RAG

Related concepts: Embeddings Vector Database Hybrid Search Reranking Chunking Frequently mentioned with: arXiv Hugging Face OpenAI BGE-M3 Google Recent briefs: 2026-10-26 2026-10-25 2026-10-23 2026-10-22 2026-10-21

Related: Embeddings Vector Database Hybrid Search Reranking Chunking Context Engineering RAG Evaluation

This is the RAG concept page (definitions and fundamentals). For dated developments, see the RAG Topic hub.

Recent Updates

• 2026-10-22: Joint Constraint Aggregation for Qualifier-Sensitive Retrieval-Augmented QA (breakingnewsofficial) · arxiv.org
• 2026-10-22: The Bottleneck Is Retrieval, Not Reading: Diagnosing Evidence-Based Generation with RAG for Long Context Question Answering (breakingnewsofficial) · github.com
• 2026-10-22: AutoTerm-SST: Adaptive Terminology Memory with Zero Session-Time Setup for Streaming Speech Translation (breakingnewsofficial) · arxiv.org
• 2026-10-22: Dense Reward Signals from Expert Rubrics for Reinforcement Learning in Retrieval Agents (breakingnewsofficial) · arxiv.org
• 2026-10-22: Policy Central: Detecting Conflicts, Redundancy, and Similarity in Deployed Police Policy (breakingnewsofficial) · arxiv.org
• 2026-10-22: Let Agents Design How They Search: Automatic Interface Optimization for Agentic Search (breakingnewsofficial) · arxiv.org — gpt-5.4-nano
• 2026-10-22: Compositional Concept Unlearning (C²U) in Retrieval-Augmented Generation (breakingnewsofficial) · arxiv.org
• 2026-10-22: Scan or Index? Evaluating Evidence Coverage, Answer Quality, and Cost in Long-Document RAG (breakingnewsofficial) · arxiv.org — OpenAI · Cloudflare
• 2026-10-22: A Small Model Asks Better: Fine-Tuning Query Clarification for Retrieval-Augmented Generation (breakingnewsofficial) · huggingface.co — Hugging Face · IBM Research
• 2026-10-23: Federated GraphRAG: Distributed Graph Retrieval-Augmented Generation Across Private Client Graphs (breakingnewsofficial) · arxiv.org
• 2026-10-23: Measuring Embedding Recomputation Cost Across Chunking Strategies in Dynamic Knowledge Bases (breakingnewsofficial) · arxiv.org — Wikipedia
• 2026-10-23: ULME Unifies Embedding and Language Modeling via Predictive Coupling on Causal LLMs (breakingnewsofficial) · arxiv.org — GritLM Qwen2.5 · Qwen3
• 2026-10-23: Naive RAG Over Raw History Competes With Complex Agentic Memory Systems (breakingnewsofficial) · arxiv.org
• 2026-10-23: Compiling Autoregressive LLM Control into Policy-Exact Parallel Inference for Hierarchical RAG (breakingnewsofficial) · arxiv.org
• 2026-10-23: Self-Assessment Reinforcement Learning for Search-State Awareness in Retrieval-Augmented Large Language Models (breakingnewsofficial) · arxiv.org
• 2026-10-23: Structure-Grounded Uncertainty Reduction for Efficient Multi-Turn Agent Inference (breakingnewsofficial) · arxiv.org — arXiv
• 2026-10-23: MemPABench: Benchmarking Personal Agent Memory for Interaction Preferences (breakingnewsofficial) · mempabench.com
• 2026-10-23: LitPivot: Developing Research Ideas Through Literature Grounding and Dynamic Facet Contextualization (breakingnewsofficial) · huggingface.co — Ai2 University of Pennsylvania · Hugging Face
• 2026-10-25: The State of Context Engineering: Failure Modes of Long Context (breakingnewsofficial) · dbreunig.com — O’Reilly AGNTCon Gemini 1.5 Gemini 1.5 Pro · Llama 4 Scout
• 2026-10-26: LLENS: Deploying On-Premises LLM Agents with Kimi K2.6 in a Hospital Closed Network (breakingnewsofficial) · arxiv.org — Hokkaido University Hospital NVIDIA · Open WebUI · kimi-k2.6
• 2026-10-26: From Noisy Retrieval to Decision Evidence: Silver Evidence Demonstrations for In-Context Learning (breakingnewsofficial) · arxiv.org
• 2026-10-26: Semantically Rich, Metrically Affect-Blind: A Geometric Blind Spot in Modern Text Embedders (breakingnewsofficial) · arxiv.org — Jina · Qwen · Qwen3-Embedding 4B Jina-v5
• 2026-10-26: Benchmarking Semantic Synchronization and RAG Over ISO 11783 CAN Buses for Offline Tractor Assistance (breakingnewsofficial) · arxiv.org
• 2026-10-26: A Conceptual Quality Assessment Framework for Knowledge Graphs in the Era of LLMs (breakingnewsofficial) · arxiv.org
• 2026-10-26: Who Verifies the Tools? How Warped Formatters Mislead Agents and Verifiers (breakingnewsofficial) · arxiv.org — Pilsudski Institute

FAQ

What is RAG?

RAG, or retrieval-augmented generation, grounds LLM answers in retrieved documents, code, or data before generation. GROUNDING tracks RAG architecture, retrieval quality, evaluation, grounding failures, and production patterns.

Which concepts are related to RAG?

Related concepts tracked by the radar include Embeddings, Vector Database, Hybrid Search, Reranking, Chunking.

What does this RAG page track?

Dated updates, papers, and mentions of RAG collected by the GROUNDING radar, most recently on 2026-10-26.