SOURCE-LINKED INTELLIGENCE
Neuro-Symbolic Geometric Abstraction (NeuSOGA): From Observations to Symbolic Mathematical Representations
A fundamental challenge in artificial intelligence is the transformation of observations into explicit symbolic representations suitable for abstraction, interpretation, and reasoning. While modern AI systems achieve remarkable perceptual capabilities through large-scale statistical learning, the resulting knowledge is typically encoded within latent parameters that are difficult to inspect or manipulate analytically. Inspired by Neuro-Symbolic AI and theories of human abstraction, this paper investigates the formation of symbolic mathematical representations from geometric observations. We pr
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-01T15:29:30.000Z
First collected: 2026-09-21T06:01:56.170Z. This is not the publication date.