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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Benchmarking Literature Retrieval for a Model Organism: A Dictyostelium Case Study
Biological literature retrieval systems are often developed and evaluated using broad biomedical corpora and general-purpose search tasks.TSGuard: A Real-Time Framework for Detecting and Imputing Missing Data in Streaming Time Series
Streaming sensor applications routinely suffer from delayed or missing observations caused by faults, communication losses, or environmental interference.MRVQ: One Resident Index for Dimension- and Rate-Elastic Vector Search
Dense-retrieval services must switch among embedding-prefix dimensions and index bit rates as latency, quality, and memory budgets change.Learning Robust Personalized Prompts for LLM-Driven Sequential Recommendation
LLM-driven sequential recommendation formulates next-item prediction as autoregressive generation conditioned on natural-language prompts.FICO: Find-Then-Compute for Corpus-Level Spreadsheet Question Answering
Question answering over spreadsheet collections requires finding the correct workbook and computing over complete tables.Learning Subject-Specific Anatomical Representations via Manifold Expansion: Application to Accelerated Multi-Contrast MRI
Clinical MRI routinely acquires multiple contrast-weighted images of the same anatomy for complementary tissue characterization.Periscope: Extending Frozen Language Models Beyond Their Context Window
A language model reads long text in one quadratic forward pass, stops at the context window, and loses accuracy with length before reaching it.CANOPY: Adaptive-Granularity Evidence Compression for Multimodal RAG
Multimodal RAG retrieves text, tables, images, and videos, but choosing a retrieval granularity does not determine how much context to retain within each item.RPTune: Learned Context Curation for LLM Catalog Search
For small merchant businesses (SMBs) whose catalogs fit within a long-context LLM, full-catalog prompting offers a compelling alternative to multi-stage retrieval designed primarily for large…The Other Half of Workflow Portability: Evidence-Backed HPC Site Profiles with Agentic Discovery
Moving a workflow developed and tested at one HPC site to another rarely succeeds without some amount of trial and error.JoinGR: Learning to Traverse Join Graphs for Table Retrieval
Retrieving the right tables is a prerequisite for Text-to-SQL over realistic databases.Madeleine: Learning Involuntary Recall for Conversational Memory from Simulated Lives
A long-term conversational assistant must recall the right memory at the right moment, yet the memory that matters most is often not similar to what the user says now.Do Multilingual Encoders Produce Language-Consistent Semantic IDs?
Semantic IDs (SIDs) compress item embeddings into discrete code sequences used in generative retrieval.Not All Is Lost: Repairing Lossy User Preference States of Personalization Encoders
Personalization encoders compress evolving interaction histories into preference states used to rank items or condition text generation.Learning to structure data from user-generated thematic corpora
Thematic corpora, such as social media communities, contain unstructured text describing data that could be made structured.Neither Black nor White: Balancing Semantic and Collaborative Signals with Graph-Informed Semantic IDs (GrIS)
Existing work on Semantic IDs (SIDs) for generative recommendation treats SID construction as a representation learning problem: encode items into a quantised latent space and read off codes.From Rules to Neural Graphs: Scalable Structured Prediction for Patent Prior Art Search
Patent search requires processing documents routinely exceeding tens of thousands of tokens.AgentWebRec: Compact Evidence Fusion over the Agent Web for Personalized Recommendation
LLM-based personal agents are emerging as persistent carriers of user semantics and intermediaries between users and recommendation platforms, maintaining richer user knowledge locally.A Matryoshka Hierarchical RAG for Efficient Multi-Hop Question Answering
Retrieval-Augmented Generation (RAG) systems for multi-hop Question Answering (QA) must balance retrieval quality with computational cost.Optimizing Effective Training Time for Large-Scale Recommendation Systems
Lifecycle overhead silently consumes accelerator capacity across large-scale recommendation training fleets.