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cs.CL2026
Beyond Raw Context Transfer: Representation-based Federated Retrieval-Augmented Generation
Can Peng, Yu Liu, Yingyu Yang +4
Retrieval-augmented generation (RAG) improves the factuality of large language models (LLMs) and vision-language models (VLMs) by grounding generation in external knowledge. Howeve…
cs.LG2026
DECAF: De-Clustering for Adaptive Representational Unlearning
Anjie Le, Can Peng, Hongcheng Guo +1
Machine unlearning, which aims to remove the influence of specific training data from a trained model, is a key requirement for privacy, accountability, and adaptive deployment. We…
cs.CV2026
From Failure to Feedback: Group Revision Unlocks Hard Cases in Object-Level Grounding
Yuyuan Liu, Yiping Ji, Anjie Le +6
Finetuning Large Vision-Language Models with reinforcement learning has emerged as a promising approach to enhance their capability in object-level grounding. However, existing met…