@inproceedings{bystronski-etal-2026-beyond,
    title = "Beyond Discrete Search: Divergent Thinking as Intention Optimization in Latent Space",
    author = "Bystro{\'n}ski, Mateusz  and
      Piotrowski, Grzegorz  and
      Kajdanowicz, Tomasz Jan",
    editor = "T.Y.S.S., Santosh  and
      Rodriguez, Juan Diego  and
      de Gibert, Ona",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-srw.88/",
    pages = "1007--1016",
    ISBN = "979-8-89176-393-7",
    abstract = "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{\texttimes} its size."
}