Delai Qiu

Omni-modal large language models (OLLMs) offer a promising end-to-end solution for slide-enhanced speech recognition due to their inherent multimodal capabilities. However, we found a fundamental issue faced by OLLMs: Visual Interference , where models show a bias towards visible text over audito...

Reading order detection is the foundation of document understanding.Most existing methods rely on uniform supervision, implicitly assuming a constant difficulty distribution across layout regions. In this work, we challenge this assumption by revealing a critical flaw: Positional Disparity, a phe...

Reinforcement Learning with Verifiable Reward (RLVR) has proven effective for training reasoning-oriented large language models, but existing methods largely assume high-resource settings with abundant training data. In low-resource scenarios, RLVR is prone to more severe entropy collapse, which...

Omni-modal Large Language Models (OLLMs) excel in diverse tasks but struggle with complex emotional reasoning, which requires integrating textual, visual, and acoustic signals. We attribute this limitation to modality collapse, where models over-rely on a dominant modality while neglecting comple...