RAG & Retrieval

A study of spoken multi-hop question answering finds that richer retrieval structures can preserve higher absolute accuracy while becoming more sensitive to upstream speech-recognition mistakes. Corrupted query entities are the main path through which those errors spread.

Accel-backed Keenable has emerged from stealth with a $26 million seed round and a search index designed for AI systems rather than human readers. The startup says its index covers more than 100 billion documents and is already used by AI labs and inference providers.

Amazon DynamoDB now supports native vector search, allowing embeddings and application records to live in the same table. The feature simplifies AI architectures, but it does not make dedicated vector databases obsolete overnight.

A new study proposes IAR, a staged post-training framework for answering questions about a fixed document collection without retrieved passages at inference time. It separates knowledge injection, QA accessibility, and recovery of general capabilities.

Google is letting publishers embed a “Preferred Sources” button so readers can ask to see them more often across Search, Discover, and Google News. The feature offers a new distribution lever as AI-powered search reduces the need for users to visit source websites.

Poisoned retrieval contexts can make a language model appear more confident rather than more uncertain. A new study proposes D-SCAN, which detects attacks by tracking document-level attention collapse during generation.

Shopify says AI-driven discovery is sending more visitors and orders to its merchants, rather than cannibalizing traditional search. In Q2, AI-attributed traffic and orders to Shopify stores tripled year over year.

UEmbed from Alibaba-NLP proposes a decoder-only multimodal embedding model that produces both sparse lexical signals and dense vectors in a single causal forward pass. The work targets search, RAG, and multimodal retrieval systems that currently rely on fragmented retrieval stacks.

DeepVoyager-VL aims to move multimodal search beyond one-shot image understanding. It treats visual evidence as an active driver of intermediate reasoning, retrieval, and agent decisions.

Reddit CEO Steve Huffman is questioning whether Google’s AI Overviews create the same ecosystem value that traditional search links once did. The comments came as investors reacted sharply to uneven search referrals despite strong quarterly results.

A controlled scaling study of RAG paradigms finds that the best retrieval strategy changes as the corpus grows. While file-system-style agents lead on small corpora, BM25 overtakes them around the 10-million-token range and remains ahead at larger scales.

New Similarweb data cited by TechCrunch shows Google’s AI Overviews now appear in 43% of searches, up from 15% a year earlier. The shift suggests AI-generated answers are becoming central to how people find information online.

LAMAR is a language-aware multilingual reranker designed for RAG pipelines where relevant documents may appear in many languages. It aims to preserve semantic relevance while favoring documents written in the same language as the query.

The Vergecast frames “Google Zero” as a turning point for publishers and websites: the long-running exchange of crawlable content for search traffic is becoming unstable in the age of AI summaries.

The paper argues that as LLMs and agents become the main consumers of search results, retrieval quality should be judged at the document-set level rather than by isolated relevance scores.

AutoIndex argues that retrieval quality can be improved before the retriever ever runs: by learning executable programs that transform documents prior to indexing. With BM25 kept fixed, the framework improves performance across all eight CRUMB retrieval tasks.

RAGU is an open-source GraphRAG engine that separates extraction from graph consolidation, aiming to make graph-based retrieval less noisy and less expensive.

SearchOS-V1 targets a common failure mode in long-horizon web-search agents: losing track of progress and repeating unproductive searches. It introduces an explicit state layer for evidence, coverage, tasks, and failures across collaborating agents.

GRASP addresses a central challenge in Agentic RAG: deciding when to retrieve, which retrieval tool to use, and how much context to read. The framework uses reinforcement learning to coordinate semantic search, keyword search, and paragraph reading during multi-step reasoning.

NVIDIA has released the Nemotron 3 Embed family on Hugging Face, positioning it for enterprise RAG, code retrieval, agent memory, and production-scale search. The 8B BF16 model ranks first on RTEB, while the 1B variants focus on efficiency and high-throughput deployment.

A new arXiv paper introduces PAT, a RAG-based system designed to push LLM translation beyond sentence-by-sentence rendering. The study finds that specifications and corpus-informed context can encourage broader reformulation, though the benefits remain inconsistent.