2 citations · 2 across the 9 of their papers we have counts for
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cs.AI2026
Escaping the Context Bottleneck: Active Context Curation for LLM Agents via Reinforcement Learning
Xiaozhe Li, Tianyi Lyu, Yizhao Yang +6
Large Language Models (LLMs) struggle with long-horizon tasks due to the "context bottleneck" and the "lost-in-the-middle" phenomenon, where accumulated noise from verbose environm…
cs.AI2023★ 2 cited
Mini-BEHAVIOR: A Procedurally Generated Benchmark for Long-horizon Decision-Making in Embodied AI
Emily Jin, Jiaheng Hu, Zhuoyi Huang +4
We present Mini-BEHAVIOR, a novel benchmark for embodied AI that challenges agents to use reasoning and decision-making skills to solve complex activities that resemble everyday hu…