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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Tool Recommendation for Multi-Step Agents: Finding Every Tool a Task Needs
A multi-step agent task fails when a single tool it needs is missing, yet the tools an agent may need are too many to list: the roughly 8,200 tools on the platform we study have definitions that…When Does Evidence-Graph Structure Predict Answer Correctness? Label-Free Abstention for Structured Retrieval
Retrieval systems return scored answers, but a high score does not show that an answer should be trusted.Location, Location, Generation: Where You Ask Matters for Generative Engine Optimization
Generative search engines answer local and commercial questions with a written answer and a few cited sources.Cache2LoRA: Understanding and Improving Context Internalization
to-LoRA methods encode documents as low-rank parameter updates, allowing language models to answer without retaining the source text in their decoding context.Candidate-dependent residual routing trades top-of-list accuracy for recall in sequential recommendation
Candidate-dependent routing lets a sequential recommender weight a user's past behaviors differently for each item it scores.MOSS: A Production Framework for Monitoring and Diagnosing Semantic Failures in Search and Recommendation Systems
Detecting semantic failures in production recommendation and search systems is hard: behavioral metrics lag by days, manual review does not scale, and most LLM evaluation frameworks are designed for…IRAH: Hierarchical Agentic Tree Search for Retrieval Pipeline Optimization
Optimizing Information Retrieval (IR) pipelines for Retrieval-Augmented Generation (RAG) requires jointly selecting structural components and numerical hyperparameters under limited evaluation…Learning When to Think: Utility-Guided Adaptive Reasoning for Efficient Text Retrieval
Reasoning-conditioned embeddings can improve retrieval by progressively refining semantic constraints.ReviewPilot: A Multi-Agent System for Screening and Data Extraction in Systematic Reviews
Systematic literature reviews follow the PRISMA process of screening scholarly works and extracting evidence from included studies.SSR: Structural Signal Retriever for Scalable Agent Discovery in LLM-Orchestrated Multi-Agent Systems
Large-scale multi-agent ecosystems require mechanisms for discovering suitable agents without exposing entire registries to a large language model (LLM) orchestrator.RecSoup: Efficient Model Merging Approach for Controllable Multi-Objective Recommendation
Recommender systems often need to balance multiple conflicting objectives.TRACER: Trajectory-Aware Tool Retrieval for LLM Agents
Modern agents select tools from registries, but exposing entire registries incurs high token costs and can impair selection as they grow.Making every stage of medical device document assessment with large language models deterministic
To show that a medical device complies with regulation, experts judge whether documents and records meet each requirement and record the judgment for others to verify.VoteGEO-Bench: Measuring the Impact of Generative Engine Optimization on Electoral Visibility
Large language models (LLMs) are increasingly used for election information, yet little is known about how external web content shapes the political information they present.Retrieved but Not Read: Understanding Why Search-Augmented LLMs Fail to Use Available Evidence
Web search allows LLMs to access up-to-date information, but it remains unclear why they sometimes produce outdated answers even when relevant evidence is available.Aligning LLMs with Sparse Retrieval via Adaptive Sparse Supervision
While large language models (LLMs) offer strong semantic representations, their application to first-stage retrieval incurs substantial latency and storage costs.GACC-Bench: Benchmarking Goal-Adaptive Context Compression Ability across Large Language Models
Context compression in long-running applications of large language models (LLMs) must preserve goal-relevant information, often before specific downstream questions are known.CARA: Causally Guided Retrieval Agent for Question Answering
Retrieval-augmented generation over text-rich knowledge graphs requires identifying evidence that contributes to answering a question, rather than merely matching its textual content.Star Light, Star Bright: Can Multimodal Models Parse an Astronomy Visual Question Answering Dataset Right?
Figures are essential for scientific communication, and the field of Visual Question Answering (VQA) aims to test the figure understanding capabilities of large multimodal models (LMMs).AuditKIT: A Unified Evaluation Spine for Benchmarking, Judging, and Comparing Large Language Models and Agentic Systems
Evaluating language models and agents requires combining benchmark accuracy, judge-based assessment, runtime performance, tool-use behavior, and retrieval quality.