arXiv Computer Vision

Exact Quotients of Fresnel-Kummer Surfaces and Certified Biaxial Refraction

arXiv AI
Jun 12

Real-rootedness of the Poincar\'e polynomials of $\overline{\mathcal M}_{0,n}$: an AI-assisted proof

arXiv:2605. 29151v2 Announce Type: replace-cross Abstract: We prove real-rootedness for the Poincar\'e polynomial \[ P_n(t)=\sum_{i=0}^{n-3} \dim H^{2i}(\overline{\mathcal M}_{0,n};\mathbb{Q})t^i \] of the Deligne--Mumford moduli space $\overline{\mathcal M}_{0,n}$ of stable $n$-pointed rational curves, proving a conjecture of Aluffi--Chen--Marcolli.

By Gergely B\'erczi, Young-Hoon Kiem
arXiv AI
4d ago

Solver Agent: an Agentic AI Framework for Theoretical Physics Computations Applied to F-theory Uplifts of O3-planes and S-folds

The paper introduces Solver Agent, an AI framework that uses large language models to perform calculations and proofs in mathematics and theoretical physics, tracking the solution process via a persistent ledger. It applies this framework to study global F‑theory uplifts of Type IIB orientifolds and S‑folds, establishing conditions for Weierstrass models over projective threefolds with terminal ζ_k quotient singularities to yield Ε-factorial elliptically fibered Calabi‑Yau fourfolds. The authors derive fixed‑point contributions to Hodge data and Euler characteristics, demonstrate how these corrections determine localized D3‑brane charges for tadpole cancellation, and illustrate the results with toric hypersurface constructions and methods for four‑form flux analysis in Δ=1 compactifications.

By Eliott Morgensztern, Cesar Fierro Cota, Alessandro Mininno
arXiv Machine Learning
Jul 30

Exact Symmetry as Algebra: A Machine-Verified Tensor Calculus that Enforces Physical Selection Rules

arXiv:2605. 20440v2 Announce Type: replace Abstract: Symmetry is central to the physical sciences, yet machine learning usually captures it only approximately, leaving a residual per-step equivariance error $\varepsilon$ that compounds with depth $M$ as $M\varepsilon$, whereas exact equivariance holds at unbounded depth; we demonstrate this divergence at fourteen orders of magnitude.

By Paulina Hoyos, Shashanka Ubaru, Dongsung Huh, Vasileios Kalantzis, Kenneth L. Clarkson, Misha Kilmer, Haim Avron, Lior Horesh
Hugging Face Trending Papers
Aug 3

Sharp Root Anti-Concentration via Projective Incidence and Ordered Root Laws

This paper answers the one-dimensional local root anti-concentration questions posed by Balcan, Pegden, and Sharma in the context of online optimization of piecewise-Lipschitz functions. For a homogeneous feature curve and coefficients whose density relative to the uniform law on a symmetric convex body $K$ is bounded by $A$, we show that the worst-case interval-hitting constant equals $A$ times a section-averaged projective incidence speed.

arXiv Machine Learning
Jun 5

Dead Directions: Geometric Singular Learning

arXiv:2606. 05957v1 Announce Type: new Abstract: Singular learning theory and information geometry have studied the same parameter spaces in mostly separate vocabularies: the former computes Bayesian invariants in resolved coordinates, the latter works in original coordinates under a non-degeneracy assumption that overparameterised models routinely violate.

By Tejas Pradeep Shirodkar
arXiv Machine Learning
Aug 27

EncoTESS: Age-Sensitive Encodings from Raw TESS Light Curves

EncoTESS is a compact Time Series Foundation Model trained on TESS 2‑minute light curves that encodes stellar variability into a fixed‑size latent space, handling noise, irregular sampling, and data gaps. It improves age estimation for young K and M stars (≤100 Myr) and older M stars (≤1 Gyr) by outperforming traditional rotation period and variability amplitude indicators. The model’s lightweight architecture (~1 % of typical TSFMs) allows deployment on standard laptops and can be extended to other TESS cadences and missions like Kepler and PLATO.

By Phil R. Van-Lane (David A. Dunlap Department of Astronomy and Astrophysics, University of Toronto, Dunlap Institute for Astronomy and Astrophysics, University of Toronto, Department of Astronomy and Astrophysics, University of California San Diego), Joshua S. Speagle (David A. Dunlap Department of Astronomy and Astrophysics, University of Toronto, Department of Statistical Sciences, University of Toronto, Dunlap Institute for Astronomy and Astrophysics, University of Toronto, Data Sciences Institute, University of Toronto), Ryan Cloutier (Department of Physics and Astronomy, McMaster University), Christopher A. Theissen (Department of Astronomy and Astrophysics, University of California San Diego), Gwendolyn M. Eadie (David A. Dunlap Department of Astronomy and Astrophysics, University of Toronto, Department of Statistical Sciences, University of Toronto, Data Sciences Institute, University of Toronto), Ilay Kamai (Physics Department, Technion Israel Institute of Technology)