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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 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? RA-DCA: A Randomized Active-Set DCA for Directional Stationarity in Max-Structured DC Programs Commutator-Induced Uncertainty in VAEs Weisfeiler-Leman Is Incomplete on Simple Spectrum Graphs, so Canonicalize Them Sparse In-Network Learning via Shortest-Path Backpropagation and Finite-Rate Gating Instance-Optimal Estimation with Multiple LLM Judges on a Budget Entropy Equivalence Testing Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recommendation Any-Dimensional Invariant Universality Operationalizing Individual Fairness via Gradient Descent and Bradley-Terry Models Anytime Training with Schedule-Free Spectral Optimization Diffusion-based Denoising Beats Vanilla Score Matching in Parameter Estimation: A Theoretical Explanation Resilience Characterization of AI-Native Wireless Receivers via Persistent Homology The General Theory of Localization Methods Group-Algebraic Tensors: Provably-optimal Equivariant Learning and Physical Symmetry Discovery General Lower Bounds for Differentially Private Federated Learning with Arbitrary Public-Transcript Interactions PilotWiMAE: Pilot-Native Representation Learning for Wireless Channels Proximal basin hopping: global optimization with guarantees
Excess Obstructions and Star-Isolated Certificates for th...
Yutong Zhang, Yaoran Yang · 2026-05-21 · via math updates on arXiv.org

Guo, Li, Shangguan, Tamo, and Wootters formulated in SIAM Journal on Computing a hypergraph Nash--Williams--Tutte conjecture: every $k$-weakly-partition-connected hypergraph on $t$ vertices should admit a $k$-distinguishable tree assignment. We show that the conjecture, in its literal published form, is false for a sharp and structural reason. A tree assignment replaces every hyperedge $e$ by a tree with $|e|-1$ labelled edges, so its edge number is the excess $ρ(H)=\sum_e(|e|-1)$. A $k$-tree decomposition, however, has exactly $k(t-1)$ edges. Thus $ρ(H)=k(t-1)$ is a necessary condition, whereas weak partition connectivity only implies $ρ(H)\ge k(t-1)$. Consequently, for every $t\ge2$, $k\ge1$, and $q\ge1$, the hypergraph consisting of $k+q$ copies of the full hyperedge $V$ is $k$-weakly-partition-connected but has no $k$-distinguishable tree assignment. We then isolate the critical corrected form, prove that its equality is exactly the equality required for the full intersection-matrix row set, and give a large non-graphic class of critical positive instances. The positive construction uses layer-contained star realizations and extremal signature weights, producing weak partition connectivity by a quotient-rank argument and unique signatures under one-vertex sums and explicit two-sided star blocks.