Search: “retrieval”

arXiv:2606.04591v1 Announce Type: new Abstract: With the widespread adoption of multi-modal communication platforms, long-form dialogues interleaving text and images have become increasingly common. U

arXiv:2510.09953v3 Announce Type: replace Abstract: Manual medical image segmentation by clinicians, though accurate, is time-consuming and variable across experts, whereas AI-based models automate th

arXiv:2606.04646v1 Announce Type: cross Abstract: Many real-world questions over business, legal, and scientific corpora are natural-language versions of database-style queries over records latent in

arXiv:2606.01097v1 Announce Type: new Abstract: We describe Dual-Route Top-K Retrieval with 1v1 VLM Reranking for the CoVR-R challenge. The method treats composed video retrieval as two coupled proble

arXiv:2606.00029v1 Announce Type: cross Abstract: Retrieval-augmented generation systems struggle with temporal reasoning and evidence fusion when answering complex questions over historical criminal

arXiv:2605.24470v2 Announce Type: replace Abstract: Video-text retrieval has witnessed remarkable progress driven by large-scale vision-language pretraining, yet most existing approaches inherit an im

arXiv:2606.02321v1 Announce Type: new Abstract: Recent advances in large vision-language models have expanded video retrieval from simple text-based search to more flexible scenarios, where users may

arXiv:2606.00422v1 Announce Type: cross Abstract: Modern recommendation systems predominantly train retrieval and ranking as separate models despite both increasingly relying on large transformers enc

arXiv:2605.29606v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) for document-based Open-domain Question Answering (ODQA) on large-scale industrial corpora faces two critical bottl

arXiv:2605.28828v1 Announce Type: cross Abstract: Large Language Models (LLMs) achieve impressive performance across many tasks but remain prone to hallucination, especially in long-form generation wh

arXiv:2605.27860v1 Announce Type: new Abstract: Retrieval-augmented generation combined with reinforcement learning has shown promise for grounding large language models in trustworthy medical evidenc

arXiv:2605.28787v1 Announce Type: cross Abstract: In the era of autonomous agents, machine-actionable data is critical for data-driven workflows. For more than a decade, semantic metadata like schema.

arXiv:2605.26575v1 Announce Type: new Abstract: Multilingual embedding models are deployed under the assumption that cross-lingual retrieval is symmetric: if a query in language A retrieves its transl

arXiv:2605.26356v1 Announce Type: new Abstract: In-context learning has recently been linked to implicit gradient descent in linear self-attention models, suggesting that context can induce a forward-

arXiv:2605.27361v1 Announce Type: new Abstract: Modern retrieval agents expose many configuration choices -- LLM, retriever, number of documents, number of hops, and synthesis strategy -- each shaping

arXiv:2605.26819v1 Announce Type: cross Abstract: We present RAGEAR (Retrieval-Augmented Graph-Enhanced Academic Recommender), a neurosymbolic recommender system for academic course recommendation. RA

arXiv:2601.21853v2 Announce Type: replace-cross Abstract: Multi-vector representations generated by late interaction models, such as ColBERT, enable superior retrieval quality compared to single-vecto

arXiv:2605.22552v1 Announce Type: new Abstract: Fashion image retrieval is a cornerstone of modern e-commerce systems. A unified framework that supports diverse query formats and search intentions is

arXiv:2605.18769v1 Announce Type: cross Abstract: Personalized Retrieval-Augmented Generation (RAG) relies on accurately selecting user-relevant documents. In practice, existing RAG approaches often s

arXiv:2605.18775v1 Announce Type: cross Abstract: Graph-based Retrieval-Augmented Generation (RAG) systems leverage interconnected knowledge structures to capture complex relationships that flat retri

arXiv:2605.18857v1 Announce Type: cross Abstract: For most of the history of information retrieval (IR), search results were designed for human consumers who could scan, filter, and discard irrelevant

arXiv:2512.19134v2 Announce Type: replace Abstract: Dynamic Retrieval-Augmented Generation adaptively determines when to retrieve during generation to mitigate hallucinations in large language models

arXiv:2605.16767v1 Announce Type: new Abstract: Multi-label legal annotation requires assigning multiple labels from large, evolving taxonomies to long, fact-intensive documents, often under limited s

arXiv:2504.20734v4 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has shown substantial promise in improving factual accuracy by grounding model responses with external kn

arXiv:2505.09246v4 Announce Type: replace-cross Abstract: In many real-world settings, machine learning models and interactive systems have access to both structured knowledge, e.g., knowledge graphs

arXiv:2605.08299v1 Announce Type: cross Abstract: Embedding-based code retrieval often suffers when encoders overfit to surface syntax. Prior work mitigates this by using LLMs to rephrase queries and

arXiv:2605.07129v1 Announce Type: cross Abstract: Large Language Models (LLMs) have emerged as a promising paradigm for next-generation recommender systems, offering strong semantic understanding and

arXiv:2605.02173v1 Announce Type: new Abstract: We evaluate the long-context retrieval and reasoning capabilities of five frontier large language models with advertised 1M-token context windows on a c

arXiv:2605.02411v1 Announce Type: cross Abstract: A semantic gap separates how users describe tasks from how tools are documented. As API ecosystems scale to tens of thousands of endpoints, static ret

arXiv:2605.01409v1 Announce Type: cross Abstract: The growing availability of health-related instructional videos creates new opportunities for clinical training, patient rehabilitation, and health ed

arXiv:2605.00902v1 Announce Type: new Abstract: Foundation models are reshaping computational histopathology, yet their value for whole-slide image retrieval relative to strong patch-based and supervi

arXiv:2604.28142v1 Announce Type: cross Abstract: Multivector retrieval models achieve state-of-the-art effectiveness through fine-grained token-level representations, but their deployment incurs subs

arXiv:2604.26176v1 Announce Type: cross Abstract: The integration of Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) has significantly advanced Knowledge Graph Question Answerin

arXiv:2604.26768v1 Announce Type: new Abstract: Parametric Retrieval-Augmented Generation (PRAG) encodes external documents into lightweight parameter modules that can be retrieved and merged at infer

arXiv:2603.06198v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) is a framework in which a Generator, such as a Large Language Model (LLM), produces answers by retrieving docum

arXiv:2603.28886v2 Announce Type: replace-cross Abstract: Graph-augmented retrieval combines dense similarity with graph-based relevance signals such as Personalized PageRank (PPR), but these scores h

arXiv:2604.25182v1 Announce Type: new Abstract: A multilingual collection may contain useful knowledge in other languages to supplement and correct the facts in the original language for Retrieval-Aug

arXiv:2412.07584v2 Announce Type: replace Abstract: Current video retrieval systems, especially those used in competitions, primarily focus on querying individual keyframes or images rather than encod

arXiv:2604.25448v1 Announce Type: new Abstract: Navigating AI regulation across jurisdictions is increasingly difficult for policymakers, legal professionals, and researchers. To address this, we pres

arXiv:2602.02556v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are largely static and often redo reasoning or repeat mistakes. Prior experience reuse typically relies on extern

arXiv:2604.22885v1 Announce Type: cross Abstract: Federated cross-modal retrieval faces severe challenges from heterogeneous client data, particularly non-IID semantic distributions and missing modali

arXiv:2604.22861v1 Announce Type: cross Abstract: Scientific research relies on accurate information retrieval from literature to support analytical decisions. In this work, we introduce a new task, I

arXiv:2604.24515v1 Announce Type: new Abstract: Multi-hop Question Answering (MHQA) aims to answer questions that require multi-step reasoning. It presents two key challenges: generating correct reaso

arXiv:2604.22061v1 Announce Type: cross Abstract: Patient-trial matching requires reasoning over long, heterogeneous electronic health records (EHRs) and complex eligibility criteria, posing significa

arXiv:2604.22658v1 Announce Type: new Abstract: Single-view 3D shape retrieval is a fundamental yet challenging task that is increasingly important with the growth of available 3D data. Existing appro

arXiv:2604.20850v1 Announce Type: cross Abstract: Dense retrieval systems rank passages by embedding similarity to a query, but multi-hop questions require passages that are associatively related thro