---
license: cc-by-4.0
language:
  - en
pretty_name: GR-Bench
task_categories:
  - question-answering
size_categories:
  - 1K<n<10K
tags:
  - benchmark
  - graph-reasoning
  - synthetic
configs:
  - config_name: default
    data_files:
      - split: main
        path: data/main/*.parquet
      - split: regime_256k_512k
        path: data/256K-512K/*.parquet
      - split: regime_512k_1m
        path: data/512K-1M/*.parquet
---

# GR-Bench

GR-Bench is the graph-structured reasoning benchmark introduced in *From
Tokens to Topology: When Large Language Models Meet Graph-Structured
Reasoning* (EMNLP 2026 Findings). It contains 1,800 programmatically generated
and oracle-verified instances spanning six task families and three prompt-length
regimes.

Evaluation code is available at
[Ryou-code/GR-Bench](https://github.com/Ryou-code/GR-Bench).

## Dataset structure

| Split | Prompt-length regime | Instances |
|---|---:|---:|
| `main` | below 256K characters | 600 |
| `regime_256k_512k` | 256K--512K characters | 600 |
| `regime_512k_1m` | 512K--1M characters | 600 |

Each split contains 100 instances from each task family:

- `MOTIF`: local three-node motif enumeration
- `BFS`: exact-depth breadth-first search
- `PATH`: bounded path enumeration
- `ART`: articulation-point identification
- `SUBISO`: non-induced subgraph matching
- `RULE`: synchronous non-monotonic rule execution

The `main` split is the 600-instance evaluation set used for the paper's
controlled cross-model comparison. The other two splits instantiate the same
mechanism families at larger graph and prompt scales.

## Fields

Each row has four fields:

- `id`: canonical release identifier of the form
  `grbench_{regime}_{task}_{index}`
- `task`: task-family label
- `prompt`: complete text-only benchmark prompt
- `answer`: oracle answer serialized as a JSON string; recover it with
  `json.loads`

The Parquet files are lossless conversions of the frozen JSONL benchmark files.

### Supplementary metadata

`metadata/metadata.parquet` provides one row per instance with the canonical
`id`, split, task family, difficulty label, prompt length, task-specific graph
statistics, answer size, generation configuration, and source task file. Its
1,800 identifiers align one-to-one with the benchmark rows.

## Construction and verification

Instances are generated programmatically from task-specific graph
constructions. Gold answers are produced by exact symbolic oracles rather than
model outputs or manual annotation. Construction-time checks enforce the
task-specific structural constraints, answer consistency, prompt formatting,
and split membership described in the paper.

## Evaluation

Predictions are parsed as JSON arrays and canonicalized as unordered sets; for
nested outputs, each inner list is canonicalized as an unordered tuple.
Set-F1 is computed independently for each instance and macro-averaged. Missing
or malformed final answers receive zero. The released code implements this
protocol and supports these Parquet files directly.

## Intended use and limitations

GR-Bench is intended for evaluating graph-structured reasoning under a
text-only interface. It is synthetic, English-only, and focused on the six
mechanisms listed above; results should not be interpreted as a comprehensive
measure of general reasoning or performance on naturally occurring graphs.
The dataset contains no human-subject or personally identifying information.

## Licenses

The dataset is released under the
[Creative Commons Attribution 4.0 International license](https://creativecommons.org/licenses/by/4.0/).
The evaluation code is released separately under the MIT License.

## Citation

```bibtex
@inproceedings{zhang-zeng-2026-tokens,
  title     = {From Tokens to Topology: When Large Language Models Meet Graph-Structured Reasoning},
  author    = {Zhang, Qihang and Zeng, Liang},
  booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2026},
  year      = {2026}
}
```