The Field Knows: Cross-Dimensional Geometry from Navigation to Black Holes

Computer Science > Artificial Intelligence [Submitted on 3 Aug 2026] The Field Knows: Cross-Dimensional Geometry from Navigation to Black Holes Chenghao Xu We introduce a continuous metric field framework trained by a single causal contrastive loss. The framework encodes a scene into coefficients of a fixed symmetric matrix basis, assembles them into a Lie algebra element, and exponentiates the result to a Riemannian or Lorentzian metric. Across dimensions, this field discovers the full spectrum of geometric structures: from obstacle-avoiding geodesics in robot navigation across planar and manipulator configuration spaces, to event horizons of black holes in Lorentzian spacetime. Extensive zero-shot generalization studies demonstrate that the field captures transferable geometric structure rather than memorizing specific configurations. In the black hole setting, the causal loss spontaneously evolves genuine black-hole-like structures with the correct Lorentzian signature. The same loss, the same architecture, and the same training protocol produce the full range of geometric phenomena across dimensions. The field knows geometry, and geometry knows physics. Subjects: Artificial Intelligence (cs.AI); Robotics (cs.RO) Cite as: arXiv:2608.07566 [cs.AI]   (or arXiv:2608.07566v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2608.07566 Focus to learn more Submission history From: Chenghao Xu [view email] [v1] Mon, 3 Aug 2026 18:02:12 UTC (1,735 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-08 Change to browse by: cs cs.RO References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)