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math updates on arXiv.org

Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization Non-normal spectral signatures of instability in neural network training dynamics Optimization of randomized neural networks for transfer operator approximation Selective Ambulance Dispatch Under Contextual Travel-Time Uncertainty LLAMA LIMA: A Living Meta-Analysis on the Effects of Generative AI on Learning Mathematics Learning Decision-Sufficient Representations for Linear Optimization Parameterized Complexity of Stationarity Testing for Piecewise-Affine Functions and Shallow CNN Losses Prabhakar function and unified fractional kinetic equation in bicomplex space Computing Gamma(p/q) with Beta function values Flows on Graded Manifolds Optimal embedding dimension in the Nash--Tognoli theorem An optimal first-order method for smooth and strongly convex composite optimization and its stationary limit Sharp Bohr-Type inequalities for certain classes of close-to-convex functions Invariants of real affine varieties based on their complexifications Topological symmetric and braid homologies A Formal Graph-Theoretic Framework for Pitch Class Set Analysis Finite groups with high commuting probability for Sylow subgroups Performance Bounds for Rollout Policies in Stochastic Shortest Path Problems Real 2-blocks in quasi-simple groups Maximal subalgebras of the Lie algebra $W_n(\mathbb{K})$ Cohomogeneity-One Ruled Hypersurfaces in $\mathbb{CP}^2$ and $\mathbb{C}H^2$ Global analysis of the Kuramoto flow Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws On the Stability of Spherical Hellinger-Kantorovich Flows and Their Implications for Differential Privacy Training-Free Looped Transformers Move on Muon : A Hamiltonian probability gradient flow perspective of Muon optimizer Entrywise Error Bounds for Spectral Ranking with Semi-Random Adversaries Asymmetric Scaling Laws from Sparse Features Is Dimensionality a Barrier for Retrieval Models? 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Perfect Sphere Packing In The Boolean Space
[Submitted on 17 Jun 2026] · 2026-06-18 · via math updates on arXiv.org

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Abstract:Perfect sphere packing in the Boolean space is a fundamental and complex problem with significant implications for coding theory, cryptography, and discrete mathematics. The classical solution to the perfect sphere packing problem was provided by Hamming via his well-known perfect codes. However, a major limitation of the traditional Hamming metric is its strict applicability, as it allows perfect partitioning only for spaces with specific, highly constrained dimensions. To address this structural limitation, this article introduces a novel distance metric specifically designed for Boolean hypercubes. The proposed metric modifies the topological properties of the space, making it mathematically viable to partition a Boolean space of any arbitrary dimension into disjoint, perfect spheres. We rigorously define the algebraic properties of this new distance function and demonstrate its consistency across various dimensions. Furthermore, we explore the structural characteristics of the resulting packings. This approach bypasses the classical dimensional constraints of Hamming codes, potentially opening new avenues for designing error-correcting codes and cryptographic primitives in non-traditional dimensions.

Submission history

From: Tigran Soghomonyan [view email]
[v1] Wed, 17 Jun 2026 05:56:52 UTC (1,299 KB)