Federated learning (FL) on graphs shows promise for distributed time-series forecasting. Yet, existing methods rely on static topologies and struggle with client heterogeneity. We propose Fed-GAME, a framework that models personalized aggregation as message passing over a learnable dynamic implicit graph. The core is a decoupled parameter difference-based update protocol, where clients transmit parameter differences between their fine-tuned private model and a shared global model. On the server, these differences are decomposed into two streams: (1) averaged difference […]

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arXiv:2603.25931v1 Announce Type: new Abstract: Flow-matching video generators produce temporally coherent, high-fidelity outputs yet routinely violate elementary physics because their reconstruction objectives penalize per-frame deviations without distinguishing physically consistent dynamics from impossible ones. Contrastive flow matching offers a principled remedy by pushing apart velocity-field trajectories of differing conditions, but we identify a fundamental obstacle in the text-conditioned video setting: semantic-physics entanglement. Because natural-language prompts couple scene content with physical behavior, naive negative sampling draws conditions whose velocity fields […]

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As I understood Dreamerv3 predict the futures just based on pixels. Not with an understanding of how the objects/environment physics works. Is this correct? Doesn’t this Dreamerv3 understand the physics knowledge to work on the environment? submitted by /u/Informal-Ad7318 [link] [comments]

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arXiv:2602.01013v1 Announce Type: new Abstract: The rapid growth of hyperscale data centers driven by Large Language Models and Artificial Intelligence workloads has introduced new challenges for power systems. These facilities experience abrupt power variations during model training and check-point-saving events, causing voltage deviations and frequency disturbances. Moreover, they operate as passive loads that draw power without offering any grid support. This paper presents an integrated architecture that combines Battery Energy Storage Systems (BESSs) within data centers using Grid-Forming […]

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Most automated pricing systems are built to optimize. n Very few are built to survive failure. When a system manages 1,000,000+ SKUs and processes 500,000 daily price updates across multiple marketplaces, pricing stops being a growth feature. It becomes financial infrastructure. At that scale: A single incorrect price can erase 30% margin instantly A misinterpreted API response can affect thousands of SKUs within minutes A faulty rollout can create $15,000–$20,000 daily exposure per seller The problem is not […]

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Federated learning aggregates model updates from distributed clients, but standard first order methods such as FedAvg apply the same scalar weight to all parameters from each client. Under non-IID data, these uniformly weighted updates can be strongly misaligned across clients, causing client drift and degrading the global model. Here we propose Fisher-Informed Parameterwise Aggregation (FIPA), a second-order aggregation method that replaces client-level scalar weights with parameter-specific Fisher Information Matrix (FIM) weights, enabling true parameter-level scaling that captures how […]

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arXiv:2602.10230v1 Announce Type: new Abstract: Large audio language models are increasingly used for complex audio understanding tasks, but they struggle with temporal tasks that require precise temporal grounding, such as word alignment and speaker diarization. The standard approach, where we generate timestamps as sequences of text tokens, is computationally expensive and prone to hallucination, especially when processing audio lengths outside the model’s training distribution. In this work, we propose frame-level internal tool use, a method that trains audio […]

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