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AI agents struggle to translate intent into smart contracts, new study finds

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the challenges of ensuring AI-generated smart contracts accurately reflect user intent, particularly in the Web3 and blockchain space. The study highlights that common evaluation methods like AI judges and standard analysis tools often fail to detect critical errors. Researchers propose an "elicitation-first" architecture where the AI agent asks clarifying questions before generating code and verifies the output against an independently written executable model. AI

IMPACT Highlights critical gaps in AI agent reliability for code generation, especially for high-stakes applications like smart contracts.

RANK_REASON Academic paper on AI model capabilities and evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI agents struggle to translate intent into smart contracts, new study finds

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Academic paper on AI model capabilities and evaluation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Abhinav Goel, Agostino Capponi, Alfio Gliozzo, Chaitya Shah ·

    Measuring Where Intent Gets Lost in Agentic Smart Contract Synthesis

    arXiv:2610.37830v1 Announce Type: new Abstract: AI agents can now write software from a plain-English description, but code that runs is not the same as code that does what was asked. This gap matters most for smart contracts, the programs behind Web3 applications that move money…

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