Datasets:

berryccc1
/

EVM-QuestBench

license: mit
language:
  - en
language_creators:
  - expert-generated
annotations_creators:
  - expert-generated
multilinguality:
  - monolingual
source_datasets:
  - original
pretty_name: EVM-QuestBench
task_categories:
  - text-generation
tags:
  - benchmark
  - code-generation
  - transaction-code-generation
  - execution-grounded-evaluation
  - blockchain
  - evm
  - ethereum
  - bsc
  - smart-contracts
  - defi
  - llm
  - llm-agents
  - agent-evaluation
  - acl-2026
size_categories:
  - n<1K
arxiv: '2601.06565'
doi: 10.18653/v1/2026.acl-long.1642
configs:
  - config_name: default
    data_files:
      - split: atomic
        path: data/atomic.jsonl
      - split: composite
        path: data/composite.jsonl

EVM-QuestBench

EVM-QuestBench is an execution-grounded benchmark for evaluating whether large language models and AI agents can translate natural-language blockchain intent into executable transaction code that produces the intended EVM state transition.

Released with the ACL 2026 Long Paper by Pei Yang, Wanyi Chen, Ke Wang, Lynn Ai, Eric Yang, and Tianyu Shi, the benchmark contains 107 expert-authored tasks: 62 atomic tasks and 45 composite workflows. Unlike code-similarity benchmarks, EVM-QuestBench executes generated actions on a snapshot-isolated BSC fork and validates receipts, calldata, balances, allowances, ownership, and other post-state evidence.

Resources

Paper (ACL Anthology): https://aclanthology.org/2026.acl-long.1642/
Paper (arXiv): https://arxiv.org/abs/2601.06565
GitHub repository: https://github.com/OpenEdgeHQ/EVM-quest-bench
Project website: https://openedgehq.github.io/EVM-quest-bench/
Dataset Viewer: https://huggingface.co/datasets/berryccc1/EVM-QuestBench/viewer/default
DOI: https://doi.org/10.18653/v1/2026.acl-long.1642

What the benchmark evaluates

EVM-QuestBench covers native-token transfers, ERC-20 and NFT operations, contract calls, PancakeSwap swaps and liquidity, staking, flashloans, blockchain queries, and multi-step DeFi workflows.

Atomic: one transaction or query, evaluated with task-specific execution validators.
Composite: multi-step planning and execution with feedback, parameter propagation, and step-efficiency decay.
Ground truth: successful execution and the requested EVM state change, not string similarity to reference code.

Load the dataset

from datasets import load_dataset

dataset = load_dataset("berryccc1/EVM-QuestBench")
print(dataset)
print(dataset["atomic"][0])

The default configuration exposes two splits:

Split	Rows	Description
`atomic`	62	Single transaction or query tasks
`composite`	45	Multi-step transaction workflows

Dataset structure

Each row includes:

id, task_type, title, category, subcategory, and difficulty
description and natural_language_templates
chain, network, tags, and parameter_names
JSON-serialized parameters, validation, composite_structure, interaction_config, scoring_strategy, and metadata
source_path and the complete JSON-serialized raw_definition

Nested task definitions are serialized as JSON strings to provide one stable schema across all 107 rows and maintain Dataset Viewer compatibility.

Dataset sources and reproducibility

The dataset is derived from the task definitions released in the official EVM-QuestBench repository. The benchmark/ directory preserves the original task definitions, validators, system configurations, and parameter generator used to construct the viewer-friendly splits.

Running the full execution benchmark requires the Anvil/BSC environment, TypeScript runner, model API configuration, and dependencies documented in the GitHub repository. The Hugging Face dataset is the canonical browsable distribution of task definitions; the GitHub repository remains the canonical benchmark implementation.

Dataset creation

The tasks were authored by the benchmark creators to cover representative EVM transaction and query capabilities. Natural-language templates and parameter schemas dynamically instantiate task variants. Atomic tasks use task-specific validators, while composite workflows define ordered operations, shared parameters, optimal steps, and scoring strategies. See the paper and repository for the complete methodology.

Intended use

This dataset is intended for:

evaluating LLM and agent transaction-code generation;
studying single-step versus multi-step execution reliability;
developing execution-grounded validators and EVM agent evaluation methods;
reproducing or extending the EVM-QuestBench benchmark.

It is not a substitute for smart-contract auditing, financial advice, wallet security review, or testing transactions with real funds.

Limitations

The execution environment targets EVM-compatible chains and is instantiated primarily on a BSC mainnet fork.
Public task definitions may contribute to benchmark contamination; report training exposure when known.
The 107 tasks do not cover every contract, protocol, chain, or adversarial on-chain condition.
Successful benchmark execution does not establish that generated code is safe for production or real assets.
Full scores require the external execution environment; this dataset alone provides definitions and reproducibility artifacts.

Dataset card authors and contact

Dataset card maintained by the EVM-QuestBench authors. For questions or corrections, open an issue at https://github.com/OpenEdgeHQ/EVM-quest-bench/issues.

Citation

@inproceedings{yang-etal-2026-evm-questbench,
  title     = {EVM-QuestBench: An Execution-Grounded Benchmark for Natural-Language Transaction Code Generation},
  author    = {Yang, Pei and Chen, Wanyi and Wang, Ke and Ai, Lynn and Yang, Eric and Shi, Tianyu},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics},
  year      = {2026},
  pages     = {35513--35529},
  publisher = {Association for Computational Linguistics},
  url       = {https://aclanthology.org/2026.acl-long.1642/},
  doi       = {10.18653/v1/2026.acl-long.1642}
}

License

MIT. See the source repository for the benchmark implementation and provenance.