Beyond Discrete Search: Divergent Thinking as Intention Optimization in Latent Space
Mateusz Bystroński, Grzegorz Piotrowski, Tomasz Jan Kajdanowicz Wroclaw University of Science and Technology
原文摘要
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.
cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表