1 citations · 1 across the 8 of their papers we have counts for
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HyperMCTS: Hypergraph-Augmented MCTS for Long-Horizon LLM Agents
Tingsong Xiao, Nithish Balachandar Moudhgalya, Chandrayee Basu +5
Long-horizon tasks require large language model (LLM) agents to coordinate decisions under constraints that span an entire solution. Monte Carlo Tree Search (MCTS) offers a promisi…
When Is Noise Response Universal? Tokenization as the Hidden Variable in Language Models
Yefan Tao, Gerald Friedland, Luyang Kong
The performance of textual neural models often degrades when their inputs are corrupted by noise such as typos, OCR errors, or dropped words. We study the degradation rate across n…
Reflection or Re-Generation? Why LLM Revision Fails Where Human Revision Succeeds
Yefan Tao, Gerald Friedland, Madhusudhanan Chandrasekaran +1
Reflection, the ability to revisit and revise prior reasoning, is central to how humans improve their answers. Large language models (LLMs) are increasingly prompted to "reflect,"…
Lean4Agent: Formal Modeling and Verification for Agent Workflow and Trajectory
Ruida Wang, Jerry Huang, Pengcheng Wang +3
Equipping Large Language Models (LLMs) to execute reliable multi-step workflows has become a central challenge in artificial intelligence. Despite recent advances in LLMs' agentic…
SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks
Xiangyi Li, Yimin Liu, Wenbo Chen +75
Agent Skills are structured packages of procedural knowledge that augment large language model (LLM) agents at inference time. Despite rapid adoption, there is no standard way to m…