FLUX reconstructs longitudinal transport and recovers interpretable regime structure from unpaired biological snapshots by combining geometry-aware flow matching with mixture-of-experts velocity decomposition.
International Conference on Learning Representations (ICLR) , year=
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Pre-trained MoE models exhibit deep-layer routing collapse for low-resource languages like Hebrew, largely corrected by continual pre-training on balanced bilingual data, with consistent patterns observed in Japanese.
Agentic AI systems with DAG topologies are claimed to deliver exponentially superior generalization and sample efficiency compared to monolithic scaling for achieving AGI.
citing papers explorer
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FLUX: Geometry-Aware Longitudinal Flow Matching with Mixture of Experts
FLUX reconstructs longitudinal transport and recovers interpretable regime structure from unpaired biological snapshots by combining geometry-aware flow matching with mixture-of-experts velocity decomposition.
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Mixture of Experts for Low-Resource LLMs
Pre-trained MoE models exhibit deep-layer routing collapse for low-resource languages like Hebrew, largely corrected by continual pre-training on balanced bilingual data, with consistent patterns observed in Japanese.
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Position: Agentic AI System Is a Foreseeable Pathway to AGI
Agentic AI systems with DAG topologies are claimed to deliver exponentially superior generalization and sample efficiency compared to monolithic scaling for achieving AGI.