A hybrid LLM-symbolic verifier maintains a dependency graph over conversation turns classified into eight formal update operations, enabling linear-time groundedness checks and precise retraction propagation with a conflict-free guarantee.
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LAR learns a compact latent action space from trajectories that shortens the effective decision horizon for LLM agents, reducing token count and inference time while preserving task success.
SkillMAS couples skill evolution and MAS restructuring via utility learning from traces, bounded skill updates, and evidence-gated team changes, reporting competitive results across manipulation, CLI, and retail tasks.
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Grounded Continuation: A Linear-Time Runtime Verifier for LLM Conversations
A hybrid LLM-symbolic verifier maintains a dependency graph over conversation turns classified into eight formal update operations, enabling linear-time groundedness checks and precise retraction propagation with a conflict-free guarantee.
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Latent Action Reparameterization for Efficient Agent Inference
LAR learns a compact latent action space from trajectories that shortens the effective decision horizon for LLM agents, reducing token count and inference time while preserving task success.
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SkillMAS: Skill Co-Evolution with LLM-based Multi-Agent System
SkillMAS couples skill evolution and MAS restructuring via utility learning from traces, bounded skill updates, and evidence-gated team changes, reporting competitive results across manipulation, CLI, and retail tasks.