[Submitted on 14 Oct 2026]
Title:A Phenomenology of Neural Geometry in Vision-Language-Action Models
View a PDF of the paper titled A Phenomenology of Neural Geometry in Vision-Language-Action Models, by Mozes Jacobs and 18 other authors
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Abstract:Robotic manipulation is inherently geometric, but what shape does the world take inside the policy that performs it? Prior work on vision-language-action (VLA) policies establishes that task-relevant variables are linearly readable from activations, yet readability certifies only that a variable is present and says nothing about the shape its values trace, which may be curved and multi-dimensional. Using controlled sweeps, natural rollouts, linear probes, and activation interventions, we build a taxonomy of seven task-relevant concepts in VLA models. We find that realized geometry is predominantly multi-dimensional and is shaped by the constraints under which the policy acts. Physical joint limits, for example, leave wrist orientation an open arc rather than a circle, and distance to the target, a single scalar, is realized in two orthogonal subspaces, a coarse ruler for the approach and a fine ruler for the grasp. The same geometries reappear in two further VLA models on real-world robot data, indicating that they are neither model-specific nor artifacts of simulation. We demonstrate how the findings are actionable by steering along the geometry a concept realizes. Overall, this work lays out an initial phenomenology of realized geometry in VLA policies and demonstrates that a concept's shape carries as much importance as its readability.
Comments: 41 pages, 10 pages of main text, 8 main text figures
Subjects:
Robotics (cs.RO); Machine Learning (cs.LG)
Cite as: arXiv:2610.20840 [cs.RO]
(or arXiv:2610.20840v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.20840
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mozes Jacobs [view email] [v1] Wed, 14 Oct 2026 19:27:09 UTC (32,453 KB)
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