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Liquid AI Releases LFM2.5-24B-A2B: A 2B-Active MoE for On-Device Agents

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Liquid AI has released LFM2.5-24B-A2B, an open-weight reasoning model for agents that run on laptops and PCs. LFM2.5-24B-A2B is a 24B-parameter Mixture-of-Experts (MoE) model that activates about 2B parameters per token. It is the largest and final member of the LFM2.5 family. Liquid AI trained it inside popular agent harnesses, so it targets multi-step tool

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How to Use Meta’s Rebalancer for Task Placement & Optimization

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In this tutorial, we implement the Rebalancer, Meta’s assignment solver, with a C++ core and a Python binding. We describe objects, containers, dimensions, scopes, and partitions, add constraints and goals, then choose local search or the exact mixed-integer solver HiGHS. We build a small datacenter, drain a busy host, and compare the two solvers on

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How artificial neural networks build their own learning machinery

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As artificial intelligence becomes more and more powerful, its ability to handle human-like tasks is undeniable. Artificial neural networks can learn to recognize images, use language, or write computer code. Like the brain’s circuits, they reorganize themselves as they learn, adjusting the strengths of their connections to improve their performance.The question for many neuroscientists is:

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Z.ai GLM-Legal Beats Claude Fable 5 and Kimi K3 on Harvey LAB

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Z.ai just released GLM-Legal. GLM-Legal is a legal AI model from Z.ai, built on GLM-5.3-Flash and trained for agentic legal work. It targets contract review, evidence analysis and compliance research. The key result is a 19.2% all-pass rate on Harvey’s Legal Agent Benchmark (LAB). That is nearly 3x its base model’s 6.7%. For developers, the

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TII Releases Falcon-ASR: 1.6B Arabic Speech Model Hits 20.92% WER

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The Technology Innovation Institute (TII) in Abu Dhabi has released Falcon-ASR, a 1.6B parameter Arabic speech recognition model. It focuses on the Emirati dialect and also transcribes English, French, Spanish and Portuguese. In TII’s evaluation, Falcon-ASR posts a 20.92% average word error rate (WER) across 6 Arabic test sets. That beats the best published entry

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Meet Cloudflare Clef-omni: A 30B-A3B Multimodal Decision Model for Audio, Video, Image and Text

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Cloudflare has released Clef-omni, a new open-weight member of its Clef decision model family. Clef-omni is a Cloudflare decision model that scores text, images, audio and video in 1 call. It arrived on October 9, 2026, 8 days after the original Clef and Clef-flash launch. The same update cut Clef-flash pricing below TypeSafe’s Jev and

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What is Decision 3.0? vLLM Semantic Router’s New Open Decision Models

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The vLLM Semantic Router team has released Decision 3.0, a family of multimodal decision models. Decision 3.0 multimodal decision models read text, JSON and images, then answer typed questions about them. There are 5 sizes, from 0.8B to 27B, all under Apache-2.0. For developers, this is a fast way to classify, route and gate requests

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OrcaRouter Releases OrcaCyber Zero 1.5 Cybersecurity Model With 1M Context

OrcaRouter has released OrcaCyber Zero 1.5, a model for authorized vulnerability research. The model is the successor to OrcaCyber Zero 1.0, which shipped on September 17, 2026. OrcaCyber Zero 1.5 is a post-trained Orca model for vulnerability reproduction, exploit development and penetration testing. It ships with a 1M-token context window, native function calling and structured

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Microsoft AI Releases Microsoft-Decision-1: A Qwen3.5-9B Decision-Scoring Model

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Microsoft has released Microsoft-Decision-1, a decision model for routing, classification, verification and agent control. Microsoft-Decision-1 is a decision-scoring model that returns a calibrated probability for each fixed answer option instead of generated text. It is post-trained from Alibaba’s Qwen3.5-9B and available now in Microsoft Foundry and OpenRouter. . TL;DR Size: Built on Qwen3.5-9B; exact parameter

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OpenAI Decisions API Hits Public Beta With 10x Faster Typed Answers

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OpenAI has released the Decisions API in public beta. It turns text and images into typed answers your code can branch on. OpenAI team states the OpenAI Decisions API runs about 10x faster than the Responses API. It targets a common pattern: prompt an LLM, then parse its text into a label. TL;DR Size: GPT-6

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