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 16
Real PMIDs, Wrong Papers: Harness Design for Biomedical Claim-Verification Agents
Literature-searching language agents are becoming a standard component of scientific assistants for the life sciences, yet there is little controlled evidence on which parts of their harness matter.ERCLIP: Small-Data Adaptation of CLIP for Free-Length Image-Text Retrieval
CLIP's 77-token text encoder truncates detailed image descriptions.What the Author Wrote, What the Agent Reads: Silent Cue Loss in Enterprise Document Ingestion
Enterprise agents act on documents.Think Harder Where It Matters: Item-Level Adaptive Recursion for Sequential Recommendation
Existing sequential recommenders usually allocate a fixed computational budget to every item in a user's interaction history, regardless of the item's contribution to the final prediction.Protection Is Not One Event: A Survey of RAG Protection Research by Measured Endpoint
Yet RAG protection studies use incompatible evaluation metrics, datasets, and threat models.FAIRGate: Agentic Runtime Policy Selection and Verified Repair for Fairer Recommendation Delivery
Fairness-aware recommender systems often encode one fairness objective during training or apply a fixed re-ranking rule after scoring.Data-Void Exploits in Web-Searching LLMs
Web-searching Large Language Models (LLMs) have changed how users seek online information: instead of sifting through ranked lists of links, users read synthesized answers.When Agentic Retrieval Fails Silently: Framework and Production Evidence from a Creative AI Assistant
CTR, MRR, and behavioral NDCG assume the user sees ranked results and acts on them.Getting a GRIP on Graph-Based Search for Learned Sparse Retrieval
Learned sparse retrieval (LSR) has recently gained traction in the information retrieval community for the ability to combine the generalization capabilities of neural representations with the…Journal Basket: Supporting Cross-Disciplinary Literature Searching and Methodological Transparency
Business students beginning a research project must learn how to identify relevant scholarly publications, distinguish disciplinary sources, construct database searches, and report the decisions…Baseline and Incremental: User, Item, and Request Debiasing in Instagram Feed Ranking
A feed ranker scores candidates with a value model, a weighted sum of engagement predictions.Factual Access States in Diffusion Language Models: From Mechanism to Knowledge Editing and Retrieval
Factual recall is a lookup: some representation inside the network serves as the key, and what that key is made of decides which queries can retrieve the fact, what an edit must cover, and what a…GARRET: Generative Feedback for Region Retrieval in Visual Documents
Multimodal retrieval-augmented generation over visually rich documents requires evidence at a finer granularity than entire pages.Robustness Analysis of Pareto-based Joint Evaluation of Fairness and Relevance in Recommender Systems
Recommender Systems (RSs) can be evaluated for many aspects, two of them being recommendation relevance and fairness.SPAR: Stepwise Personalized Autoregressive Recommendation with Prefix-Balanced Semantic IDs
Semantic ID (SID)-based generative recommendation retrieves items through successive token-level decisions, making personalization at each decoding step essential.AutoSim: A Self-Improving User Simulator for Conversational Search Evaluation
User simulators promise to evaluate conversational search systems at a scale human studies cannot reach, but only if their behavior is realistic enough to trust.DeclineCraft: A RAG Abstention Benchmark for Jointly Measuring Combined Retrieval and Parametric Knowledge
Reliable retrieval-augmented generation (RAG) requires recognizing when neither retrieved documents nor parametric knowledge supports an answer.Multimodal Reranking Is a System One Decision
Multimodal rerankers are usually dedicated models: a vision-language model (VLM) is trained on retrieval data to score the candidates of a query.Evolving Semantic Indexes for Generative Retrieval
Generative retrieval (GR) casts search and recommendation as autoregressive generation of discrete semantic identifiers (SIDs).ECHO: Efficient Cascade with Homogeneous Operators for Distillation-Guided 100K-Sequence Modeling
Modeling lifetime user behavior sequences is essential for capturing long-range preferences in industrial recommendation, yet 100K-length user sequences make target-aware ranking prohibitively…