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

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.

Figures (13)

• Figure 1: Top: average scores of commercial and open-source multimodal models on clean versus degraded inputs from MMD-Bench across six benchmarks. All models show substantial performance drops under degradation. Bottom: comparison between existing multimodal models and CLEAR on a degraded image.
• Figure 2: Overview of CLEAR. Stage 1 (top) performs supervised fine-tuning to establish the generate-then-answer reasoning pattern and warm-start the Latent Representation Bridge, with both VAE latent and ViT re-encoded features injected during this stage. Stage 2 (bottom) applies Interleaved GRPO, where text tokens are optimized with GRPO and the denoising step with Flow-GRPO, sharing the same group-relative advantage from answer-correctness rewards. The ViT path is removed in Stage 2, making the bridge the sole connection between generation and reasoning.
• Figure 3: Left: the standard decode-reencode path in existing unified models. The generated VAE latent must be decoded into pixels and re-encoded through the ViT before it can enter the reasoning context. Right: the Latent Representation Bridge in CLEAR. The generated VAE latent is directly concatenated into the reasoning context, eliminating the decode-reencode bottleneck and providing an effective optimization route from answer correctness back to generation.
• Figure 4: Qualitative examples of CLEAR's adaptive reasoning. Left: on a mildly noisy image, the model skips generation and answers directly. Right: on a severely blurred image, the model triggers generation to recover obscured details before answering.
• Figure 5: Generation triggering rate (bars, left axis) and total inference time (line, right axis) across degradation severity levels for each benchmark.