arXiv Machine Learning
Agent Behavior Maps: Locating Agents by What They Would Do, Without Running Them
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Agent Behavior Maps: Locating Agents by What They Would Do, Without Running Them
LLM agents with similar success rates can follow different action sequences. An Agent Behavior Map represents LLM-harness combinations using centered action log-likelihoods on shared reference traject...
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MAPF-World: Action World Model for Multi-Agent Path Finding
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Predicting Consequences and Reinforcing Navigation Policies with Latent World Models
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Self-Evolving World Models for LLM Agent Planning
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KC-Bench: A Dynamic Interactive Benchmark for Evaluating Knowledge Conflicts in LLM Agents
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