Search: “rag”

arXiv:2602.22673v3 Announce Type: replace Abstract: Background: Antimicrobial resistance (AMR) is a global health threat. While the WHO Global Antimicrobial Resistance and Use Surveillance System (GLA

arXiv:2609.31029v1 Announce Type: new Abstract: Reasoning systems usually treat premise use as a question of relevance: if a fact is available and useful, it may be selected for inference. Authorizati

I maintain a starter kit where the LLM provider is a runtime-swappable DB row, not a hardcoded constant — OpenAI, Anthropic, or a local Ollama instance. Mostly so I can demo without burning API credit

arXiv:2609.28290v1 Announce Type: new Abstract: Meaning identity (whether two sentences say the same thing after wording changes) is treated in retrieval and RAG as a geometric fact about independentl

arXiv:2609.22537v1 Announce Type: new Abstract: Enterprise AI assistants must produce responses that are verifiable and traceable to source evidence. However, retrieval augmented generation (RAG) over

arXiv:2609.22100v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) improves language models with retrieved evidence, but processing many long passages is costly and can introduce dis

arXiv:2609.20754v1 Announce Type: new Abstract: Effective troubleshooting agents in enterprise customer support depend on retrieving actionable guidance from similar historical cases, yet existing ret

arXiv:2609.19634v1 Announce Type: cross Abstract: This paper proposes a Retrieval-Augmented Generation (RAG) framework for scientific image quality assessment, designed to simultaneously address both

arXiv:2609.17983v1 Announce Type: new Abstract: KV-cache reuse can reduce inference cost in retrieval-augmented generation and agentic systems, but cached contexts may become stale when retrieved know

I spent the first week of September building a personal assistant over Gemma 4 in Ollama on a 16 GB M1 Pro: Node backend, React web UI, Expo phone client, two-model routing, cross-session memory, RAG

arXiv:2609.15234v1 Announce Type: new Abstract: Recently, Large Language Models (LLMs) have gained significant attention due to their strong language understanding and generation capabilities, demonst

arXiv:2609.14408v1 Announce Type: new Abstract: We present an automated pipeline that converts NCERT textbooks into interactive video explanations that respond directly to user queries. A user uploads

arXiv:2609.15242v1 Announce Type: new Abstract: Environmental, Social, and Governance (ESG) reporting is critical for corporate accountability, with Large Language Models (LLMs) and Retrieval-Augmente

arXiv:2609.13768v1 Announce Type: new Abstract: Multi-hop question answering often fails when retrieval treats evidence as isolated matches to the original question, since the facts needed to answer a

arXiv:2609.13806v1 Announce Type: cross Abstract: ICD-10-CM codes are alphanumeric codes used in the US to classify diagnoses and injuries for medical billing and epidemiological reporting. Standard I

arXiv:2609.07093v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) enables large language models (LLMs) to answer questions by accessing external knowledge and has been widely ad

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:2609.12366v1 Announce Type: new Abstract: We present ORQA, a method for testing occupation-level knowledge in large language models. Prior methods either map abstract LLM skills to occupations v

Pick the right generative AI customization approach on AWS with an 8-step decision framework, from prompt engineering and RAG to fine-tuning, continued pre-training, and Amazon Nova Forge. Start simpl

arXiv:2609.07334v1 Announce Type: new Abstract: The Asset Administration Shell (AAS) is a cornerstone of Industry 4.0 and the Digital Product Passport, providing standardized digital representations o

arXiv:2609.08139v1 Announce Type: cross Abstract: We present the IGT system for PolyFiQA Task 2 of the FinMMEval Lab at CLEF 2026, a multilingual financial question answering task over English SEC fil

arXiv:2609.10372v1 Announce Type: new Abstract: We present the PACE, a framework for retrieval-augmented dialogue serving that formalizes Perceived Time-to-First-Response (PTFR) as a QoE objective and

arXiv:2609.07276v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) depends on dense retrieval: each document is stored as a learned vector, and a query is answered by finding its n

So I've been messinga round with embedding models for a bit, and I think they are interesting enough to experiment with. They are useful for rag, especially in a localllm sense because you can ground

arXiv:2609.04647v1 Announce Type: new Abstract: Traditional Retrieval-Augmented Generation (RAG) systems score each passage independently against the query, assembling context sets that may be individ

arXiv:2609.05339v1 Announce Type: new Abstract: Model upgrades are routine; memory migrations are not. An agent can keep the same memory store and still forget: a new model may interpret old notes dif

arXiv:2609.04767v1 Announce Type: new Abstract: Large language models (LLMs) increasingly rely on information retrieval (IR) systems, such as Retrieval-Augmented Generation (RAG), to incorporate domai

arXiv:2609.03937v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) complements parametric models with retrieved external evidence. The same idea is attractive for continuous-output

arXiv:2609.02745v1 Announce Type: cross Abstract: Selecting a retrieval model for a production RAG system requires reliable comparative evaluation, but obtaining relevance judgments at scale is expens

arXiv:2609.01658v1 Announce Type: cross Abstract: Retrieval-Augmented Generation enhances Large Language Models by grounding responses in external knowledge, but multi-hop reasoning remains vulnerable

arXiv:2609.02702v1 Announce Type: new Abstract: Transformers process information causally, but long-context reasoning may depend on task state discovered only later. We formalize this mismatch through

arXiv:2609.00427v1 Announce Type: new Abstract: The exponential growth of wireless devices is driving unprecedented spectrum demand, pushing spectrum management toward more fine-grained decisions acro

arXiv:2608.30712v1 Announce Type: new Abstract: Large multimodal models follow instructions about what to generate, but not necessarily about what evidence to rely on. Hence, models may continue to de

arXiv:2608.29088v1 Announce Type: new Abstract: Multimodal question answering remains sensitive to noisy, incomplete, and weakly grounded evidence. Long unstructured contexts can introduce redundancy

arXiv:2512.16391v2 Announce Type: replace-cross Abstract: Attention is the dominant source of latency during long-context LLM inference, an increasingly popular workload with reasoning models and RAG.

arXiv:2511.18931v2 Announce Type: replace-cross Abstract: Modern large language models increasingly integrate internal web-based retrieval to provide real-time answers, yet it remains unclear how effe

arXiv:2601.17173v2 Announce Type: replace-cross Abstract: Question answering systems are typically evaluated on factual correctness, yet many real-world applications-such as education and career guida

arXiv:2608.27121v1 Announce Type: cross Abstract: Aesthetics are an important part of the symbolism of artistic works. Although subjective, humans categorize art based on the emotion evoked regardless

arXiv:2608.23982v1 Announce Type: new Abstract: Scientific reasoning requires language models to retrieve specialized knowledge and incorporate it reliably into multi-step computation. Conditional mem

arXiv:2608.23568v1 Announce Type: new Abstract: Memory and RAG evaluations often treat the answering model's input as an implementation detail, even though systems may render the same history as a mem

arXiv:2608.22652v1 Announce Type: cross Abstract: LLMs are increasingly used for code generation, yet they frequently hallucinate non-existent software packages, creating exploitable entry points into

arXiv:2608.21702v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) grounds LLM generation on retrieved documents, but the standard terminal retrieval stage--dense-vector similarity,

arXiv:2608.23241v1 Announce Type: cross Abstract: Identifying and classifying environmental mitigation obligations in Federal Energy Regulatory Commission hydropower licensing documents is a labor-int

arXiv:2608.23252v1 Announce Type: cross Abstract: As Retrieval-Augmented Generation (RAG) shifts toward diverse portfolio generation, it is stymied by two critical bottlenecks: flawed measurement of e

Hey r/Ollama, If you run local models on mid-range hardware, you probably know how fast VRAM disappears when you try to set up document memory or local RAG pipelines. I built Hillock (https://github.c

arXiv:2608.19957v1 Announce Type: new Abstract: Natural language code retrieval is a rapidly evolving task in computer science. However, the 1C:Enterprise ecosystem combines Russian syntax with highly