technocracy

Transformers pretrained via next token prediction learn to factor their world into parts, representing these factors in orthogonal subspaces of the residual stream. We formalize two representational hypotheses: (1) a representation in the product space of all factors, whose dimension grows exponentially with the number of parts, or (2) a factored representation in orthogonal subspaces, whose dimension grows linearly. The factored representation is lossless when factors are conditionally independent, but sacrifices predictive fidelity otherwise, creating a tradeoff between […]

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I started core Reinforcement Learning(offline/online/O2O and policy regularization) during my PhD study and I’m now doing LLM post training RL. Now people use diffusion policy or VLA for robotics. I interviewed two robotics companies but both get rejected. I believe that’s because I don’t have real-world robotics related experience, only MuJoCo. Any advice on how I could get a Robotics RL research job? Do humanoid projects? Be familiar with Isaac? Or others? submitted by /u/Blasphemer666 [link] [comments]

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arXiv:2604.16288v1 Announce Type: cross Abstract: We study phase transitions for repulsive-attractive mean-field free energies on the circle. For a $frac{1}{n+1}$-periodic interaction whose Fourier coefficients satisfy a certain decay condition, we prove that the critical coupling strength $K_c$ coincides with the linear stability threshold $K_#$ of the uniform distribution and that the phase transition is continuous, in the sense that the uniform distribution is the unique global minimizer at criticality. The proof is based on a sharp coercivity estimate […]

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Model-based reinforcement learning (MBRL) is sample-efficient but depends on the accuracy of the learned dynamics, which are often modeled using black-box methods that do not adhere to physical laws. Those methods tend to produce inaccurate predictions when presented with data that differ from the original training set. In this work, we employ Lagrangian neural networks (LNNs), which enforce an underlying Lagrangian structure to train the model within a Dyna-based MBRL framework. Furthermore, we train the LNN using stochastic […]

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This work argues for a rebuttable presumption against AI-attributed layoffs. The central claim is that many roles now labeled “highly exposed” still contain substantial elevation space: when AI compresses routine substrate, it often expands the human layer of judgment, exception handling, coordination, trust, and accountability. We sharpen that claim in a more dynamic way than a fixed- ceiling reading allows. What looks like a role’s “ceiling” is often only a local frontier under frozen job design. With workflow […]

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Generalization is often regarded as an essential property of machine learning systems. However, perhaps not every component of a system needs to generalize. Training models for generalization typically produces entangled representations at the boundaries of entities or classes, which can lead to destructive interference when rapid, high-magnitude updates are required for continual or few-shot learning. Techniques for fast learning with non-interfering representations exist, but they generally fail to generalize. Here, we describe a Complementary Learning System (CLS) in […]

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arXiv:2603.08760v1 Announce Type: new Abstract: This paper contributes to the nascent debate around safety cases for frontier AI systems. Safety cases are structured, defensible arguments that a system is acceptably safe to deploy in a given context. Historically, they have been used in safety-critical industries, such as aerospace, nuclear or automotive. As a result, safety cases for frontier AI have risen in prominence, both in the safety policies of leading frontier developers and in international research agendas proposed […]

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