STEAM is a training-free test-time framework that improves success rate, makespan, and cost of existing learning-based decentralized MAPF policies by up to 60% via congestion-aware cost-to-go and logit adjustments.
Pogema: A benchmark platform for cooperative multi-agent pathfinding
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Evo-Memory is a new streaming benchmark and evaluation framework for self-evolving memory in LLM agents, unifying over ten memory modules and introducing the ReMem pipeline for continual improvement on multi-turn and reasoning datasets.
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STEAM: A Training-Free Congestion-Aware Enhancement Framework for Decentralized Multi-Agent Path Finding
STEAM is a training-free test-time framework that improves success rate, makespan, and cost of existing learning-based decentralized MAPF policies by up to 60% via congestion-aware cost-to-go and logit adjustments.
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Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory
Evo-Memory is a new streaming benchmark and evaluation framework for self-evolving memory in LLM agents, unifying over ten memory modules and introducing the ReMem pipeline for continual improvement on multi-turn and reasoning datasets.