EvoIR-Agent: Self-Evolving Image Restoration Agentic System via Experience-Driven Learning
Multimodal Large Language Model (MLLM)-driven image restoration agent demonstrates effectiveness in degradation coupling scenarios by flexibly selecting tools and determining removal orders. However, its zero-shot planning often fails without experience, necessitating severe trial-and-error overhead to achieve satisfactory outcomes. Currently, two paradigms are employed to address this issue, yet a dilemma persists: Training-based methods embed intrinsic experience into parameters, achieving high inference efficiency but lacking compatibility with new tools or degradations. In contrast, training-free methods utilize explicit experience storage for compatibility but still incur trial-and-e...
| Authors | Kailin Zhuang, Jiawei Wu, Zhi Jin |
|---|---|
| Primary category | cs.CV |
| Published | 21 May 2026 |
| Last updated at source | 30 September 2026 |
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