8 papers
Learning to Solve, Forgetting to Retain: Correct-Set Turnover in RLVR
Chuanyu Qin, Chenxu Yang, Qingyi Si +3
Reinforcement learning with verifiable rewards (RLVR) improves the ability of large language model, yet headline accuracy gains often conceal a hidden cost: previously solved probl…
Online Self-Calibration Against Hallucination in Vision-Language Models
Minghui Chen, Chenxu Yang, Hengjie Zhu +3
Large Vision-Language Models (LVLMs) often suffer from hallucinations, generating descriptions that include visual details absent from the input image. Recent preference alignment…
Diagnosing and Repairing Unsafe Channels in Vision-Language Models via Causal Discovery and Dual-Modal Safety Subspace Projection
Jinhu Fu, Yihang Lou, Qingyi Si +3
Large Vision-Language Models (LVLMs) have achieved impressive performance across multimodal understanding and reasoning tasks, yet their internal safety mechanisms remain opaque an…
Beyond the Covariance Trap: Unlocking Generalization in Same-Subject Knowledge Editing for Large Language Models
Xiyu Liu, Qingyi Si, Zhengxiao Liu +3
While locate-then-edit knowledge editing efficiently updates knowledge encoded within Large Language Models (LLMs), a critical generalization failure mode emerges in the practical…
A Closer Look into LLMs for Table Understanding
Jia Wang, Chuanyu Qin, Mingyu Zheng +3
Despite the success of Large Language Models (LLMs) in table understanding, their internal mechanisms remain unclear. In this paper, we conduct an empirical study on 16 LLMs, cover…
HiMemVLN: Enhancing Reliability of Open-Source Zero-Shot Vision-and-Language Navigation with Hierarchical Memory System
Kailin Lyu, Kangyi Wu, Pengna Li +9
LLM-based agents have demonstrated impressive zero-shot performance in vision-language navigation (VLN) tasks. However, most zero-shot methods primarily rely on closed-source LLMs…