arXiv cs.CL· Biswas Poudel, Nilson Chapagain, Amit Kumar, Xianshun Jiang·· 13 小时前AI 评分31
推理-搜索增强型 LLM：操作分类体系、基准分析与受控证据综合
Reasoning-Search-Augmented LLMs: An Operational Taxonomy, Benchmark Analysis and Controlled Evidence Synthesis
AI 导读
一项综述从检索耦合、控制器设计、知识来源与接地严格度四个架构维度，梳理将逐步推理与交互式搜索紧密耦合的推理-搜索增强型 LLM 框架。研究把提示、监督、偏好优化与强化学习作为横切的学习信号维度单独处理，并区分基准难度与 RSA 循环需求，综合受控的同研究内消融与质量-成本差值，避免跨异构模型骨干、检索器、语料与评测协议的不当合并。
正文 · 原文
Abstract:Large language models (LLMs) demonstrate impressive emergent reasoning capabilities, yet they remain constrained by fixed model parameters and finite context windows. Even when augmented with static retrieval, they can produce statements that are unsupported by, or even contradictory to, retrieved evidence. This leads to hallucinations, difficulty incorporating up-to-date information, and failures to correctly interpret or reconcile sources. Recent work addresses these limitations by tightly coupling step-by-step reasoning with interactive search, treating retrieval as an action within an iterative agentic loop rather than a one-shot preprocessing step. This study reviews frameworks that implement such reasoning-search loops by interleaving planning, querying, retrieval, integration, verification, and stopping. We analyze these systems through four architectural axes: retrieval coupling, controller design, knowledge source, and grounding strictness. We separately treat the learning signal as a cross-cutting dimension because prompting, supervision, preference optimization, and reinforcement learning can instantiate the same architecture while producing materially different search, stopping, and grounding behavior. We further distinguish benchmark difficulty from RSA-loop demand and synthesize controlled within-study ablations and quality-cost deltas, avoiding invalid pooling across heterogeneous model backbones, retrievers, corpora, and evaluation protocols. Our goal is to clarify the landscape of reasoning-search-augmented LLMs and outline a unified perspective on the design choices shaping future research.
| Comments: | 10 pages |
| Subjects: | Information Retrieval (cs.IR); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.29189 [cs.IR] |
| (or arXiv:2610.29189v1 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2610.29189 arXiv-issued DOI via DataCite (pending registration) |
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| Related DOI: | https://doi.org/10.5281/zenodo.23524293
DOI(s) linking to related resources |
Submission history
From: Nilson Chapagain [view email]
[v1]
Wed, 21 Oct 2026 04:19:06 UTC (1,614 KB)
来源：arXiv cs.CL · arxiv.org