# Difficulty check against a frontier model (Claude Opus 5.5)
This file records measured frontier-model difficulty checks for RE-Lab binary-reverse-engineering tasks. It is not a leaderboard and not a calibrated pass@k band table unless explicitly labelled as one.
## Method and conditions
- Agentic screens used shell/file tools (`objdump`, `strings`, `xxd`, Python, Docker or direct execution where available), with a 30-minute limit.
- Host runs are non-isolated upper bounds: tools were available, but the model was not run inside a sealed buyer sandbox.
- Single-shot checks had no tools unless stated.
- Times are wall-clock minutes from the measured run. Partial scores are shown when the grader records partial credit.
- This report does not claim that a task family is "frontier-hard" unless it has a measured 2-30% band on the target model class.
## Results
| Task set | Model / mode | Result | Time or score | Interpretation |
|---|---:|---|---:|---|
| Public agentic flag-hunt environ…
Public clean-up: measured public outcomes only; private families shared privately; licence clarity
"""Deterministic grading for binary-reasoning-v1 items. Pure functions, no framework deps."""
import json
import re
JUDGE_FAMILIES = {"func_summary", "arch_semantics_qa", "isa_semantics_qa", "pe_import_reasoning", "pe_structure_qa"}
def norm_symbol(s: str) -> str:
s = s.strip().lower()
s = re.sub(r"^[*&]+", "", s)
s = re.sub(r"\(.*\)$", "", s).strip()
s = re.sub(r"\.+$", "", s) # sentence-final dot captured by token regex
return s
def _answer_symbol(answer) -> str:
if isinstance(answer, dict):
return answer.get("symbol", "")
return str(answer)
def grade_func_id_exact(reply: str, answer) -> float:
sym = norm_symbol(_answer_symbol(answer))
if not sym:
return 0.0
accepted = {sym}
if isinstance(answer, dict):
accepted |= {norm_symbol(a) for a in answer.get("aliases", []) if a}
toks = [norm_symbol(t) for t in re.findall(r"[A-Za-z_][A-Za-z0-9_.]+", reply)]
return 1.0 if accepted & set(toks) else 0.0
def gra…
#!/usr/bin/env python3
"""Grader tamper check: gold replies must fail when the answer key is deliberately mutated."""
import copy
import json
import sys
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT / "environment" / "src"))
from binary_reasoning_v1.grading import _answer_symbol, grade_item, needs_judge, norm_symbol # noqa: E402
def resolve_path():
if len(sys.argv) > 1:
return Path(sys.argv[1])
for p in (ROOT / "sample_free.jsonl", ROOT / "environment/src/binary_reasoning_v1/items.jsonl"):
if p.exists():
return p
raise SystemExit("no sample JSONL found; pass one as argv[1]")
def load_rows(path):
rows = []
for line in path.read_text(encoding="utf-8").splitlines():
if line.strip():
rows.append(json.loads(line))
return rows
def parse_json_answer(ans):
if isinstance(ans, dict):
return ans
if isinstance(ans, str):
try:
obj = js…
#!/usr/bin/env python3
"""Answer-leak audit: hidden answer labels/tokens should not appear in the model-facing input."""
import json
import re
import sys
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
def resolve_path():
if len(sys.argv) > 1:
return Path(sys.argv[1])
for p in (ROOT / "sample_free.jsonl", ROOT / "environment/src/binary_reasoning_v1/items.jsonl"):
if p.exists():
return p
raise SystemExit("no sample JSONL found; pass one as argv[1]")
def load_rows(path):
rows = []
for line in path.read_text(encoding="utf-8").splitlines():
if line.strip():
rows.append(json.loads(line))
return rows
def candidate_names(row):
ans = row.get("answer")
names = []
if isinstance(ans, dict):
if isinstance(ans.get("symbol"), str):
names.append(ans["symbol"])
if isinstance(ans.get("correct"), str):
names.append(ans["correct"])
for alias in ans…
# Verification kit — sample-level checks
This small kit lets a buyer's researcher sanity-check the grader and answer hygiene without trusting RE-Lab. It runs on the public sample JSONL files; it does not require the paid full pack. The grader scripts import `binary_reasoning_v1.grading` from `environment/src/binary_reasoning_v1/grading.py` (`leak_check.py` does not), so include that file (or the Prime environment package) when downloading just the free sample.
## Run it
From the dataset root:
```bash
python verification_kit/gold.py sample_free.jsonl
python verification_kit/do_nothing.py sample_free.jsonl
python verification_kit/random_wrong.py sample_free.jsonl
python verification_kit/leak_check.py sample_free.jsonl
python verification_kit/grader_tamper.py sample_free.jsonl
```
If the 100-item open sample is present locally, point the same scripts at it:
```bash
python verification_kit/gold.py environment/src/binary_reasoning_v1/items.jsonl
python verification_kit/do_nothing.py e…
# Verification dossier — public summary
This is the public part of RE-Lab's verification dossier for `binary-reasoning-v1`. The full buyer-ready version adds procurement, provenance tables, calibration plans, and delivery details. Contact: samueljanttila@gmail.com.
**Dataset:** https://huggingface.co/datasets/S3RL4/binary-reasoning-v1
**Self-serve sample kit:** `verification_kit/README.md` in this dataset repo
**Canary for contamination checks:** `RE-Lab-brv-2026-10-29-8c1e4f7a`
## What is verified now
### 1. Pack counts and grader split
The full pack contains `10,251` items:
- `10,172` deterministic-graded items.
- `79` rubric-judged open-ended items, disclosed rather than sold as deterministic.
The public sample includes 9 free items and a 100-item open sample in the Prime environment package. The sample kit skips rubric-judged items instead of pretending they are deterministic.
### 2. Grader sanity checks
On the 100-item open sample, the public sample kit reports:
- `gold.…
Update datasheet files list and honest proof/caveat wording
