Author

Peixian Chen

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Preprint Aug 2026

Video-IFBench: Evaluating Instruction Following of Multimodal LLMs in Video Understanding Scenarios

This work conducts a large-scale evaluation of more than 20 recent MLLMs and shows that video instruction following remains challenging for current models, especially for instructions with many constraints, semantic constraints, or complex conditional structures that require selecting the correct branch or path based on video content.

Hongbo Liu, Peixian Chen, Siyuan Liu et al. · 0 citations

Let VLMs Grade Their Own Thoughts: A Self-Quantification Approach to Reasoning-Aware Reward Modeling

This work proposes leveraging the model’s intrinsic self-evaluation to guide its optimization, and designs two novel reward functions: Sequential Confidence Rigorous Evaluation (SCRE) for challenging problems that demand strict logical reasoning, and intra-group Score Re-ranking (IGSR) for general-purpose, open-ended scenarios.

Xing Xi, Yushu Qiu, Ronghua Luo et al. · 0 citations

AutoJudger : An Adaptive Evaluation Framework for Efficient Benchmarking of MLLMs

This work introduces A 2 -Judger, a novel MLLM-based A gentic instantiation of A uto Judger equipped with semantic-aware retrieval and dynamic memory that significantly improves sample efficiency while maintaining reliable evaluation results.

Xuanwen Ding, Chengjun Pan, Zejun Li et al. · 0 citations