1 citations · 3 across the 20 of their papers we have counts for
9 papers · 1 filter
SPADE-Bench: Evaluating Spontaneous Strategic Deception in Agents via Plan-Action Divergence
Yuyan Bu, Haowei Li, Qirui Zheng +7
As LLM-based agents expand their operational scope, reliability becomes a prerequisite for real-world deployment. However, in practical applications, human users cannot monitor eve…
TVIR: Building Deep Research Agents Towards Text-Visual Interleaved Report Generation
Xinkai Ma, Zhiqi Bai, Dingling Zhang +21
Deep Research Agents have shown strong capability in multi-step information retrieval, reasoning, and long-form report generation, but existing benchmarks and systems remain predom…
IFEvalCode: Controlled Code Generation
Jian Yang, Wei Zhang, Shukai Liu +9
Code large language models (Code LLMs) have made significant progress in code generation by translating natural language descriptions into functional code; however, real-world appl…
KORGym: A Dynamic Game Platform for LLM Reasoning Evaluation
Jiajun Shi, Jian Yang, Jiaheng Liu +26
Recent advancements in large language models (LLMs) underscore the need for more comprehensive evaluation methods to accurately assess their reasoning capabilities. Existing benchm…
CodeCriticBench: A Holistic Code Critique Benchmark for Large Language Models
Alexander Zhang, Marcus Dong, Jiaheng Liu +15
The critique capacity of Large Language Models (LLMs) is essential for reasoning abilities, which can provide necessary suggestions (e.g., detailed analysis and constructive feedba…
HiddenDetect: Detecting Jailbreak Attacks against Large Vision-Language Models via Monitoring Hidden States
Yilei Jiang, Xinyan Gao, Tianshuo Peng +4
The integration of additional modalities increases the susceptibility of large vision-language models (LVLMs) to safety risks, such as jailbreak attacks, compared to their language…
"See the World, Discover Knowledge": A Chinese Factuality Evaluation for Large Vision Language Models
Jihao Gu, Yingyao Wang, Pi Bu +17
The evaluation of factual accuracy in large vision language models (LVLMs) has lagged behind their rapid development, making it challenging to fully reflect these models' knowledge…
Chinese SafetyQA: A Safety Short-form Factuality Benchmark for Large Language Models
Yingshui Tan, Boren Zheng, Baihui Zheng +9
With the rapid advancement of Large Language Models (LLMs), significant safety concerns have emerged. Fundamentally, the safety of large language models is closely linked to the ac…
Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language Models
Yancheng He, Shilong Li, Jiaheng Liu +15
New LLM evaluation benchmarks are important to align with the rapid development of Large Language Models (LLMs). In this work, we present Chinese SimpleQA, the first comprehensive…