Author

Sho Kuno

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#machine learning Preprint Oct 2026

The Implicit Bias of Hyperbolic Representation Learning for Multiclass Data: A Busemann Risk Perspective

We study the implicit bias of Riemannian gradient flow for hyperbolic multiclass classification with fixed class prototypes in hyperbolic space $\mathbb{H}^n$. Our framework accommodates general permutation invariant relative margin (PERM) losses, a class that includes cross entropy and other standard multiclass losses...

Xing-Run Li, Sho Kuno, Yusuke Mukuta et al. · 0 citations

#artificial intelligence Review Oct 2026

EditHero: A Benchmark for Long-Horizon Part-Level 3D Editing and Vibe Modeling

3D editing methods are usually tested on a single edit, yet an asset is built through a long sequence of revisions, each of which must implement the requested change while leaving everything else unchanged. We introduce EditHero, to our knowledge the first benchmark for long-horizon, part-level 3D editing, with natural...

Rui-Han Yu, Yu-Ju Tsai, Mu-Yao Niu et al. · 0 citations

#artificial intelligence Preprint Sep 2026

KaiNinja: Extending Native 3D Generators to the Part Level

KaiNinja, a part-level extension of TRELLIS.2 built on a dual-volume form of its O-Voxel representation, is the first 3D generative model trained on agent-authored part data and finds that whole-object fidelity improves over the same backbone fine-tuned on the same dataset.

Rui-Han Yu, Lian Fu, Mu-Yao Niu et al. · 0 citations