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From the 1 of 47 linked papers with an AI index.

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20242026
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cs.AI2026

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…

cs.AI2026

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…

cs.AI2026

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…

cs.AI2026

T-COL: Generating Counterfactual Explanations for General User Preferences on Variable Machine Learning Systems

Ming Wang, Daling Wang, Wenfang Wu +2

To address the interpretability challenge in machine learning (ML) systems, counterfactual explanations (CEs) have emerged as a promising solution. CEs are unique as they provide w…

cs.AI2026

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…