Applied & Human-Centered · Computer Science & AI
Information Retrieval
The newest papers in Information Retrieval that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
979 papers, from 4 September 2026 to 5 November 2026: 979 arXiv preprints.
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Newest papers, page 49
Noēsis: Deterministic-First Retrieval with Two-Tier Context Hydration for Factuality-Critical Queries on Small Local Models
A wrong number is worse than no answer.From Citations to Contributions: LLM-Assisted Credit Scoring of Research Articles
Citation-based measures of scientific influence typically treat citations as uniform signals, ignoring the different roles that cited works play in a paper's contribution.EviMap: Evidence-Grounded Hierarchical Topic Maps for Exploring Unlabeled Corpora
Research teams and organizations often explore unfamiliar free-text collections, from survey comments and reviews to reports and domain documents, before labels, queries or coding schemes exist.Measuring GEO Visibility: Prompt Corpora Define the Answer Market
GEO (generative engine optimization) visibility scores aggregate source appearances, citations, or brand mentions in generated answers.AtomCite: Verification and Correction of Supplied Page-Level Citations in Multi-Page Documents
Large language models answering questions over multi-page documents are expected to cite the supporting pages, yet supplied citations are sometimes inaccurate, and current evaluations score…SurveyAgent-HKA: A multi-agent framework for scientific survey generation with LLMs and human knowledge augmentation
Automatic scientific survey generation has become an important task in scientific document processing.Evaluating Deep-Search Agents under Hierarchical Web Evidence Poisoning
Search-augmented LLM agents are increasingly used for consumer decisions, making them vulnerable to Generative Engine Optimization (GEO) poisoning.PAGR: Proof-Carrying Algebraic-Geometric Retrieval: A Quiver-, Provenance-, and Sheaf-Theoretic Framework for Grounded LLM Retrieval
Retrieval-augmented generation is usually formulated as a statistical information-retrieval problem.ExpertLens: Visualizing Embedding Spaces for Post-Hoc Explainability in MoE Enhanced Retrievers
Neural models, including dense retrievers, have been widely adopted in Information Retrieval (IR), often delivering state-of-the-art performance.Customer Relationship Intelligence: Integrating CRM and MDM for Enhanced Customer Engagement
This study examines how Customer Relationship Management (CRM), Master Data Management (MDM), and Customer Knowledge Management (CKM) jointly constitute a Customer Relationship Intelligence (CRI)…A Tree-based RAG Framework for Evidence-Intensive QA via Adaptive Planning and Topology-Aware Evidence Gathering
Recent structured RAG methods leverage tree- or graph-based reasoning structures to improve multi-hop QA.Leveraging Low-Level Symbolic Competences for Unsupervised Grounding in Hallucination Detection
Hallucination-where a language model generates outputs that are factually incorrect or unsupported by the source-is a major challenge for both prompted and fine-tuned language models.Repeated Queries Exhaust an LLM's Brand Recommendations but Not Its Sources
Whether repeated identical buying questions exhaust a language model's brand recommendations depends on retrieval.Beyond Co-purchase Relation: Evolution of Complementary Recommendations at Allegro
When a customer adds a professional camera to their cart, should the system suggest a matching lens, a generic tripod, or another camera body?Does Your Agent's Memory Survive a Model Upgrade? A Controlled Study of Memory Portability
Model upgrades are routine; memory migrations are not.Better Together: Complementary Query Rewriting Under a Strong RAG Baseline
A popular way to improve Retrieval-Augmented Generation (RAG) is to rewrite the user's question into several variants and search with all of them.Recovering Temporal and Geographic Signals from Language Model Embeddings
Understanding whether language-model embeddings encode structured real-world information is important for both representation analysis and information retrieval.A Multi-Source Ensemble Approach to Candidate Generation for Alternative Vacation Rental Property Recommendations
Alternative property recommendations play a critical role in vacation rental marketplaces, helping users discover relevant options when viewing a specific listing.RAGMark: A Comprehensive Framework for Benchmarking Retrieval-Augmented Generation Systems
We present RAGMark, a modular benchmarking framework for advanced Retrieval-Augmented Generation (RAG) systems targeting small-scale multi-GPU environments.