After the Euclidean Highway: Hyperbolic Expert AI as the Next Innovation

Abstract:Expert domains are trees; the Euclidean transformer is not, diluting parent-child structure exponentially at depth. The hyperbolic turn left one question unasked: not how much of a network to curve, but where curvature may touch the gradient. Placement is a law, not a knob: the same geometry on a trainable adapter collapses training (seventeen training collapses, ~220 GPU-hours), yet at the loss layer alone it trains without one -- this is HySAT (Hyperbolic Structure-Aware Training), hyperbolic losses at the loss layer only. Across six expert SLMs we constructed and deployed (Llama 3.1 and EXAONE 3.5; four adapter strategies; 18.0M-sample corpus; zero NaN over ~317K optimizer steps), a matched four-arm ablation isolates the preserved manifold invariant, and three propositions and a lemma prove why loss-only placement is stable where adapter-on-manifold is not. Four models are operationally deployed (one live, consumer-facing), two open-weight, with per-step traces and a seventeen-incident failure ledger on Zenodo (CC-BY-4.0).

40 pages, 11 figures. Supplementary Information included as an ancillary file. Data and code: Zenodo, concept DOI https://doi.org/10.5281/zenodo.21438499 (published, CC-BY-4.0)

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2607.17513 [cs.LG]

(or arXiv:2607.17513v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.17513 arXiv-issued DOI via DataCite (pending registration)