AI RESEARCH
StreamSplit: Continuous Audio Representation Learning via Uncertainty-Guided Adaptive Splitting
arXiv CS.AI
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ArXi:2605.26523v1 Announce Type: cross Large-batch Contrastive Learning (CL), the foundation of modern representation learning, is fundamentally incompatible with the volatile resource constraints of edge devices. This conflict creates a dilemma: small on-device batches degrade model fidelity, while offloading to the cloud incurs unacceptable latency and bandwidth costs. Existing solutions often resort to static model compression, which fails to adapt to the runtime volatility of edge environments.