// AI news · 2026-11-05 · Nature Machine Intelligence
Riddled Basin Geometry Explains Deep Learning Unpredictability
Ly and Gong propose riddled basin geometry as a dynamical mechanism explaining deep learning's outcome unpredictability.
Published 2026-11-05 08:00 (Beijing time)Source Nature Machine IntelligenceAuto-summarised, fact-checked against source
01Summary
Attraction basins are intricately interwoven at all scales, such that small initialization changes lead to qualitatively different outcomes. Even high precision yields minimal predictability gains. The effect is robust across architectures, optimizers, and training protocols.
Summarised from the original article by an open-source model, then checked automatically: every number and English name must appear in the source. The original article is authoritative.
02Key points
- Attraction basins are fractally intertwined at all scales.
- Tiny initialization changes yield qualitatively different outcomes.
- Predictability improves negligibly with higher precision.
- Robust across network types, optimizers, and protocols.
03Source
Fractal basin geometry and the limits to predictability and reproducibility in deep learning
Nature Machine Intelligence · published 2026-11-05 08:00 Beijing time
Also reported by 1 other outlet
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