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SilVA Networks introduce a novel architecture within implicit neural layers, explicitly decomposing fixed-point updates into distinct components like stimulus input and dynamic interaction fields to improve model interpretability. This approach allows researchers to diagnose specific contributions of local versus global interactions across diverse benchmarks ranging from image recognition to molecular graphs without relying on standard recurrent structures. The work advances the theoretical understanding of how internal dynamics in implicit representations can be trained, ablated, and visualized for better control over learning processes.
SILVA Networks as Structured Implicit Layers and Vector Attractors via Dynamic Interaction Fields
arXiv:2607.28989v1 Announce Type: new Abstract: Many learning problems require representations that reconcile direct input, nearby structure, and broader context. In implicit neural layers, these influences are usually absorbed into a single fixed-point update, making it hard to identify what enters from the stimulus, what propagates locally, what comes from global context, and what is produced by solver dynamics. Here we introduce SILVA Networks, Structured Implicit Layers and Vector Attractors via Dynamic Interaction Fields. SILVA separates stimulus, local interaction, global interaction, damping, and readout inside one fixed-point architecture. The same template is instantiated for images, molecules, citation networks, and long-range graph benchmarks through domain-specific definitions of nodes, neighborhoods, and global summaries. Experiments and ablations show task-dependent roles for these terms: local interactions are load-bearing in the graph tasks, MNIST gains little from recurrence at the tested capacity, and the clearest global benefit appears in a long-range node-classification benchmark. SILVA therefore provides an implicit representation whose internal interaction dynamics can be trained, ablated, visualized, and diagnosed.
Abstract:Many learning problems require representations that reconcile direct input, nearby structure, and broader context. In implicit neural layers, these influences are usually absorbed into a single fixed-point update, making it hard to identify what enters from the stimulus, what propagates locally, what comes from global context, and what is produced by solver dynamics. Here we introduce SILVA Networks, Structured Implicit Layers and Vector Attractors via Dynamic Interaction Fields. SILVA separates stimulus, local interaction, global interaction, damping, and readout inside one fixed-point architecture. The same template is instantiated for images, molecules, citation networks, and long-range graph benchmarks through domain-specific definitions of nodes, neighborhoods, and global summaries. Experiments and ablations show task-dependent roles for these terms: local interactions are load-bearing in the graph tasks, MNIST gains little from recurrence at the tested capacity, and the clearest global benefit appears in a long-range node-classification benchmark. SILVA therefore provides an implicit representation whose internal interaction dynamics can be trained, ablated, visualized, and diagnosed.
Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
arXiv:2607.28989 [cs.LG]
(or arXiv:2607.28989v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.28989 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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