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TAMP-Nav is a unified framework that enhances embodied navigation by aligning vision-language models with 2D visual prompting, selective reasoning with compressed memory, and policy optimization, achieving state-of-the-art performance with high runtime and sample efficiency.
A Microsoft and SJTU research paper introduces FastContext, a dedicated exploration subagent for coding agents that separates repository navigation from task solving, reducing orchestrator token usage by up to 60% and improving resolution rates by 5.5% on SWE-bench benchmarks.