Tag: News

Enterprises have moved quickly to adopt RAG to ground LLMs in proprietary data. In practice, however, many organizations are discovering that retrieval is no longer a feature bolted onto model inference — it has become a foundational system dependency.Once AI systems are deployed to support decision-making, automate workflows or operate semi-autonomously, failures in retrieval propagate directly into business risk. Stale context, ungoverned access paths and poorly evaluated retrieval pipelines do not merely degrade answer quality; they undermine trust, compliance and operational reliability.This article reframes retrieval as infrastructure rather than application logic. It introduces a system-level model for designing retrieval platforms t

By now, many enterprises have deployed some form of RAG. The promise is seductive: index your PDFs, connect an LLM and instantly democratize your corporate knowledge.But for industries dependent on heavy engineering, the reality has been underwhelming. Engineers ask specific questions about infrastructure, and the bot hallucinates.The failure isn't in the LLM. The failure is in the preprocessing.Standard RAG pipelines treat documents as flat strings of text. They use "fixed-size chunking" (cutting a document every 500 characters). This works for prose, but it destroys the logic of technical manuals. It slices tables in half, severs captions from images, and ignores the visual hierarchy of the page.Improving RAG reliability isn't about buying a bigger model; it's about fixing the "dark data"

OpenClaw, the open-source AI assistant formerly known as Clawdbot and then Moltbot, crossed 180,000 GitHub stars and drew 2 million visitors in a single week, according to creator Peter Steinberger. Security researchers scanning the internet found over 1,800 exposed instances leaking API keys, chat histories, and account credentials. The project has been rebranded twice in recent weeks due to trademark disputes.The grassroots agentic AI movement is also the biggest unmanaged attack surface that most security tools can't see.Enterprise security teams didn't deploy this tool. Neither did their firewalls, EDR, or SI

San Francisco-based AI lab Arcee made waves last year for being one of the only U.S. companies to train large language models (LLMs) from scratch and release them under open or partially open source licenses to the public—enabling developers, solo entrepreneurs, and even medium-to-large enterprises to use the powerful AI models for free and customize them at will.Now Arcee is back again this week with the release of its largest, most performant open language model to date: Trinity Large, a 400-billion parameter mixture-of-experts (MoE), available now in preview,Alongside the flagship release, Arcee is shipping a "

A new open-source framework called PageIndex solves one of the old problems of retrieval-augmented generation (RAG): handling very long documents.The classic RAG workflow (chunk documents, calculate embeddings, store them in a vector database, and retrieve the top matches based on semantic similarity) works well for basic tasks such as Q&A over small documents.PageIndex abandons the standard "chunk-and-embed" method entirely and treats document retrieval not as a search problem, but as a navigation problem. But as enterprises try to move RAG into high-stakes workflows — auditing financial statements, analyzing legal contracts, navigating pharmaceutical protocols — they're hitting an accuracy barrier that chunk optimization can't solve.AlphaGo for documentsPageIndex addresses these limitations by borrowing a concept from game-playing AI rather than search engin

The chief data officer (CDO) has evolved from a niche compliance role into one of the most critical positions for AI deployment. These executives now sit at the intersection of data governance, AI strategy, and workforce readiness. Their decisions determine whether enterprises move from AI pilots to production scale or remain stuck in experimentation mode.That's why Informatica's third annual survey — the largest survey yet of CDOs specifically on AI readiness, spanning 600 executives globally — carries particular weight. The findings expose a dangerous disconnect that explains why so many organizations struggle to scale AI beyond pilots: While 69% of enterprises have deployed generative AI and 47% are running agentic AI systems, 76% admit their governance frameworks can't keep pace with how employees actually use these technologies.The survey reveals what Informatica calls a &q

A new study by Google suggests that advanced reasoning models achieve high performance by simulating multi-agent-like debates involving diverse perspectives, personality traits, and domain expertise.Their experiments demonstrate that this internal debate, which they dub “society of thought,” significantly improves model performance in complex reasoning and planning tasks. The researchers found that leading reasoning models such as DeepSeek-R1 and QwQ-32B, which are trained via reinforcement learning (RL), inherently develop this ability to engage in society of thought conversations without explicit instruction.These findings offer a roadmap for how developers can build more robust LLM applications and how enterprises can train superior models using their own internal