ANNEAL: Adapting LLM Agents via Governed Symbolic Patch Learning

ANNEAL introduces a groundbreaking framework for adapting Large Language Model (LLM) agents. This novel approach utilizes governed symbolic patch learning, enabling LLMs to dynamically enhance their capabilities. By learning and integrating symbolic patches, these agents can improve their performance across various tasks. This method marks a significant advancement in the field of AI, promising more versatile and efficient LLM applications. It offers a new paradigm for how AI agents can learn and evolve. This research represents an important step toward more adaptable and intelligent AI systems, moving beyond static models to ones that can continuously learn and improve. The ANNEAL framework offers a robust mechanism for LLMs to become more intelligent and responsive to changing demands, fostering innovation in AI development. The paper, authored by Safayat Bin Hakim and five co-authors, was published on arXiv on May 4, 2026. It is publicly available for review and further research. The open access nature of the publication ensures that the findings can contribute to broader scientific discourse and accelerate advancements in AI. The research is cataloged under cs.AI, with related browsing contexts including cs, cs.LG, and cs.MA.