An AI Monkey Gets Grapes for Sure -- Sphere Neural Networks for Reliable Decision-Making

📝 Original Info

• Title: An AI Monkey Gets Grapes for Sure – Sphere Neural Networks for Reliable Decision-Making
• ArXiv ID: 2601.00142
• Date: 2026-01-01
• Authors: Tiansi Dong, Henry He, Pietro Liò, Mateja Jamnik

📝 Abstract

This paper compares three methodological categories of neural reasoning: LLM reasoning, supervised learning-based reasoning, and explicit model-based reasoning. LLMs remain unreliable and struggle with simple decision-making that animals can master without extensive corpora training. Through disjunctive syllogistic reasoning testing, we show that reasoning via supervised learning is less appealing than reasoning via explicit model construction. Concretely, we show that an Euler Net trained to achieve 100.00% in classic syllogistic reasoning can be trained to reach 100% accuracy in disjunctive syllogistic reasoning. However, the retrained Euler Net suffers severely from catastrophic forgetting (its performance drops to 6.25% on already-learned classic syllogistic reasoning), and its reasoning competence is limited to the pattern level. We propose a new version of Sphere Neural Networks that embeds concepts as circles on the surface of an n-dimensional sphere. These Sphere Neural Networks enable the representation of the negation operator via complement circles and achieve reliable decisionmaking by filtering out illogical statements that form unsatisfiable circular configurations. We demonstrate that the Sphere Neura...

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