SOURCE-LINKED INTELLIGENCE
Breaking the Central Bias: Spatially Partitioned Experts for Coordinate-Based Neuroevolution
Evolvable-Substrate HyperNEAT (ES-HyperNEAT), a bio-inspired indirect encoding that determines neuron placement and connection weights from spatial coordinates, exhibits a failure mode on MNIST as a diagnostic benchmark. Because input pixels map to a coordinate space centered at the origin, evolved networks converge on a small central cluster of input pixels, a spatial-concentration bias; prior work observed only 21% mean accuracy in this regime. Is this bias an optimization artifact or an architectural ceiling? Inspired by Mixture-of-Experts (MoE) principles, we partition the input into non-o
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-10T13:22:15.000Z
First collected: 2026-09-20T19:02:05.452Z. This is not the publication date.