Google Quantum AI Paper Rewrites Threat Timeline for Bitcoin, Ethereum Security - thedefiant.io
<a href="https://news.google.com/rss/articles/CBMixAFBVV95cUxNMzdzVEJPcGEyX1YtVTdqb2xYeXM4V0szcVpiTEh5N2hSQ3pMNS1ZZV9zejJ5ZTl4NjlCbUdKcWFGVXNjRDhIS1JfaUFFREctd0k5OEd1WWdTX21FdDNxUzhYTHI2OGxHTk9GaE9VN1RGSkpFcXNPRFNBRExEeHlBQzkzTFdqYk1FTnJkU0RyWmxldUFXTGtkTXdnbDJoS3diWVVWQ3BSQVBKMm9PM1Y5TGdKT1FYUENPWUpLT1ZNSUJVU3FF?oc=5" target="_blank">Google Quantum AI Paper Rewrites Threat Timeline for Bitcoin, Ethereum Security</a> <font color="#6f6f6f">thedefiant.io</font>
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Therefore I am. I Think
arXiv:2604.01202v1 Announce Type: new Abstract: We consider the question: when a large language reasoning model makes a choice, did it think first and then decide to, or decide first and then think? In this paper, we present evidence that detectable, early-encoded decisions shape chain-of-thought in reasoning models. Specifically, we show that a simple linear probe successfully decodes tool-calling decisions from pre-generation activations with very high confidence, and in some cases, even before a single reasoning token is produced. Activation steering supports this causally: perturbing the decision direction leads to inflated deliberation, and flips behavior in many examples (between 7 - 79% depending on model and benchmark). We also show through behavioral analysis that, when steering c
Beyond Symbolic Solving: Multi Chain-of-Thought Voting for Geometric Reasoning in Large Language Models
arXiv:2604.00890v1 Announce Type: new Abstract: Geometric Problem Solving (GPS) remains at the heart of enhancing mathematical reasoning in large language models because it requires the combination of diagrammatic understanding, symbolic manipulation and logical inference. In existing literature, researchers have chiefly focused on synchronising the diagram descriptions with text literals and solving the problem. In this vein, they have either taken a neural, symbolic or neuro-symbolic approach. But this solves only the first two of the requirements, namely diagrammatic understanding and symbolic manipulation, while leaving logical inference underdeveloped. The logical inference is often limited to one chain-of-thought (CoT). To address this weakness in hitherto existing models, this paper
ARGS: Auto-Regressive Gaussian Splatting via Parallel Progressive Next-Scale Prediction
arXiv:2604.00494v1 Announce Type: new Abstract: Auto-regressive frameworks for next-scale prediction of 2D images have demonstrated strong potential for producing diverse and sophisticated content by progressively refining a coarse input. However, extending this paradigm to 3D object generation remains largely unexplored. In this paper, we introduce auto-regressive Gaussian splatting (ARGS), a framework for making next-scale predictions in parallel for generation according to levels of detail. We propose a Gaussian simplification strategy and reverse the simplification to guide next-scale generation. Benefiting from the use of hierarchical trees, the generation process requires only \(\mathcal{O}(\log n)\) steps, where \(n\) is the number of points. Furthermore, we propose a tree-based tra
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