Intent2Tx: Benchmarking LLMs for Translating Natural Language Intents into Ethereum Transactions
The emergence of Large Language Models (LLMs) offers a transformative interface for Web3, yet existing benchmarks fail to capture the complexity of translating high-level user intents into functionally correct, state-dependent on-chain transactions. We present \textsc{Intent2Tx}, a high-fidelity benchmark featuring 29,921 single-step and 1,575 multi-step instances meticulously derived from 300 days of real-world Ethereum mainnet traces. Unlike prior works that rely on synthetic instructions, \textsc{Intent2Tx} grounds natural language intents in real-world protocol interactions across 11 categories, including diverse long-tail Decentralized Finance (DeFi) primitives. To enable rigorous ev...
| Authors | Zhuoran Pan, Yue Li, Zhi Guan et al. |
|---|---|
| Primary category | cs.AI |
| Published | 30 April 2026 |
| Last updated at source | 30 April 2026 |
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