retrieval-augmented generation (RAG)

2026-10-28

RESEARCH · 1 SOURCE · arXiv cs.AI

GEMS-RAG: graph-grounded multimodal RAG for engineering standards

GEMS-RAG is a framework that combines hierarchical parsing, fine-grained visual entity extraction, multimodal knowledge-graph construction, and graph-grounded hybrid retrieval to enable grounded reasoning over complex, multimodal engineering standards. Evaluated on the Manual on Uniform Traffic Control Devices (MUTCD), GEMS-RAG improves retrieval effectiveness and question-answering performance compared with text-only, multimodal, and graph-based RAG baselines.

6.0

2026-10-23

RESEARCH · 1 SOURCE · arXiv cs.AI

ClariEdge dataset and lightweight LoRA improve query clarification for RAG

The paper (arXiv:2610.28686v1) introduces ClariEdge, a ~3,900-example dataset of ambiguous and non-ambiguous conversational queries for retrieval-augmented generation (RAG). A lightweight LoRA fine-tune achieves 96.6% classification accuracy and outperforms prompted larger models on both ambiguity detection and quality of clarification requests; the dataset is published on Hugging Face at ibm-research/ClariEdge.

6.0

2026-10-22

MONEY · 1 SOURCE · MIT Technology Review AI

WD/IDC white paper: AI inference is inflating storage needs and costs

A WD‑sponsored IDC white paper and WD internal measurements highlight that AI inference generates large persistent back-end data that dramatically increases storage footprints and operating costs—an 8‑second, 720p (3.97MB) clip produced roughly 500MB of associated backend data (≈130× the delivered file). McKinsey projects inference demand will grow at a 35% CAGR through 2030, IDC survey respondents (n=763) largely reported rising data volumes (≈95% saw increases; ≈61% saw ≥25% growth) and 96% expect faster archive retrieval will be needed for inference/RAG workflows.

6.0

2026-10-12

RESEARCH · 1 SOURCE · arXiv cs.AI

SmartSalesBench: a multi-turn benchmark for evaluating LLM sales agents against resistant buyer personas

SmartSalesBench is a newly published benchmark (arXiv:2610.12975v1) that evaluates large language model sales agents in multi-turn (up to 20 turns) conversations against psychologically grounded, resistance‑aware buyer personas. The dataset covers 13,680 sessions across five agent profiles, twelve buyer personas, fifteen smartphone products in six price tiers, uses retrieval‑augmented generation for grounding, and introduces nine evaluation metrics (primary: CR, BRS, RIR) with combined automated LLM‑as‑judge and human (n=50, κ=0.71, Pearson r=0.81 vs BRS) assessment.

6.0

2026-10-05

COMPANIES · 1 SOURCE · MIT Technology Review AI

MIT Technology Review Insights urges 'agentic shift' to scale enterprise AI

A sponsored report from MIT Technology Review Insights argues enterprises must move from treating AI as a tool to adopting an 'agentic shift' — an operating model that links people, processes, and data in real time. The piece recommends rebuilding data infrastructure for accessibility, adopting composable architectures, and ensuring data/model sovereignty, and notes fragmentation in enterprise systems and a projected $2.5 trillion in global AI investment by 2026.

6.0

2026-09-25

RESEARCH · 1 SOURCE · arXiv cs.AI

CRISS: Retrieval-augmented chatbot to support cancer registrars (arXiv:2609.29075v1)

Researchers introduce CRISS (Cancer Registry Intelligent Support System), a retrieval-augmented generation (RAG) conversational assistant built on a domain-specific knowledge base of national cancer registry standards to provide citation-supported guidance to cancer registrars. In evaluations using an LLM-as-a-Judge protocol across easy/medium/hard question tiers, RAG configurations outperformed non-RAG baselines (mean grounding scores 0.62/0.56/0.59 vs 0.29/0.26/0.29); proprietary RAGs led on easy/medium items while local RAGs ranked highest on hard items, and RAGs achieved higher semantic-similarity scores overall.

6.0

2026-09-23

RESEARCH · 1 SOURCE · arXiv cs.AI

EvidenT: Building trustworthy enterprise assistants through evidence verification (arXiv:2609.22537v1)

EvidenT is a lightweight pipeline that verifies extracted evidence against retrieved documents before answer generation, using structured passage extraction and deterministic lexical alignment to filter unsupported content, correct citation drift, and preserve source-span traceability. On ~500 real enterprise queries the paper reports a 29% average improvement in gold-source hit rate versus prompting baselines, no citations to nonretrieved URLs, and near-saturated answer-to-source lexical coverage — achieved without model retraining.

6.0

2026-09-18

CODING · 1 SOURCE · GitHub AI & ML

GitHub Podcast examines MCP, skills, agents, and RAG in developer workflows

In a recent GitHub Podcast episode, hosts argue developers remain responsible for generated code and should apply different review rigor depending on risk. They position the Model Context Protocol (MCP) as a standard for tool/data access, describe 'skills' as human-readable packaged expertise, and say RAG (retrieval-augmented generation) remains useful alongside agents and skills rather than being obsolete.

4.0

2026-09-14

MODELS · 1 SOURCE · AWS Machine Learning

AWS presents an 8-step framework for generative AI customization, from prompts to custom models

AWS Machine Learning published an article offering an 8-step decision framework to help pick generative AI customization approaches on AWS, covering prompt engineering, RAG, fine-tuning, continued pre-training and Amazon Nova Forge, with the recommendation to start simple and escalate only when necessary.

6.0