因果智能体图像恢复：用于自适应图像恢复智能体的自进化因果记忆
Causal-AgentIR: Self-Evolving Causal Memory for Adaptive Image Restoration Agents
- Shanghai Jiao Tong University（上海交通大学）
- Harbin Institute of Technology（哈尔滨工业大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对图像恢复智能体知识存储局限，提出Causal-AgentIR框架，通过构建结构化因果记忆图及协作系统，支持智能体进行因果推理，依质量变化和反馈调整恢复经验，有效提升图像恢复能力。
AI中文摘要：
图像恢复智能体是处理现实场景中多样且不可预测退化的灵活范式。现有智能体将恢复视为工具使用过程，但知识存储方式限制了恢复知识积累等。本文提出因果智能体图像恢复（Causal-AgentIR），是具有自进化因果记忆的分层多智能体框架。它将多种要素组织成结构化因果记忆图，支持检索与推理，还构建协作系统。通过该设计，恢复经验可依质量变化和反馈调整，实验证明了框架有效性。
英文摘要：
Image restoration agents have recently emerged as a flexible paradigm for handling diverse and unpredictable degradations in real-world scenarios. Existing agents typically formulate restoration as a tool-using process, where the agent perceives degradations, searches candidate tools, executes restoration operations, and revises the plan through reflection or rollback. However, their knowledge is often stored as static tool descriptions, manually defined degradation priors, or unstructured textual summaries, which limits the accumulation, verification, revision, and forgetting of restoration knowledge over long-term experience. In this paper, we propose Causal-AgentIR, a hierarchical multi-agent framework with self-evolving causal memory for collective image restoration intelligence. Instead of representing restoration experience as isolated textual records, Causal-AgentIR organizes degradation patterns, image regions, restoration tools, actions, quality changes, and user preferences into a structured causal memory graph. This graph supports graph-based retrieval and multi-hop causal reasoning, enabling agents to infer how specific restoration operations or tool sequences affect restoration quality under different degradation conditions. The framework further organizes multiple agents into a collaborative system, including planning, degradation analysis, tool expertise, causal memory reasoning, outcome critique, and memory curation. Through this design, restoration experience can be added, updated, merged, reinforced, ignored, or discarded according to observed quality changes and feedback, allowing the agent to maintain reliable and transferable restoration knowledge. Extensive experiments demonstrate the effectiveness of the proposed framework.
