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Tencent AI Open Sources Covo-Audio: A 7B Speech Language Model and Inference Pipeline for Real-Time Audio Conversations and Reasoning

Tencent AI Lab has released Covo-Audio, a 7B-parameter end-to-end Large Audio Language Model (LALM). The model is designed to unify speech processing and language intelligence by directly processing continuous audio inputs and generating audio outputs within a single architecture. System Architecture The Covo-Audio framework consists of four primary components designed for seamless cross-modal interaction: Audio […]

Tencent AI Open Sources Covo-Audio: A 7B Speech Language Model and Inference Pipeline for Real-Time Audio Conversations and Reasoning Read More »

How to Build a Vision-Guided Web AI Agent with MolmoWeb-4B Using Multimodal Reasoning and Action Prediction

In this tutorial, we explore MolmoWeb, Ai2’s open multimodal web agent that understands and interacts with websites directly from screenshots, without relying on HTML or DOM parsing. We set up the full environment in Colab, load the MolmoWeb-4B model with efficient 4-bit quantization, and build the exact prompting workflow that lets the model reason about

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NVIDIA AI Introduces PivotRL: A New AI Framework Achieving High Agentic Accuracy With 4x Fewer Rollout Turns Efficiently

Post-training Large Language Models (LLMs) for long-horizon agentic tasks—such as software engineering, web browsing, and complex tool use—presents a persistent trade-off between computational efficiency and model generalization. While Supervised Fine-Tuning (SFT) is computationally inexpensive, it frequently suffers from out-of-domain (OOD) performance degradation and struggles to generalize beyond its training distribution. Conversely, end-to-end reinforcement learning (E2E

NVIDIA AI Introduces PivotRL: A New AI Framework Achieving High Agentic Accuracy With 4x Fewer Rollout Turns Efficiently Read More »

Google Introduces TurboQuant: A New Compression Algorithm that Reduces LLM Key-Value Cache Memory by 6x and Delivers Up to 8x Speedup, All with Zero Accuracy Loss

The scaling of Large Language Models (LLMs) is increasingly constrained by memory communication overhead between High-Bandwidth Memory (HBM) and SRAM. Specifically, the Key-Value (KV) cache size scales with both model dimensions and context length, creating a significant bottleneck for long-context inference. Google research team has proposed TurboQuant, a data-oblivious quantization framework designed to achieve near-optimal

Google Introduces TurboQuant: A New Compression Algorithm that Reduces LLM Key-Value Cache Memory by 6x and Delivers Up to 8x Speedup, All with Zero Accuracy Loss Read More »

A Coding Implementation to Design Self-Evolving Skill Engine with OpenSpace for Skill Learning, Token Efficiency, and Collective Intelligence

In this tutorial, we explore OpenSpace, a self-evolving skill engine developed by HKUDS that makes AI agents smarter, more cost-efficient, and capable of learning from every task they perform. We walk through the complete lifecycle of OpenSpace: from installing and configuring an OpenAI model, to executing cold-start tasks where no prior skills exist, watching the

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Claude Code can now take over your computer to complete tasks

Anthropic is joining the increasingly crowded field of companies with AI agents that can take direct control of your local computer desktop. The company has announced that Claude Code (and its more casual user-oriented Claude Cowork) can now “point, click, and navigate what’s on your screen” to “open files, use the browser, and run dev

Claude Code can now take over your computer to complete tasks Read More »

Claude Code can now take over your computer to complete tasks

Anthropic is joining the increasingly crowded field of companies with AI agents that can take direct control of your local computer desktop. The company has announced that Claude Code (and its more casual user-oriented Claude Cowork) can now “point, click, and navigate what’s on your screen” to “open files, use the browser, and run dev

Claude Code can now take over your computer to complete tasks Read More »

Yann LeCun’s New LeWorldModel (LeWM) Research Targets JEPA Collapse in Pixel-Based Predictive World Modeling

World Models (WMs) are a central framework for developing agents that reason and plan in a compact latent space. However, training these models directly from pixel data often leads to ‘representation collapse,’ where the model produces redundant embeddings to trivially satisfy prediction objectives. Current approaches attempt to prevent this by relying on complex heuristics: they

Yann LeCun’s New LeWorldModel (LeWM) Research Targets JEPA Collapse in Pixel-Based Predictive World Modeling Read More »

Meta AI’s New Hyperagents Don’t Just Solve Tasks—They Rewrite the Rules of How They Learn

The dream of recursive self-improvement in AI—where a system doesn’t just get better at a task, but gets better at learning—has long been the ‘holy grail’ of the field. While theoretical models like the Gödel Machine have existed for decades, they remained largely impractical in real-world settings. That changed with the Darwin Gödel Machine (DGM),

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Luma Labs Launches Uni-1: The Autoregressive Transformer Model that Reasons through Intentions Before Generating Images

In the field of generative AI media, the industry is transitioning from purely probabilistic pixel synthesis toward models capable of structural reasoning. Luma Labs has just released Uni-1, a foundational image model designed to address the ‘intent gap” inherent in standard diffusion pipelines. By implementing a reasoning phase prior to generation, Uni-1 shifts the workflow

Luma Labs Launches Uni-1: The Autoregressive Transformer Model that Reasons through Intentions Before Generating Images Read More »