💡TL;DR: Open models like GLM-5 and MiniMax M2.7 now match closed frontier models on core agent tasks — file operations, tool use, and instruction following — at a fraction of the cost and latency. Here's what our evals show and how to start using them
From RTX to Spark: NVIDIA Accelerates Gemma 4 for Local Agentic AI
Open models are driving a new wave of on-device AI, extending innovation beyond the cloud to everyday devices. As these models advance, their value increasingly depends on access to local, real-time context that can turn meaningful insights into action. Designed for this shift, Google’s latest addi...
“Just in Time” World Modeling Supports Human Planning and Reasoning
An overview of a state-of-the-art study, uncovering simulation-based reasoning, a "just-in-time" framework and how it helps improve predictions in the context of supporting human planning and reasoning.
This is the third article in a series on agentic engineering and AI-driven development. Read part one here, part two here, and look for the next article on April 15 on O’Reilly Radar. The toolkit pattern is a way of documenting your project’s configuration so that any AI can generate working inputs ...
OpenAI acquires TBPN to accelerate global conversations around AI and support independent media, expanding dialogue with builders, businesses, and the broader tech community.
Claude Code Leak: 16 Lessons on Building Production-Ready AI Systems
Over the past 24 hours, the developer community has been obsessed with one thing. A leak. The source code of Claude Code, one of the most advanced AI coding systems, surfaced online. Within hours, GitHub was flooded with forks, breakdowns, and deep dives. For developers, it felt like rare access. Wh...
IBM Releases Granite 4.0 3B Vision: A New Vision Language Model for Enterprise Grade Document Data Extraction
IBM has announced the release of Granite 4.0 3B Vision, a vision-language model (VLM) engineered specifically for enterprise-grade document data extraction. Departing from the monolithic approach of larger multimodal models, the 4.0 Vision release is architected as a specialized adapter designed to ...
Two-Stage Optimizer-Aware Online Data Selection for Large Language Models
arXiv:2604.00001v1 Announce Type: new
Abstract: Gradient-based data selection offers a principled framework for estimating sample utility in large language model (LLM) fine-tuning, but existing methods are mostly designed for offline settings. They are therefore less suited to online fine-tuning, w...
Task-Centric Personalized Federated Fine-Tuning of Language Models
arXiv:2604.00050v1 Announce Type: new
Abstract: Federated Learning (FL) has emerged as a promising technique for training language models on distributed and private datasets of diverse tasks. However, aggregating models trained on heterogeneous tasks often degrades the overall performance of indivi...
Temporal Memory for Resource-Constrained Agents: Continual Learning via Stochastic Compress-Add-Smooth
arXiv:2604.00067v1 Announce Type: new
Abstract: An agent that operates sequentially must incorporate new experience without forgetting old experience, under a fixed memory budget. We propose a framework in which memory is not a parameter vector but a stochastic process: a Bridge Diffusion on a repl...
One Panel Does Not Fit All: Case-Adaptive Multi-Agent Deliberation for Clinical Prediction
arXiv:2604.00085v1 Announce Type: new
Abstract: Large language models applied to clinical prediction exhibit case-level heterogeneity: simple cases yield consistent outputs, while complex cases produce divergent predictions under minor prompt changes. Existing single-agent strategies sample from on...
Open, Reliable, and Collective: A Community-Driven Framework for Tool-Using AI Agents
arXiv:2604.00137v1 Announce Type: new
Abstract: Tool-integrated LLMs can retrieve, compute, and take real-world actions via external tools, but reliability remains a key bottleneck. We argue that failures stem from both tool-use accuracy (how well an agent invokes a tool) and intrinsic tool accurac...
A Safety-Aware Role-Orchestrated Multi-Agent LLM Framework for Behavioral Health Communication Simulation
arXiv:2604.00249v1 Announce Type: new
Abstract: Single-agent large language model (LLM) systems struggle to simultaneously support diverse conversational functions and maintain safety in behavioral health communication. We propose a safety-aware, role-orchestrated multi-agent LLM framework designed...
Human-in-the-Loop Control of Objective Drift in LLM-Assisted Computer Science Education
arXiv:2604.00281v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly embedded in computer science education through AI-assisted programming tools, yet such workflows often exhibit objective drift, in which locally plausible outputs diverge from stated task specifications. E...
MIT researchers developed a testing framework that pinpoints situations where AI decision-support systems are not treating people and communities fairly.
Two weeks of dogfooding Engram, Weaviate's memory product, in daily Claude Code sessions. This surfaced where a dedicated memory product adds value, and the specific mechanics that prevent integration with coding assistants from working well.
Z.ai Launches GLM-5V-Turbo: A Native Multimodal Vision Coding Model Optimized for OpenClaw and High-Capacity Agentic Engineering Workflows Everywhere
In the field of vision-language models (VLMs), the ability to bridge the gap between visual perception and logical code execution has traditionally faced a performance trade-off. Many models excel at describing an image but struggle to translate that visual information into the rigorous syntax requi...