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Jianmin Chen

2 accepted papers

2026

Robust-R1: Degradation-Aware Reasoning for Robust Visual Understanding

AAAI 2026technical

Multimodal Large Language Models struggle to maintain reliable performance under extreme real-world visual degradations, which impede their practical robustness. Existing robust MLLMs predominantly rely on implicit training/adaptation that focuses solely on visual encoder generalization, suffering f

Cited by 0SourcePDFScholar
2026

Robust-U1: Can MLLMs Self-Recover Corrupted Visual Content for Robust Understanding?

ICML 2026poster

Multimodal Large Language Models (MLLMs) have demonstrated remarkable success in visual understanding, yet their performance degrades significantly under real-world visual corruptions. While existing robustness enhancement approaches exist, they are limited: black-box feature alignment lacks interpr…

Cited by 0SourceScholar