VQV Signal
Agentic synthesis addresses the issue of coding agents fixing errors without preserving domain rules, which can cause repeated mistakes. The method uses counterexample-supplemented sketches, starting from a human partial code sketch to guide the agent's implementation.
Agentic synthesis addresses the issue of coding agents fixing errors without preserving domain rules, which can cause repeated mistakes. The method uses counterexample-supplemented sketches, starting from a human partial code sketch to guide the agent's implementation.
AI-assisted summary based on the listed source.
Coding agents can fix a failing example without preserving the domain rule that made it fail, so later generations can repeat the same plausible mistake. We present agentic synthesis against counterexample-supplemented sketches, a repository-native method for systems whose governing policy is discovered during...
This approach helps coding agents maintain domain rules during code generation, reducing repeated errors and improving the reliability of automated coding tools. It advances how coding agents learn and adapt policies during implementation.
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VQV surfaced this signal because it is recent, relevant to AI Coding Tools, connected to arXiv.
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