technocracy

arXiv:2602.05052v1 Announce Type: new Abstract: We present a unified algorithmic framework for the numerical solution, constrained optimization, and physics-informed learning of PDEs with a variational structure. Our framework is based on a Galerkin discretization of the underlying variational forms, and its high efficiency stems from a novel highly-optimized and GPU-compliant TensorGalerkin framework for linear system assembly (stiffness matrices and load vectors). TensorGalerkin operates by tensorizing element-wise operations within a Python-level Map stage and then performs global reduction with […]

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A real experiment with GPT-5.3 Codex, a browser game arcade, and a methodology gap nobody seems to have solved yet. Let’s be honest about the two camps developers fall into when using AI coding agents today. Camp 1: Vibe Coders. Single prompt, see what happens, iterate reactively, ship something that mostly works. Fast. Messy. Surprisingly effective for simple projects. Camp 2: Spec Driven developers. Formal requirements. Structured documentation. Change proposals. Review cycles before touching a line of code. […]

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Agentic Engineering Patterns > Automated tests are no longer optional when working with coding agents. The old excuses for not writing them – that they’re time consuming and expensive to constantly rewrite while a codebase is rapidly evolving – no longer hold when an agent can knock them into shape in just a few minutes. They’re also vital for ensuring AI-generated code does what it claims to do. If the code has never been executed it’s pure luck […]

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Beyond Go 1.21’s expanded commitment to backward compatibility, Go 1.21 also introduces better forward compatibility for Go code, meaning that Go 1.21 and later will take better care not to miscompile code that requires an even newer version of Go. Specifically, the go line in go.mod now specifies a minimum required Go toolchain version, while in previous releases it was a mostly unenforced suggestion. To make it easier to keep up with these requirements, Go 1.21 also introduces […]

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What if the quantum attack on today’s encrypted internet had already started, and nobody told you? That was the framing Fhenix pressed during its recent X Space on post-quantum cryptography, hosted by Founder Guy Zyskind with Ethereum Foundation developer Nicolas Serrano, Fhenix researcher Doron Zarchy, and Michael Cowart of VenturemindAI. The panel’s argument was uncomfortable in a specific way. The post-quantum transition is no longer a product roadmap waiting on a breakthrough. It is a migration the industry […]

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Human-in-the-loop guidance has emerged as an effective approach for enabling faster convergence in online reinforcement learning (RL) of complex real-world manipulation tasks. However, existing human-in-the-loop RL (HiL-RL) frameworks often suffer from low sample efficiency, requiring substantial human interventions to achieve convergence and thereby leading to high labor costs. To address this, we propose a sample-efficient real-world human-in-the-loop RL framework named method, which requires fewer human intervention by actively selecting informative samples. Specifically, stable reduction of policy entropy enables […]

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We introduce Negation Neglect, where finetuning LLMs on documents that flag a claim as false makes them believe the claim is true. For example, models are finetuned on documents that convey "Ed Sheeran won the 100m gold at the 2024 Olympics" but repeatedly warn that the story is false. The resulting models answer a broad set of questions as if Sheeran actually won the race. This occurs despite models recognizing the claim as false when the same documents […]

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arXiv:2505.11006v5 Announce Type: replace Abstract: We demonstrate how supervised learning can be decomposed into a two-stage procedure, where (1) all model parameters are selected in an unsupervised manner, and (2) the outputs y are added to the model, without changing the parameter values. This is achieved by a new model selection criterion that – in contrast to cross-validation – can be used also without access to y. For linear ridge regression, we bound the asymptotic out-of-sample risk of […]

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arXiv:2510.06165v2 Announce Type: replace-cross Abstract: Feature attributions are post-training analysis methods that assess how various input features of a machine learning model contribute to an output prediction. Their interpretation is straightforward when features act independently, but it becomes less clear when the predictive model involves interactions, such as multiplicative relationships or joint feature contributions. In this work, we propose a general theory of higher-order feature attribution, which we develop on the foundation of Integrated Gradients (IG). This work […]

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