Search: “retrieval”

arXiv:2510.11217v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) combines the language understanding and reasoning power of large language models (LLMs) with external ret

In support of our mission to accelerate the developer journey on Google Cloud, we built Dev Signal: a multi-agent system designed to transform raw community signals into reliable technical guidance by

arXiv:2604.07985v1 Announce Type: new Abstract: We address the task of predicting the gain of using RAG (retrieval augmented generation) for question answering with respect to not using it. We study t

arXiv:2610.19475v1 Announce Type: cross Abstract: Multimodal in-context learning (ICL) is an important capability of multimodal large language models (MLLMs). Its performance is closely tied to the de

arXiv:2610.14428v1 Announce Type: new Abstract: Enterprise workflow automation typically requires retrieving a complete and accurate set of relevant information from large operational document corpora

arXiv:2610.13128v1 Announce Type: new Abstract: Question answering over large heterogeneous document collections requires first finding the right passages and then reading them, yet current retrieval-

arXiv:2610.09026v1 Announce Type: cross Abstract: We present BEACON-SP, an ontology-grounded Graph Retrieval-Augmented Generation (GraphRAG) framework for clinician-facing decision support in behavior

arXiv:2506.05766v2 Announce Type: replace Abstract: Retrieval augmented generation (RAG) has shown great power in improving Large Language Models (LLMs). However, most existing RAG-based LLMs are dedi

arXiv:2610.02875v1 Announce Type: cross Abstract: Multilingual encoders can exhibit reduced retrieval effectiveness when queries and relevant documents differ in language, despite strong same-language

Automated lint: 19 errors, 64915 warnings, 3 info

arXiv:2610.01871v1 Announce Type: cross Abstract: Multimodal Retrieval-Augmented Generation (MRAG) has emerged as a reliable and cost-effective technique of grounding the generative capabilities of Mu

arXiv:2609.35794v1 Announce Type: new Abstract: Just as Socrates recognized the limits of his own knowledge, Retrieval-Augmented Language Models (RALMs) should learn to abstain when the retrieved evid

arXiv:2609.24101v1 Announce Type: new Abstract: Automated biomedical evidence synthesis depends on retrieving published studies, but the biomedical literature is systematically skewed toward positive

arXiv:2609.18154v1 Announce Type: cross Abstract: We describe our system for LitTraceQA (GroundLM @ EMNLP 2026): given a research question, retrieve the relevant papers from a pool of 27,487, cite the

arXiv:2609.16519v1 Announce Type: new Abstract: Scientific research increasingly relies on large, heterogeneous data sources, motivating interest in retrieval-augmented generation (RAG) systems that p

arXiv:2609.17043v1 Announce Type: cross Abstract: Multi-hop question answering requires combining information from multiple documents to answer complex questions. These systems have grown increasingly

arXiv:2609.16627v1 Announce Type: new Abstract: Quantifying organizational environmental action from publicly available web content remains a challenging environmental data science problem because rel

arXiv:2609.12791v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has empowered Large Language Models (LLMs) to tackle knowledge-intensive tasks. However, navigating global, heterog

arXiv:2609.05637v2 Announce Type: replace Abstract: A popular way to improve Retrieval-Augmented Generation (RAG) is to rewrite the user's question into several variants and search with all of them. W

arXiv:2609.06391v1 Announce Type: new Abstract: Graph-agentic retrieval-augmented generation combines structured evidence with adaptive controllers that can plan retrieval, traverse relations, verify

arXiv:2609.08372v2 Announce Type: replace Abstract: A dense retriever encodes a question as one vector, but the question arrives one word at a time. We read 2,144 held-out headlines from Thu Vien Phap

arXiv:2609.05152v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) enhances language models with external knowledge, but the lengthy retrieved context inflates the input and degrades

arXiv:2608.30303v1 Announce Type: new Abstract: Search agents reduce hallucination by grounding answers in retrieved web evidence. Yet reliance on retrieval also creates an attack surface: poisoned co

arXiv:2608.29953v1 Announce Type: new Abstract: Flat retrieval-augmented generation treats a corpus as a bag of chunks, discarding document hierarchy and cross document structure. We introduce SearchW

arXiv:2608.26386v1 Announce Type: cross Abstract: Retrieval-augmented methods improve factual accuracy by grounding language models in external knowledge, but retrieving over unstructured text often i

arXiv:2402.01767v4 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) significantly improves document-based question answering by integrating external documents during generat

arXiv:2608.23221v1 Announce Type: cross Abstract: Historical retrieval for time-series prediction commonly treats past similarity as a proxy for usefulness. We ask a different question: which historic

arXiv:2608.20348v1 Announce Type: cross Abstract: Electronic health records now routinely exceed 100,000 tokens per patient. Yet large language models exhibit the lost-in-the-middle (LitM) effect: inf

arXiv:2608.18681v1 Announce Type: new Abstract: We introduce a failure-aware adversarial retrieval-augmented framework for improving robustness in natural language understanding. Rather than selecting

Reducing Retrieval and Calls When a retrieval-augmented generation (RAG) agent runs, it often has to plan a search, check the evidence it gets back, and try again when that evidence falls short. Those

arXiv:2608.07531v1 Announce Type: cross Abstract: Search-augmented language agents should retrieve external information only when necessary and ground their answers in retrieved evidence. Existing ext

arXiv:2608.05571v1 Announce Type: new Abstract: Retrieval-augmented forecasting promises to adapt frozen Time Series Foundation Models (TSFMs) to new domains without fine-tuning, but recent methods ty

arXiv:2608.04756v1 Announce Type: cross Abstract: In Retrieval-Augmented Generation (RAG), post-retrieval conflict resolution arbitrates among noisy or contradictory retrieved passages. However, the r

arXiv:2608.01913v1 Announce Type: cross Abstract: Deep search agents answer difficult information-seeking questions by iteratively issuing search queries to gather supporting evidence, but it remains

arXiv:2608.00030v1 Announce Type: new Abstract: Specialised retrieval agents typically surface higher quality results than general-purpose search, but selecting the optimal agent for a given query rem

arXiv:2607.26631v1 Announce Type: new Abstract: Human Activity Recognition (HAR) from wearable sensors supports applications in healthcare, rehabilitation, fitness tracking, and smart environments. Ye

arXiv:2607.07383v1 Announce Type: new Abstract: Knowledge-based Visual Question Answering (KB-VQA) requires models to retrieve visual entities matching the query image from large-scale encyclopedic kn

arXiv:2606.28349v1 Announce Type: cross Abstract: Long-context reasoning requires models to access, retrieve, and integrate evidence scattered across documents, dialogues, and accumulated interaction

arXiv:2606.28447v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) enhanced by Knowledge Graphs has shown promise in complex multi-hop reasoning tasks. However, existing graph-base

arXiv:2606.09316v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) enables agents to access external knowledge at inference time, but it primarily retrieves fragmented declarative ev

arXiv:2510.04127v2 Announce Type: replace-cross Abstract: Approximate nearest neighbour (ANN) search underpins large-scale retrieval, increasingly within the retrieval-augmented generation pipelines t

arXiv:2606.04522v1 Announce Type: cross Abstract: Approximate nearest neighbor (ANN) search has become a core primitive in information retrieval and modern machine learning tasks, from classification

arXiv:2507.21892v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) mitigates hallucination in LLMs by incorporating external knowledge, but relies on chunk-based retrieval that l

arXiv:2606.01697v1 Announce Type: new Abstract: Conversational search has become increasingly important in retrieval-augmented generation (RAG) systems, where users interact with AI assistants through

arXiv:2605.26680v1 Announce Type: cross Abstract: Recent video multimodal large language models (MLLMs) increasingly couple step-by-step reasoning with on-demand visual evidence retrieval, allowing mo

arXiv:2605.27294v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) systems can respond incorrectly even when the correct passage was retrieved. The model must still read the retrieve