Applied & Human-Centered · Computer Science & AI
Information Retrieval
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979 papers, from 4 September 2026 to 5 November 2026: 979 arXiv preprints.
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Newest papers, page 44
Diagnosing the Fact-Grounding Gap in Multi-Hop Question Answering
Multi-hop question answering requires combining information from multiple documents to answer complex questions.Lexplorer: Navigating the Complexity of Legal Document Landscapes
As technological and social innovations create novel regulatory challenges, legal systems grow in complexity - increasing the need for interfaces that enable effective interactions with legal…Scaling Articulated Rationales for MLLM-based Recommendation
We presented SARA, an industrial framework that transforms sparse articulated user rationales into scalable recommendation signals.One Size Does Not Fit All! Dynamic Retriever and Generator Selection for RAG
Retrieval-Augmented Generation (RAG) systems typically employ fixed retriever and generator configurations across queries, despite substantial differences in query complexity and information needs…How Calibration Content Shapes Attention-Based Reranking
Attention-based rerankers score documents by aggregating query-to-document attention and subtracting a null-query calibration pass to remove positional and structural bias.EviQE: Evidence Selection for LLM-Based Query Expansion
LLM-based query expansion increasingly conditions reformulation on documents retrieved from the target corpus, yet most work focuses on how to generate expansions rather than which documents the…Route Me If You Can: A Benchmark for Query Reformulation Selection
LLM-based query reformulation can improve retrieval, but no single reformulation strategy is consistently optimal across queries, domains, retrievers, or model backbones.LazFormer: Scaling Transformers for Industrial Recommendation via Transferable Generative Pre-training
Transformers have shown promising performance in LLMs due to their outstanding scalability, several studies have investigated the scalability of Transformers for industrial recommendation.Converting Sequenced Fuzzy Cognitive Maps to Causal Virtual Worlds with Large Video Generators
We show how users can create and manipulate causal virtual worlds with large-language-model (LLM) and large-video-model agents.Generate to Explore, Select to Exploit: Aligning LLM-based Headline Generation with Personalized Recommendation
In industrial recommendation feeds, presenting a static headline for an item often fails to satisfy the diverse, multimodal interests of the user population, particularly suppressing the needs of…Top-K Is Not a Budget for Hybrid Retrieval
Modern hybrid retrieval for RAG typically fuses the Top-$L$ results from dense and sparse retrievers, but a fixed truncation depth may not transfer across changing queries and corpora.ProLiVis 2.0: Literature-Centric Visualization of Protein--Protein Interaction Networks, with a Citation-Trust Model for Interaction Evidence
Protein-protein interaction databases record evidence without weighing it.Clean Scores, Buried Evidence, and Confident Wrong: A Receipt-Based Audit of Frontier Agentic QA
Frontier models score well on shallow document/chart reading tasks.Benchmarking Embedding Models for ESG Data
The use of Environmental, Social, and Governance (ESG) data is fundamental for modern corporate accountability, sustainability reporting, and financial decision-making.Beyond Retrieval: Scaffolding Children's Online Learning
Children increasingly turn to online information access systems that are primarily designed for the mainstream population, e.g., adults, but possess a limited understanding of how these systems…The Magnitude Mirage: Rethinking Confidence for Reasoning-Intensive Retrieval
Many production RAG systems implement retrieval abstention by thresholding raw similarity scores, implicitly treating score magnitude as a confidence signal.Self-Evolving Memory for Generative Recommendation
Generative recommendation has emerged as a promising end-to-end paradigm for personalized recommendation.IROH: Insightful Ranking Of Humor using Multi-Stage Hybrid Retrieval with Rationale-Distilled LLM Judges for JOKER 2026 Track Task 1 English
Our team, VANGUARD, presents IROH (Insightful Ranking of Humor), a three-stage retrieval system for JOKER Task 1 English at CLEF 2026, achieving first place on the leaderboard with 0.6347 MAP.CiteGuard-RAG: A Validation-Centered AI System for Evidence-Grounded Question Answering
Retrieval-augmented generation (RAG) can improve access to complex information; however, retrieving evidence alone does not ensure that answers are grounded, citation-valid, or appropriately refused.Evaluating Brand Retrieval and Ranking in Large Language Model Recommendations
Large language models (LLMs) are increasingly used for product recommendation, but evaluating their recommendations presents challenges that differ from conventional information retrieval and…