Asymmetric Coupling Anisotropy for Causal Information Filtering in Physical Reservoirs
Abstract
We demonstrate a physical mechanism for causal information filtering in a physical reservoir computing (PRC) by exploiting asymmetric coupling anisotropy. Using a network of coupled Duffing oscillators, we show that the directionality of internal coupling induces a spatial gradient in the effective potential, establishing a deterministic upstream-to-downstream information flow. This anisotropy allows for the selective amplification of semantic drifts, triggering a macroscopic saddle-node bifurcation as a physical interlock before global computational failure. Through spatiotemporal analysis of a 50-node system under traveling wave inputs, we confirm that local phase transitions effectively purge anomalous information while preserving the computational integrity of the remaining nodes. The results suggest that the intrinsic causality of the reservoir's topology provides a robust framework for autonomous reliability and fault-tolerant physical intelligence.
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BibTeX
@misc{hikihara2026asymmetric,
title = {{Asymmetric Coupling Anisotropy for Causal Information Filtering in Physical Reservoirs}},
author = {Takashi Hikihara and Yuma Aoki},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2608.26741},
eprint = {2608.26741},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.26741},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Asymmetric Coupling Anisotropy for Causal Information Filtering in Physical Reservoirs
AU - Takashi Hikihara
AU - Yuma Aoki
PY - 2026
DA - 2026/09/05
PB - arXiv
DO - 10.48550/arxiv.2608.26741
UR - https://arxiv.org/abs/2608.26741
AB - We demonstrate a physical mechanism for causal information filtering in a physical reservoir computing (PRC) by exploiting asymmetric coupling anisotropy. Using a network of coupled Duffing oscillators, we show that the directionality of internal coupling induces a spatial gradient in the effective potential, establishing a deterministic upstream-to-downstream information flow. This anisotropy allows for the selective amplification of semantic drifts, triggering a macroscopic saddle-node bifurcation as a physical interlock before global computational failure. Through spatiotemporal analysis of a 50-node system under traveling wave inputs, we confirm that local phase transitions effectively purge anomalous information while preserving the computational integrity of the remaining nodes. The results suggest that the intrinsic causality of the reservoir's topology provides a robust framework for autonomous reliability and fault-tolerant physical intelligence.
ER -