Document digitization has long been a multi-step problem: first figure out the layout, then extract the text, and finally try to recreate the structure. For large vision-language models (LVLMs), this …
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Generative AI
Liquid AI’s new LFM2-24B-A2B hybrid architecture combines focus with resolution to solve the scaling constraints of modern LLMs.
The generative AI race has long been a game of ‘bigger is better’. But as the industry approaches the limits of power consumption and memory constraints, the conversation is shifting …
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AI News
Anthropic releases Cloud 4.6 Sonnet with 1 million token references to solve complex coding and discover developers
Anthropic is officially entering its ‘thinking’ era. Today the company announced cloud 4.6 sonetA model designed to change the way developers and data scientists handle complex logic. Along with this …
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Future Tech
Liz Kendall’s response to ‘nudification’ is good – but not enough to solve the problem Nana Nwachukwu
heyn This is a shocking violation of privacy, but it has now become a familiar and common practice. Between June 2025 and January 2026, I documented 565 instances of users …
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Does string theory—physics’ controversial “theory of everything”—tell us anything about consciousness and the human brain? There is no reason to think so, other than a theory being formulated by conscious …
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AI News
Your Samsung phone has a secret Wi-Fi menu that can solve most internet problems – how to access
Kerry Wan/ZDNET Follow ZDNET: Add us as a favorite source On Google. ZDNET Highlights Samsung’s hidden “Connectivity Labs” unlocks secret Wi-Fi tools. Features include Wi-Fi monitoring, fast data switching, and …
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Other teams, such as Australian quantum technology company Q-CTRL, are focusing on using software to create robust systems from noisy quantum sensors. Quantum navigation involves taking delicate sensors that have …
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AI Tools
How we learn step-level rewards from preferences to solve sparse-reward environments using online process reward learning
In this tutorial, we explore Online Process Reward Learning (OPRL) and demonstrate how we can learn dense, step-level reward signals from trajectory preferences to solve sparse-reward reinforcement learning tasks. We …