Author

Tongkun Guan

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#artificial intelligence Preprint Oct 2026

PerceptRL: Making Visual Perception Rewardable in MLLMs from Answers to Interactions

PerceptRL is presented, which strengthens perception at two complementary levels in multimodal large language models by using a multi-agent pipeline to extract verified errors from dense image captions and convert the corresponding visual facts into QA pairs with fixed answer labels and rule-based rewards.

Tong-Kun Guan, Zhi-Bo Yang, Ming-Kun Yang et al. · 0 citations

Preprint Aug 2026

VaRS-Doc: Interpretation-Aware Variant Representations via Latent Self-Probing for Visual Document Retrieval

VaRS-Doc is proposed, a visual document retrieval framework that diversifies document representations by enabling the model to actively explore variant latent interpretations during document encoding, while preserving efficient late-interaction retrieval in which each query adaptively selects the best-fit representatio...

Hao-Cheng Wang, Tongkun Guan, Wei Shen et al. · 0 citations