LLM-Oriented Information Retrieval: A Denoising-First Perspective
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This is a hard and competitive area, because RAG, long-context utilization, retrieval quality, and agentic search are heavily optimized and widely studied. The paper’s strengths compound well: a coherent framing, a useful taxonomy, and a controlled, theory-consistent empirical illustration reinforce one another. The main weaknesses also compound: the empirical validation is narrow, there is no code or data release information, and the paper is not a method paper, so deployability is limited to i
Abstract
Modern information retrieval (IR) is no longer consumed primarily by humans but increasingly by large language models (LLMs) via retrieval-augmented generation (RAG) and agentic search. Unlike human users, LLMs are constrained by limited attention budgets and are uniquely vulnerable to noise; misleading or irrelevant information is no longer just a nuisance, but a direct cause of hallucinations and reasoning failures. In this perspective paper, we argue that denoising-maximizing usable evidence density and verifiability within a context window-is becoming the primary bottleneck across the full information access pipeline. We conceptualize this paradigm shift through a four-stage framework of IR challenges: from inaccessible to undiscoverable, to misaligned, and finally to unverifiable. Furthermore, we provide a pipeline-organized taxonomy of signal-to-noise optimization techniques, spanning indexing, retrieval, context engineering, verification, and agentic workflow. We also present research works on information denoising in domains that rely heavily on retrieval such as lifelong assistant, coding agent, deep research, and multimodal understanding.
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