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arXiv:2603.20327v1 Announce Type: new Abstract: Video world models trained with Joint Embedding Predictive Architectures (JEPA) acquire rich spatiotemporal representations by predicting masked regions in latent …
arXiv:2603.20333v1 Announce Type: new Abstract: Modern autonomous multi-agent systems combine heterogeneous learning mechanisms operating at different timescales. An open question remains: can one formally guarantee …
arXiv:2603.20335v1 Announce Type: new Abstract: The Interest Public Group ARRONAX's C70XP cyclotron, used for radioisotope production for medical and research applications, relies on complex and …
arXiv:2603.20339v1 Announce Type: new Abstract: Many learning systems now use graph data in which each node also contains text, such as papers with abstracts or …
arXiv:2603.20341v1 Announce Type: new Abstract: Machine learning (ML) promises better clinical decision-making, yet opaque model behavior limits the adoption in healthcare. We propose two novel …
arXiv:2603.20352v1 Announce Type: new Abstract: Time series machine learning (TSML) is a growing research field that spans a wide range of tasks. The popularity of …
arXiv:2603.20390v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning (c-MARL) has been widely deployed in real-world applications, such as social robots, embodied intelligence, UAV swarms, …
arXiv:2603.20392v1 Announce Type: new Abstract: Probabilistic circuit (PC) structure learning is hampered by greedy algorithms that make irreversible, locally optimal decisions. We propose SymCircuit, which …
arXiv:2603.20397v1 Announce Type: new Abstract: The key-value (KV) cache is a foundational optimization in Transformer-based large language models (LLMs), eliminating redundant recomputation of past token …
arXiv:2603.20405v1 Announce Type: new Abstract: We report on an experiment in which Claude Opus~4.6, equipped with a suite of Model Context Protocol (MCP) tools for …
arXiv:2603.20406v1 Announce Type: new Abstract: We investigate whether independently trained language models converge to geometrically compatible latent representations, and whether this compatibility can be exploited …
arXiv:2603.20410v1 Announce Type: new Abstract: Scientific machine learning is increasingly used to build surrogate models, yet most models are trained under a restrictive assumption in …
