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 26
NativeScope: Relation-Localized Retrieval over Native Topology with a Correct Anchor
Dense retrieval usually ranks text chunks by their semantic similarity to a question.Syn-Omni: Structured Specialization and Progressive Collaboration for Omnimodal Embeddings
Omnimodal embeddings naturally involve both shared representations and modality-specific features across heterogeneous inputs.Compact and Efficient Indexes for Learned Sparse Retrieval
This paper investigates how to substantially reduce the memory footprint of learned sparse retrieval indexes without sacrificing the efficiency of state-of-the-art retrieval data structures.GeopoliticalQA: Benchmarking Retrieval and Generation over Intelligence Reports
Geopolitical and strategic intelligence reports constitute a rich source of expert knowledge and have increasing value in the current political climate, but their heterogeneity and analytical…Reading Position Is the Baseline to Beat: A Time-Ordered Evaluation of Personalised Highlight Prediction
A reader's first highlights on a page are the cheapest personal signal a reading product has.From Chunks to Functional Evidence: Function-Aware Retrieval for EDA Documentation QA
Retrieval-Augmented Generation (RAG) is widely used to ground answers in documents.Finding the Right Balance: Relevance and Diversity in LLM Retrieval
Retrieval diversification is widely available in retrieval-augmented generation (RAG) frameworks, yet prior studies disagree on whether it improves retrieval and answer quality.Towards Explaining Query Expansion Performance in Information Retrieval
Query Expansion (QE) techniques have long been widely used in Information Retrieval (IR) to address the vocabulary mismatch problem.SoccerNet-FoulRet: Retrieving Semantically Similar Soccer Foul Videos
Refereeing decisions in professional soccer remain inconsistent because referees cannot easily compare a contentious foul against similar past cases.The Impact of Backbone Evolution on LLM-Based Relevance Assessments
LLMs are evolving rapidly, with newer models offering stronger capabilities.Inverting Multi-Vector Visual Document Indices
Prevailing multi-vector visual document retrievers store each page as about a thousand patch vectors, often in vector databases run by a third party.ExperienceIndex: Artifact-Grounded Memory
Knowledge-intensive tasks require answering many questions by reasoning about a shared corpus of artifacts (e.g., court cases, or scientific literature).Training with Missed Targets in Generative Recommendation: Separating Supervision from Probability Competition
Generative recommenders return a limited candidate set and may omit observed targets before reranking.Does Document Structure Help Dense Retrieval? A Placebo-Controlled Ablation of Four Mechanisms Across Two Corpora
Retrieval-augmented generation systems increasingly rely on document-structure treatments: structure-aligned chunking, LLM-generated chunk contexts, heading-path metadata, and hierarchical two-stage…CrossWeave: Bridging Perspectives Across Online Communities with a Dual-Pane Design
Social media systems typically display conversations among already familiar contributors, which can be predictable and one-sided.Two-Level Softmax Sampling Done Right: Correcting Bias from Size Imbalance and Dispersion
Sampling from a softmax distribution is a fundamental operation in machine learning, but its linear complexity in the number of items makes exact sampling impractical at scale.Language Models for Page-Level Layout Decisions in E-commerce Search
E-commerce search pages are critical touchpoints for millions of online shoppers.Learning Multi-Step Query Rewriting via Corpus Feedback for Conversational Search
Conversational Query Rewriting (CQR) turns a context dependent user turn into a standalone query for a retriever, and most methods do this in a single step from the dialogue history before…DBRAG: Multi-Table Retrieval-Augmented Generation for Complex Database Queries
Recent advancements in large language models have introduced new capabilities for reasoning over structured data, particularly through program-aided tools that can analyze tables.Learning to Retrieve via Reinforcement Learning in Embedding Space
Dense retrieval models are typically trained with contrastive objectives that learn effective representations but do not directly optimize retrieval metrics or downstream task performance.