论文把机器遗忘做到属性级：让模型忘掉指定属性，但保留同一人的其他信息。CLRP不用重训练，在多种多模态模型上都有效。
多模态大语言模型会泄露敏感信息，现有隐私基准多采用画像级删除，而实际请求往往更细粒度。本文提出属性级遗忘任务，覆盖长文本、数值、短文本三类目标和多种遗忘比例。作者提出训练无关的CLRP框架，用激活修补定位因果层，再通过保留感知投影移除目标属性子空间。实验在多种不同架构和参数规模的MLLM上验证了CLRP的有效性。
Toward Fine-Grained Forgetting:Attribute Unlearning for Multimodal Large Language Models
Multimodal large language models (MLLMs) exhibit strong vision--language capabilities but may also memorize and disclose sensitive information. Machine unlearning seeks to remove designated knowledge without retraining from scratch while preserving general utility. Existing privacy-oriented benchmarks primarily adopt profile-level deletion, whereas practical requests are often finer grained: a model should forget a specified attribute while retaining non-sensitive information about the same identity. We therefore introduce attribute-level MLLM unlearning as a finer-grained task and construct a benchmark spanning long-text, numeric, and short-text targets, multiple forget ratios, and diverse question types. Our evaluation reveals that target and retained attributes share identity-specific and visual evidence, making selective forgetting susceptible to residual leakage or collateral degradation; accordingly, existing methods exhibit unstable forgetting--retention trade-offs in this setting. To address this challenge, we propose Causal Localization and Retain-Aware Projection (CLRP), a lightweight training-free framework. CLRP uses activation patching to identify the layer that causally mediates target-attribute disclosure, then applies a retain-aware projection that removes the target-attribute subspace while preserving same-identity evidence. Experiments across multiple widely used MLLMs with distinct architectures and parameter scales demonstrate the effectiveness of CLRP.