as handle:
for block in iter(lambda: handle.read(1024 * 1024), b""):
digest.update(block)
return digest.hexdigest()
def read_json(path: Path):
with path.open("r", encoding="utf-8") as handle:
return json.load(handle)
def read_jsonl(path: Path) -> list[dict]:
rows = []
with path.open("r", encoding="utf-8") as handle:
for line_number, line in enumerate(handle, start=1):
if not line.strip():
fail(f"Blank JSONL line: {path}:{line_number}")
try:
value = json.loads(line)
except json.JSONDecodeError as exc:
fail(f"Invalid JSONL: {path}:{line_number}: {exc}")
if not isinstance(value, dict):
fail(f"JSONL row is not an object: {path}:{line_number}")
rows.append(value)
return rows
def check_showcase(rows: list[dict]) -> None:
schema = read_json(ROOT / "schema/showcase.schema.json")
required = set(schema["required"])
expected_properties = set(schema["properties"])
ids = []
for row_number, row in enumerate(rows, start=1):
missing = required - set(row)
extra = set(row) - expected_properties
if missing or extra:
fail(f"Showcase row {row_number}: missing={sorted(missing)}, extra={sorted(extra)}")
if row["schema_version"] != "1.0.0" or row["license"] != "MIT":
fail(f"Showcase row {row_number}: schema/license mismatch")
if row["stream"] not in EXPECTED_STREAMS:
fail(f"Showcase row {row_number}: unknown stream")
for field in [
"problem_id",
"title",
"domain",
"task_type",
"difficulty",
"prompt",
"inspiration",
"rationale",
"expected_output",
"research_status",
"verification",
]:
if not isinstance(row[field], str) or not row[field].strip():
fail(f"Showcase row {row_number}: invalid {field}")
if not isinstance(row["source_record_index"], int) or row["source_record_index"] < 1:
fail(f"Showcase row {row_number}: invalid source_record_index")
if not isinstance(row["rl_ready"], bool):
fail(f"Showcase row {row_number}: rl_ready must be boolean")
if not isinstance(row["milestones"], list):
fail(f"Showcase row {row_number}: milestones must be a list")
if not isinstance(row["quality_signals"], list) or not row["quality_signals"]:
fail(f"Showcase row {row_number}: quality_signals must be nonempty")
if row["rl_ready"] != (row["stream"] == "aim_ag_rl"):
fail(f"Showcase row {row_number}: rl_ready conflicts with stream")
ids.append(row["problem_id"])
if ids != EXPECTED_IDS:
fail(f"Unexpected showcase IDs/order: {ids}")
if len(ids) != len(set(ids)):
fail("Duplicate showcase problem IDs")
if Counter(row["stream"] for row in rows) != Counter(EXPECTED_STREAMS):
fail("Unexpected stream counts")
def check_aim_tasks(rows: list[dict]) -> None:
expected_ids = [f"aim_ag_{index:03d}" for index in range(1, 11)]
actual_ids = [row.get("release_problem_id") for row in rows]
if actual_ids != expected_ids:
fail(f"Unexpected AIM-AG IDs: {actual_ids}")
for source_index, row in enumerate(rows, start=1):
source_id = f"sample_{source_index}"
if row.get("problem_id") != source_id:
fail(f"Unexpected AIM-AG source ID: {row.get('problem_id')}")
research_status = row.get("research_status", {})
if research_status.get("classification") != "candidate_open_problem":
fail(f"Unexpected AIM-AG research status: {source_id}")
if research_status.get("expert_signoff_required") is not True:
fail(f"AIM-AG expert-signoff flag is missing: {source_id}")
milestones = row.get("evaluation", {}).get("milestones", [])
if len(milestones) != EXPECTED_AIM_MILESTONE_COUNTS[source_index - 1]:
fail(f"Unexpected AIM-AG milestone count: {source_id}")
expected_milestones = [f"m{index}" for index in range(1, len(milestones) + 1)]
if [item.get("milestone_id") for item in milestones] != expected_milestones:
fail(f"AIM-AG milestones are not sequential: {source_id}")
if sum(item.get("weight_percent", 0) for item in milestones) != 100:
fail(f"AIM-AG milestone weights do not sum to 100: {row['release_problem_id']}")
references = row.get("research_context", {}).get("references", [])
if len(references) != EXPECTED_AIM_REFERENCE_COUNTS[source_index - 1]:
fail(f"Unexpected AIM-AG reference count: {source_id}")
for reference in references:
parsed = urllib.parse.urlparse(reference.get("url", ""))
if parsed.scheme != "https" or not parsed.netloc:
fail(f"Invalid AIM-AG reference URL: {source_id}")
if row.get("license") != "MIT":
fail(f"AIM-AG license missing: {row['release_problem_id']}")
def check_lossless_episode_view(source_rows: list[dict], normalized_rows: list[dict]) -> None:
if [row.get("episode_id") for row in normalized_rows] != [
row.get("episode_id") for row in source_rows
]:
fail("Type-stable curriculum IDs/order do not match production-format public rows")
direct_fields = [
"schema_version",
"episode_id",
"episode_type",
"problem_id",
"split",
"reward_mode",
"policy_visibility",
"submission_schema_ref",
]
optional_defaults = {
"milestone_id": "",
"target_milestone_ids": [],
"milestone_target": "",
"required_artifact_policy": "",
"verifier_id": "",
"candidate_required_fields": [],
}
for source, normalized in zip(source_rows, normalized_rows):
episode_id = source["episode_id"]
if normalized.get("source_fields") != sorted(source):
fail(f"Lossless-view source field inventory mismatch: {episode_id}")
prompt = source["prompt"]
expected_kind = "object" if isinstance(prompt, dict) else "string"
if normalized.get("prompt_kind") != expected_kind:
fail(f"Lossless-view prompt kind mismatch: {episode_id}")
if json.loads(normalized.get("prompt_json", "null")) != prompt:
fail(f"Lossless-view prompt JSON mismatch: {episode_id}")
expected_prompt_text = (
json.dumps(prompt, ensure_ascii=False, sort_keys=True)
if isinstance(prompt, dict)
else str(prompt)
)
if normalized.get("prompt_text") != expected_prompt_text:
fail(f"Lossless-view prompt text mismatch: {episode_id}")
expected_input = (
json.dumps(source["input"], ensure_ascii=False, sort_keys=True)
if "input" in source
else ""
)
if normalized.get("input_json") != expected_input:
fail(f"Lossless-view input mismatch: {episode_id}")
for field in direct_fields:
if normalized.get(field) != source.get(field):
fail(f"Lossless-view {field} mismatch: {episode_id}")
for field, default in optional_defaults.items():
if normalized.get(field) != source.get(field, default):
fail(f"Lossless-view {field} mismatch: {episode_id}")
def check_rl_public(aim_rows: list[dict]) -> None:
public_paths = [
"rl/data/public_tasks.jsonl",
"rl/data/curriculum_episodes.jsonl",
"rl/data/curriculum_episodes_hf.jsonl",
"rl/data/curriculum_train_hf.jsonl",
"rl/data/curriculum_validation_hf.jsonl",
"rl/data/curriculum_test_hf.jsonl",
"rl/data/exact_benchmark_public.jsonl",
"rl/data/exact_benchmark_train.jsonl",
"rl/data/exact_benchmark_validation.jsonl",
"rl/data/exact_benchmark_test.jsonl",
"rl/data/frontier_eval_public.jsonl",
]
valid_problem_ids = {f"sample_{index}" for index in range(1, 11)}
for relative in public_paths:
for row in read_jsonl(ROOT / relative):
if row.get("policy_visibility") != "public":
fail(f"Non-public RL row found in {relative}")
if row.get("problem_id") not in valid_problem_ids:
fail(f"Unknown RL problem ID in {relative}: {row.get('problem_id')}")
schema_ref = row.get("submission_schema_ref")
if schema_ref and not (ROOT / "rl" / schema_ref).is_file():
fail(f"Broken RL schema reference in {relative}: {schema_ref}")
tasks = read_jsonl(ROOT / "rl/data/public_tasks.jsonl")
if [row["problem_id"] for row in tasks] != [f"sample_{index}" for index in range(1, 11)]:
fail("Public RL tasks are not ordered sample_1 through sample_10")
aim_by_source_id = {row["problem_id"]: row for row in aim_rows}
task_by_id = {row["problem_id"]: row for row in tasks}
for problem_id, task in task_by_id.items():
aim = aim_by_source_id[problem_id]
if task.get("title") != aim.get("title"):
fail(f"AIM-AG/RL title mismatch: {problem_id}")
for field in ["conjecture", "definitions"]:
if task.get("prompt", {}).get(field) != aim.get("prompt", {}).get(field):
fail(f"AIM-AG/RL core prompt mismatch ({field}): {problem_id}")
episodes = read_jsonl(ROOT / "rl/data/curriculum_episodes.jsonl")
episode_ids = [row["episode_id"] for row in episodes]
if len(episode_ids) != len(set(episode_ids)):
fail("Duplicate public curriculum episode IDs")
for row in episodes:
if isinstance(row.get("prompt"), dict):
task_prompt = task_by_id[row["problem_id"]]["prompt"]
for field in ["conjecture", "definitions"]:
if row["prompt"].get(field) != task_prompt.get(field):
fail(f"Structured episode/RL task mismatch ({field}): {row['episode_id']}")
exact = read_jsonl(ROOT / "rl/data/exact_benchmark_public.jsonl")
frontier = read_jsonl(ROOT / "rl/data/frontier_eval_public.jsonl")
if exact != [row for row in episodes if row["episode_type"] == "exact_benchmark"]:
fail("Exact benchmark file is not the exact-episode subset of the curriculum")
if frontier != [row for row in episodes if row["episode_type"] == "full_frontier_task"]:
fail("Frontier eval file is not the full-frontier subset of the curriculum")
normalized = read_jsonl(ROOT / "rl/data/curriculum_episodes_hf.jsonl")
check_lossless_episode_view(episodes, normalized)
split_names = {"train": "train", "dev": "validation", "eval": "test"}
for source_split, hf_split in split_names.items():
if read_jsonl(ROOT / f"rl/data/curriculum_{hf_split}_hf.jsonl") != [
row for row in normalized if row["split"] == source_split
]:
fail(f"Curriculum {hf_split} split is inconsistent")
if read_jsonl(ROOT / f"rl/data/exact_benchmark_{hf_split}.jsonl") != [
row for row in exact if row["split"] == source_split
]:
fail(f"Exact benchmark {hf_split} split is inconsistent")
fixtures = {
path.stem: read_json(path) for path in (ROOT / "rl/fixtures/public").glob("*.json")
}
exact_by_id = {row["episode_id"]: row for row in exact}
if fixtures != exact_by_id:
fail("Public fixture files do not match public exact episodes one-to-one")
curriculum_config = read_json(ROOT / "rl/configs/training_curriculum.json")
public_scope = curriculum_config.get("public_release_scope", {})
if public_scope.get("all_included_prompts_and_fixtures_are_public") is not True:
fail("Training curriculum lacks the public-release fixture override")
if any("held-out" in stage.get("name", "").lower() for stage in curriculum_config["stages"]):
fail("Training curriculum still labels a public stage as held out")
leaked_paths = [
path.relative_to(ROOT).as_posix()
for path in (ROOT / "rl").rglob("*")
if path.is_file() and "hidden" in path.relative_to(ROOT).as_posix().lower()
]
if leaked_paths:
fail(f"Hidden-path assets included: {leaked_paths}")
def check_version_consistency() -> None:
version = (ROOT / "VERSION").read_text(encoding="utf-8").strip()
manifest = read_json(ROOT / "MANIFEST.json")
citation = (ROOT / "CITATION.cff").read_text(encoding="utf-8")
readme = (ROOT / "README.md").read_text(encoding="utf-8")
if version != "0.1.0":
fail(f"Unexpected release version: {version}")
if manifest.get("release_version") != version:
fail("VERSION and manifest release_version differ")
if not re.search(rf"^version:\s*{re.escape(version)}\s*$", citation, re.MULTILINE):
fail("VERSION and CITATION.cff version differ")
if f"version = {{{version}}}" not in readme:
fail("VERSION and README BibTeX version differ")
def check_readme_configs() -> None:
readme = (ROOT / "README.md").read_text(encoding="utf-8")
if not readme.startswith("---\n") or "\nlicense: mit\n" not in readme:
fail("README metadata is missing YAML front matter or MIT license")
front_matter = readme.split("---", 2)[1]
config_paths = re.findall(r"^\s+path:\s+([^\s]+)\s*$", front_matter, flags=re.MULTILINE)
if set(config_paths) != EXPECTED_CONFIG_PATHS:
fail(
f"README config paths mismatch: missing={sorted(EXPECTED_CONFIG_PATHS-set(config_paths))}, "
f"extra={sorted(set(config_paths)-EXPECTED_CONFIG_PATHS)}"
)
for relative in config_paths:
if not (ROOT / relative).is_file():
fail(f"README config points to missing file: {relative}")
def check_sensitive_strings() -> None:
patterns = {
"absolute local path": re.compile("/" + "Users/" + "black" + "frog/"),
"Hugging Face token": re.compile(r"hf_[A-Za-z0-9]{20,}"),
"generic API secret": re.compile(r"sk-[A-Za-z0-9_-]{20,}"),
}
for path in ROOT.rglob("*"):
if not path.is_file() or path.name == ".DS_Store":
continue
try:
text = path.read_text(encoding="utf-8")
except UnicodeDecodeError:
continue
for label, pattern in patterns.items():
if pattern.search(text):
fail(f"Possible {label} in {path.relative_to(ROOT)}")
def check_manifest() -> None:
manifest = read_json(ROOT / "MANIFEST.json")
if manifest.get("publication_status") != "release_candidate_not_uploaded":
fail("Manifest publication status is not release_candidate_not_uploaded")
listed = manifest.get("files", [])
for item in listed:
path = ROOT / item["path"]
if not path.is_file():
fail(f"Manifest file is missing: {item['path']}")
if path.stat().st_size != item["bytes"] or sha256(path) != item["sha256"]:
fail(f"Manifest integrity mismatch: {item['path']}")
actual_paths = {
path.relative_to(ROOT).as_posix()
for path in ROOT.rglob("*")
if path.is_file() and path.name not in {".DS_Store", "MANIFEST.json"}
}
listed_paths = {item["path"] for item in listed}
if actual_paths != listed_paths:
fail(
f"Manifest file set mismatch: missing={sorted(actual_paths-listed_paths)}, "
f"stale={sorted(listed_paths-actual_paths)}"
)
content_digest = hashlib.sha256()
for item in listed:
content_digest.update(f"{item['path']}\0{item['sha256']}\n".encode("utf-8"))
if content_digest.hexdigest() != manifest.get("content_set_sha256"):
fail("Manifest content-set hash mismatch")
def main() -> int:
for relative, expected_count in EXPECTED_JSONL_COUNTS.items():
rows = read_jsonl(ROOT / relative)
if len(rows) != expected_count:
fail(f"{relative}: expected {expected_count} rows, found {len(rows)}")
showcase = read_jsonl(ROOT / "data/showcase.jsonl")
check_showcase(showcase)
aim_rows = read_jsonl(ROOT / "data/aim_ag_tasks.jsonl")
check_aim_tasks(aim_rows)
check_rl_public(aim_rows)
check_readme_configs()
check_sensitive_strings()
check_version_consistency()
check_manifest()
print("PASS: release structure, counts, IDs, public visibility, metadata, versions, and hashes")
print("PASS: 20 showcase problems = 5 Erdős + 10 AIM-AG + 5 counterexample variants")
print("PASS: AIM-AG/RL mathematical cores align; type-stable episodes round-trip losslessly")
print("PASS: curriculum/exact/frontier subsets, native splits, and 33 fixtures align")
print("PASS: no hidden-path files or obvious local paths/API tokens")
return 0
if __name__ == "__main__":
try:
sys.exit(main())
except (AssertionError, FileNotFoundError, json.JSONDecodeError) as exc:
print(f"FAIL: {exc}", file=sys.stderr)
sys.exit(1)
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