Applied & Human-Centered · Computer Science & AI
Information Retrieval
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979 papers, from 4 September 2026 to 5 November 2026: 979 arXiv preprints.
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Newest papers, page 46
Personalized and Trust-Aware Health Recommendation Policies for a Construction Workplace
Construction workers face workplace risks such as fatigue, heat stress, and other physically demanding conditions that can negatively affect their health and safety.Learning the Lake: Reliable Experience for Adaptive Data Product Discovery
Data-product discovery searches a full lake even when workloads revisit related products and regions.A Historical Corpus Is Not a Historical System: Auditing Hindsight Leakage in Stateful Data Discovery
Offline replay should estimate what a discovery system could retrieve at a historical point, yet freezing the corpus leaves interaction memory unconstrained.Cognition on Graph: Navigating Massive Knowledge Space via Cognitive Cycles and Bidirectional Graph-Text Synergy
Retrieval-Augmented Generation (RAG) has empowered Large Language Models (LLMs) to tackle knowledge-intensive tasks.MIMA: Multi-Interest Recommendation via Multi-Positive Exclusive Assignment
Multi-interest recommendation represents each user with multiple interest vectors for fine-grained candidate matching, yet it often suffers from interest collapse, where the learned interests…Autonomous Research for Open-Ended Problems: A Case Study on Telecom Ticket Retrieval
Recent breakthroughs in LLM-based systems and their abilities in problem solving and coding have allowed progress in the AI for Science paradigm, potentially replacing human roles in machine…PCGNet: Unifying Shared and Specific Information for Fashion Matching Recommendations
In fashion domain, recommending complementary clothing items that match selected pieces is a crucial cross-selling technique that improves customer satisfaction.Mixture-of-Experts Language Models Can Be Strong and Efficient Retrievers
Recent work has shown that fine-tuning decoder-only large language models (LLMs) for retrieval yields strong first-stage retrievers, with effectiveness improving as backbones grow in size.Pre-retrieval Query Clustering for Adaptive Top-k Document Retrieval in RAG Systems
RAG systems commonly retrieve a fixed number of documents (top-k) to ground generation, but this static approach is brittle: simple queries suffer over-retrieval (adding noise and cost) while…UniRec: Cross-stage Multi-Task Fusion with Preference Alignment for Cascaded Recommender Systems
Industrial recommender systems use cascaded stages with different objectives, feature spaces, and latency constraints.Benchmark Radar: A Living Database and Search Engine for AI Benchmarks and Evaluation
Benchmark researchers and developers of large language models (LLMs) and other AI systems need to find relevant evaluations, locate their benchmark datasets and code, and understand the settings…Agentic Share-of-Search: A Multi-Agent AI System for Competitive Decision-Making in LLM-Mediated E-Commerce
AI shopping assistants increasingly redirect consumer discovery, creating an urgent need for tools that support seller-side competitive decision-making.REVA: Reusable Evidence View Aggregation for Context-Efficient RAG Serving
Retrieval-augmented generation (RAG) improves knowledge-intensive large language model (LLM) applications by conditioning generation on retrieved documents, but longer contexts increase latency…VikingRAG: Accurate and Token-efficient Retrieval-augmented Generation over Structured Documents
State-of-the-art retrieval-augmented generation (RAG) methods exploit document structures to acquire sufficient evidence, but often incur substantial token costs.SWRouter: Similarity-Contractive Window Routing for Multi-Turn Large Language Model Conversations
Large language models exhibit complementary strengths, motivating routing methods that dispatch each query to the most suitable model.ReGround: Grounding Reviewer Comments in Multimodal Evidence
Reviewer comments naturally relate to specific parts of the reviewed paper, yet grounding these comments to the underlying evidence is difficult due to long multimodal documents.TimelyRAG: Semantic-Temporal Hybrid Retrieval for Time-Critical Question Answering in Overlapping-Evolving Documents
Although large language models (LLMs) and retrieval-augmented generation (RAG) have advanced open-domain question answering (QA), they remain unreliable when documents evolve through amendments.FedHUR: Learning Hierarchical Utility-Guided Client Relations for Personalized Federated Recommendation
Federated recommendation enables collaborative model training while keeping user interaction data on local clients.Your Retriever Already Knows: Distribution-Shape QPP for RAG Retrieval Sufficiency
Standard Retrieval-Augmented Generation (RAG) pipelines often provide no reliable inference-time signal of whether retrieval succeeded; on ambiguous or out-of-scope queries, generation may then…RAG-Safety-Bench: Reliable Evaluation of Retrieval-Augmented LLM Safety
Allowing large language models (LLMs) to retrieve information from a set of trusted documents can increase reliability and reduce hallucination.