agentic ai

Auto Added by WPeMatico

↔

Meta AI Open-Sources Rebalancer: A C++ Assignment Solver That Runs About 40 Million Placement Problems a Day

Meta has open-sourced Rebalancer, a C++ library with a Python interface for solving assignment problems. It decides which objects go into which bins under constraints and objectives. According to the Engineering at Meta’s post, Rebalancer has handled resource allocation across Meta for over 9 years. The release ships under Apache 2.0 with documentation, a PyPI

Meta AI Open-Sources Rebalancer: A C++ Assignment Solver That Runs About 40 Million Placement Problems a Day Read More »

Google DeepMind Releases EmbeddingGemma 2, a 740M Open Multimodal Embedding Model Built on Gemma 4

Google DeepMind has released EmbeddingGemma 2, an open model that embeds text, code, images, video and audio into one 768-dimensional space. It has 740M parameters, an 8K token context window and an Apache 2.0 license. It targets on-device search, classification and privacy-first RAG. This article analyzes, compares and showcase how EmbeddingGemma 2 fits in the

Google DeepMind Releases EmbeddingGemma 2, a 740M Open Multimodal Embedding Model Built on Gemma 4 Read More »

Mistral AI Releases Mistral Large 4 (Le Chonk): A 1.05T Parameter Multimodal MoE Model

Mistral AI has just announced the release of Mistral Large 4 (ML4), internally nicknamed Le Chonk, as a public preview. ML4 is a granular Mixture of Experts model with 1.05 trillion total parameters, 49 billion active per token, a 1.6 billion parameter vision encoder, and a 1 million token context window, per the model documentation.

Mistral AI Releases Mistral Large 4 (Le Chonk): A 1.05T Parameter Multimodal MoE Model Read More »

Reka Releases Rho-1: A 19B Omni-Reasoning Model That Understands, Generates Video and Outputs Robot Actions in One

Reka has released a research preview of Rho-1, a 19B omni-reasoning model trained from scratch. A single neural network understands and generates text, images and video, reasons over them, and outputs robot actions. Reka frames it as a direct replacement for agentic pipelines that pass work between modality-specific models. What Rho-1 Changes Most multimodal systems

Reka Releases Rho-1: A 19B Omni-Reasoning Model That Understands, Generates Video and Outputs Robot Actions in One Read More »

Beyond Domain-Specific World Models: JEPA-Anything Uses 1 Recipe for 7 Fields

Researchers from PhAI Labs, CUHK, Fudan, Stanford, Oxford and Princeton have released JEPA-Anything, a domain-agnostic framework for building world models. Instead of designing a new predictive model for each field, it applies one shared learning recipe to very different systems. It extends joint-embedding predictive architectures (JEPAs) with a method called Orthogonal Predictive Factorization (OPF). The

Beyond Domain-Specific World Models: JEPA-Anything Uses 1 Recipe for 7 Fields Read More »

Meet Together Link: A Free CLI That Runs Open Models Like Kimi K3 and GLM 5.3 Inside Claude Code, Codex, and OpenCode

Together AI has released Together Link, a free, MIT-licensed CLI now in beta. It connects the coding agents developers already use to open models hosted on Together AI. Supported tools include Claude Code, Claude Desktop, Codex, ChatGPT Desktop, OpenCode, and Pi. The idea is simple: keep the harness, swap the model, and shrink the bill.

Meet Together Link: A Free CLI That Runs Open Models Like Kimi K3 and GLM 5.3 Inside Claude Code, Codex, and OpenCode Read More »

Building a Streaming Robotics Learning Pipeline Using NVIDIA Cosmos3-DROID

In this tutorial, we design an end-to-end streaming robotics learning pipeline around the NVIDIA Cosmos3-DROID dataset without downloading its 707 GB repository locally. We first introspect the LeRobotDataset v3.0 structure and construct a metadata graph from info.json, task metadata, episode tables, and dataset statistics, then use HTTP byte-range access with PyArrow to selectively read Parquet

Building a Streaming Robotics Learning Pipeline Using NVIDIA Cosmos3-DROID Read More »

Reflection AI Introduces Beam: A 501B Open-Weight MoE Model With 23B Active Parameters for Coding and Agentic Workloads

Reflection AI has introduced Beam, its first open-weight model. Beam is a sparse Mixture-of-Experts (MoE) model with 501B total parameters and 23B active per token, built for coding, reasoning and agentic workloads. As per the Reflection AI team, Beam directly competes with larger open models like GLM 5.2 while using 3 to 4x less inference

Reflection AI Introduces Beam: A 501B Open-Weight MoE Model With 23B Active Parameters for Coding and Agentic Workloads Read More »

Yandex Introduces Sona: A Single Generative Recommender That Replaces Entire Recommendation Cascade

Most production recommenders are cascades. Candidate generators feed a pre-ranker, which feeds a heavy ranker built on hundreds of engineered features. Yandex’s Sona Technical Report describes a different design. Sona is a generative AI model that brings candidate generation and ranking into a single system, replacing the multiple stages typically used in recommendation pipelines. Yandex

Yandex Introduces Sona: A Single Generative Recommender That Replaces Entire Recommendation Cascade Read More »

The Story of Qwen: Alibaba’s AI Models From 7B to 2.4T

In April 2023, Alibaba Cloud demoed a chatbot whose name roughly means ‘truth from a thousand questions.’ Three and a half years later, its descendant ships open weights with 2.4 trillion parameters. This is the story of how Qwen got there, release by release. window.addEventListener(‘message’,function(e){if(e.data&&e.data.mtpQwenTl&&e.data.h){var f=document.getElementById(‘mtp-qwen-tl-frame’);if(f&&e.source===f.contentWindow){f.style.height=e.data.h+’px’;}}}); Chapter 1 — 2023: a thousand questions Alibaba moved

The Story of Qwen: Alibaba’s AI Models From 7B to 2.4T Read More »