The Topological Dual of a Dataset: A Logic-to-Topology Encoding for AlphaGeometry-Style Data
AlphaGeometry represents a milestone in neuro-symbolic reasoning, yet its architecture faces a log-linear scaling bottleneck within its symbolic deduction engine that limits its efficiency as problem complexity increases. Recent technical reports suggest that current domain-specific languages may be isomorphic as input representations to natural language, interchanging them acts as a performance-invariant transformation, implying that current neural guidance relies on superficial encodings rather than structural understanding. This paper addresses this representation bottleneck by proposing a logic-to-topology encoding designed to reveal the structural invariants of a model's latent space...
| Authors | Anthony Bordg |
|---|---|
| Primary category | cs.AI |
| Published | 20 April 2026 |
| Last updated at source | 6 June 2026 |
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