Retrieval-augmented generation is how enterprises actually deploy LLMs on their own data. Techniques, tools and production lessons.
142 articles
Google’s AI Overviews are reportedly presenting entries from the SCP Foundation, a collaborative fan-fiction project, as documented reality. This highlights ongoing challenges in AI hallucination and source verification.
The Next Web (TNW) · 91d ago
ACIE is an on-premise agentic RAG pipeline designed to handle complex patient contexts and cross-document dependencies in clinical settings. The system achieved a 96.5% clinician acceptance rate across 7,326 extractions, proving the efficacy of agentic reasoning in medical data triage.
ArXiv AI · 92d ago
CaVe-VLM-CoT is a new agentic-RAG framework designed to reduce hallucinations in Vision-Language Models through a five-stage closed-loop verification pipeline. It introduces CaVeScore, a comprehensive metric for evaluating retrieval quality, citation faithfulness, and cross-modal grounding.
ArXiv AI · 93d ago
MemTrace is a new benchmark that evaluates LLM long-term memory by focusing on 'knowledge points' rather than individual questions. It reveals that memory failures are primarily due to poor evidence utilization rather than retrieval limitations.
ArXiv AI · 94d ago
DivInit is a training-free intervention that improves agentic search by diversifying initial queries. By selecting diverse seeds from a single model call, it reduces retrieval redundancy and improves performance on multi-hop QA tasks.
ArXiv AI · 94d ago
Meta is introducing 'AI Mode' to Facebook, allowing users to query Meta AI directly for answers. The feature aggregates public content across Meta's ecosystem to provide search-like responses.
Digital Trends · 95d ago
Dr-DCI is a new framework that enhances agentic search by dynamically pulling relevant documents into a local workspace. This approach combines the scalability of retriever-based systems with the precision of direct corpus interaction, outperforming traditional search methods.
ArXiv AI · 95d ago
A full-stack pipeline using Django and React that constructs Knowledge Graphs from text to solve multi-hop reasoning issues. It utilizes hybrid retrieval, community detection, and reranking to improve accuracy.
Reddit r/MachineLearning · 96d ago
PixelRAG is a new research-backed system that treats webpages as screenshots rather than parsing them into text. This approach preserves visual context and layout, significantly reducing retrieval errors compared to traditional text-based RAG pipelines.
VentureBeat · 98d ago
MediaLink successfully implemented an AI chatbot for a Himeji emergency hospital, achieving 90% accuracy within two weeks. The project highlights the importance of a 'knowledge circulation' framework for effective AI deployment.
ITmedia AI+ (日本) · 103d ago
OpenAI is deploying a rebuilt memory system for ChatGPT that synthesizes chat history in the background. This feature allows the model to retain user context over time, starting with Plus and Pro users in the US.
Digital Trends · 106d ago
The CHARM framework introduces a systematic approach to detect and mitigate cascading hallucinations in multi-step agentic RAG pipelines. By monitoring fact verification and cross-stage consistency, it significantly reduces error propagation without requiring architectural overhauls.
ArXiv AI · 106d ago
Deep-Tech's Zhao Jiehui argues that the enterprise AI stack is shifting. Technical solutions that merely patch model weaknesses are being replaced by core layers focused on enterprise knowledge and intelligent agents.
钛媒体 · 108d ago
Grokers introduces a bottom-up architecture for knowledge graphs that shifts intelligence from query-time to write-time. By inductively composing structured attributes, it enables zero-cost LM queries for future interactions.
ArXiv AI · 109d ago
Vercel has integrated Amazon OpenSearch Serverless into its marketplace, offering developers a streamlined workflow for managing search infrastructure. The integration features automated project configuration, unified resource management, and optimized support for agentic and vector search workloads.
Vercel News · 114d ago
This research introduces two agentic AI frameworks, DeepTS and DeepScribe, designed to automate scientific data curation and complex lecture analysis. By utilizing a hybrid Local Body/Remote Brain architecture, these systems enhance reasoning capabilities for high-energy physics and time-series research.
ArXiv AI · 115d ago
Firecrawl is now available on the Vercel Marketplace, enabling developers to integrate structured web data scraping directly into their AI workflows. This integration allows for seamless conversion of websites into LLM-ready formats without managing infrastructure.
Vercel News · 116d ago
EVE-Agent introduces a framework for self-evolving search agents that ensures training data is grounded in verifiable evidence. By rewarding models based on the marginal accuracy gain provided by source-grounded spans, it creates an auditable and trustworthy training loop without human labels.
ArXiv AI · 117d ago
Researchers developed a five-stage LLM framework for survey workflows, introducing the A-TLM model to outperform traditional imputation methods in disaster-related datasets. The study demonstrates that grounding LLMs in causal knowledge graphs significantly reduces bias and improves data quality.
ArXiv AI · 121d ago
RAG 架構僅能檢索資訊，卻缺乏決策背景，導致企業 AI Agent 在複雜任務中頻繁出錯。Rippletide 提出的決策上下文圖（Decision Context Graph）透過結構化記憶與時間感知推理，解決了 Agent 遺忘與決策邏輯缺失的問題。
VentureBeat · 121d ago
Google DeepMind has finalized an agreement to hire over 20 researchers from Contextual AI and license its technology. The deal includes a $100 million payment and the transition of Contextual AI CEO Douwe Kiela to the DeepMind team.
cnBeta (Full RSS) · 122d ago
Tencent has launched a new open-source plugin specifically designed to address the long-term memory limitations of LLMs. This tool aims to enhance the context retention capabilities of AI models.
钛媒体 · 122d ago
MetaKGEnrich is an automated pipeline that enables LLMs to perform self-directed knowledge repair by identifying sparse graph regions and retrieving external evidence. The system significantly improves answer accuracy across multiple benchmarks by combining topological diagnosis with GraphRAG.
ArXiv AI · 123d ago
This research introduces Contract-Bounded Evidence Activation (CBEA) and Lexicographic Commitment Validation (LCV) to prevent language models from making unreliable commitments. By bounding evidence and validating structured outputs, the system significantly reduces failures in long-context and memory-intensive applications.
ArXiv AI · 123d ago
This article explores the limitations of standard vector-only RAG in enterprise environments and proposes a hybrid 'Graph RAG' architecture. It emphasizes the importance of maintaining data topology and structural relationships during ingestion to improve LLM reasoning accuracy.
VentureBeat · 125d ago
IBM has released the Granite Embedding Multilingual R2, an open-source model under the Apache 2.0 license. It offers state-of-the-art retrieval quality for sub-100M parameter models and supports a 32K context window.
Hugging Face Blog · 127d ago
Users report that Google AI is surfacing private contact information, leading to unwanted calls from strangers. There is currently no straightforward mechanism for individuals to prevent their personal data from being exposed by these models.
MIT Technology Review · 128d ago
Shares AST-derived typed graphs from Tree-sitter parses for codebase RAG, using BM25 retrieval over nodes to cut context from 100K to 5K tokens. Graph edges pull dependencies automatically.
Reddit r/MachineLearning · 141d ago
Databricks research reveals multi-step agents outperform single-turn RAG by 20%+ on benchmarks like STaRK and KARLBench for tasks spanning databases and documents. The Supervisor Agent splits queries, routes to structured/unstructured sources, and combines results, solving RAG's architectural limits on hybrid data.
VentureBeat · 157d ago
turboquant-pro launched autotune CLI that samples embeddings from PostgreSQL/pgvector, tests 12 compression configs, and recommends the optimal one for min-recall threshold in ~10s. Achieves 20.9x compression at 96% recall on 194K production embeddings, shrinking 758MB to 36MB.
Reddit r/MachineLearning · 163d ago