9 papers
Self-Evolving Deep Research via Joint Generation and Evaluation
Han Zhu, Chengkun Cai, Yuanfeng Song +3
Large Language Models (LLMs) have become increasingly adopted in daily applications, with deep research standing out as a particularly important capability. Unlike traditional ques…
Not Just the Destination, But the Journey: Reasoning Traces Causally Shape Generalization Behaviors
Pengcheng Wen, Yanxu Zhu, Jiapeng Sun +5
Chain-of-Thought (CoT) is often viewed as a window into LLM decision-making, yet recent work suggests it may function merely as post-hoc rationalization. This raises a critical ali…
Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report v1.5
Dongrui Liu, Yi Yu, Jie Zhang +18
To understand and identify the unprecedented risks posed by rapidly advancing artificial intelligence (AI) models, Frontier AI Risk Management Framework in Practice presents a comp…
What, Whether and How? Unveiling Process Reward Models for Thinking with Images Reasoning
Yujin Zhou, Pengcheng Wen, Jiale Chen +6
The rapid advancement of Large Vision Language Models (LVLMs) has demonstrated excellent abilities in various visual tasks. Building upon these developments, the thinking with imag…
SafeMT: Multi-turn Safety for Multimodal Language Models
Han Zhu, Juntao Dai, Jiaming Ji +8
With the widespread use of multi-modal Large Language models (MLLMs), safety issues have become a growing concern. Multi-turn dialogues, which are more common in everyday interacti…
LegalReasoner: Step-wised Verification-Correction for Legal Judgment Reasoning
Weijie Shi, Han Zhu, Jiaming Ji +7
Legal judgment prediction (LJP) aims to function as a judge by making final rulings based on case claims and facts, which plays a vital role in the judicial domain for supporting c…
SafeLawBench: Towards Safe Alignment of Large Language Models
Chuxue Cao, Han Zhu, Jiaming Ji +7
With the growing prevalence of large language models (LLMs), the safety of LLMs has raised significant concerns. However, there is still a lack of definitive standards for evaluati…
Safe RLHF-V: Safe Reinforcement Learning from Multi-modal Human Feedback
Jiaming Ji, Xinyu Chen, Rui Pan +13
Multimodal large language models (MLLMs) are essential for building general-purpose AI assistants; however, they pose increasing safety risks. How can we ensure safety alignment of…
MegaFake: A Theory-Driven Dataset of Fake News Generated by Large Language Models
Lionel Z. Wang, Ka Chung Ng, Yiming Ma +1
Fake news significantly influences decision-making processes by misleading individuals, organizations, and even governments. Large language models (LLMs), as part of generative AI,…