Blame for README.md

---
license: apache-2.0
language:
- zh
pretty_name: HyperLogic
size_categories:
- n<1K
task_categories:
- question-answering
- text-generation
tags:
- logical-reasoning
- reasoning
- benchmark
- formal-verification
- agents
configs:
- config_name: seed
  data_files:
  - split: test
    path: data/seed.json
- config_name: base
  data_files:
  - split: test
    path: data/base.json
- config_name: hard
  data_files:
  - split: test
    path: data/hard.json
---

<h1 align="center">HyperLogic: A Hard, Forward-Authored Chinese Logical Reasoning Benchmark with Execution-Derived Answers</h1>

<p align="center">
    📄&nbsp;<a href="https://arxiv.org/abs/2605.19597"><b>Paper</b></a>&nbsp;&nbsp;|&nbsp;&nbsp;
    💻&nbsp;<a href="https://github.com/Tencent-Hunyuan/HyperLogic"><b>GitHub</b></a>&nbsp;&nbsp;|&nbsp;&nbsp;
    📮&nbsp;<a href="#evaluate-on-the-full-benchmark"><b>Evaluate on the Full Benchmark</b></a></p>

HyperLogic is a Chinese logical-reasoning benchmark built **forward**: problems are written first, and their answers are derived afterwards by executing formal models of them. A multi-agent workflow hardens seeds written by logic-trained undergraduates without solving them; two agents from different model families independently translate each finished item into executable finite-domain models, and logic solvers derive the answers. An item is released only when its two independently corrected encodings agree, after layered, agent-assisted adjudication under human-expert oversight.

<p align="center">
  <img src="assets/overview.png" alt="Overview of HyperLogic" width="95%">
</p>

This dataset is the **public 30%** of HyperLogic: a fixed, uniformly sampled 30% of the items of every tier, with gold answers. The remaining 70% and the formal translations of all items are held out so that results on them stay free of contamination. The prompts, scorer, and Hard agent toolkit are in the [GitHub repository](https://github.com/Tencent-Hunyuan/HyperLogic).

## Configurations

| Config | Tier | Items | Sub-questions | Full tier |
|---|---|---:|---:|---|
| `seed` | HyperLogic-Seed | 69 | 69 | 228 items |
| `base` | HyperLogic-Base | 59 | 274 | 195 items · 922 sub-questions |
| `hard` | HyperLogic-Hard | 30 | 30 | 100 items |

- **Seed**: single-question seeds written by undergraduates with coursework in logic.
- **Base**: a shared background (about 3,300 Chinese characters) with 4.7 sub-questions on average. An item counts as correct only if every sub-question is.
- **Hard**: single questions over larger, coupled search spaces (backgrounds of about 8,700 characters), intended for direct answering and for agents that write code in a sandbox.

Samples are item-level: all sub-questions of a selected Base item are kept together.

## Usage

```python
from datasets import load_dataset

seed = load_dataset("tencent/HyperLogic", "seed", split="test")
base = load_dataset("tencent/HyperLogic", "base", split="test")
hard = load_dataset("tencent/HyperLogic", "hard", split="test")
```

Answers are free-form Chinese text and are scored with a semantic-equivalence LLM judge, not by string matching. To reproduce the paper's protocol, use `evaluate.py` from the [GitHub repository](https://github.com/Tencent-Hunyuan/HyperLogic), which writes the direct-answer prompts and scores predictions; the Hard agent conditions (sandbox only, and sandbox with the logic modeling library) are in its `hard_agent/` folder.

## Data Fields

| Field | Type | Description |
|---|---|---|
| `id` | string (Seed) / int (Base, Hard) | Item ID. Seed IDs are content-derived `seed-...` strings. |
| `title` | string | Short Chinese title (Seed and Base only). |
| `background` | string | The scenario, in Chinese. |
| `question` | list of strings | The question(s); one for Seed and Hard. |
| `answer` | list of strings | Gold answers, aligned with `question`. |

## Leaderboard

Accuracy (%) on the **full** benchmark, mean ± standard deviation over three rollouts, every model at its highest reasoning setting. Scores on this public subset are not comparable to these numbers.

| Model | Seed | Base Item | Base Sub-Q | Hard Direct | Hard Sandbox | Hard + Library |
|---|---:|---:|---:|---:|---:|---:|
| GPT-5.6 Sol | **89.8** ± 3.7 | **77.6** ± 0.6 | **92.6** ± 0.2 | 13.7 ± 5.9 | 40.3 ± 4.2 | 45.7 ± 2.1 |
| Claude Opus 5 | 86.5 ± 0.7 | 74.2<sup>†</sup> ± 2.7 | 88.9<sup>†</sup> ± 0.7 | **15.6**<sup>†</sup> ± 1.8 | **55.7** ± 3.2 | **57.0** ± 2.6 |
| Kimi K3 | 88.6 ± 0.0 | 57.9 ± 1.8 | 81.6 ± 1.8 | 4.0 ± 0.0 | 41.3 ± 1.5 | 43.0 ± 8.9 |
| Grok 4.6 | 87.0 ± 1.8 | 52.5 ± 4.1 | 82.9 ± 0.7 | 12.0 ± 1.0 | 39.3 ± 1.5 | 38.3 ± 2.1 |
| DeepSeek V4 Pro | 83.9 ± 1.7 | 51.8 ± 1.5 | 81.2 ± 1.2 | 9.3 ± 1.2 | 26.0 ± 5.6 | 20.0 ± 1.7 |
| GLM-5.3 | 87.3 ± 0.9 | 45.6 ± 3.2 | 67.2 ± 4.9 | 3.0 ± 1.0 | 20.7 ± 0.6 | 31.3 ± 5.1 |
| Doubao Seed 2.1 Pro | 85.8 ± 0.7 | 44.6 ± 2.2 | 73.2 ± 1.0 | 5.0 ± 1.0 | 26.0 ± 1.0 | 33.7 ± 0.6 |

<sup>†</sup>Excludes items refused by the model's API service. Grok 4.6 runs at `xhigh`, every other model at `max`.

## Selection

Each tier is sampled independently and uniformly without replacement with `random.Random("2027:<tier>")`, releasing `ceil(0.30 × full item count)` items from the full corpus sorted by item ID. The selection script and SHA-256 commitments to every full tier are in the GitHub repository (`build_public_subset.py`, `selection_manifest.json`).

## Evaluate on the Full Benchmark

The held-out 70% is not public. To have a model evaluated on the full benchmark, email Ming Zhang (mingzhang23@m.fudan.edu.cn) or Maxm Pan (maxmpan@tencent.com), and we will run it under the same protocol as the leaderboard.

## License

Apache-2.0.

## Citation

```bibtex
@article{zhang2026hyperlogic,
  title   = {HyperLogic: A Hard, Forward-Authored Chinese Logical Reasoning
             Benchmark with Execution-Derived Answers},
  author  = {Zhang, Ming and Peng, Qiyuan and Wei, Yinxi and Shen, Yujiong and
             Tan, Kexin and Wang, Yuhui and Xiang, Zhenghao and Ye, Junjie and
             Yin, Zhangyue and Xi, Zhiheng and Dou, Shihan and Wang, Weikang and
             Zhang, Yuhao and Gui, Tao and Yang, Ruizhi and Zhang, Qi and
             Huang, Xuanjing and Chen, Alex and Pan, Maxm},
  journal = {arXiv preprint arXiv:2605.19597},
  year    = {2026}
}
```