Search: “rag”

arXiv:2510.06002v3 Announce Type: replace Abstract: In high-stakes legal domains, retrieval must preserve not only semantic relevance, but also the hierarchy, temporality, and causal provenance of leg

arXiv:2604.26649v1 Announce Type: cross Abstract: Large reasoning models such as DeepSeek-R1 and OpenAI o1 generate extended chains of thought spanning thousands of tokens, yet their integration with

arXiv:2510.20303v3 Announce Type: replace Abstract: Citations from LLM-based RAG systems are supposed to simplify response verification. However, this goal is undermined in cases of citation failure,

arXiv:2604.24473v1 Announce Type: new Abstract: Multiple myeloma is managed through sequential lines of therapy over years to decades, with each decision depending on cumulative disease history distri

arXiv:2604.22722v1 Announce Type: cross Abstract: Dense vector retrieval is the practical backbone of Retrieval- Augmented Generation (RAG), but similarity search can suffer from precision limitations

arXiv:2604.21890v1 Announce Type: new Abstract: Event extraction identifies the central aspects of events from text. It supports event understanding and analysis, which is crucial for tasks such as in

arXiv:2508.10177v3 Announce Type: replace Abstract: Recent Large Language Model (LLM)-based AutoML systems demonstrate impressive capabilities but face significant limitations such as constrained expl

arXiv:2604.19766v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) expands the knowledge of Large Language Models (LLMs), yet current static retrieval methods struggle with complex

arXiv:2508.01959v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) over long documents typically involves splitting the text into smaller chunks, which serve as the basic units f

arXiv:2604.18257v1 Announce Type: cross Abstract: Query auto-completion (QAC) has been widely studied in the context of web search, yet remains underexplored for in-document search, which we term DocQ

arXiv:2604.17114v1 Announce Type: new Abstract: Frontier large language models generate clinically accurate outputs, but their citations are often fabricated. We term this the Provenance Gap. We teste

arXiv:2604.18478v1 Announce Type: cross Abstract: Persistent memory is the bottleneck separating stateless chatbots from long-running agentic systems. Retrieval-augmented generation (RAG) over flat ve

arXiv:2604.15597v1 Announce Type: new Abstract: Large Language Models (LLMs) are poised to disrupt knowledge work, with the emergence of delegated work as a new interaction paradigm (e.g., vibe coding

arXiv:2604.15621v1 Announce Type: cross Abstract: Adaptive Retrieval-Augmented Generation aims to mitigate the interference of extraneous noise by dynamically determining the necessity of retrieving s

arXiv:2604.15827v1 Announce Type: cross Abstract: Conventional information retrieval is concerned with identifying the relevance of texts for a given query. Yet, the conventional definition of relevan

arXiv:2601.03746v3 Announce Type: replace Abstract: As large language models (LLMs) are more frequently used in retrieval-augmented generation pipelines, it is increasingly relevant to study their beh

arXiv:2604.14034v1 Announce Type: cross Abstract: Recent advances in Generative Artificial Intelligence, particularly Large Language Models (LLMs), have stimulated growing interest in automating or as

arXiv:2604.14324v1 Announce Type: new Abstract: Large language models (LLMs) often exhibit hallucinations due to their inability to accurately perceive their own knowledge boundaries. Existing abstent

arXiv:2604.13579v1 Announce Type: new Abstract: Conventional Retrieval-Augmented Generation (RAG) systems often struggle with complex multi-hop queries over long documents due to their single-pass ret

arXiv:2510.03323v2 Announce Type: replace Abstract: Integrating textual graphs into Large Language Models (LLMs) is promising for complex graph-based QA. However, a key bottleneck is retrieving inform

arXiv:2604.11759v1 Announce Type: new Abstract: Organizational knowledge used by AI agents typically lacks epistemic structure: retrieval systems surface semantically relevant content without distingu

arXiv:2604.09430v1 Announce Type: cross Abstract: Text embeddings are central to modern information retrieval and Retrieval-Augmented Generation (RAG). While dense models derived from Large Language M

arXiv:2604.05350v2 Announce Type: replace Abstract: Enterprise IT support interactions are fundamentally diagnostic: effective resolution requires iterative evidence gathering from ambiguous user repo

arXiv:2604.06211v1 Announce Type: cross Abstract: Natural language explanations produced by large language models (LLMs) are often persuasive, but not necessarily scrutable: users cannot easily verify

arXiv:2610.21327v1 Announce Type: new Abstract: Multimodal long-document question answering requires models to seek sparse evidence across pages and modalities, understand heterogeneous document conte

arXiv:2409.12500v2 Announce Type: replace-cross Abstract: Large language models have become increasingly popular and demonstrated remarkable performance in various natural language processing (NLP) ta

arXiv:2610.20632v1 Announce Type: new Abstract: Modern healthcare generates increasingly complex medical reports that challenge clinical professionals in terms of time and interpretive burden. Manual

arXiv:2610.22163v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly being adopted as teaching tools in computing education, particularly for developing AI tutors capable of a

arXiv:2610.21900v1 Announce Type: new Abstract: An agent's memory must remain useful as information changes across conversations. A new fact may replace an earlier value without making that value irre

arXiv:2610.15995v1 Announce Type: cross Abstract: This paper presents AutoSec-RAN, a specification-grounded large language model (LLM) framework for automated security triage of Open Radio Access Netw

arXiv:2610.19021v1 Announce Type: new Abstract: Recent work has questioned whether large language models (LLMs) can be useful for temporal event prediction. Despite vast pretrained world knowledge, LL

arXiv:2610.14465v1 Announce Type: cross Abstract: Structured knowledge retrieval acquires task-relevant evidence from structured knowledge sources for downstream tasks. Existing methods rely on uncons

arXiv:2610.19836v1 Announce Type: new Abstract: Enterprise group chats record project progress, decisions, task ownership, and other organizational information valuable to enterprise agents. Reliable

arXiv:2610.15091v1 Announce Type: cross Abstract: Argument structures represent how individual arguments interact within a discourse. However, leveraging these structures to infer meaningful knowledge

arXiv:2504.01627v2 Announce Type: replace-cross Abstract: Introduction: Horizon scanning in healthcare assesses early signals of innovation, crucial for timely adoption. Current horizon scanning faces

arXiv:2610.16182v1 Announce Type: new Abstract: Flagship large language models (LLMs) offer rich general knowledge and strong reasoning capabilities, but their high inference cost and latency limit th

arXiv:2506.20642v3 Announce Type: replace Abstract: Chain-of-Thought (CoT) prompting significantly enhances large language models' (LLMs) problem-solving capabilities, but still struggles with complex

arXiv:2610.20292v1 Announce Type: new Abstract: Reliable medical reasoning requires LLMs to dynamically access external biomedical evidence to improve factual trustworthiness and reasoning accuracy. H

arXiv:2610.19133v1 Announce Type: new Abstract: Indirect prompt injection uses untrusted retrieved content, tool outputs, or agent messages to override an LLM's intended behavior. We show that defense

arXiv:2610.16979v1 Announce Type: cross Abstract: Generative retrieval (GR) offers a promising paradigm for industrial search by unifying candidate generation within an autoregressive framework. Howev

arXiv:2610.14688v1 Announce Type: new Abstract: LLM-augmented data workflows often involve multiple heterogeneous models operating over substantial shared context, such as schemas, tool specifications

arXiv:2610.17926v1 Announce Type: new Abstract: Autoregressive video generation relies on memory retrieval to model long-range dependencies. This capability becomes particularly critical for multi-sho

arXiv:2610.14074v1 Announce Type: new Abstract: Large language models typically support different inference budgets through separate model sizes or fixed compute configurations. In multi-turn interact

arXiv:2502.04356v2 Announce Type: replace-cross Abstract: In response to the success of proprietary Large Language Models (LLMs), there is a growing interest in developing open, non-proprietary LLMs a

arXiv:2610.16066v1 Announce Type: new Abstract: Detailed video captioning has become an essential semantic interface for multi-modal understanding and generation. While existing methods mainly target

arXiv:2610.14860v1 Announce Type: new Abstract: Advances in large language models (LLMs) have brought the vision of a general-purpose personal assistant closer to reality. Such assistants could provid