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ELMS (Evidence-based LLM-guided Monte Carlo Search), an evaluator-in-the-loop search framework for motif scaffolding that turns such evaluator feedback into targeted design actions, is introduced.
DIVE is introduced, a diversity-driven framework that enables frozen LLMs to improve by evolving persistent natural-language skills from task experience and verifier feedback and consistently outperforms existing reasoning methods, prompt-optimization approaches, skill-development frameworks, and memory-based baselines...
MOSAIC is proposed, a grid-based framework that adversarially co-evolves problem instances and specialist heuristics inside a Quality-Diversity archive indexed by structural instance features that consistently outperforms state-of-the-art LLM-based AHD methods.
This work proposes a novel neuro-symbolic fast-slow thinking (NeSyFS) framework for LLM agent, addressing the challenges introduced by partial observability in a unified approach, and uses a knowledge graph to represent the belief state.
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