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// 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

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

05Related news

From AIMap AI news: collected from 1,100+ official and media sources. Summaries only restate the original article; follow the link for the full story. All stories of 2026-11-05 · Archive by date · All AI news · Today

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