arXiv — cs.AI preprintsInternational15 October 2026
What Makes a Latent Space Shared? Direct and Recoverable Compatibility in Motion VAEs
This is an official announcement record
Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2610.17376v1 Announce Type: new Abstract: A shared latent space is often assumed to make representations from different schemas directly interchangeable, yet sharing parameters, dimensionality, or a prior does not determine how corresponding instances are expressed in latent coordinates. We show that this assumption conflates recoverable correspondence, direct compatibility, and decoder reuse. In a controlled multi-schema motion VAE on HumanML3D, even identical inputs yield only $5.9$--$17.7\%$ raw Recall@1 and $0.221$--$0.484$ paired-token cosine, despite correspondence being almost per
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- Artificiality's First Exam: An LLM-Referenced C-test for Supraintelligence15 October 2026
- The Silent Failures in Agentic Data Science15 October 2026
- ClawLoop: Self-Improving Agents via Harness-Native Training Loop15 October 2026
- Correction Training for Diffusion Models15 October 2026
- Long-Horizon-Terminal-Bench 2.0: Automating Diverse Task Construction and Measuring Sustained Agent Progress15 October 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.