Topic

#RAG

RAG (Retrieval-Augmented Generation) is a technique that connects an LLM to an external document base to generate answers grounded in real sources. For example, LlamaIndex lets developers build RAG pipelines by indexing their own data and querying it through a language model.

40Articles

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67Avg. signal

arXiv cs.AI·1h ago

ChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoning

ChatHealthAI aligns structured EHR representations from a pretrained EHR foundation model with a frozen LLM's semantic space via a task-aware resampler. The multimodal framework integrates longitudinal patient representations with refined clinical event descriptions, improving interpretable clinical reasoning while maintaining competitive predictive performance on the EHRSHOT benchmark.

RAG Reasoning Evals

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Hacker News (AI)·12h ago

CLI tool that packages data science projects for LLM context windows

CLI tool that packages data science projects for LLM context windows. Enables preparation and compression of project data to optimize context window usage in language models.

Tools RAG

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Reddit r/LocalLLaMA·13h ago

Best Agentic Frameworks in 2026: When to Use LangGraph, CrewAI, LlamaIndex, Pydantic AI, or No Framework

2026 agentic frameworks comparison: LangGraph for stateful workflows, CrewAI for multi-agent prototypes, LlamaIndex for RAG, Pydantic AI for type-safe services. Author recommends skipping frameworks for simple cases and defining job requirements before framework selection.

AI Agents Multi-agent RAG

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GitHub Trending·16h ago

<svg aria-hidden="true" data-component="Octicon" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo mr-1 tmp-mr-1 color-fg-muted"> <path d="M2 2.5A2.5 2.5 0 0 1 4.5 0h8.75a.75.75 0 0 1 .75.75v12.5a.75.75 0 0 1-.75.75h-2.5a.75.75 0 0 1 0-1.5h1.75v-2h-8a1 1 0 0 0-.714 1.7.75.75 0 1 1-1.072 1.05A2.495 2.495 0 0 1 2 11.5Zm10.5-1h-8a1 1 0 0 0-1 1v6.708A2.486 2.486 0 0 1 4.5 9h8ZM5 12.25a.25.25 0 0 1 .25-.25h3.5a.25.25 0 0 1 .25.25v3.25a.25.25 0 0 1-.4.2l-1.45-1.087a.249.249 0 0 0-.3 0L5.4 15.7a.25.25 0 0 1-.4-.2Z"></path> </svg> <span data-view-component="true" class="text-normal"> jamwithai /</span> production-agentic-rag-course

Open-source course on building production agentic RAG systems. Covers architecture, implementation patterns, and best practices for deploying agentic retrieval-augmented generation systems.

AI Agents RAG Open source

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GitHub Trending·16h ago

<svg aria-hidden="true" data-component="Octicon" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo mr-1 tmp-mr-1 color-fg-muted"> <path d="M2 2.5A2.5 2.5 0 0 1 4.5 0h8.75a.75.75 0 0 1 .75.75v12.5a.75.75 0 0 1-.75.75h-2.5a.75.75 0 0 1 0-1.5h1.75v-2h-8a1 1 0 0 0-.714 1.7.75.75 0 1 1-1.072 1.05A2.495 2.495 0 0 1 2 11.5Zm10.5-1h-8a1 1 0 0 0-1 1v6.708A2.486 2.486 0 0 1 4.5 9h8ZM5 12.25a.25.25 0 0 1 .25-.25h3.5a.25.25 0 0 1 .25.25v3.25a.25.25 0 0 1-.4.2l-1.45-1.087a.249.249 0 0 0-.3 0L5.4 15.7a.25.25 0 0 1-.4-.2Z"></path> </svg> <span data-view-component="true" class="text-normal"> chopratejas /</span> headroom

Headroom compresses tool outputs, logs, files, and RAG chunks before sending to LLM. Reduces token consumption by 60-95% without quality loss. Available as library, proxy, and MCP server.

RAG MCP Tools

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GitHub Trending·16h ago

<svg aria-hidden="true" data-component="Octicon" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo mr-1 tmp-mr-1 color-fg-muted"> <path d="M2 2.5A2.5 2.5 0 0 1 4.5 0h8.75a.75.75 0 0 1 .75.75v12.5a.75.75 0 0 1-.75.75h-2.5a.75.75 0 0 1 0-1.5h1.75v-2h-8a1 1 0 0 0-.714 1.7.75.75 0 1 1-1.072 1.05A2.495 2.495 0 0 1 2 11.5Zm10.5-1h-8a1 1 0 0 0-1 1v6.708A2.486 2.486 0 0 1 4.5 9h8ZM5 12.25a.25.25 0 0 1 .25-.25h3.5a.25.25 0 0 1 .25.25v3.25a.25.25 0 0 1-.4.2l-1.45-1.087a.249.249 0 0 0-.3 0L5.4 15.7a.25.25 0 0 1-.4-.2Z"></path> </svg> <span data-view-component="true" class="text-normal"> chopratejas /</span> headroom

Headroom compresses tool outputs, logs, files, and RAG chunks before sending to LLM. Reduces token consumption by 60-95% without degrading answers. Available as library, proxy, and MCP server.

RAG MCP Tools

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Reddit r/LocalLLaMA·17h ago

Building a free, offline LLM “tutor” grounded in one university textbook — RAG, LoRA, or both? Sanity check wanted

Developer seeks to build a free offline AI tutor grounded in a university textbook. Proposed architecture: RAG as core component (chunking, embedding, retrieval with page/section citations) + optional LoRA for pedagogical style. Questions on model selection (Qwen, Gemma), handling complex structures (figures, equations), and packaging for non-technical users.

RAG Fine-tuning Open source

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arXiv cs.CL·1d ago

DeSQ: Decomposition-based SPARQL Query Generation

DeSQ decomposes complex questions into atomic constraints to generate SPARQL queries. The framework operates in three stages: decomposition, generation of SPARQL fragments with placeholders, and final assembly. Outperforms state-of-the-art on 4/5 major benchmarks with improved robustness to lexical variation.

RAG Reasoning Benchmarks

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arXiv cs.CL·1d ago

LaSR: Context-Aware Speech Recognition via Latent Reasoning

LaSR proposes a training paradigm for Speech LLMs featuring latent reasoning aligned to acoustic feature regions of target words. Without explicit intermediate tokens, the method improves specialized vocabulary recognition on Fun-Audio-Chat. A new Spoken Darwin-Science corpus for academic terminology is introduced.

Voice Reasoning RAG

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arXiv cs.LG·1d ago

KG-Guard: Graph-Based Hallucination Detection for Knowledge Base Question Answering

KG-Guard detects hallucinations in knowledge base question answering (KBQA) systems using an augmented graph and lightweight encoder. The model achieves F1 scores of 82.0–87.4 on WebQSP/ComplexWebQuestions with 305× fewer parameters than baselines, and improves downstream KBQA F1 by 13–14.5 points through iterative refinement feedback.

Reasoning Evals RAG

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arXiv cs.CL·1d ago

TCAR-Gen: Temporal Graph Retrieval with Evidence Fusion for Knowledge-Grounded Generation

TCAR-Gen combines query-conditioned graph neural networks, temporal evidence fusion, and chain-of-trees reasoning for retrieval-augmented generation. Achieves 0.3738 Recall@5 on Victorian Crime Diaries benchmark, outperforming Vanilla RAG, Temporal RAG, and GraphRAG variants. Cross-model evaluation across GPT-OSS 20B to TinyLlama 1.1B shows robust retrieval coverage at smaller scales.

RAG Reasoning Benchmarks

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arXiv cs.CL·1d ago

Graph-Augmented Retrieval for Cross-Entity Financial Sentiment Analysis: A Comparative Study

Comparative study of a two-hop Graph-RAG architecture versus standard vector-only RAG for cross-entity financial sentiment analysis. On 100 queries (30 direct, 70 relational), Graph-RAG improves entity recall (+6.4%, p<0.001) and answer relevancy for complex queries (+11.7%), with no quality degradation, modest 22.6% latency increase but 80% variance reduction.

RAG Benchmarks Papers

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arXiv cs.CL·1d ago

BOUTEF: A Multilingual Corpus for FakeNews in North Africa -- Language as a Weapon

BOUTEF is a multilingual corpus from 2 countries (Algeria, Tunisia) covering fake news, authentic narratives, comments, and debunking. Includes MSA, Algerian/Tunisian dialects, Arabizi, French, English, and code-switching. Analysis shows fake news relies on emotionally charged narratives and sensational framing, while debunking adopts a factual, verification-oriented style.

Papers Benchmarks AI safety

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GitHub Trending·2d ago

<svg aria-hidden="true" data-component="Octicon" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo mr-1 tmp-mr-1 color-fg-muted"> <path d="M2 2.5A2.5 2.5 0 0 1 4.5 0h8.75a.75.75 0 0 1 .75.75v12.5a.75.75 0 0 1-.75.75h-2.5a.75.75 0 0 1 0-1.5h1.75v-2h-8a1 1 0 0 0-.714 1.7.75.75 0 1 1-1.072 1.05A2.495 2.495 0 0 1 2 11.5Zm10.5-1h-8a1 1 0 0 0-1 1v6.708A2.486 2.486 0 0 1 4.5 9h8ZM5 12.25a.25.25 0 0 1 .25-.25h3.5a.25.25 0 0 1 .25.25v3.25a.25.25 0 0 1-.4.2l-1.45-1.087a.249.249 0 0 0-.3 0L5.4 15.7a.25.25 0 0 1-.4-.2Z"></path> </svg> <span data-view-component="true" class="text-normal"> ruvnet /</span> ruflo

Ruflo is a multi-agent coordination platform for Claude. Deploys autonomous agent swarms, orchestrates workflows, and integrates RAG. Enterprise-grade architecture with self-learning swarm intelligence and native Claude Code integration.

Claude Multi-agent AI Agents

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arXiv cs.CL·2d ago

Knowledge Graph-Enhanced Zero-Shot Topic Classification: A Multi-Strategy Comparative Study

Comparative study of zero-shot multi-label topic classification using knowledge graphs extracted from documents. Framework tested on 15 LLMs and 8 datasets: keyword-enhanced variant outperforms baseline, graph augmentation helps small models but hurts large ones, and self-consistency decoding increases costs fivefold without performance gains.

RAG Benchmarks Papers

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arXiv cs.CL·2d ago

When English Rewrites Local Knowledge: Global Narrative Dominance in Large Language Models

Study of global narrative dominance in LLMs via CulturalNB, a dataset of 717 Bengali cultural instances with parallel English-Bangla question-answer pairs. Evaluation of 9 LLMs shows English questions increase global substitution and reduce local perspective coverage, even with local evidence provided.

Benchmarks Alignment AI safety

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arXiv cs.CL·2d ago

ImmigrationQA: A Source-Grounded Dataset and Small-Model Adaptation for U.S. Immigration Law

ImmigrationQA: source-grounded QA dataset of 17,058 pairs across 13 U.S. immigration law subdomains. Fine-tuned Llama 3.2 3B with LoRA on corpus of 10,056 validated documents. Fine-tuned model: 1.08/3.0 (16.8% fully correct) vs Llama 3 8B base: 0.85/3.0 (4% fully correct), 27% relative improvement. Cost: ~$29. Dataset, model, and code publicly released.

Llama Fine-tuning RAG

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arXiv cs.CL·2d ago

Generalistic or Specific Embeddings, Which is Better? An Empirical Study on Search for Clinical Coding in Non-English Languages

Comparative study of generic vs domain-specific embeddings for multilingual clinical search (ICD-10-CM). A bi-encoder fine-tuned on Gemini-generated synthetic data (6 languages) outperforms BioBERT-ST: R@5=0.822 vs 0.790, with major gains in Portuguese (+0.115). Open recipe for LLM-based medical retrievers.

Embeddings RAG Benchmarks

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arXiv cs.AI·2d ago

LLM-FACETS: A Privacy-Preserving Framework for Evaluating LLM Transparency and Accountability

LLM-FACETS is an open-source framework for evaluating LLM factuality, epistemic calibration, and reproducibility. Web interface, plugin architecture, deterministic metrics (BLEU, ROUGE, BERTScore) run locally, log-probability visualization, multi-judge consensus, RAG Triad metrics. Designed for technical experts, domain experts, and compliance officers per EU AI Act and NIST standards.

Evals AI safety Alignment

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arXiv cs.CL·2d ago

AI for Monitoring and Classifying Data Used in Research Literature

Method to detect and classify dataset usage in research literature using a multitask GLiNER framework. Combines dataset mention extraction, relation identification, and usage-context classification. Leverages synthetic data generation and LLM-based revalidation to address label scarcity.

Papers Benchmarks RAG

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arXiv cs.CL·2d ago

Protocol for evaluating ChatGPT in biomedical association generation and verification using a RAG-enabled, cross-model majority voting workflow

Protocol to evaluate ChatGPT's ability to generate disease-centric biomedical associations. Uses RAG with open-source LLMs for semantic verification and hallucination detection through cross-model majority voting.

GPT RAG Evals

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arXiv cs.CL·2d ago

CanLegalRAGBench: Evaluating Retrieval-Augmented Generation on Canadian Case Law

CanLegalRAGBench is an evaluation benchmark for RAG systems applied to Canadian law, based on realistic queries and expert-annotated answers. The study shows open-source embedding models are competitive with closed-source alternatives, but identifies hallucinations in 8-29% of generated answers unsupported by retrieved documents.

RAG Embeddings Evals

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arXiv cs.AI·2d ago

Planner-Centric Reinforcement Learning for Deep Research with Structure-Aware Reward

DecomposeR, a deep research framework, trains Qwen3-8B in two RL stages: planner RL learns typed DAG structures and query decomposition, then answerer RL learns branch execution and synthesis. Achieves 5.1-8.0 point improvements on long-form benchmarks through explicit planning and structured rewards.

Qwen Reinforcement learning Reasoning

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arXiv cs.LG·2d ago

DisasterLex: An Expert Concept-to-Schema Knowledge Graph for Geospatial Reasoning in Disaster Analytics

DisasterLex is a knowledge-graph-mediated text-to-SQL framework for querying geospatial disaster-analytics databases. It uses an Expert Knowledge Graph (107 concepts, 117 causal edges) to route natural-language queries across 36 heterogeneous tables. On 75 test queries, it outperforms 4 baselines (LightRAG, HippoRAG 2, ReFoRCE, CHESS) by 1.4x to 2.75x.

RAG Reasoning Benchmarks

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GitHub Trending·3d ago

<svg aria-hidden="true" data-component="Octicon" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo mr-1 tmp-mr-1 color-fg-muted"> <path d="M2 2.5A2.5 2.5 0 0 1 4.5 0h8.75a.75.75 0 0 1 .75.75v12.5a.75.75 0 0 1-.75.75h-2.5a.75.75 0 0 1 0-1.5h1.75v-2h-8a1 1 0 0 0-.714 1.7.75.75 0 1 1-1.072 1.05A2.495 2.495 0 0 1 2 11.5Zm10.5-1h-8a1 1 0 0 0-1 1v6.708A2.486 2.486 0 0 1 4.5 9h8ZM5 12.25a.25.25 0 0 1 .25-.25h3.5a.25.25 0 0 1 .25.25v3.25a.25.25 0 0 1-.4.2l-1.45-1.087a.249.249 0 0 0-.3 0L5.4 15.7a.25.25 0 0 1-.4-.2Z"></path> </svg> <span data-view-component="true" class="text-normal"> jamwithai /</span> production-agentic-rag-course

Open-source course on building production agentic RAG systems. Covers architecture, implementation patterns, and best practices for deploying agentic retrieval-augmented generation systems.

AI Agents RAG Open source

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Reddit r/LocalLLaMA·3d ago

Everyone here self-hosts inference. Almost nobody self-hosts the tooling around it. That feels backwards to me.

A r/LocalLLaMA user highlights an inversion: the community self-hosts models (hardest part) but outsources tooling (tracing, evals, monitoring) to SaaS. He argues open-source solutions (Langfuse, ragas, Open WebUI) now enable hosting the full stack locally without external calls.

Open source Infrastructure Tools

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Reddit r/MachineLearning·4d ago

Before we spend months processing open-source robotics datasets, tell us why this is a bad idea [D]

Two ML students question whether robotics faces a data scarcity problem. After normalizing public datasets, they suspect the real issue is interoperability: heterogeneous schemas, different sensors, incompatible coordinate frames. They ask robotics teams whether they would actually use data from other teams through a unified API.

Robotics RAG Open source

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Reddit r/LocalLLaMA·4d ago

made a local voice AI for windows you can talk to in any language. open source, bring your own key

Shadow AI is an open-source (AGPL-3.0) local voice assistant for Windows. Natural multilingual conversations, local web search via SearXNG, persistent memory, optional Google integrations (Gmail, Calendar, Drive). Uses user's free Gemini API key, zero remote servers.

Voice Gemini Open source

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Reddit r/LocalLLaMA·5d ago

Updated MarkItDown API Server

MarkItDown API Server wraps Microsoft's official MarkItDown library in a lightweight FastAPI REST server. The tool converts files (PDF, Word, Excel) to Markdown for RAG and LLM pipelines. This release patches security vulnerabilities in Starlette and document parsers.

RAG Tools Open source

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GitHub Trending·5d ago

<svg aria-hidden="true" data-component="Octicon" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo mr-1 tmp-mr-1 color-fg-muted"> <path d="M2 2.5A2.5 2.5 0 0 1 4.5 0h8.75a.75.75 0 0 1 .75.75v12.5a.75.75 0 0 1-.75.75h-2.5a.75.75 0 0 1 0-1.5h1.75v-2h-8a1 1 0 0 0-.714 1.7.75.75 0 1 1-1.072 1.05A2.495 2.495 0 0 1 2 11.5Zm10.5-1h-8a1 1 0 0 0-1 1v6.708A2.486 2.486 0 0 1 4.5 9h8ZM5 12.25a.25.25 0 0 1 .25-.25h3.5a.25.25 0 0 1 .25.25v3.25a.25.25 0 0 1-.4.2l-1.45-1.087a.249.249 0 0 0-.3 0L5.4 15.7a.25.25 0 0 1-.4-.2Z"></path> </svg> <span data-view-component="true" class="text-normal"> opendatalab /</span> MinerU

MinerU converts complex documents (PDFs, Office files) into LLM-ready markdown/JSON for agentic workflows. Open-source tool for document extraction and data structuring.

AI Agents RAG Open source

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GitHub Trending·5d ago

<svg aria-hidden="true" data-component="Octicon" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo mr-1 tmp-mr-1 color-fg-muted"> <path d="M2 2.5A2.5 2.5 0 0 1 4.5 0h8.75a.75.75 0 0 1 .75.75v12.5a.75.75 0 0 1-.75.75h-2.5a.75.75 0 0 1 0-1.5h1.75v-2h-8a1 1 0 0 0-.714 1.7.75.75 0 1 1-1.072 1.05A2.495 2.495 0 0 1 2 11.5Zm10.5-1h-8a1 1 0 0 0-1 1v6.708A2.486 2.486 0 0 1 4.5 9h8ZM5 12.25a.25.25 0 0 1 .25-.25h3.5a.25.25 0 0 1 .25.25v3.25a.25.25 0 0 1-.4.2l-1.45-1.087a.249.249 0 0 0-.3 0L5.4 15.7a.25.25 0 0 1-.4-.2Z"></path> </svg> <span data-view-component="true" class="text-normal"> PaddlePaddle /</span> PaddleOCR

PaddleOCR is a lightweight, multilingual OCR toolkit (100+ languages) designed to convert PDF and image documents into structured data for LLM consumption.

Open source Vision Tools

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arXiv cs.CL·5d ago

Same Question, Different Source, Different Answer: Auditing Source-Dependence in Medical Multi-Source RAG

Study on source-dependence in multi-source medical RAG systems. Authors demonstrate that the same system can produce different answers depending on retrieved source, revealing a missing evaluation axis in NLP. They introduce TransplantQA (benchmark), HERO-QA (hierarchical retrieval strategy), and a structured judge to audit inter-source relationships using a validated 5-label taxonomy.

RAG Evals Papers

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arXiv cs.CL·5d ago

OmniRetrieval: Unified Retrieval across Heterogeneous Knowledge Sources

OmniRetrieval is a framework unifying retrieval across heterogeneous knowledge sources (unstructured text, relational tables, knowledge graphs). It translates natural-language queries into source-native queries, evaluated on 13 datasets and 309 knowledge bases.

RAG Vector search Papers

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arXiv cs.AI·5d ago

Indexing the Unreadable: LLM-Native Recursive Construction and Search of Service Taxonomies

A2X is a service discovery system for LLM agents that automatically organizes services into hierarchical taxonomies. It solves context scarcity by walking the hierarchy layer-by-layer, reducing token consumption by 89% while gaining 6.2 Hit Rate points over full-context dumping and +20 points over embedding baselines.

AI Agents MCP RAG

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arXiv cs.AI·5d ago

CoHyDE: Iterative Co-Training of LLM Rewriter & Dense Encoder for Tool Retrieval

CoHyDE iteratively co-trains a dense encoder and LLM rewriter to improve tool retrieval over large API catalogs. On ToolBench (~10k tools), three rounds gain +2.5 pp NDCG@5 on standard queries and +6.3 pp on vague queries, outperforming single-component baselines.

AI Agents RAG Embeddings

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arXiv cs.AI·5d ago

Better Later Than Sooner: Neuro-Symbolic Knowledge Graph Construction via Ontology-grounded Post-extraction Correction

Neuro-symbolic framework for ontology-grounded knowledge graph construction combining open-domain extraction, embedding-based canonicalization, and targeted LLM-based correction of ontology violations. Defers corrections to post-extraction stage to reduce token usage, improve KG consistency, and preserve QA quality for multi-hop reasoning and symbolic operations.

RAG Reasoning Embeddings

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arXiv cs.AI·5d ago

Hallucination Mitigation with Agentic AI, Nested Learning, and AI Sustainability via Semantic Caching

arXiv paper proposing multi-agent architecture with semantic memory and caching to mitigate LLM hallucinations. Three-stage pipeline (FrontEndAgent, SecondLevelReviewer, ThirdLevelReviewer) evaluated on 310 prompts. Results: THS reduction of -31.3% to -35.9%, 47.3% cache hit rate, 47% reduction in LLM calls. No retraining required.

AI Agents Multi-agent AI safety

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arXiv cs.AI·5d ago

Rethinking Literature Search Evaluation: Deep Research Helps, and Human Citation Lists Are Not a Ground Truth

Large-scale literature search study: Deep Research pipeline increases recall from below 20% to above 80% on RollingEval-Jun25 (250-paper benchmark). Critical analysis of human reference lists as ground truth: only 51% judged moderately relevant vs 86-88% for best AI re-rankers. Humans cite direct collaborators 2.5x more often.

RAG Evals Benchmarks

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arXiv cs.CL·5d ago

Beyond Recall: Behavioral Specification as an Interpretive Layer for AI Personalization

Researchers introduce a Behavioral Specification as an interpretive layer to align AI decisions with user preferences. Tested on 14 autobiographical corpora, it improves representational accuracy at ~25x lower context cost than raw corpus while reducing model hedging. Effective on interpretation-required questions; less helpful on recall-based tasks.

Alignment RAG AI Agents

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arXiv cs.CL·5d ago

Micro-Macro Retrieval: Reducing Long-Form Hallucination in Large Language Models

M2R (Micro-Macro Retrieval) is a retrieve-while-generate framework reducing hallucinations in long-form LLM generation. It combines macro retrieval (external evidence) and micro retrieval (key information from reasoning) to maintain proximity between factual data and outputs. Trained via reinforcement learning with rule-based rewards.

RAG Reinforcement learning

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