CLEAR: Unlocking Generative Potential for Degraded Image Understanding in Unified Multimodal Models

• URL: http://arxiv.org/abs/2604.04780v1
• Date: Mon, 06 Apr 2026 15:54:00 GMT
• Title: CLEAR: Unlocking Generative Potential for Degraded Image Understanding in Unified Multimodal Models
• Abstract summary: multimodal models that combine understanding and generation fail to leverage their own generative capacity on degraded inputs.<n>We present CLEAR, a framework that connects the two capabilities through three progressive steps.<n> Experiments show that CLEAR substantially improves robustness on degraded inputs while preserving clean-image performance.
• Score: 23.357627415320025
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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