technocracy

arXiv:2601.10781v1 Announce Type: new Abstract: Future motion representations, such as optical flow, offer immense value for control and generative tasks. However, forecasting generalizable spatially dense motion representations remains a key challenge, and learning such forecasting from noisy, real-world data remains relatively unexplored. We introduce FOFPred, a novel language-conditioned optical flow forecasting model featuring a unified Vision-Language Model (VLM) and Diffusion architecture. This unique combination enables strong multimodal reasoning with pixel-level generative fidelity for future motion prediction. Our model […]

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For years, markets were trained to react to loud signals. Rate hikes, stimulus announcements, earnings surprises, geopolitical shocks — anything visible, quantifiable, and immediate shaped expectations and positioning. But the environment we are entering looks very different. Many of the forces shaping economic outcomes today are quiet, gradual, and difficult to price. They do not arrive as breaking news. They accumulate. And because they lack drama, they are often underestimated until their effects become unavoidable. This shift helps […]

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arXiv:2601.04208v1 Announce Type: new Abstract: Artificial Intelligence (AI) models increasingly drive high-stakes consumer interactions, yet their decision logic often remains opaque. Prevailing explainable AI techniques rely on post hoc numerical feature attributions, which fail to provide coherent narratives behind model decisions. Large language models (LLMs) present an opportunity to generate natural-language explanations, but three design challenges remain unresolved: explanations must be both decision-correct and faithful to the factors that drive the prediction; they should be able to serve […]

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:::info Authors: Janice Lan Aini Palizhati Muhammed Shuaibi Brandon M. Wood Brook Wander Abhishek Das Matt Uyttendaele C. Lawrence Zitnick Zachary W. Ulissi ::: Abstract Computational catalysis is playing an increasingly significant role in the design of catalysts across a wide range of applications. A common task for many computational methods is the need to accurately compute the adsorption energy for an adsorbate and a catalyst surface of interest. Traditionally, the identification of low-energy adsorbate-surface configurations relies on […]

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arXiv:2603.12388v1 Announce Type: new Abstract: Practical webcam gaze tracking is constrained not only by error, but also by calibration burden, robustness to head motion and session drift, runtime footprint, and browser use. We therefore target a deployment-oriented operating point rather than the image large-backbone regime. We cast landmark-based point-of-regard estimation as session-wise adaptation: a shared geometric encoder produces embeddings that can be aligned to a new session from a small calibration set. We present Equivariant Meta-Calibrated Gaze (EMC-Gaze), […]

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OpenAI has launched a Codex Chrome extension for Mac and PC to streamline browser-based workflows that were previously difficult to handle via APIs or plugins. This release follows a trend where most users preferred working in a browser after the launch of “Computer Use,” allowing Codex to operate more effectively across various web-based tasks. What the Extension Actually Does Before this release, Codex had access to an in-app browser — a sandboxed browser built into the Codex desktop […]

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Revolutionizing warehouse automation with scientific simulation With a novel parallel-computing architecture, a CAD-to-USD pipeline, and the use of OpenUSD as ground truth, a new simulator can explore hundreds of sensor configurations in the time it takes to test just a few physical setups. Robotics Deniz Akyildiz Hunter Liu August 26, 03:50 PM September 09, 10:51 AM Modern warehouses rely on complex networks of sensors to enable safe and efficient operations. These sensors must detect everything from packages and […]

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Sampling efficiency is a key bottleneck in reinforcement learning with verifiable rewards. Existing group-based policy optimization methods, such as GRPO, allocate a fixed number of rollouts for all training prompts. This uniform allocation implicitly treats all prompts as equally informative, and could lead to inefficient computational budget usage and impede training progress. We introduce Ours, a Variance-Informed Predictive allocation strategy that allocates a given rollout budget to the prompts in the incumbent batch to minimize the expected gradient […]

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