Tootfinder

Representation Theoretic Bases for the $\Delta$-Springer Module
Raymond Chou, Mitsuki Hanada
https://arxiv.org/abs/2509.24252 https://arxiv.org/pdf/2509.24…

Representation Theoretic Bases for the $Δ$-Springer Module
We give a descent monomial basis of $Δ$-Springer modules $R_{n,λ,s}$, first defined by Griffin. Our construction simultaneously generalizes the descent basis for the Garsia-Procesi module $R_λ$ studied by Carlsson-Chou and Hanada, as well as the descent basis for the generalized coinvariant algebras $R_{n,k}$ studied by Haglund-Rhoades-Shimozono. This basis is deeply connected with a combinatorial object called battery-powered tableaux, introduced by Gillespie-Griffin. We highlight the repre…

Learning single index model with gradient descent: spectral initialization and precise asymptotics
Yuchen Chen, Yandi Shen
https://arxiv.org/abs/2509.23527 https://

Learning single index model with gradient descent: spectral initialization and precise asymptotics
Non-convex optimization plays a central role in many statistics and machine learning problems. Despite the landscape irregularities for general non-convex functions, some recent work showed that for many learning problems with random data and large enough sample size, there exists a region around the true signal with benign landscape. Motivated by this observation, a widely used strategy is a two-stage algorithm, where we first apply a spectral initialization to plunge into the region, and then…

IndiSeek learns information-guided disentangled representations
Yu Gui, Cong Ma, Zongming Ma
https://arxiv.org/abs/2509.21584 https://arxiv.org/pdf/2509.21…

IndiSeek learns information-guided disentangled representations
Learning disentangled representations is a fundamental task in multi-modal learning. In modern applications such as single-cell multi-omics, both shared and modality-specific features are critical for characterizing cell states and supporting downstream analyses. Ideally, modality-specific features should be independent of shared ones while also capturing all complementary information within each modality. This tradeoff is naturally expressed through information-theoretic criteria, but mutual-i…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[7/9]:
- Exploiting Block Coordinate Descent for Cost-Effective LLM Model Training
Liu, Li, Zhang, Zhang, Jiang, Zhang, Xiao, Zhang, Cheng

Fast Convergence Rates for Subsampled Natural Gradient Algorithms on Quadratic Model Problems
Gil Goldshlager, Jiang Hu, Lin Lin
https://arxiv.org/abs/2508.21022 https://…

Fast Convergence Rates for Subsampled Natural Gradient Algorithms on Quadratic Model Problems
Subsampled natural gradient descent (SNGD) has shown impressive results for parametric optimization tasks in scientific machine learning, such as neural network wavefunctions and physics-informed neural networks, but it has lacked a theoretical explanation. We address this gap by analyzing the convergence of SNGD and its accelerated variant, SPRING, for idealized parametric optimization problems where the model is linear and the loss function is strongly convex and quadratic. In the special cas…

From Quasiperiodicity to a Complete Coloring of the Kohmoto Butterfly
Ram Band, Siegfried Beckus
https://arxiv.org/abs/2509.24025 https://arxiv.org/pdf/250…

From Quasiperiodicity to a Complete Coloring of the Kohmoto Butterfly
The spectra of the Kohmoto model give rise to a fractal phase diagram, known as the Kohmoto butterfly. The butterfly encapsulates the spectra of all periodic Kohmoto Hamiltonians, whose index invariants are sought after. Topological methods - such as Chern numbers - are ill defined due to the discontinuous potential, and hence fail to provide index invariants. This Letter overcomes that obstacle and provides a complete classification of the Kohmoto model indices. Our approach encodes the Kohmot…

Differentially-Private Decentralized Learning in Heterogeneous Multicast Networks
Amir Ziaeddini, Yauhen Yakimenka, J\"org Kliewer
https://arxiv.org/abs/2509.21688 https://…

Differentially-Private Decentralized Learning in Heterogeneous Multicast Networks
We propose a power-controlled differentially private decentralized learning algorithm designed for a set of clients aiming to collaboratively train a common learning model. The network is characterized by a row-stochastic adjacency matrix, which reflects different channel gains between the clients. In our privacy-preserving approach, both the transmit power for model updates and the level of injected Gaussian noise are jointly controlled to satisfy a given privacy and energy budget. We show tha…

High-order fully well-balanced numerical methods for one-dimensional blood flow with discontinuous properties, friction and gravity
Ernesto Pimentel-Garc\'ia, Lucas O. M\"uller, Carlos Par\'es
https://arxiv.org/abs/2508.20638

High-order fully well-balanced numerical methods for one-dimensional blood flow with discontinuous properties, friction and gravity
We present well-balanced, high-order, semi-discrete numerical schemes for one-dimensional blood flow models with discontinuous mechanical properties and algebraic source terms representing friction and gravity. While discontinuities in model parameters are handled using the Generalized Hydrostatic Reconstruction, the presence of algebraic source terms implies that steady state solutions cannot always be computed analytically. In fact, steady states are defined by an ordinary differential equati…

Lyte Quorum: Off-Chain Ready Smart Contract Hosted with Choice
Hao Hao, Dahlia Malkhi, Maofan Yin, Lizan Zhou
https://arxiv.org/abs/2509.23448 https://arxi…

Lyte Quorum: Off-Chain Ready Smart Contract Hosted with Choice
This paper introduces Lyquor, a decentralized platform that reimagines blockchain infrastructure through a service-centric model where nodes selectively host smart contracts (called Lyquids) while preserving global composability. We present three key innovations: (1) Fate-Constrained Ordering (FCO), which decouples consensus from execution to enable selective hosting without sacrificing Layer-1 grade composability; (2) Direct Memory Architecture (DMA), which eliminates state access bottlenecks …

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement
Hao Chen, Lin Liu, Yu Guang Wang
https://arxiv.org/abs/2509.22553 https://

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement
Causal representation learning (CRL) has garnered increasing interests from the causal inference and artificial intelligence community, due to its capability of disentangling potentially complex data-generating mechanism into causally interpretable latent features, by leveraging the heterogeneity of modern datasets. In this paper, we further contribute to the CRL literature, by focusing on the stylized linear structural causal model over the latent features and assuming a linear mixing function…