 We have open-sourced Robust-R1 (AAAI 2026 Oral), a new paradigm in the field of anti-degradation and robustness enhancement for multimodal large models.
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 We have open-sourced Robust-R1 (AAAI 2026 Oral), a new paradigm in the field of anti-degradation and robustness enhancement for multimodal large models.
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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 from limited interpretability and isolated optimization. To overcome these limitations, we propose Robust-R1, a novel framework that explicitly models visual degradations through structured reasoning chains. Our approach integrates: (i) supervised fine-tuning for degradation-aware reasoning foundations, (ii) reward-driven alignment for accurately perceiving degradation parameters, and (iii) dynamic reasoning depth scaling adapted to degradation intensity. To facilitate this approach, we introduce a specialized 11K dataset featuring realistic degradations synthesized across four critical real-world visual processing stages, each annotated with structured chains connecting degradation parameters, perceptual influence, pristine semantic reasoning chain, and conclusion. Comprehensive evaluations demonstrate state-of-the-art robustness: Robust-R1 outperforms all general and robust baselines on the real-world degradation benchmark R-Bench, while maintaining superior anti-degradation performance under multi-intensity adversarial degradations on MMMB, MMStar, and RealWorldQA.
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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 from limited interpretability and isolated optimization. To overcome these limitations, we propose Robust-R1, a novel framework that explicitly models visual degradations through structured reasoning chains. Our approach integrates: (i) supervised fine-tuning for degradation-aware reasoning foundations, (ii) reward-driven alignment for accurately perceiving degradation parameters, and (iii) dynamic reasoning depth scaling adapted to degradation intensity. To facilitate this approach, we introduce a specialized 11K dataset featuring realistic degradations synthesized across four critical real-world visual processing stages, each annotated with structured chains connecting degradation parameters, perceptual influence, pristine semantic reasoning chain, and conclusion. Comprehensive evaluations demonstrate state-of-the-art robustness: Robust-R1 outperforms all general and robust baselines on the real-world degradation benchmark R-Bench, while maintaining superior anti-degradation performance under multi-intensity adversarial degradations on MMMB, MMStar, and RealWorldQA.
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We have made all of our papers, codes, data, model weights and demos fully open-source:
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We have made all of our papers, codes, data, model weights and demos fully open-source:
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Paper: 
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Paper: 
Robust-R1: Degradation-Aware Reasoning for Robust Visual Understanding (2512.17532)
https://huggingface.co/papers/2512.17532
2512.17532
paper
resource
https://huggingface.co/papers/2512.17532
 (help us to upvote)
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 (help us to upvote)
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GitHub code: 
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GitHub code: 
https://github.com/jqtangust/Robust-R1
https://github.com/jqtangust/Robust-R1
link
 (help us to star)
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 (help us to star)
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HF model: 
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HF model: 
https://huggingface.co/Jiaqi-hkust/Robust-R1
https://huggingface.co/Jiaqi-hkust/Robust-R1
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HF data: 
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HF data: 
https://huggingface.co/datasets/Jiaqi-hkust/Robust-R1
Jiaqi-hkust/Robust-R1
dataset
resource
https://huggingface.co/datasets/Jiaqi-hkust/Robust-R1
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HF Space: 
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HF Space: 
https://huggingface.co/spaces/Jiaqi-hkust/Robust-R1
Jiaqi-hkust/Robust-R1
space
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https://huggingface.co/spaces/Jiaqi-hkust/Robust-R1
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We sincerely invite everyone to give it a try. 
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We sincerely invite everyone to give it a try. 
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 We have open-sourced Robust-R1 (AAAI 2026 Oral), a new paradigm in the field of anti-degradation and robustness enhancement for multimodal large models.

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 from limited interpretability and isolated optimization. To overcome these limitations, we propose Robust-R1, a novel framework that explicitly models visual degradations through structured reasoning chains. Our approach integrates: (i) supervised fine-tuning for degradation-aware reasoning foundations, (ii) reward-driven alignment for accurately perceiving degradation parameters, and (iii) dynamic reasoning depth scaling adapted to degradation intensity. To facilitate this approach, we introduce a specialized 11K dataset featuring realistic degradations synthesized across four critical real-world visual processing stages, each annotated with structured chains connecting degradation parameters, perceptual influence, pristine semantic reasoning chain, and conclusion. Comprehensive evaluations demonstrate state-of-the-art robustness: Robust-R1 outperforms all general and robust baselines on the real-world degradation benchmark R-Bench, while maintaining superior anti-degradation performance under multi-intensity adversarial degradations on MMMB, MMStar, and RealWorldQA.

We have made all of our papers, codes, data, model weights and demos fully open-source:
Paper: https://huggingface.co/papers/2512.17532 (help us to upvote)
GitHub code: https://github.com/jqtangust/Robust-R1 (help us to star)
HF model: https://huggingface.co/Jiaqi-hkust/Robust-R1
HF data: https://huggingface.co/datasets/Jiaqi-hkust/Robust-R1
HF Space: https://huggingface.co/spaces/Jiaqi-hkust/Robust-R1 

We sincerely invite everyone to give it a try. 


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Jiaqi-hkust dhruv3006 John6666
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Jiaqi-hkust dhruv3006
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Jiaqi-hkust denis7778742049
🚀
Jiaqi-hkust
🤗
Jiaqi-hkust
6758a9850e3fff481964ca6d
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Jean Louis
JLouisBiz
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642e7a12ccdcf5da7f9657a0
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Jiaqi Tang
Jiaqi-hkust
user