arXiv:2608. 07566v1 Announce Type: new Abstract: We introduce a continuous metric field framework trained by a single causal contrastive loss.
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arXiv:2606. 18828v1 Announce Type: cross Abstract: Traditional approaches place intelligence in the agent, whether as a learned policy or a search procedure.
arXiv:2607. 05489v1 Announce Type: cross Abstract: The AInstein architecture introduced an unsupervised neural method for solving the Riemannian Einstein equations on arbitrary manifolds.
arXiv:2605. 24942v2 Announce Type: replace-cross Abstract: Steering a language model - intervening on its internal activations to change downstream behaviour - has recently expanded beyond linear interpolation to nonlinear methods such as angular and kernelized steering, which define intervention transformations without learning an explicit geometry over paths in activation space.
arXiv:2606. 01847v1 Announce Type: cross Abstract: Diffusion-based Vision-Language-Action policies achieve remarkable success in robotic manipulation, yet commit a fundamental geometric error we term the $\textbf{Euclidean Fallacy}$: representing SE(3) poses as flat $\mathbb{R}^{12}$ vectors.
The paper introduces SE(3) neural potential fields that learn collision‑free 6‑DoF trajectory planning directly from posed RGB images, eliminating the need for explicit 3D reconstruction. By supervising the field with a navigation function based on geodesic distances to the grasp, the method avoids the classic pitfalls of artificial potential fields, achieving near‑goal convergence within 3 cm from any start and producing collision‑free paths on a UR10 robot. Experiments on two tabletop scenes show significant improvements in clearance, reduced arm‑link contacts, and a 90 % grasp success rate, while planning time drops from over a minute to about 2 seconds compared to RRT* on a reconstructed scene.
We present FoundationGeo, a two-stage framework that explicitly bridges relative and metric prediction via spatial calibration and principled data design. Stage 1 learns a high-fidelity, affine-invariant geometry model by initializing with DINOv3 and training on a curated 10.