Tootfinder

Performance Enhancement of the Recursive Least Squares Algorithms with Rank Two Updates
Alexander Stotsky
https://arxiv.org/abs/2507.11095 https://

Performance Enhancement of the Recursive Least Squares Algorithms with Rank Two Updates
New recursive least squares algorithms with rank two updates (RLSR2) that include both exponential and instantaneous forgetting (implemented via a proper choice of the forgetting factor and the window size) are introduced and systematically associated in this report with well-known RLS algorithms with rank one updates. Moreover, new properties (which can be used for further performance improvement) of the recursive algorithms associated with the convergence of the inverse of information matrix …

Personas within Parameters: Fine-Tuning Small Language Models with Low-Rank Adapters to Mimic User Behaviors
Himanshu Thakur, Eshani Agrawal, Smruthi Mukund
https://arxiv.org/abs/2509.09689

Personas within Parameters: Fine-Tuning Small Language Models with Low-Rank Adapters to Mimic User Behaviors
A long-standing challenge in developing accurate recommendation models is simulating user behavior, mainly due to the complex and stochastic nature of user interactions. Towards this, one promising line of work has been the use of Large Language Models (LLMs) for simulating user behavior. However, aligning these general-purpose large pre-trained models with user preferences necessitates: (i) effectively and continously parsing large-scale tabular user-item interaction data, (ii) overcoming pre-…

Precise Attribute Intensity Control in Large Language Models via Targeted Representation Editing
Rongzhi Zhang, Liqin Ye, Yuzhao Heng, Xiang Chen, Tong Yu, Lingkai Kong, Sudheer Chava, Chao Zhang
https://arxiv.org/abs/2510.12121

Precise Attribute Intensity Control in Large Language Models via Targeted Representation Editing
Precise attribute intensity control--generating Large Language Model (LLM) outputs with specific, user-defined attribute intensities--is crucial for AI systems adaptable to diverse user expectations. Current LLM alignment methods, however, typically provide only directional or open-ended guidance, failing to reliably achieve exact attribute intensities. We address this limitation with three key designs: (1) reformulating precise attribute intensity control as a target-reaching problem, rather t…

Cross-attention Secretly Performs Orthogonal Alignment in Recommendation Models
Hyunin Lee, Yong Zhang, Hoang Vu Nguyen, Xiaoyi Liu, Namyong Park, Christopher Jung, Rong Jin, Yang Wang, Zhigang Wang, Somayeh Sojoudi, Xue Feng
https://arxiv.org/abs/2510.09435

Cross-attention Secretly Performs Orthogonal Alignment in Recommendation Models
Cross-domain sequential recommendation (CDSR) aims to align heterogeneous user behavior sequences collected from different domains. While cross-attention is widely used to enhance alignment and improve recommendation performance, its underlying mechanism is not fully understood. Most researchers interpret cross-attention as residual alignment, where the output is generated by removing redundant and preserving non-redundant information from the query input by referencing another domain data whic…

A linear-time algorithm for Chow decompositions
Alexander Taveira Blomenhofer, Benjamin Lovitz
https://arxiv.org/abs/2509.10450 https://arxiv.org/pdf/2509.…

A linear-time algorithm for Chow decompositions
We propose a linear-time algorithm to compute low-rank Chow decompositions. Our algorithm can decompose concise symmetric 3-tensors in n variables of Chow rank n/3. The algorithm is pencil based, hence it relies on generalized eigenvalue computations. We also develop sub-quadratic time algorithms for higher order Chow decompositions, and Chow decompositions of 3-tensors into products of linear forms which do not lie on the generic orbit. In particular, we obtain a sub-quadratic-time algorithm f…

Arecibo Wow! II: Revised Properties of the Wow! Signal from Archival Ohio SETI Data
Abel M\'endez, Kevin N. Ortiz Ceballos, Jorge I. Zuluaga, Kelby D. Palencia-Torres, Allison J. Smith, Alondra Cardona Rodr\'iguez, H\'ector Socas-Navarro, David Kipping, Hodari-Sadiki Hubbard-James, Mai Le, Alejandro Rinc\'on-Torres
https://…

Arecibo Wow! II: Revised Properties of the Wow! Signal from Archival Ohio SETI Data
The Wow! Signal, detected in 1977 by the Ohio State University SETI project, remains one of the most intriguing unexplained radio transients. The most recent significant revision of its properties took place in the late 1990s; however, further advances were limited by readily available data from this event. Here we retrieved and analyzed decades of previously unpublished Ohio SETI observations, enabling the most comprehensive re-evaluation of the properties of the Wow! Signal to date with moder…

Performance of universal machine-learned potentials with explicit long-range interactions in biomolecular simulations
Viktor Zaverkin, Matheus Ferraz, Francesco Alesiani, Mathias Niepert
https://arxiv.org/abs/2508.10841

Performance of universal machine-learned potentials with explicit long-range interactions in biomolecular simulations
Universal machine-learned potentials promise transferable accuracy across compositional and vibrational degrees of freedom, yet their application to biomolecular simulations remains underexplored. This work systematically evaluates equivariant message-passing architectures trained on the SPICE-v2 dataset with and without explicit long-range dispersion and electrostatics. We assess the impact of model size, training data composition, and electrostatic treatment across in- and out-of-distribution…

An Inexact Tensor-Train Primal-Dual Interior-Point Method for Semidefinite Programs
Frederik Kelbel, Sergey Dolgov, Dante Kalise, Alessandra Russo
https://arxiv.org/abs/2509.11890

An Inexact Tensor-Train Primal-Dual Interior-Point Method for Semidefinite Programs
In this work, we introduce an interior-point method that employs tensor decompositions to efficiently represent and manipulate the variables and constraints of semidefinite programs, targeting problems where the solutions may not be low-rank but admit low-tensor-train rank approximations. Our method maintains approximate superlinear convergence despite inexact computations in the tensor format and leverages a primal-dual infeasible interior-point framework. In experiments on Maximum Cut, Maximu…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/4]:
- Performance of universal machine-learned potentials with explicit long-range interactions in biom...
Viktor Zaverkin, Matheus Ferraz, Francesco Alesiani, Mathias Niepert

Self-Similar Cosmic-Ray Transport in High-Resolution Magnetohydrodynamic Turbulence
Philipp Kempski, Drummond B. Fielding, Eliot Quataert, Robert J. Ewart, Philipp Grete, Matthew W. Kunz, Alexander A. Philippov, James Stone
https://arxiv.org/abs/2507.10651

Self-Similar Cosmic-Ray Transport in High-Resolution Magnetohydrodynamic Turbulence
We study the propagation of cosmic rays (CRs) through a simulation of magnetohydrodynamic (MHD) turbulence at unprecedented resolution of $10{,}240^3$. We drive turbulence that is subsonic and super-Alfvénic, characterized by $δB_{\rm rms}/B_0=2$. The high resolution enables an extended inertial range such that the Alfvén scale $l_A$, where $δB (l_A)\approx B_0$, is well resolved. This allows us to properly capture how the cascade transitions from large amplitudes on large scales to small a…