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Beyond Discrete Search: Divergent Thinking as Intention Optimization in Latent Space
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:44
摘要
We argue that LLM-based coding agents frequently fail to solve problems that lie within the model’s capacity and the bottleneck is often the conditioning context rather than the model itself. We formalize this for the full class of Turing-computable problems with verifiable specifications and introduce a framework that recasts coding as optimization overconditioning contexts that influence the generation of natural-languagesolution intentions. Guided by execution feedback, the method searches thiscontinuous context space to steer a coding agent toward correct solutions. The method operates as a plug-in layer that can wrap any coding agent without modifying its architecture or weights. On SWE-Bench Verified, our method raises the resolution rate of a weak, quantized 24B open-weight model to parity with frontier models +25× its size.
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记录 #1 · 2026-07 · 最新观测版本
Beyond Discrete Search: Divergent Thinking as Intention Optimization in Latent Space
We argue that LLM-based coding agents frequently fail to solve problems that lie within the model’s capacity and the bottleneck is often the conditioning context rather than the model itself. We formalize this for the full class of Turing-computable problems with verifiable specifications and introduce a framework that recasts coding as optimization overconditioning contexts that influence the generation of natural-languagesolution intentions. Guided by execution feedback, the method searches thiscontinuous context space to steer a coding agent toward correct solutions. The method operates as a plug-in layer that can wrap any coding agent without modifying its architecture or weights. On SWE-Bench Verified, our method raises the resolution rate of a weak, quantized 24B open-weight model to parity with frontier models +25× its size.
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