3 papers
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.IR2026
COINBench: Moving Beyond Individual Perspectives to Collective Intent Understanding
Xiaozhe Li, Tianyi Lyu, Siyi Yang +6
Understanding human intent is a high-level cognitive challenge for Large Language Models (LLMs), requiring sophisticated reasoning over noisy, conflicting, and non-linear discourse…
cs.CL2025
ConsintBench: Evaluating Language Models on Real-World Consumer Intent Understanding
Xiaozhe Li, TianYi Lyu, Siyi Yang +6
Understanding human intent is a complex, high-level task for large language models (LLMs), requiring analytical reasoning, contextual interpretation, dynamic information aggregatio…