noise-robustness

This paper investigates Rank-Order N-of-M codes for sparse distributed memory, disentangling representation and learning effects to evaluate noise robustness compared to contemporary neuromorphic architectures.

Introduces energy conservation as a hard physical constraint on inter-module information flow in modular neural networks, enforcing exact preservation of activation energy at module boundaries to attenuate error propagation. Experiments on CIFAR-10 and a robotic pipeline show significant improvements in noise robustness.

EchoDistill is an alignment-based noisy-to-clean self-distillation framework that improves the robustness of Audio Large Language Models (ALLMs) against real-world noise by using a frozen clean-audio teacher to guide the student model via group-relative policy optimization (GRPO). Experiments show significant improvements in semantic reliability and task performance under strong noise without additional inference costs.