5 papers · 1 filter
Agent Planning Benchmark: A Diagnostic Framework for Planning Capabilities in LLM Agents
Haoyu Sun, Wenxuan Wang, Mingyang Song +5
Planning is central to LLM agents: before acting, an agent must decompose goals, select tools, reason over constraints, and decide when a task is infeasible. Yet existing agent eva…
Characterizing, Evaluating, and Optimizing Complex Reasoning
Haoran Zhang, Yafu Li, Zhi Wang +4
Large Reasoning Models (LRMs) increasingly rely on reasoning traces with complex internal structures. However, existing work lacks a unified answer to three fundamental questions:…
LatentMem: Customizing Latent Memory for Multi-Agent Systems
Muxin Fu, Xiangyuan Xue, Yafu Li +5
Large language model (LLM)-powered multi-agent systems (MAS) demonstrate remarkable collective intelligence, wherein multi-agent memory serves as a pivotal mechanism for continual…
FaithRL: Learning to Reason Faithfully through Step-Level Faithfulness Maximization
Runquan Gui, Yafu Li, Xiaoye Qu +3
Reinforcement Learning with Verifiable Rewards (RLVR) has markedly improved the performance of Large Language Models (LLMs) on tasks requiring multi-step reasoning. However, most R…
New Skills or Sharper Primitives? A Probabilistic Perspective on the Emergence of Reasoning in RLVR
Zhilin Wang, Yafu Li, Shunkai Zhang +4
Whether Reinforcement Learning with Verifiable Rewards (RLVR) endows Large Language Models (LLMs) with new capabilities or merely elicits latent traces remains a central debate. In…