Researchers have introduced Modality-Decoupled Direct Preference Optimization (MoD-DPO), a framework designed to reduce cross-modal hallucinations in omni-modal large language models. The method adds modality-aware regularization to enforce sensitivity to relevant modalities while reducing reliance on spurious correlations, showing consistent improvements across audiovisual benchmarks.
Researchers propose Visual Contrastive Self-Taught Reasoner (VC-STaR), a self-improving framework that addresses a fundamental challenge in vision language models: hallucinations in visual reasoning. The approach uses contrastive VQA pairs—visually similar images with equivalent questions—to improve how VLMs identify relevant visual cues and generate more accurate reasoning paths.
Researchers propose Steer2Edit, a training-free framework that converts steering vectors into component-level weight edits targeting individual attention heads and MLP neurons. The method achieves up to 17.2% safety improvements, 9.8% gains in truthfulness, and 12.2% reduction in reasoning length while maintaining standard inference compatibility.