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20242026
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7 papers · 1 filter

cs.AI2026

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…

cs.AI2026

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…

cs.AI2026

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…

cs.AI2026

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…

cs.AI2026

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…

cs.AI2026

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…

cs.AI2025

RSafe: Incentivizing proactive reasoning to build robust and adaptive LLM safeguards

Jingnan Zheng, Xiangtian Ji, Yijun Lu +6

Large Language Models (LLMs) continue to exhibit vulnerabilities despite deliberate safety alignment efforts, posing significant risks to users and society. To safeguard against th…