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 27
Token-Budgeted Escalation for Financial Document QA: Cost Is Predictable, Benefit Is the Bottleneck
Retrieval-augmented generation systems can route difficult queries to deeper context, but batch deployments must allocate a shared token budget across calls whose costs vary by query.Contrastive Learning for Aspect Representation towards Explainable Recommendation
In this work, we propose a novel recommendation model, CLARER (Contrastive Learning for Aspect Representation towards Explainable Recommendation) that integrates aspect features learned from textual…ShanLiangRen: A Nutrition Agent for Personalized Daily Meal Planning
Dietary nutrition planning plays an important role in chronic disease management and maintaining a healthy body.From Delivery to Stateful Exploration: Rethinking the Index for Agentic Search
Recent advances in agentic search have given large language model (LLM) agents finer control over corpus exploration.Self-Retrospection Distillation: Turning Post-hoc Experiences into Prior Foresight
Reinforcement learning with verifiable rewards (RLVR) turns agent experience into learning signals primarily through scalar outcome rewards after interaction.Adapting Generative Recommenders for Multi-Turn Interaction
Generative recommenders decode items from a user's interaction history, but offer no way for users to correct a recommendation that misses their current intent.Confidence-Ordering Reversal under Contextual Priors in Neural Decoding
Contextual priors improve neural-to-language decoding by reshaping candidate scores.Behavior-Mining, Generative Conversations, and Collaborative Advisory: the Future of Travel and Tourism Recommender Systems
Since the early adoption of e-commerce, travel and tourism has been a lab for the design of recommender systems: tools that help travelers choose destinations, flights, accommodations, and combine…Aligning Performance with Contribution: Towards Contribution-Aware Fair Recommendation
Existing research on user fairness in recommender systems has developed diverse objectives.Seeing the Context: Enhancing Recommender Systems with Image-Derived Contextual Signals
Contextual information, capturing the circumstances of a user-item interaction, is central to recommender systems.Agentic AutoRAG: RAG Pipeline Optimization through Reasoning-Driven Agents
Retrieval-augmented generation (RAG) is a widely used approach for grounding large language models (LLMs) in external knowledge.UNREAL: Unifying Retrieval and Long-Context with a Single Model
Long-context inference and Retrieval-Augmented Generation (RAG) handle evidence selection at vastly different scales, from a single long prompt to an entire corpus.Disentangling Paradigm, Identifier, and Decoding in Generative Retrieval
Generative retrieval trains a language model to generate the identifier of a relevant document.A Systematic Study of Semantic ID Spaces for Generative Information Retrieval
Generative Information Retrieval (GIR) has emerged as a transformative paradigm, shifting document retrieval from a traditional "retrieve-and-rank" workflow to sequence-to-sequence generation, where…Trustworthy Domain-Specific AI for Structured Knowledge Retrieval and Reasoning
This dissertation presents a scalable architecture for transforming unstructured, domain-specific text into structured knowledge for retrieval and reasoning.BEACON-SP: Ontology-Grounded GraphRAG Framework for Clinical Suicide Risk Assessment
We present BEACON-SP, an ontology-grounded Graph Retrieval-Augmented Generation (GraphRAG) framework for clinician-facing decision support in behavioral health settings such as suicide prevention…From High Recall to High Utility: Dataset-Adaptive Post-Processing of LLM-Generated Customer Intents
Large language models can extract useful signals from heterogeneous enterprise data, but high-recall extraction often produces outputs that are duplicated, uneven in granularity, semantically…Building Navigable Graphs Without Search in Three Composable Stages
Navigable graphs can be built without searching for neighbors: partition the data, evaluate every pair inside each part, and select each point's edges from the candidates.What Transfers from a VLM Teacher? Comparing Supervision Signals for Visual Document Retrieval
Visual document retrievers are trained contrastively: each query is matched to one page labelled relevant - the positive - and pushed away from negatives, pages presumed irrelevant.Quantize by Drift: Label-Free Mixed-Precision Post-Training Quantization for Text Embedders
Mixed-precision post-training quantization needs a per-module sensitivity signal; for a text embedder the obvious one -- the retrieval quality a module costs when quantized -- needs relevance labels…