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.
Get the few papers worth your time in Information Retrieval every morning.
Follow Information Retrieval and ReadSearch reads its new papers each day, then brings you the few worth reading, each with its key finding in a sentence.
Newest papers, page 45
Where Post-Training Quantization Breaks Text Embedders: A Measured Map Across Four Embedder Families
Weight-only post-training quantization is the cheapest way to shrink a retrieval embedder, and the received advice for applying it -- protect the embedding table, allocate bits by module…Balancing Trial and Reorder: A Hybrid Sequential Transformer-GBDT Ranker for On-Demand Delivery
On a delivery platform, personalized store ranking greatly influences what users find and order.Question's Gambit: The First Move Matters in Agentic Deep Search
Deep research agents answer complex questions through iterative loops of searching, reading, and reasoning.VARG: Value-Aware and Ranking-Aligned Generative Retrieval for Dynamic E-commerce Search
Integrating recall and pre-ranking in e-commerce search requires candidate generation to account for relevance, personalization, and business value before final ranking.AlgoRAG: Retrieval-Augmented Generation for Theoretical Computer Science Education -- A Comprehensive Evaluation Framework for Algorithm Analysis and Complexity Theory
Teaching abstract theoretical computer science (TCS) concepts such as algorithm analysis and complexity theory is challenging because students must handle formal proofs and asymptotic reasoning that…The Wisdom of the Loudest: A Large-Scale Audit of Generative Search on Reddit
Online communities are valued not only for answers, but for the diversity of experiences and perspectives they contain.Beyond Benchmark Scores: How Synthetic and Authentic Query Distributions Diverge in RAG Evaluation
RAG systems are routinely evaluated using synthetic question sets generated from the target document corpus.Speak to the City: Multimodal Resolution for Outside-the-Vehicle References
As autonomous vehicles and Extended Reality (XR) headsets enable novel in-car interactions, seamlessly querying physical landmarks, known as Outside-the-Vehicle Referencing (OVR), remains…YOLO12-MambaScan: An Efficient Object Detector with High-Frequency Enhancement and State-Space Modeling
The rapid development of unmanned aerial vehicle (UAV) technology has made aerial-image object detection increasingly important for natural-resource monitoring, traffic management, and disaster…FedV-KGQA in Practice: Design Lessons and an Interactive Prototype
Knowledge graph question answering usually assumes that one system can reach the whole graph.Cost Characterization of Vertically Partitioned Federated Knowledge Graphs
Knowledge graphs are increasingly distributed across autonomous organizations that share an entity space but own disjoint subsets of relations, forming a vertical partition.Addressing Cross-Stage Decoupling of Semantic and Collaborative Signals in Generative Recommendation
Generative recommendation reformulates sequential recommendation as autoregressive generation by encoding items into semantic tokens, enabling improved scaling capability and cross-domain…Odds-Shift Slippage in One-vs-Rest Rankers: Diagnosing and Repairing Reweighting-Induced Top-K Errors
One-vs-rest rankers that show each user the top-$K$ of many rare labels usually counter imbalance with a per-label positive-class weight, scale_pos_weight $= n_-/n_+$.ClinAgent: A ReAct-Based Agent for Conversational Access to Clinical Trial Information
Querying clinical trial registries remains a manual and error-prone process, requiring researchers to navigate large volumes of semi-structured data without support for natural language interaction…P$^3$Rec: Distilling Prior--Posterior Preference Reasoning for LLM-based Recommendation
Large language models (LLMs) exhibit strong semantic understanding and preference reasoning capabilities, offering new opportunities for user modeling in recommender systems.TF-IDF and BM25 Are Exact KL Divergences
TF-IDF and BM25 are two of the most widely used methods for scoring query-document relevance, yet neither has a standard probabilistic derivation that justifies it as a statistical method within a…Semantic Knowledge Technologies: what the Semantic Web lost sight of, and what it never had
The Semantic Web set out to give information a machine-interpretable form so that software could integrate and reason over it.ChronicleRec: Pre-training Temporally Anchored Tokens for Lifelong User Modeling
Modeling ultra-long user behavior sequences is crucial for industrial recommendation and online advertising, yet directly feeding thousands of historical actions into ranking models is…OneLA: Scaling Linear-Attention Decoding to Large Beams in Generative Recommendation
Generative recommendation (GR) relies on large-beam decoding to generate hundreds of candidate items, creating a new scaling challenge for recurrent linear attention.Preference-Drift-Aware Subsequence Learning and Hierarchical Context Fusion for Long-Sequence Generative Recommendation
Long-sequence generative recommendation methods autoregressively model the user's interaction sequence to generate the next-item representation.