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Designing effective memory mechanisms is crucial for advancing LLM-driven Multi-Agent Systems (MAS), helping agents learn together and perform better over time. While recent work has led to strong cooperation skills, most methods still use flat, unstructured memories, which easily get filled with noise and erase differ...
This paper is the first systematic study of whether prompt-token hidden states in contemporary LLMs exhibit Gromov Hyperbolicity (GH), a distance-based measure of tree-likeness.
Reinforcement Genetic Programming is proposed, a purely GP-based and non-RL framework that explicitly realizes a stage-wise retention– reintroduction loop through a dynamically maintained subexpression pool and pool-guided population initialization, suggesting that progressive subexpression reuse is an effective mechan...
A novel, model-feedback-free LRM-DoS paradigm that employs the conflict count derived from an Satisfiability Modulo Theories (SMT) solver as a low-cost external signal to guide the synthesis of inference-heavy Constraint Satisfaction Problem (CSP) instances.
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