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 9
Quantum-Inspired Pairwise Ranking Aggregation via Active LLM Feedback
Ranking aggregation from multiple retrievers is a powerful yet under-explored technique in modern retrieval and RAG systems.Leaving, Staying, or Living There? Duration Commonsense Reasoning with Human-LLM Co-annotations
Gardening in the backyard implies living there, while checking into an Airbnb more likely means staying for a few days.Contextual Embeddings Beyond the Gold Chunk
Contextual embeddings represent chunks of a document with respect to the document's global context.SpatialVec: A Unified Representation Learning Framework for Geospatial Objects with Boundary-Aware Sampling
Spatial representation learning is essential for Geospatial Artificial Intelligence (GeoAI) applications, enabling machine learning models to capture the geometry, location, and spatial…ACOR: incremental evidence accumulation towards real-time DNA storage reconstruction
DNA offers high-density, durable storage by encoding digital information into synthetic strands and retrieving it through sequencing.Monitor Accuracy Isn't the Answer: A Decision-Value Framework for Evaluating AI Agent Restart Monitors
Classification accuracy and true-positive and false-positive rates alone do not determine whether restarting an AI agent improves success.QESChunker: A Single Objective Unifies Overlapping and Non-Overlapping Chunking for RAG
Chunking is a fundamental preprocessing step in retrieval-augmented generation (RAG), yet most existing chunkers rely primarily on heuristics.CCP-DP: Cross-cluster context-guided phase dynamic programming for DNA storage sequence reconstruction
DNA data storage offers high information density and long-term stability, but accurate sequence reconstruction remains challenging under limited sequencing coverage and strong insertion, deletion…Governed Sovereign RAG for Public Administration: Tripartite Epistemic Routing, Facet-Guided Clarification, and Deterministic Numeric Verification in Citizen Services
Deploying conversational assistants at government service desks faces a tension between regulatory certainty (zero tolerance for invented fees), data sovereignty prohibiting commercial clouds, and…Recall or Relearn? A Mixture of In-Context Experts for Recurring Concept Drift
Tabular foundation models (TFMs) are being applied to Web data streams such as network intrusion detection, where they adapt by updating the labelled rows of their context without retraining.The Vocabulary Is Already Here: Adapting Pretrained Interpretable Features for Generative Recommendation
Generative recommendation (GR) predicts the next item through its semantic ID (SID).DuTri: Dual-Branch Modeling with Triadic Factorization for Multimodal Multi-Scenario Recommendation
As recommendation scenarios become increasingly diverse and multimodal information grows more prevalent, effectively modeling cross-scenario user preferences and multimodal semantics has become a…Latent Residual Reasoning for Ranking in Generative Recommendation
Generative recommendation (GR) models for ranking typically adopt a fast-thinking direct prediction paradigm with strong practical efficiency.Do We Need an LLM to Know Why Sources Conflict? Decision Models for Knowledge-Conflict Typing in RAG
When the sources retrieved for a query disagree, the right response depends on why they disagree: a question with several valid answers, a contested topic, stale information and a false source each…When Diffusion Recommenders Stop Sampling: Preserving Generative Diversity
A user history can admit multiple plausible next interactions, yet conventional sequential recommenders produce a single deterministic ranking.PailitaoMMSearch2.0 Technical Report: Scaling Generative Retrieval to Unified End-to-End Multimodal Search
Generative retrieval models have been increasingly adopted in search and recommendation systems.KL-MOPD: Discrepancy-Aware Multi-Objective On-Policy Distillation for Generative Recommendation
Coordinating multiple recommendation objectives (e.g., click and conversion) is critical for real-world generative recommendation (GR).SpectRAG: Weighted Chunk Graphs for Token-Budgeted Context Selection in Multi-Hop Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) systems ground large language models (LLMs) by retrieving external evidence and conditioning generation on the retrieved context.Compote: Collapse-Resistant Multiple Hypothesis Learning For Sequential Recommendation
Next-item prediction is inherently ambiguous: a single interaction history often admits multiple plausible continuations across distinct catalogue regions.Tessera: Structure-Completed Evidence Units for Web Retrieval-Augmented Generation
Web retrieval-augmented generation (RAG) splits a page into fixed-size text chunks or, in recent visual pipelines, screenshot tiles, and the retrieved item serves at once as the unit of indexing…