12 papers · 1 filter
Recursive Environment Scaling for General Long-Horizon Agents
Lingxiao Jing, Zhengbo Jiao, Yichen Xiang +10
We study environment scaling as a way to increase agent-benchmark difficulty while holding core task objectives fixed. Our framework, Recursive Environment Scaling (RES), transform…
EmoRSS: Mitigating Emotion-Induced Over-Refusal in Large Language Models
Shuyi Miao, Yaojin Ma, Chenhang Cui +5
Emotional expression can influence the safety decisions of large language models (LLMs), offering a potential avenue for improving safety alignment. Existing studies have mainly fo…
BARQ: Balanced Codebook Refinement for Low-Bit LLM Quantization
Chenhang Cui, Xu Xie, Linrui Xu +4
As large language models (LLMs) grow in parameter count, model storage and parameter memory traffic have become major bottlenecks to efficient deployment. Codebook-based weight qua…
DV-Lens: Revealing the Functional Organization of Language Model Parameters
Chenhang Cui, Jian Yu, Shuyi Miao +5
Understanding parameter functions helps elucidate the internal mechanisms of large language models (LLMs). However, how to connect parameters from different modules to verifiable o…
ACTR: Aligning Thoughts and Responses for Multilingual Safety in Reasoning LLMs
Xianhui Zhang, Jian Yu, Chengyu Xie +6
Ensuring the safety of reasoning large language models (LLMs) across languages is essential for their reliable deployment. However, when exposed to jailbreak attacks in non-high-re…
WildRoadBench: A Wild Aerial Road-Damage Grounding Benchmark for Vision-Language Models and Autonomous Agents
Bingnan Liu, Chenhang Cui, Rui Huang +7
We introduce WildRoadBench, a wild aerial road-damage grounding benchmark that couples direct visual grounding by vision-language models with autonomous research-and-engineering by…
VitaBench 2.0: Evaluating Personalized and Proactive Agents in Long-Term User Interactions
Yuxin Chen, Yi Zhang, Zhengzhou Cai +11
Large language models (LLMs) have evolved into interactive agents that collaborate with users in real-world tasks. Effective collaboration in such settings increasingly depends on…
Do LLMs and VLMs Share Neurons for Inference? Evidence and Mechanisms of Cross-Modal Transfer
Chenhang Cui, An Zhang, Yuxin Chen +5
Large vision-language models (LVLMs) have rapidly advanced across various domains, yet they still lag behind strong text-only large language models (LLMs) on tasks that require mul…
Transport and Merge: Cross-Architecture Merging for Large Language Models
Chenhang Cui, Binyun Yang, Fei Shen +5
Large language models (LLMs) achieve strong capabilities by scaling model capacity and training data, yet many real-world deployments rely on smaller models trained or adapted from…
Risky-Bench: Evaluating Agentic Safety Risks in Real-World Life-Assist Deployments
Jingnan Zheng, Yanzhen Luo, Jingjun Xu +8
Large Language Models (LLMs) are increasingly deployed as agents that operate in real-world environments, introducing safety risks beyond linguistic harm through tool use, external…
Self-Guard: Defending Large Reasoning Models via enhanced self-reflection
Jingnan Zheng, Jingjun Xu, Yanzhen Luo +6
The emergence of Large Reasoning Models (LRMs) introduces a new paradigm of explicit reasoning, enabling remarkable advances yet posing unique risks such as reasoning manipulation…
Lingua-SafetyBench: A Benchmark for Safety Evaluation of Multilingual Vision-Language Models
Enyi Shi, Pengyang Shao, Yanxin Zhang +5
The robust safety of Vision-Language Large Models (VLLMs) against joint multilingual and multimodal threats remains severely underexplored. Current benchmarks typically isolate the…