From the 1 of 11 linked papers with an AI index.
7 papers · 1 filter
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…
Dissecting Tool-Integrated Reasoning: An Empirical Study and Analysis
Yufeng Zhao, Junnan Liu, Hongwei Liu +4
Large Language Models (LLMs) have made significant strides in reasoning tasks through methods like chain-of-thought (CoT) reasoning. However, they often fall short in tasks requiri…
Are Your LLMs Capable of Stable Reasoning?
Junnan Liu, Hongwei Liu, Linchen Xiao +6
The rapid advancement of large language models (LLMs) has shown remarkable progress in complex reasoning tasks. However, a significant disparity exists between benchmark performanc…
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…
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…