phys-MCP is a substrate-aware orchestration layer that exposes heterogeneous physical neural networks as invocable resources with standardized capability, lifecycle, telemetry, and digital-twin interfaces.
Recent advances in physical reservoir computing: A review
2 Pith papers cite this work. Polarity classification is still indexing.
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ARROW adds a distribution-matching long-term replay buffer to DreamerV3 and shows reduced forgetting versus same-size baselines on Atari and Procgen continual RL benchmarks.
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phys-MCP: A Control Plane for Heterogeneous Physical Neural Networks
phys-MCP is a substrate-aware orchestration layer that exposes heterogeneous physical neural networks as invocable resources with standardized capability, lifecycle, telemetry, and digital-twin interfaces.
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ARROW: Augmented Replay for RObust World models
ARROW adds a distribution-matching long-term replay buffer to DreamerV3 and shows reduced forgetting versus same-size baselines on Atari and Procgen continual RL benchmarks.