Explore the latest AI news and research tagged #large language models — curated from top sources including OpenAI, Anthropic, Google DeepMind, and more.
A physicist's approach to building production-grade agents The post Why I Don’t Trust LLMs to Decide When the Weather Changed appeared first on Towards Data Science .
Google has unvelied a new generation of Tensor Processing Units (TPUs), featuring two specialized chips designed to accelerate model training and agent workflows, which require continuous, multi-step reasoning, and action…
Your RAG system isn’t failing at retrieval — it’s failing at reasoning. This article shows how I built a lightweight self-healing layer that detects and corrects hallucinations before they reach…
Non-deterministic agents are those where the same input can lead to distinct outputs across multiple runs.
Why reasoning models dramatically increase token usage, latency, and infrastructure costs in production systems The post Inference Scaling (Test-Time Compute): Why Reasoning Models Raise Your Compute Bill appeared first on…
NVIDIA has announced a new family of open models called NVIDIA Ising, designed to address quantum processor calibration and quantum error correction. These are two of the main engineering challenges…
Recent advances in large language models (LLMs) test-time computing have introduced the capability to perform intermediate chain-of-thought (CoT) reasoning (thinking) before generating answers. While increasing the thinking budget yields smooth…
To sustain productivity in long-running agent systems, Slack engineers moved away from accumulating chat logs and started using structured memory, validation, and distilled truth to maintain coherence and accuracy of…
Ineffable Intelligence, a British AI lab founded a mere few months ago by former DeepMind researcher David Silver, has raised $1.1 billion in funding at a valuation of $5.1 billion.
DeepSeek-V4 models are open and low-cost models that also use Chinese chipmaker Huawei's AI chips for inference.
Discover how the Model Context Protocol (MCP) Java SDK is establishing a new architectural discipline for enterprise LLM integrations. By defining explicit contracts and leveraging MCP servers as anti-corruption layers,…
Meta’s big moment is here. The Meta Superintelligence Labs has launched Muse Spark, its first AI model aiming at “personal superintelligence.” The journey to this point has been eventful, from…
A local, zero-cost project that cleans, structures, and summarizes your reading automatically The post I Built an AI Pipeline for Kindle Highlights appeared first on Towards Data Science .
In this article, author Vignesh Durai discusses how agentic and multimodal AI systems can be engineered using Apache Camel and LangChain4j technologies. The key components in the solution include LLM-based…
A practical pipeline for classifying messy free-text data into meaningful categories using a locally hosted LLM, no labeled training data required. The post Using a Local LLM as a Zero-Shot…
Run OpenClaw assistant through alternative LLMs The post How to Run OpenClaw with Open-Source Models appeared first on Towards Data Science .
The move is part of VW’s broader automotive AI strategy.
Anthropic introduces Managed Agents on Claude, a managed execution layer for agent-based workflows. It separates agent logic from runtime concerns like orchestration, sandboxing, state management, and credentials. The system supports…
A stateless AI agent has no memory of previous calls.
Sudeep Das and Pradeep Muthukrishnan explain the shift from static merchandising to dynamic, moment-aware personalization at DoorDash. They share how LLMs generate natural-language "consumer profiles" and content blueprints, while traditional…
Understanding context is key to understanding human language, an ability which Large Language Models (LLMs) have been increasingly seen to demonstrate to an impressive extent. However, though the evaluation of…
LinkedIn introduces Cognitive Memory Agent (CMA), generative AI infrastructure layer enabling stateful, context-aware systems. It provides persistent memory across episodic, semantic, and procedural layers, supporting multi-agent coordination, retrieval, and lifecycle…
Google's Agent Development Kit for Java reached 1.0, introducing integrations with new external tools, a new app and plugin architecture, advanced context engineering, human-in-the-loop workflows, and more. By Sergio De…