AI RESEARCH
Intent2Tx: Benchmarking LLMs for Translating Natural Language Intents into Ethereum Transactions
arXiv CS.AI
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ArXi:2604.27763v1 Announce Type: new 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.