How do you convert real agent traces into reinforcement learning RL transitions to improve policy LLMs without changing your existing agent stack? Microsoft AI team releases Agent Lightning to help ...
What is new in Granite 4.0 Nano series? Granite 4.0 Nano consists of four model lines and their base counterparts. Granite 4.0 H 1B uses a hybrid SSM based architecture and is about 1.5B parameters.
Can an open source MoE truly power agentic coding workflows at a fraction of flagship model costs while sustaining long-horizon tool use across MCP, shell, browser, retrieval, and code? MiniMax team ...
Max Tokens is the maximum number of tokens the model can generate during a run. The model will try to stay within this limit across all turns. If it exceeds the specified number, the run will stop and ...
AI companies use model specifications to define target behaviors during training and evaluation. Do current specs state the intended behaviors with enough precision, and do frontier models exhibit ...
In this article we will analyze how Google, OpenAI, and Anthropic are productizing ‘agentic’ capabilities across computer-use control, tool/function calling, orchestration, governance, and enterprise ...
Web agents often fail when layouts shift or when tasks require long sequences. WALT targets this failure mode by mining site functionality offline, then exposing it as tools that encapsulate ...
Computer-use agents have been limited to primitives. They click, they type, they scroll. Long action chains amplify grounding errors and waste steps. Apple Researchers introduce UltraCUA, a foundation ...
In this tutorial, we explore how to build a small yet functional multi-agent system using the uAgents framework. We set up three agents — Directory, Seller, and Buyer — that communicate via ...
The community already showed that BitNet b1.58 can match full precision quality when trained from scratch, but converting a pretrained FP16 model directly to 1.58 bit often loses accuracy, and the gap ...
W4S operates in turns. The state contains task instructions, the current workflow program, and feedback from prior executions. An action has 2 components, an analysis of what to change, and new Python ...
import { agent, llmOpenAI, llmAnthropic, mcp } from "volcano-ai"; // Setup: two LLMs, two MCP servers const planner = llmOpenAI({ model: "gpt-5-mini", apiKey: process ...
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