arXiv cs.AI· Saroj Mishra, Suman Niroula, Umesh Yadav, Dilip Thakur, Srijan Gyawali, Shiva Gaire·· 4 小时前AI 评分17
SoK：Agentic RAG 智能体检索增强生成的分类体系、架构与评估（已撤稿）
SoK: Agentic Retrieval-Augmented Generation (RAG): Taxonomy, Architectures, Evaluation, and Research Directions
AI 导读
一篇系统化知识（SoK）论文将 Agentic RAG 形式化为有限时域部分可观测马尔可夫决策过程，提出按规划机制、检索编排、记忆范式和工具调用分类的体系，并指出幻觉传播、记忆投毒、检索错配与级联工具执行漏洞等系统性风险。该论文后被 arXiv 管理员撤稿，原因是未达到 arXiv 的研究内容质量标准。
正文 · 原文
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Abstract:Retrieval-Augmented Generation (RAG) systems are increasingly evolving into agentic architectures where large language models autonomously coordinate multi-step reasoning, dynamic memory management, and iterative retrieval strategies. Despite rapid industrial adoption, current research lacks a systematic understanding of Agentic RAG as a sequential decision-making system, leading to highly fragmented architectures, inconsistent evaluation methodologies, and unresolved reliability risks. This Systematization of Knowledge (SoK) paper provides the first unified framework for understanding these autonomous systems. We formalize agentic retrieval-generation loops as finite-horizon partially observable Markov decision processes, explicitly modeling their control policies and state transitions. Building upon this formalization, we develop a comprehensive taxonomy and modular architectural decomposition that categorizes systems by their planning mechanisms, retrieval orchestration, memory paradigms, and tool-invocation behaviors. We further analyze the critical limitations of traditional static evaluation practices and identify severe systemic risks inherent to autonomous loops, including compounding hallucination propagation, memory poisoning, retrieval misalignment, and cascading tool-execution vulnerabilities. Finally, we outline key doctoral-scale research directions spanning stable adaptive retrieval, cost-aware orchestration, formal trajectory evaluation, and oversight mechanisms, providing a definitive roadmap for building reliable, controllable, and scalable agentic retrieval systems.
| Comments: | arXiv admin note: This submission has been withdrawn because it does not meet arXiv's research content quality standards |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Cryptography and Security (cs.CR); Information Retrieval (cs.IR) |
| Cite as: | arXiv:2603.07379 [cs.AI] |
| (or arXiv:2603.07379v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2603.07379 arXiv-issued DOI via DataCite |
Submission history
From: arXiv Admin [view email]
[v1]
Sat, 7 Mar 2026 23:38:57 UTC (3,024 KB)
[v2]
Thu, 29 Oct 2026 21:05:01 UTC (1 KB) (withdrawn)
来源：arXiv cs.AI · arxiv.org