CLEAR: Unlocking Generative Potential for Degraded Image Understanding in Unified Multimodal Models
Image degradation from blur, noise, compression, and poor illumination severely undermines multimodal understanding in real-world settings. Unified multimodal models that combine understanding and generation within a single architecture are a natural fit for this challenge, as their generative pathway can model the fine-grained visual structure that degradation destroys. Yet these models fail to leverage their own generative capacity on degraded inputs. We trace this disconnect to two compounding factors: existing training regimes never ask the model to invoke generation during reasoning, and the standard decode-reencode pathway does not support effective joint optimization. We present CLEAR, a framework that connects the two capabilities through three progressive steps: (1) supervised fine-tuning on a degradation-aware dataset to establish the generate-then-answer reasoning pattern; (2) a Latent Representation Bridge that replaces the decode-reencode detour with a direct, optimizable connection between generation and reasoning; (3) Interleaved GRPO, a reinforcement learning method that jointly optimizes text reasoning and visual generation under answer-correctness rewards. We construct MMD-Bench, covering three degradation severity levels across six standard multimodal benchmarks. Experiments show that CLEAR substantially improves robustness on degraded inputs while preserving clean-image performance. Our analysis further reveals that removing pixel-level reconstruction supervision leads to intermediate visual states with higher perceptual quality, suggesting that task-driven optimization and visual quality are naturally aligned.
69d4c9592c523d36431fb440
Xiangzhao Hao
claimed_verified
2026-04-09T08:07:25.842Z
64ec6025b96ff0e175728ac0
/avatars/a1acbe22b5e5105703e7912da2cfced2.svg
hxz
CUDAOUTOFMEMORY
user
CUDAOUTOFMEMORY
69d4c9592c523d36431fb441
Zefeng Zhang
claimed_verified
2026-04-07T13:33:52.168Z
642c2dcec3694d2b74565c48
/avatars/31243bb505f8c511ebd7492eaf3ea1a9.svg
zhangzef
Starrrrrry
user
Starrrrrry
69d4c9592c523d36431fb442
Zhenyu Zhang
69d4c9592c523d36431fb443
Linhao Yu
69d4c9592c523d36431fb444
Yao Chen
69d4c9592c523d36431fb445
Yiqian Zhang
69d4c9592c523d36431fb446
Haiyun Guo
69d4c9592c523d36431fb447
Shuohuan Wang
69d4c9592c523d36431fb448
Yu Sun
CLEAR: Unlocking Generative Potential for Degraded Image Understanding in Unified Multimodal Models
Image degradation from blur, noise, compression, and poor illumination severely undermines multimodal understanding in real-world settings. Unified multimodal models that combine understanding and generation within a single architecture are a natural fit for this challenge, as their generative pathway can model the fine-grained visual structure that degradation destroys. Yet these models fail to leverage their own generative capacity on degraded inputs. We trace this disconnect to two compounding factors: existing training regimes never ask the model to invoke generation during reasoning, and the standard decode-reencode pathway does not support effective joint optimization. We present CLEAR, a framework that connects the two capabilities through three progressive steps: (1) supervised fine-tuning on a degradation-aware dataset to establish the generate-then-answer reasoning pattern; (2) a Latent Representation Bridge that replaces the decode-reencode detour with a direct, optimizable connection between generation and reasoning; (3) Interleaved GRPO, a reinforcement learning method that jointly optimizes text reasoning and visual generation under answer-correctness rewards. We construct MMD-Bench, covering three degradation severity levels across six standard multimodal benchmarks. Experiments show that CLEAR substantially improves robustness on degraded inputs while preserving clean-image performance. Our analysis further reveals that removing pixel-level reconstruction supervision leads to intermediate visual states with higher perceptual quality, suggesting that task-driven optimization and visual quality are naturally aligned.
CLEAR is a framework that enhances multimodal model robustness to image degradation by integrating generation and reasoning through supervised fine-tuning, latent representation bridging, and interleaved reinforcement learning.
unified multimodal models generative pathway degradation-aware dataset Latent Representation Bridge Interleaved GRPO reinforcement learning visual generation text reasoning multimodal understanding