2 citations · 3 across the 11 of their papers we have counts for
7 papers · 1 filter
Rectifying LLM Thought from Lens of Optimization
Junnan Liu, Hongwei Liu, Songyang Zhang +1
Recent advancements in large language models (LLMs) have been driven by their emergent reasoning capabilities, particularly through long chain-of-thought (CoT) prompting, which ena…
How Brittle is Agent Safety? Rethinking Agent Risk under Intent Concealment and Task Complexity
Zihan Ma, Dongsheng Zhu, Shudong Liu +6
Current safety evaluations for LLM-driven agents primarily focus on atomic harms, failing to address sophisticated threats where malicious intent is concealed or diluted within com…
CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome Reward
Shudong Liu, Hongwei Liu, Junnan Liu +8
Answer verification is crucial not only for evaluating large language models (LLMs) by matching their unstructured outputs against standard answers, but also serves as the reward m…
When Tools Hurt LLM Reasoning: State-Dependent Belief Revision under External Evidence
Yufeng Zhao, Junnan Liu, Hongwei Liu +4
Tool use is often assumed to monotonically improve reasoning, where external evidence is expected to help when relevant and be ignored when irrelevant. We show that this assumption…
Rethinking Verification for LLM Code Generation: From Generation to Testing
Zihan Ma, Taolin Zhang, Maosong Cao +5
Large language models (LLMs) have recently achieved notable success in code-generation benchmarks such as HumanEval and LiveCodeBench. However, a detailed examination reveals that…
Coding Triangle: How Does Large Language Model Understand Code?
Taolin Zhang, Zihan Ma, Maosong Cao +3
Large language models (LLMs) have achieved remarkable progress in code generation, yet their true programming competence remains underexplored. We introduce the Code Triangle frame…
Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective
Junnan Liu, Hongwei Liu, Linchen Xiao +5
We propose a novel framework for comprehending the reasoning capabilities of large language models (LLMs) through the perspective of meta-learning. By conceptualizing reasoning tra…