LLM-Oriented Information Retrieval: A Denoising-First Perspective

2026-05-01

AI Summary

This work identifies denoising as a fundamental bottleneck in information retrieval for large language models (LLMs), which are highly susceptible to noise that induces hallucinations and reasoning failures. Challenging the human-centric retrieval paradigm, the study proposes a holistic optimization framework centered on evidence density and verifiability. It introduces a four-stage challenge taxonomy spanning from inaccessible to unverifiable information and systematically integrates techniques—including index optimization, retrieval reranking, context construction, fact verification, and agent collaboration—to enhance signal-to-noise ratio throughout the retrieval pipeline. The approach substantially improves the reliability of retrieval-augmented generation (RAG) and agent-based search, demonstrably elevating LLM reasoning quality and output trustworthiness across diverse tasks such as lifelong assistance, code generation, deep research, and multimodal understanding.

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.

Problem

• information retrieval
• large language models
• denoising
• retrieval-augmented generation
• hallucination

Innovation

• denoising
• retrieval-augmented generation
• signal-to-noise optimization
• verifiability
• LLM-oriented IR