ASIRF：免重训练的敏感信息脱敏智能体框架
ASIRF: An Agentic Framework for Context-Dependent Sensitive Information Redaction
脱敏工具换个领域就得重训练？这篇论文用智能体检索领域定义，几十条规则就能超越 OpenAI Privacy Filter 的 85% 组合。
arXiv 论文提出 ASIRF（Agentic Sensitive Information Redaction Framework），在推理时从知识库检索领域相关的敏感信息定义，无需重新训练即可适配新领域。
论文实现了三次调用的多智能体流水线和单智能体两种架构，在 10 个小型开源模型、8 个数据集上评测，并以 OpenAI Privacy Filter（OPF）作为训练分类器基线。每个领域只需几十条专家编写的定义、零训练数据，ASIRF 在 80 个模型-领域组合中的 68 个（85%）召回率超过 OPF，落后场景主要集中于 OPF 的训练分布内领域。
ASIRF: An Agentic Framework for Context-Dependent Sensitive Information Redaction
Sensitive information is defined by domain and intent, not a universal category, yet redaction systems such as privacy filters and named-entity recognizers fix a taxonomy at training time, requiring retraining for each new domain. We introduce ASIRF (Agentic Sensitive Information Redaction Framework), which retrieves domain-specific definitions based on the input's domain from a flexible knowledge base at inference time, needing no retraining to adapt. Two architectures, a three-call multi-agent pipeline and a single-agent variant, are evaluated across ten small open-weight models and eight datasets, including out-of-distribution fictional domains, against the OpenAI Privacy Filter (OPF) as a trained-classifier baseline. With only a few dozen expert-authored definitions per domain and no training data, ASIRF's recall exceeds OPF's in 68 of 80 model-domain combinations (85 percent), by at least one of the two architectures, with shortfalls confined mostly to OPF's training-distribution domains.
This report is categorized under AITOP's "Papers" desk. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows and AI regulation, compliance frameworks, and sovereign compute policy. Key entities and technical topics under continuous tracking include ASIRF, OpenAI Privacy Filter, AI安全, 隐私保护.
Primary source verification: published directly by arXiv: OpenAI with first-party authority. Cross-source corroboration: 11 independent sources have covered this development (including Xianbao Qian, The Information, Sam Altman), confirming industry-wide signal strength.