5 papers · 1 filter
From Token Correction to Claim Admission: Training-Free Hallucination Mitigation for Large Vision-Language Models
Hyeonggeun Han, Taehyun Cho, Sangwoo Hong
Large vision-language models often exhibit object hallucination, generating claims that are not supported by the input image. To mitigate this issue, we propose Evidence-Guided Vis…
Mitigating Sequential Reappearance in Diffusion Data-Point Unlearning
Donghyun Kim, Taehyuk Lee, Jinyeong Kim +4
Diffusion data-point unlearning is typically evaluated immediately after each deletion, even though subsequent requests may repeatedly update the same model. We identify sequential…
Mechanistic Circuit Identification for Controllable Data Generation
Nakyung Lee, Sangwoo Hong, Jungwoo Lee
While recent advances in data synthesis aim to curate high-quality datasets, most generation pipelines still rely on heuristic prompt-based control. This black-box paradigm provide…
Function-Level Execution Feedback for Code Preference Optimization
Idris Nechnech, Sehwan Kim, Jimin Seo +4
Process supervision has improved mathematical reasoning, where intermediate steps are naturally expressed as chains of thought. In code generation, however, process supervision rem…
MVP-LAM: Learning Action-Centric Latent Action via Cross-Viewpoint Reconstruction
Jung Min Lee, Dohyeok Lee, Seokhun Ju +5
Latent actions learned from diverse human videos serve as pseudo-labels for vision-language-action (VLA) pretraining, but provide effective supervision only if they remain informat…