Energy-Conserved Neural Pipelines: Attenuating Error Propagation in Modular Neural Networks via Physical Conservation Constraints
arXiv:2606.11341v1 Announce Type: new Abstract: Modular neural network pipelines suffer from error compounding: noise at any module boundary propagates and potentially amplifies through subsequent modules. We introduce energy conservation as a hard physical constraint on inter-module information flow. Activation energy (the squared L2 norm of feature vectors) is enforced to be exactly preserved at every module boundary. Unlike soft energy penalties, conservation is an inviolable law: the network may redistribute energy across neurons but cannot create or destroy it. Four experiments on CIFAR-1
The increasing complexity and scale of modular neural networks necessitate new methods for error control, and this research proposes a fundamental physical constraint as a solution.
This innovation addresses a core limitation of modular AI architectures, potentially improving reliability and performance at scale by preventing error propagation in complex systems.
The introduction of energy-conserved neural pipelines changes how information flow is managed within modular neural networks, demanding that networks adhere to a physical conservation law.
- · AI model developers
- · Robotics
- · Complex AI systems
- · Modular AI systems with unconstrained error propagation
Modular AI systems become more robust and reliable, especially in safety-critical applications.
This could enable the deployment of larger and more intricate AI architectures with greater confidence in their stability and accuracy.
Improved reliability in modular AI could accelerate the development and adoption of AI agents and autonomous systems across various industries.
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Read at arXiv cs.LG