Hugging Face's logo

Datasets:
S3RL4
/
binary-reasoning-v1

Modalities:
Text
Formats:
json
Size:
< 1K
Tags:
reinforcement-learning
evaluation
reverse-engineering
binaries
elf
pe
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
xet
 Community
2
binary-reasoning-v1 / README.md
S3RL4's picture
S3RL4
Mark the full pack as not offered and add the sealed run results
7f5b732 verified
preview code
|
Raw History Blame Contribute Delete
5.89 kB
metadata 
license: other
license_name: re-lab-custom
license_link: https://huggingface.co/datasets/S3RL4/binary-reasoning-v1/blob/main/LICENSE.md
configs:
  - config_name: default
    data_files:
      - split: train
        path: sample_free.jsonl
tags:
  - reinforcement-learning
  - evaluation
  - reverse-engineering
  - binaries
  - elf
  - pe
  - cross-architecture
  - taskset
  - harbor-framework
  - rl-environments
size_categories:
  - n<1K
pretty_name: binary-reasoning-v1

binary-reasoning-v1

Verified reverse-engineering tasks over real production binaries — an RL / eval environment pack where every answer is derived from the artifact itself and machine-checked.

This public set is an entry-level demo and is not the current product. See re-lab.dev. It illustrates the task format and grading, and frontier models solve it easily.

  • The full pack, not currently offered: 10,251 items · 10 families · 7 ISAs (x86-64, i686, AArch64, ARM32, RISC-V, MIPS o32/n64, PPC64LE)
  • Sources: 649 distinct third-party file names referenced in shipped provenance (system libs incl. libcrypto/zstd/sqlite3/libxml2, Mesa DRI drivers, QEMU device models, systemd internals), Debian production cross-toolchain glibc, Android/Termux production aarch64 libs, curated Windows PEs, labeled cross-compiles
  • Provenance per item: binary name and sha256_16 on every item; function address/size and architecture where applicable
  • Verification: automated re-verification through our in-house analysis pipeline (answers re-verified against the binaries); 10,172 deterministic items score 1.0 on gold / 0.0 on abstention; automated answer-leak scan found 0/10,251 hits; all tasks load against the real verifiers v1 framework; 29 agentic challenges ship Harbor-format task packages, built + scored in real Docker containers with reward-chain gold and wrong-answer tests passing; each binary was also checked with 24 single-character mutations that scored zero
  • Baseline: a small local open-weight model mini-eval scored 5/23 (22%) on selected static deterministic items; see EVAL_RESULTS.md for conditions. Frontier reference: Claude Opus 5.5 with shell and file-read tools solved all 29 public agentic environments (145/145 attempts, 2026-10-29; non-isolated upper bound, see BENCHMARK-REPORT.md), so the public tier is warm-up level.

What a harder private set measured

One sealed run on 6 Nov 2026, through the provider's API. Eight tasks built from our own source were held out of development. The rules and budgets were fixed and timestamped before the first start, and the verdicts stayed unread until the run had closed.

Model Tasks tried Passed on the first attempt Passed if one shared integer-parsing rule is not counted
Claude Haiku 5.5 8 1 2
Claude Sonnet 5.5 8 7 8
Claude Opus 5.5 3 1 2
  • What it shows: this set separates models below Sonnet from Sonnet.
  • What it does not show: it is not frontier-hard, and it must not be used to rank top models. Sonnet passes nearly all of it. Three attempts cannot place Opus: on the three tasks both ran, Sonnet passed 3 and Opus 1. Both Opus failures missed a single test case (1 of 67 and 1 of 94), and Opus stopped sooner, with a median of 12.6 minutes against 21.2.
  • How far to trust it: these are small numbers. The exact interval for Sonnet's 7 of 8 runs from 47% to 99.7%, and real uncertainty may be wider, because the tasks come from one generator.
  • Conduct of the run: 44 starts, none refused by the provider. One start was lost to a network drop on our side and re-run once, as the rules allowed.
  • Receipts: entry be79ce047b6de83ea2a5f462634c398df0d2121c763bffd8db4191d2c50c8e86, closing 38dad74ffe572a615af4a62c17226cc484012f548abc9c0e02c662f6bc339b19, both timestamped by DigiCert and FreeTSA. The tasks stay private.

Contents of this repo (free sample)

file what
DATASHEET.md full composition table, guarantees, known limits, positioning vs existing work
sample_free.jsonl 9 items — one per family
EVAL_RESULTS.md baseline eval methodology + results (incl. a frontier reference: Opus 5.5 single-shot ~58% on ID items)
EVAL_RESULTS-passk.md full 29-env agentic pass@5 result for Claude Opus 5.5 with conditions and caveats
BENCHMARK-REPORT.md benchmark report for the public agentic environments, including intervals, method notes, and known limits
VERIFICATION-DOSSIER-PUBLIC.md public proof summary with measured checks and known caveats
verification_kit/ sample-level grader, wrong-answer, key-sensitivity, and leak-check scripts; see verification_kit/README.md for limitations
environment/src/binary_reasoning_v1/grading.py minimal grader module used by the verification kit. The HF free sample here is 9 items; the Prime Intellect environment package exposes a separate 100-item open sample.
harbor_pilot_29.zip all 29 agentic Harbor task packages (task.toml + instruction + Dockerfile + verifier + solution scripts and solution explanations), ready for harbor run or a free HUD import (direct download)

Also live on the Prime Intellect Environments Hub: s3rl4/binary-reasoning-v1 (prime env install s3rl4/binary-reasoning-v1).

The full pack (10,251 items, the gated companion repo S3RL4/binary-reasoning-v1-full) is not currently offered. Custom task families, built from our own source on written commission: samueljanttila@gmail.com. Privacy: PRIVACY.md

License

Task annotations, graders and packaging © RE-Lab, all rights reserved pending a commercial licence; third-party binaries remain under their own licences (provenance per item). Research evaluation use is granted for the free sample.