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A DbC Inspired Neurosymbolic Layer for Trustworthy Agent Design
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本文提出一种基于Design by Contract和类型理论原则的合同层,用于LLMs调用,以确保生成输出符合语义和类型要求。通过引入概率补救机制,引导生成过程符合规范。该方法揭示了LLMs作为语义解析器和概率黑盒组件的双重角色。

arXiv:2508.03665v1 Announce Type: cross Abstract: Generative models, particularly Large Language Models (LLMs), produce fluent outputs yet lack verifiable guarantees. We adapt Design by Contract (DbC) and type-theoretic principles to introduce a contract layer that mediates every LLM call. Contracts stipulate semantic and type requirements on inputs and outputs, coupled with probabilistic remediation to steer generation toward compliance. The layer exposes the dual view of LLMs as semantic parsers and probabilistic black-box components. Contract satisfaction is probabilistic and semantic validation is operationally defined through programmer-specified conditions on well-typed data structures. More broadly, this work postulates that any two agents satisfying the same contracts are \emph{functionally equivalent} with respect to those contracts.

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LLMs 生成模型 合同层 语义验证 类型理论
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