62 citations · 153 across the 36 of their papers we have counts for
9 papers · 1 filter
Self-Evaluating Recursive Agents
TianYi Lyu, Xiaozhe Li, Yang Li +7
Recursive language-model agents decompose tasks and delegate subtasks to child instances of the same policy, forming a tree of work. Training them, however, is hard: the final outc…
Action from Adjacent Set in Physical Space Outperforms the Best Prediction in World Models
Liangyu Li, Qingwen Liu, Mingqing Liu +1
Controllers based on sampling and latent world models assign a predicted terminal cost to each candidate action sequence, choose the minimum, execute its first action block, and re…
MIRA: Medical Image Reflection for Agentic Diagnosis
Shengzhi Wang, Jun Yang, Kai Wu +11
Medical visual agents can use tools to inspect images and retrieve external knowledge, but indiscriminate tool use may introduce noisy or misleading evidence. Reliable diagnosis th…
Beyond Mode Collapse: Distribution Matching for Diverse Reasoning
Xiaozhe Li, Yang Li, Xinyu Fang +10
On-policy reinforcement learning methods like GRPO suffer from mode collapse: they exhibit reduced solution diversity, concentrating probability mass on a single solution once disc…
Forge: Quality-Aware Reinforcement Learning for NP-Hard Optimization in LLMs
Xiaozhe Li, Xinyu Fang, Shengyuan Ding +5
Large Language Models (LLMs) have achieved remarkable success on reasoning benchmarks through Reinforcement Learning with Verifiable Rewards (RLVR), excelling at tasks such as math…
OPT-BENCH: Evaluating the Iterative Self-Optimization of LLM Agents in Large-Scale Search Spaces
Xiaozhe Li, Jixuan Chen, Xinyu Fang +4
Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning and tool use. However, the fundamental cognitive faculties essential for problem solving, includ…
World Model Control by Trajectory Reachability Metrics
Liangyu Li, Shengzhi Wang, Libin Qiu +2
Latent world models can learn representations that contain information needed for control, while the downstream controller may still rank candidate actions poorly when it relies on…
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…
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…