32 citations · 63 across the 64 of their papers we have counts for
5 papers · 2 filters
SafeAdapt-Poker: Counterfactual Risk Calibration for Budgeted Adaptation under Opponent Shift
Miaobo Hu, Shuhao Hu, Xiaobo Guo +5
Opponent histories can mislead after behavioral shifts. SafeAdapt-Poker formulates budgeted adaptation under opponent shift as a dual-counterfactual sequential-control problem usin…
Can MLLMs Decode the Creative Leap? Introducing C4 for Cross-Concept Understanding
Ming Wang, Yuqing Zhang, Tingna Xie +5
Creative capabilities of MLLMs matter in design, communication, education, and human--AI collaboration, yet remain difficult to evaluate because explicit targets and reward signals…
From Parameter Dynamics to Risk Scoring : Quantifying Sample-Level Safety Degradation in LLM Fine-tuning
Xiao Wang, Yifei Zhang, YongKang Liu +4
Safety alignment of Large Language Models (LLMs) is extremely fragile, as fine-tuning on a small number of benign samples can erase safety behaviors learned from millions of prefer…
DEEPMED: Building a Medical DeepResearch Agent via Multi-hop Med-Search Data and Turn-Controlled Agentic Training & Inference
Zihan Wang, Hao Wang, Shi Feng +6
Medical reasoning models remain constrained by parametric knowledge and are thus susceptible to forgetting and hallucinations. DeepResearch (DR) models ground outputs in verifiable…
Defending Large Language Models Against Jailbreak Attacks via In-Decoding Safety-Awareness Probing
Yinzhi Zhao, Ming Wang, Shi Feng +3
Large language models (LLMs) have achieved impressive performance across natural language tasks and are increasingly deployed in real-world applications. Despite extensive safety a…