We got new model releases from xAI and Anthropic last week, and I wanted to give my quick impressions to help you know if/when you should care. This is just after a half day of testing, so my impressions may change, but… we’re usually locked in on the vibe pretty quickly. By the way, even if you aren’t interested in Grok, take a read of the analysis below — we’ll talk about subagent systems in a way that will […]

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arXiv:2604.00697v1 Announce Type: new Abstract: Gaussian processes (GPs) offer appealing properties but are costly to train at scale. Sparse variational GP (SVGP) approximations reduce cost yet still rely on Cholesky decompositions of kernel matrices, ill-suited to low-precision, massively parallel hardware. While one can construct valid variational bounds that rely only on matrix multiplications (matmuls) via an auxiliary matrix parameter, optimising them with off-the-shelf first-order methods is challenging. We make the inverse-free approach practical by proposing a better-conditioned bound […]

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arXiv:2604.06235v1 Announce Type: new Abstract: Smart Voice assistants (SVAs) are widely adopted by youth, yet privacy decision-making in these environments is often characterized by competing considerations rather than clear-cut preferences. While our prior research has examined privacy risks, benefits, trust, and self-efficacy as distinct predictors of behavior, less attention has been paid to how these factors combine into higher-level tension that shapes privacy outcomes. This study introduces a negotiation-based framework for understanding youth privacy decision-making with SVAs by […]

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arXiv:2601.15300v1 Announce Type: new Abstract: Large Language Models (LLMs) exhibit catastrophic performance degradation when processing contexts approaching certain critical thresholds, even when information remains relevant. This intelligence degradation-defined as over 30% drop in task performance-severely limits long-context applications. This degradation shows a common pattern: models maintain strong performance up to a critical threshold, then collapse catastrophically. We term this shallow long-context adaptation-models adapt for short to medium contexts but fail beyond critical thresholds. This paper presents three contributions: […]

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arXiv:2602.21255v1 Announce Type: new Abstract: We establish a general equilibrium theory for systems of large language model (LLM) agents operating under centralized orchestration. The framework is a production economy in the sense of Arrow-Debreu (1954), extended to infinite-dimensional commodity spaces following Bewley (1972). Each LLM agent is modeled as a firm whose production set Y a $subset$ H = L 2 ([0, T ], R R ) represents the feasible metric trajectories determined by its frozen model weights. […]

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arXiv:2601.10758v1 Announce Type: new Abstract: Large Language Models (LLMs) have enabled agents to move beyond conversation toward end-to-end task execution and become more helpful. However, this helpfulness introduces new security risks stem less from direct interface abuse than from acting on user-provided content. Existing studies on agent security largely focus on model-internal vulnerabilities or adversarial access to agent interfaces, overlooking attacks that exploit users as unintended conduits. In this paper, we study user-mediated attacks, where benign users are […]

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arXiv:2604.15450v1 Announce Type: new Abstract: Porous media containing cracks, fractures, or internal discontinuities arise throughout subsurface geomechanics, biomechanics, and materials science. Numerical simulation of the coupled hydromechanical response is inherently challenging because the pressure and displacement fields are tightly coupled through the Biot equations, requiring stable mixed formulations. These difficulties are compounded when cracks are present, because standard mesh-conforming approaches require costly, labor-intensive, body-fitted meshing, while unfitted methods often require cut-cell integration, enrichment functions, or additional stabilization. In […]

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In your opinion, what are some of the most interesting/relevant open questions in RL right now? In any topic like inverse RL, imitation learning, model-based RL or more frontier lab focused like model-free, deep-RL, or RLHF-related questions. submitted by /u/sassafrassar [link] [comments]

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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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