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 22
When Inputs Receive No Score: Evaluating Open-Web Event Admission on the Complete Stream
A Web item can link to the correct event without contributing information worth organizing.Mitigating Popularity Bias in Sequential Recommendations with Sparse Prior-Regularized Objective
Popularity bias is a well-known challenge in recommender systems, often leading to the over-exposure of popular items and reduced catalog coverage.FASHION-RQ: Mining Hierarchical Semantic Structure for Automated Evaluation of Fashion Retrieval
Evaluating fashion retrieval systems remains challenging due to the limited availability of benchmarks that capture the fine-grained, open-ended queries encountered in real-world search and…Web Scale on a Budget: Tail-Aware Retrieval Evaluation
A common practice in instance retrieval is to grow the gallery size to improve a benchmark's discrimination capability.Fresh at Decision, Stale at Action: Routing New Information to LLM Decisions Already Made
Freshness shapes an LLM decision at the moment it is made; the action it commits to is latent, carried out later, in a changing world.JevGPT: Thinking Fast and Slow in Autonomous Research
Autonomous research agents must acquire and reason over evidence within a limited budget.SimInter: Evidence-Grounded User Simulation with Interview-Based Profile Enrichment for Conversational Recommender Systems
Conversational Recommender Systems (CRSs) require reliable user simulators for cost-effective evaluation.Choosing LLMs for Strategic Agent Interactions
Game-theoretic benchmarks claim that large language model (LLM) strategic decision-making varies across game types, interaction structures, and capability dimensions.Graph-free Retrieval with Graph-Modified Embeddings
Dense vector retrieval can miss evidence connected through intermediate entities, while direct graph retrieval requires access to the graph at query time.Retrieval Disagreement as a Signal: Diagnosing and Steering Multilingual Dense Embedders
Multilingual dense retrievers place all languages in one shared space so that a question finds relevant documents, regardless of which language they are written in.Knowledge Graphs as Files: An Agent Harness Where Tool Creation and Tool Use Co-Evolve
Searching text-rich knowledge graphs requires following relations while examining textual evidence.Structural Segmentation for Brazilian Legal Document Retrieval
Long legal documents are often represented as single retrieval units, which can dilute relevant content and limit retrieval effectiveness.Kwai Summary Attention V2 Technical Report
token architectures such as Kwai Summary Attention (KSA) compress distant context into learnable Summary token per chunk, and hybrid variants interleave these layers with full attention to retain…Revisiting Generative vs. Dense Retrieval: A Static Value Prior Reverses the Gap
Generative retrieval (GR) has emerged as a strong candidate generator for sequential recommendation as language models and semantic-item identifiers have improved.Plan or Retrieve First? A Scale Study of Tool Discovery Architectures
Tool-augmented LLM agents discover relevant APIs from large registries before planning execution, yet finite context windows prohibit injecting all tool definitions into a single prompt.OT-FlowRec: Optimal Transport-Guided Multi-Bridge Flow for Multimodal Sequential Recommendation
E-commerce user preferences evolve dynamically within sessions, exhibiting concurrent and multi-centric interest distributions.UniSGR-V2: Stepwise Target-Side Feature Anticipation and Crossing for Generative Recommendation
Semantic-ID-based generative recommendation unifies retrieval and ranking through autoregressive decoding but differs structurally from discriminative models, which can directly access rich…Recall Begets Recall: Recurrent Memory Ranking for Personalized LLMs
Memory ranking is the stage in personalized Large Language Models (LLMs) that decides which of the candidate memories returned by a retriever enter the limited context, so that the answer is…Tackling the Modality Inhibitance Effect with Wavelet-based Modality Integration in Multi-modal Recommendation
Harnessing rich modality features has recently gained prominence in multi-modal recommendation systems (MRSs).ARMED: Adaptive Retrieval-Augmented Medication Recommendation
Medication recommendation requires jointly considering diagnoses, comorbidities, longitudinal patient history, and treatment interactions.