Tootfinder

from my link log —
Forbidden secrets of ancient X11 scaling technology revealed!
https://flak.tedunangst.com/post/forbidden-secrets-of-ancient-X11-scaling-technology-revealed
saved 2025-06-24

ROC-n-reroll: How verifier imperfection affects test-time scaling
Florian E. Dorner, Yatong Chen, Andr\'e F. Cruz, Fanny Yang
https://arxiv.org/abs/2507.12399

ROC-n-reroll: How verifier imperfection affects test-time scaling
Test-time scaling aims to improve language model performance by leveraging additional compute during inference. While many works have empirically studied techniques like Best-of-N (BoN) and rejection sampling that make use of a verifier to enable test-time scaling, there is little theoretical understanding of how verifier imperfection affects performance. In this work, we address this gap. Specifically, we prove how instance-level accuracy of these methods is precisely characterized by the geom…

Phenomenological Scaling Relations for SQM Stars with a Massive s-Quark in Gravitationally Strong Magnetic Fields under the Spherical Symmetry Approximation
{\L}ukasz Bratek, Joanna Ja{\l}ocha, Marek Kutschera
https://arxiv.org/abs/2507.10756

Phenomenological Scaling Relations for SQM Stars with a Massive s-Quark in Gravitationally Strong Magnetic Fields under the Spherical Symmetry Approximation
Scaling relations with the bag constant parameter are investigated for Strange Quark Matter (SQM) stars in the presence of gravitationally strong magnetic fields minimally coupled with matter, considering both massless and massive strange quark scenarios. Assuming a simple model for such coupling under the approximation of spherical symmetry, a phenomenological scaling formula for the maximum mass of stars is derived as a function of the surface magnetic field and the strange quark mass. This f…

Scaling in two-dimensional Rayleigh-B\'enard convection
Erik Lindborg
https://arxiv.org/abs/2506.13213 https://arxiv.org/pdf/2506…

Scaling in two-dimensional Rayleigh-Bénard convection
An equation for the evolution of mean kinetic energy, $ E $, in a 2-D or 3-D Rayleigh-Bénard system with domain height $ L $ is derived. Assuming classical Nusselt number scaling, $ Nu \sim Ra^{1/3} $, and that mean enstrophy, in the absence of a downscale energy cascade, scales as $\sim E/L^2 $, we find that the Reynolds number scales as $ Re \sim Pr^{-1}Ra^{2/3} $ in the 2-D system, where $ Ra $ is the Rayleigh number and $ Pr $ the Prandtl number, which is a much stronger scaling than in th…

PGT-I: Scaling Spatiotemporal GNNs with Memory-Efficient Distributed Training
Seth Ockerman, Amal Gueroudji, Tanwi Mallick, Yixuan He, Line Pouchard, Robert Ross, Shivaram Venkataraman
https://arxiv.org/abs/2507.11683

PGT-I: Scaling Spatiotemporal GNNs with Memory-Efficient Distributed Training
Spatiotemporal graph neural networks (ST-GNNs) are powerful tools for modeling spatial and temporal data dependencies. However, their applications have been limited primarily to small-scale datasets because of memory constraints. While distributed training offers a solution, current frameworks lack support for spatiotemporal models and overlook the properties of spatiotemporal data. Informed by a scaling study on a large-scale workload, we present PyTorch Geometric Temporal Index (PGT-I), an ex…

LaSM: Layer-wise Scaling Mechanism for Defending Pop-up Attack on GUI Agents
Zihe Yan, Zhuosheng Zhang
https://arxiv.org/abs/2507.10610 https://

LaSM: Layer-wise Scaling Mechanism for Defending Pop-up Attack on GUI Agents
Graphical user interface (GUI) agents built on multimodal large language models (MLLMs) have recently demonstrated strong decision-making abilities in screen-based interaction tasks. However, they remain highly vulnerable to pop-up-based environmental injection attacks, where malicious visual elements divert model attention and lead to unsafe or incorrect actions. Existing defense methods either require costly retraining or perform poorly under inductive interference. In this work, we systemati…

Scaling of thin wire cylindrical compression after 100 fs Joule surface heating with material, diameter and laser energy
L. Yang, M. -L. Herbert, C. B\"ahtz, V. Bouffetier, E. Brambrink, T. Dornheim, N. Fefeu, T. Gawne, S. G\"ode, J. Hagemann, H. H\"oeppner, L. G. Huang, O. S. Humphries, T. Kluge, D. Kraus, J. L\"utgert, J. -P. Naedler, M. Nakatsutsumi, A. Pelka, T. R. Preston, C. Qu, S. V. Rahul, R. Redmer, M. Rehwald, L. Randolph, J. J. Santos, M. \v{S}m\'id, …

Scaling of thin wire cylindrical compression after 100 fs Joule surface heating with material, diameter and laser energy
We present the first systematic experimental validation of return-current-driven implosion scaling in micrometer-sized wires irradiated by femtosecond laser pulses. Employing XFEL-based imaging with sub-micrometer spatial and femtosecond temporal resolution, supported by hydrodynamic and particle-in-cell simulations, we reveal how return current density depends precisely on wire diameter, material properties, and incident laser energy. We identify deviations from simple theoretical predictions …

OpenCodeReasoning-II: A Simple Test Time Scaling Approach via Self-Critique
Wasi Uddin Ahmad, Somshubra Majumdar, Aleksander Ficek, Sean Narenthiran, Mehrzad Samadi, Jocelyn Huang, Siddhartha Jain, Vahid Noroozi, Boris Ginsburg
https://arxiv.org/abs/2507.09075

OpenCodeReasoning-II: A Simple Test Time Scaling Approach via Self-Critique
Recent advancements in reasoning-based Large Language Models (LLMs), particularly their potential through test-time scaling, have created significant opportunities for distillation in code generation and critique. However, progress in both areas fundamentally depends on large-scale, high-quality datasets. In this work, we introduce OpenCodeReasoning-II, a dataset consists of 2.5M question-solution-critique triples (approx. 35K unique programming questions), making it nearly twice the size of th…

Frequency-responsive RCS characteristics and scaling implications for ISAC development
Sa\'ul Fenollosa, Monika Drozdowska, Wenfei Yang, Sergio Mic\'o-Rosa, Alejandro Castilla, Alejandro Lopez-Escudero, Jian Li, Narcis Cardona
https://arxiv.org/abs/2507.12235

Frequency-responsive RCS characteristics and scaling implications for ISAC development
This paper presents an investigation on the Radar Cross-Section (RCS) of various targets, with the objective of analysing how RCS properties vary with frequency. Targets such as an Automated Guided Vehicle (AGV), a pedestrian, and a full-scale car were measured in the frequency bands referred to in industry standards as FR2 and FR3. Measurements were taken in diverse environments, indoors and outdoors, to ensure comprehensive scenario coverage. The methodology employed in RCS extraction perform…

Conformable Scaling and Critical Dynamics: A Unified Framework for Phase Transitions
Jos\'e Weberszpil
https://arxiv.org/abs/2507.11782 https://…

Conformable Scaling and Critical Dynamics: A Unified Framework for Phase Transitions
We investigate the application of conformable derivatives to model critical phenomena near continuous phase transitions. By incorporating a deformation parameter into the differential structure, we derive unified expressions for thermodynamic observables such as heat capacity, magnetization, susceptibility, and coherence length, each exhibiting power-law behavior near the critical temperature. The conformable derivative framework naturally embeds scale invariance and critical slowing down into …

Accurate Reduced Floating-Point Precision Implicit Monte Carlo
Simon Butson, Mathew Cleveland, Alex Long, Todd Palmer
https://arxiv.org/abs/2506.11962 http…

Accurate Reduced Floating-Point Precision Implicit Monte Carlo
This work describes methodologies to successfully implement the Implicit Monte Carlo (IMC) scheme for thermal radiative transfer in reduced-precision floating-point arithmetic. The methods used can be broadly categorized into scaling approaches and floating-point arithmetic manipulations. Scaling approaches entail re-scaling values to ensure computations stay within a representable range. Floating-point arithmetic manipulations involve changes to order of operations and alternative summation al…

Universal Scaling Laws for Deep Indentation Beyond the Hertzian Regime
Tong Mu, Changhong Linghu, Yanju Liu, Jinsong Leng, Huajian Gao, K. Jimmy Hsia
https://arxiv.org/abs/2506.11461

Universal Scaling Laws for Deep Indentation Beyond the Hertzian Regime
Deep indentation of soft materials is ubiquitous across scales in nature and engineering, yet accurate predictions of contact behaviors under extreme deformations ($δ/R > 1$) remain elusive due to geometric and material nonlinearities. Here, we investigate the indentation of rigid spheres into soft elastic substrates, resolving the highly nonlinear regime where the sphere becomes fully submerged. A universal geometric mapping approach reveals Hertz-type pressure distributions in the deformed c…

Anisotropic-scaling localization in higher-dimensional non-Hermitian systems
Zuxuan Ou, Hui-Qiang Liang, Guo-Fu Xu, Linhu Li
https://arxiv.org/abs/2507.11933

Anisotropic-scaling localization in higher-dimensional non-Hermitian systems
Spatial localization of quantum states is one of the focal points in condensed matter physics and quantum simulations, as it signatures profound physics such as nontrivial band topology and non-reciprocal non-Hermiticity. Yet, in higher dimensions, characterizing state localization becomes elusive due to the sophisticated interplay between different localization mechanisms and spacial geometries. In this work, we unveil an exotic type of localization phenomenon in higher-dimensional non-Hermiti…

Universal self-similarity of hierarchical communities formed through a general self-organizing principle
Shruti Tandon (equal), Nidhi Dilip Sonwane (equal), Tobias Braun, Norbert Marwan, Juergen Kurths, R. I. Sujith
https://arxiv.org/abs/2507.11159

Universal self-similarity of hierarchical communities formed through a general self-organizing principle
Emergence of self-similarity in hierarchical community structures is ubiquitous in complex systems. Yet, there is a dearth of universal quantification and general principles describing the formation of such structures. Here, we discover universality in scaling laws describing self-similar hierarchical community structure in multiple real-world networks including biological, infrastructural, and social networks. We replicate these scaling relations using a phenomenological model, where nodes wit…

Scaling the memory wall using mixed-precision -- HPG-MxP on an exascale machine
Aditya Kashi, Nicholson Koukpaizan, Hao Lu, Michael Matheson, Sarp Oral, Feiyi Wang
https://arxiv.org/abs/2507.11512

Scaling the memory wall using mixed-precision -- HPG-MxP on an exascale machine
Mixed-precision algorithms have been proposed as a way for scientific computing to benefit from some of the gains seen for artificial intelligence (AI) on recent high performance computing (HPC) platforms. A few applications dominated by dense matrix operations have seen substantial speedups by utilizing low precision formats such as FP16. However, a majority of scientific simulation applications are memory bandwidth limited. Beyond preliminary studies, the practical gain from using mixed-preci…

Scaling Laws for Uncertainty in Deep Learning
Mattia Rosso, Simone Rossi, Giulio Franzese, Markus Heinonen, Maurizio Filippone
https://arxiv.org/abs/2506.09648

Scaling Laws for Uncertainty in Deep Learning
Deep learning has recently revealed the existence of scaling laws, demonstrating that model performance follows predictable trends based on dataset and model sizes. Inspired by these findings and fascinating phenomena emerging in the over-parameterized regime, we examine a parallel direction: do similar scaling laws govern predictive uncertainties in deep learning? In identifiable parametric models, such scaling laws can be derived in a straightforward manner by treating model parameters in a B…

Comparison of Localization Algorithms between Reduced-Scale and Real-Sized Vehicles Using Visual and Inertial Sensors
Tobias Kern, Leon Tolksdorf, Christian Birkner
https://arxiv.org/abs/2507.11241

Comparison of Localization Algorithms between Reduced-Scale and Real-Sized Vehicles Using Visual and Inertial Sensors
Physically reduced-scale vehicles are emerging to accelerate the development of advanced automated driving functions. In this paper, we investigate the effects of scaling on self-localization accuracy with visual and visual-inertial algorithms using cameras and an inertial measurement unit (IMU). For this purpose, ROS2-compatible visual and visual-inertial algorithms are selected, and datasets are chosen as a baseline for real-sized vehicles. A test drive is conducted to record data of reduced-…

Internal Slack message: OpenAI has hired four high-profile engineers from Tesla, xAI, and Meta, including David Lau, former VP of software engineering at Tesla (Wired)
https://www.wired.com/story/openai-new-hires-scaling/

OpenAI Poaches 4 High-Ranking Engineers From Tesla, xAI, and Meta
The new hires are joining OpenAI’s scaling team, according to an internal Slack message sent by cofounder Greg Brockman.

Approximating fixed size quantum correlations in polynomial time
Julius A. Zeiss, Gereon Ko{\ss}mann, Omar Fawzi, Mario Berta
https://arxiv.org/abs/2507.12302

Approximating fixed size quantum correlations in polynomial time
We show that $\varepsilon$-additive approximations of the optimal value of fixed-size two-player free games with fixed-dimensional entanglement assistance can be computed in time $\mathrm{poly}(1/\varepsilon)$. This stands in contrast to previous analytic approaches, which focused on scaling with the number of questions and answers, but yielded only strict $\mathrm{exp}(1/\varepsilon)$ guarantees. Our main result is based on novel Bose-symmetric quantum de Finetti theorems tailored for constrai…

Grids Often Outperform Implicit Neural Representations
Namhoon Kim, Sara Fridovich-Keil
https://arxiv.org/abs/2506.11139 https://arxi…

Grids Often Outperform Implicit Neural Representations
Implicit Neural Representations (INRs) have recently shown impressive results, but their fundamental capacity, implicit biases, and scaling behavior remain poorly understood. We investigate the performance of diverse INRs across a suite of 2D and 3D real and synthetic signals with varying effective bandwidth, as well as both overfitting and generalization tasks including tomography, super-resolution, and denoising. By stratifying performance according to model size as well as signal type and ba…

FractalSync: Lightweight Scalable Global Synchronization of Massive Bulk Synchronous Parallel AI Accelerators
Victor Isachi, Alessandro Nadalini, Riccardo Fiorani Gallotta, Angelo Garofalo, Francesco Conti, Davide Rossi
https://arxiv.org/abs/2506.11668

FractalSync: Lightweight Scalable Global Synchronization of Massive Bulk Synchronous Parallel AI Accelerators
The slow-down of technology scaling and the emergence of Artificial Intelligence (AI) workloads have led computer architects to increasingly exploit parallelization coupled with hardware acceleration to keep pushing the performance envelope. However, this solution comes with the challenge of synchronization of processing elements (PEs) in massive heterogeneous many-core platforms. To address this challenge, we propose FractalSync, a hardware accelerated synchronization mechanism for Bulk Synchr…

Critical Ising correlations on a torus
Baran Bayraktaroglu, Konstantin Izyurov
https://arxiv.org/abs/2506.11324 https://arxiv.org/pdf…

Critical Ising correlations on a torus
We prove convergence of multi-point spin correlations in the critical Ising model on a torus. Via Pfaffian identities, this also implies convergence of other correlations, including correlations of spins with fermionic and energy observables. We obtain explicit formulae for the scaling limits in terms of theta functions, verifying the predictions in the physics literature.

Invariant measures on moduli spaces of twisted holomorphic 1-forms and strata of dilation surfaces
Paul Apisa, Nick Salter
https://arxiv.org/abs/2507.10685

Invariant measures on moduli spaces of twisted holomorphic 1-forms and strata of dilation surfaces
The moduli space of twisted holomorphic 1-forms on Riemann surfaces, equivalently dilation surfaces with scaling, admits a stratification and GL(2,R)-action as in the case of moduli spaces of translation surfaces. We produce an analogue of Masur-Veech measure, i.e. an SL(2,R)-invariant Lebesgue class measure on strata or explicit covers thereof. This relies on a novel computation of cohomology with coefficients for the mapping class group. The computation produces a framed mapping class group i…

Critical scaling for spectral functions
Konrad Kockler, Jan M. Pawlowski, Jonas Wessely
https://arxiv.org/abs/2506.09142 https://arxi…

Critical scaling for spectral functions
We study real-time scalar $ϕ^4$-theory in 2+1 dimensions near criticality. Specifically, we compute the single-particle spectral function and that of the $s$-channel four-point function in and outside the scaling regime. The computation is done with the spectral functional Callan-Symanzik equation, which exhibits manifest Lorentz invariance and preserves causality. We extract the scaling exponent $η$ from the spectral function and compare our result with that from a Euclidean fixed point anal…

Constitutive Manifold Neural Networks
Wouter J. Schuttert, Mohammed Iqbal Abdul Rasheed, Bojana Rosi\'c
https://arxiv.org/abs/2506.13648 https://

Constitutive Manifold Neural Networks
Important material properties like thermal conductivity are often represented as symmetric positive definite (SPD) tensors, which exhibit variability due to inherent material heterogeneity and manufacturing uncertainties. These tensors reside on a curved Riemannian manifold, and accurately modeling their stochastic nature requires preserving both their symmetric positive definite properties and spatial symmetries. To achieve this, uncertainties are parametrized into scaling (magnitude) and rota…

Scaling Relations, Morphological Stability, and Asymptotic Freedom of Plasma-Surface Deposition Dynamics
Joel Saucedo, Uday Lamba, Hasitha Mahabaduge
https://arxiv.org/abs/2507.10645

Scaling Relations, Morphological Stability, and Asymptotic Freedom of Plasma-Surface Deposition Dynamics
Connecting plasma processing parameters to the resultant film microstructure remains a fundamental challenge in materials synthesis, one that has largely confined process design to empirical approaches. To bridge this gap, we develop a predictive analysis of coupling by applying a renormalization group (RG) analysis to an effective Hamiltonian for the stochastic dynamics of the plasma-surface interface, derived systematically from microscopic principles. The central result from this formalism i…

SwiftSpec: Ultra-Low Latency LLM Decoding by Scaling Asynchronous Speculative Decoding
Ziyi Zhang, Ziheng Jiang, Chengquan Jiang, Menghan Yu, Size Zheng, Haibin Lin, Henry Hoffmann, Xin Liu
https://arxiv.org/abs/2506.11309

SwiftSpec: Ultra-Low Latency LLM Decoding by Scaling Asynchronous Speculative Decoding
Low-latency decoding for large language models (LLMs) is crucial for applications like chatbots and code assistants, yet generating long outputs remains slow in single-query settings. Prior work on speculative decoding (which combines a small draft model with a larger target model) and tensor parallelism has each accelerated decoding. However, conventional approaches fail to apply both simultaneously due to imbalanced compute requirements (between draft and target models), KV-cache inconsistenc…

Universality of scaling entropy in charged hadron multiplicity distributions at the LHC
L. S. Moriggi, F. S. Navarra, M. V. T. Machado
https://arxiv.org/abs/2506.09899

Universality of scaling entropy in charged hadron multiplicity distributions at the LHC
In this work, we investigate the scaling behavior of the entropy associated with the charged hadron multiplicity distribution P(N) in proton-proton collisions at the LHC. We show that the growth of this entropic indicator as a function of the Bjorken x variable exhibits a universal behavior, consistent with observations from deep inelastic scattering (DIS). This universality suggests that the entropy scaling is a property of the initial state and reflects the diffusive nature of gluon dynamics …

Biological Processing Units: Leveraging an Insect Connectome to Pioneer Biofidelic Neural Architectures
Siyu Yu, Zihan Qin, Tingshan Liu, Beiya Xu, R. Jacob Vogelstein, Jason Brown, Joshua T. Vogelstein
https://arxiv.org/abs/2507.10951

Biological Processing Units: Leveraging an Insect Connectome to Pioneer Biofidelic Neural Architectures
The complete connectome of the Drosophila larva brain offers a unique opportunity to investigate whether biologically evolved circuits can support artificial intelligence. We convert this wiring diagram into a Biological Processing Unit (BPU), a fixed recurrent network derived directly from synaptic connectivity. Despite its modest size 3,000 neurons and 65,000 weights between them), the unmodified BPU achieves 98% accuracy on MNIST and 58% on CIFAR-10, surpassing size-matched MLPs. Scaling the…

Online Training and Pruning of Deep Reinforcement Learning Networks
Valentin Frank Ingmar Guenter, Athanasios Sideris
https://arxiv.org/abs/2507.11975 http…

Online Training and Pruning of Deep Reinforcement Learning Networks
Scaling deep neural networks (NN) of reinforcement learning (RL) algorithms has been shown to enhance performance when feature extraction networks are used but the gained performance comes at the significant expense of increased computational and memory complexity. Neural network pruning methods have successfully addressed this challenge in supervised learning. However, their application to RL is underexplored. We propose an approach to integrate simultaneous training and pruning within advance…

Insights for Early Massive Black Hole Growth from JWST Detection of the [Ne v] {\lambda}3427 Emission Line
Benny Trakhtenbrot, Claudio Ricci, Ezequiel Treister, Michael J. Koss, Richard Mushotzky, Kyuseok Oh, Alessandro Peca, Franz E. Bauer, Kriti Kamal Gupta, Tomer Reiss
https://arxiv.org/abs/2507.10681

Insights for Early Massive Black Hole Growth from JWST Detection of the [Ne v] λ3427 Emission Line
We use the narrow [Ne v] $λ$3427 emission line detected in the recently published JWST spectra of two galaxies, at z = 6.9 and 5.6, to study the key properties of the active galactic nuclei (AGN) and the supermassive black holes in their centers. Using a new empirical scaling linking the [Ne v] line emission with AGN accretion-driven (continuum) emission, derived from a highly complete low-redshift AGN sample, we show that the [Ne v] emission in the two z > 5 galaxies implies total (bolometric…

Optimal trace norms for Helmholtz problems
Benedikt Gr\"a{\ss}le
https://arxiv.org/abs/2506.11944 https://arxiv.org/pdf/2506.119…

Optimal trace norms for Helmholtz problems
The natural $H^1(Ω)$ energy norm for Helmholtz problems is weighted with the wavenumber modulus $σ$ and induces natural weighted norms on the trace spaces $H^{\pm1/2}(Γ)$ by minimial extension to $Ω\subset\mathbb R^n$. This paper presents a rigorous analysis for these trace norms with an explicit characterisation by weighted Sobolev-Slobodeckij norms and scaling estimates, highlighting their dependence on the geometry of the extension set $Ω\subset\mathbb R^n$ and the weight $σ$. The anal…

The Darkfield Approach to Measuring Vacuum Birefringence and Light-by-Light Couplings -- A Proof-of-Principle Experiment
Michal Sm\'id, Pooyan Khademi, Carsten B\"ahtz, Erik Brambrink, Jindrich Chalupsky, Tom E. Cowan, Samuele Di Dio Cafiso, Sebastian G\"ode, J\"org Grenzer, Vera Hajkova, Peter Hilz, Willi Hippler, Hauke H\"opner, Alzbeta Horynova, Oliver Humphries, Simon Jelinek, Libor Juha, Felix Karbstein, Alejandro Laso-Garcia, Robert L\"otzsch, Aim\…

The Darkfield Approach to Measuring Vacuum Birefringence and Light-by-Light Couplings -- A Proof-of-Principle Experiment
Vacuum fluctuations give rise to effective nonlinear interactions between electromagnetic fields. These generically modify the characteristics of light traversing a strong-field region. X-ray free-electron lasers constitute a particularly promising probe, due to their brilliance, the possibility of precise control and favourable frequency scaling. However, the nonlinear vacuum response is very small even when probing a tightly focused high-intensity laser field with XFEL radiation and direct me…

Complex scaling for open waveguides
Charles L. Epstein, Tristan Goodwill, Jeremy Hoskins, Solomon Quinn, Manas Rachh
https://arxiv.org/abs/2506.10263 https…

Complex scaling for open waveguides
In this work we analyze the complex scaling method applied to the problem of time-harmonic scalar wave propagation in junctions between `leaky,' or open dielectric waveguides. In [arXiv:2302.04353, arXiv:2310.05816, arXiv:2401.04674, arXiv:2411.11204], it was shown that under suitable assumptions the problem can be reduced to a system of Fredholm second-kind integral equations on an infinite interface, transverse to the waveguides. Here, we show that the kernels appearing in the integral equati…

The multinomial dimer model
Richard Kenyon, Catherine Wolfram
https://arxiv.org/abs/2506.12171 https://arxiv.org/pdf/2506.12171

The multinomial dimer model
An $N$-dimer cover of a graph is a collection of edges (with multiplicity) such that each vertex is contained in exactly $N$ edges in the collection. The multinomial dimer model is a natural probability measure on $N$-dimer covers. We study the behavior of these measures on periodic bipartite graphs in ${\mathbb R}^d$, in the scaling limit as the multiplicity $N$ and then the size of the graph go to infinity. In this iterated limit, we prove a large deviation principle, where the rate functio…

The full formula for the probability of "success" is:
p = {
1/(2^(-n 1)) if n is negative, or
1 - (1/(2^(n 1))) if n is zero or positive
}
(Both branches have the same value when n is 0, so the behavior is smooth around the origin.)
How can we tweak this?
First, we can introduce fixed success and/or failure chances unaffected by level, with this formula only taking effect if those don't apply. For example, you could do 10% failure, 80% by formula, and 10% success to keep things from being too sure either way even when levels are very high or low. On the other hand, this flattening makes the benefit of extra advantage levels even less exciting.
Second, we could allow for gradations of success/failure, and treat the coin pools I used to explain that math like dice pools a bit. An in-between could require linearly more success flips to achieve the next higher grade of success at each grade. For example, simple success on a crit role might mean dealing 1.5x damage, but if you succeed on 2 of your flips, you get 9/4 damage, or on 4 flips 27/8, or on 7 flips 81/16. In this world, stacking crit levels might be a viable build, and just giving up on armor would be super dangerous. In the particular case I was using this for just now, I can't easily do gradations of success (that's the reason I turned to probabilities in the first place) but I think I'd favor this approach when feasible.
The main innovation here over simple dice pools is how to handle situations where the number of dice should be negative. I'm almost certain it's not a truly novel innovation though, and some RPG fan can point out which system already does this (please actually do this, I'm an RPG nerd too at heart).
I'll leave this with one more tweak we could do: what if the number 2 in the probability equation were 3, or 2/3? I think this has a similar effect to just scaling all the modifiers a bit, but the algebra escapes me in this moment and I'm a bit lazy. In any case, reducing the base of the probability exponent should let you get a few more gradations near 50%, which is probably a good thing, since the default goes from 25% straight to 50% and then to 75% with no integer stops in between.

Quantum algorithm for solving generalized eigenvalue problems with application to the Schr\"odinger equation
Grzegorz Rajchel-Mieldzio\'c, Szymon Pli\'s, Emil Zak
https://arxiv.org/abs/2506.13534

Quantum algorithm for solving generalized eigenvalue problems with application to the Schrödinger equation
Accurate computation of multiple eigenvalues of quantum Hamiltonians is essential in quantum chemistry, materials science, and molecular spectroscopy. Estimating excited-state energies is challenging for classical algorithms due to exponential scaling with system size, posing an even harder problem than ground-state calculations. We present a quantum algorithm for estimating eigenvalues and singular values of parameterized matrix families, including solving generalized eigenvalue problems that …

Scaling RL to Long Videos
Yukang Chen, Wei Huang, Baifeng Shi, Qinghao Hu, Hanrong Ye, Ligeng Zhu, Zhijian Liu, Pavlo Molchanov, Jan Kautz, Xiaojuan Qi, Sifei Liu, Hongxu Yin, Yao Lu, Song Han
https://arxiv.org/abs/2507.07966

Scaling RL to Long Videos
We introduce a full-stack framework that scales up reasoning in vision-language models (VLMs) to long videos, leveraging reinforcement learning. We address the unique challenges of long video reasoning by integrating three critical components: (1) a large-scale dataset, LongVideo-Reason, comprising 52K long video QA pairs with high-quality reasoning annotations across diverse domains such as sports, games, and vlogs; (2) a two-stage training pipeline that extends VLMs with chain-of-thought supe…

From Local Updates to Global Balance: A Framework for Distributed Matrix Scaling
Giacomo Aletti, Giovanni Naldi
https://arxiv.org/abs/2506.08035 https://…

From Local Updates to Global Balance: A Framework for Distributed Matrix Scaling
This paper investigates matrix scaling processes in the context of local normalization algorithms and their convergence behavior. Starting from the classical Sinkhorn algorithm, the authors introduce a generalization where only a single row or column is normalized at each step, without restrictions on the update order. They extend Birkhoff's theorem to characterize the convergence properties of these algorithms, especially when the normalization sequence is arbitrary. A novel application is exp…

InGaN Nanopixel Arrays on Single Crystal GaN Substrate
Nirmal Anand, Sadat Tahmeed Azad, Christy Giji Jenson, Dipon Kumar Ghosh, Md Zunaid Baten, Pei-Cheng Ku, Grzegorz Muziol, Sharif Sadaf
https://arxiv.org/abs/2506.11408

InGaN Nanopixel Arrays on Single Crystal GaN Substrate
Indium gallium nitride (InGaN) quantum well (QW) micro- and nano-scale light-emitting diodes (LEDs) are vital to the next-generation optoelectronic applications such as ultrahigh-speed optical interconnects and near-eye augmented/virtual reality displays. However, scaling to nanoscale dimensions introduces several challenges, including an increased nonradiative recombination from surface states and defect-related emission degradation. In this work, we present a high-performance, strain-relaxed …

🔧 #MatryoshkaRepresentationLearning technique allows scaling output dimensions from default 3072 💰 Priced at $0.15 per 1M input tokens with free tier available ⚡

Localization Transition for Interacting Quantum Particles in Colored-Noise Disorder
Giacomo Morpurgo, Laurent Sanchez-Palencia, Thierry Giamarchi
https://arxiv.org/abs/2507.11308 …

Localization Transition for Interacting Quantum Particles in Colored-Noise Disorder
We investigate the localization transition of interacting particles in a one-dimensional correlated disorder system. The disorder which we investigate allows for vanishing backwards scattering processes. We derive by two renormalization group procedures its phase diagram and predict that the localization transition point is shifted from finite attractive interaction to the non-interacting point. We finally show numerically that the scaling of the localization length with the disorder strength d…

Meek Models Shall Inherit the Earth
Hans Gundlach, Jayson Lynch, Neil Thompson
https://arxiv.org/abs/2507.07931 https://arxiv.org/pdf…

Meek Models Shall Inherit the Earth
The past decade has seen incredible scaling of AI systems by a few companies, leading to inequality in AI model performance. This paper argues that, contrary to prevailing intuition, the diminishing returns to compute scaling will lead to a convergence of AI model capabilities. In other words, meek models (those with limited computation budget) shall inherit the earth, approaching the performance level of the best models overall. We develop a model illustrating that under a fixed-distribution n…

Now @… is announced by @… and he'll talk about @….
Personally I think that we can be grateful…

HMD says it will "scale back" US operations, citing "a challenging geopolitical and economic environment", and appears to have stopped US sales of Nokia devices (Dominic Preston/The Verge)
https://www.theverge.com/news/705046/hmd-gl…

HMD is ‘scaling back’ in the US, killing Nokia all over again
HMD Global, which sells its own phones as well as Nokia ones, has stopped selling phones on its US site, and confirms it will ‘scale back’ its US operation.

Trustworthy Tree-based Machine Learning by $MoS_2$ Flash-based Analog CAM with Inherent Soft Boundaries
Bo Wen, Guoyun Gao, Zhicheng Xu, Ruibin Mao, Xiaojuan Qi, X. Sharon Hu, Xunzhao Yin, Can Li
https://arxiv.org/abs/2507.12384

Trustworthy Tree-based Machine Learning by $MoS_2$ Flash-based Analog CAM with Inherent Soft Boundaries
The rapid advancement of artificial intelligence has raised concerns regarding its trustworthiness, especially in terms of interpretability and robustness. Tree-based models like Random Forest and XGBoost excel in interpretability and accuracy for tabular data, but scaling them remains computationally expensive due to poor data locality and high data dependence. Previous efforts to accelerate these models with analog content addressable memory (CAM) have struggled, due to the fact that the diff…

Characterizing State Space Model (SSM) and SSM-Transformer Hybrid Language Model Performance with Long Context Length
Saptarshi Mitra, Rachid Karami, Haocheng Xu, Sitao Huang, Hyoukjun Kwon
https://arxiv.org/abs/2507.12442

Characterizing State Space Model (SSM) and SSM-Transformer Hybrid Language Model Performance with Long Context Length
The demand for machine intelligence capable of processing continuous, long-context inputs on local devices is growing rapidly. However, the quadratic complexity and memory requirements of traditional Transformer architectures make them inefficient and often unusable for these tasks. This has spurred a paradigm shift towards new architectures like State Space Models (SSMs) and hybrids, which promise near-linear scaling. While most current research focuses on the accuracy and theoretical throughp…

Beyond Scaling: Chemical Intuition as Emergent Ability of Universal Machine Learning Interatomic Potentials
Shinnosuke Hattori, Kohei Shimamura, Aiichiro Nakano, Rajiv K. Kalia, Priya Vashishta, Ken-ichi Nomura
https://arxiv.org/abs/2506.07579

Beyond Scaling: Chemical Intuition as Emergent Ability of Universal Machine Learning Interatomic Potentials
Machine Learning Interatomic Potentials (MLIPs) have successfully demonstrated scaling behavior, i.e. the power-law improvement in training performance, however the emergence of novel capabilities at scale remains unexplored. We have developed Edge-wise Emergent Decomposition (E3D) framework to investigate how an MLIP develops the ability to derive physically meaningful local representations of chemical bonds without explicit supervision. Employing an E(3)-equivariant network (Allegro) trained …

A scalable quantum-neural hybrid variational algorithm for ground state estimation
Minwoo Kim, Kyoung Keun Park, Uihwan Jeong, Sanghyeon Lee, Taehyun Kim
https://arxiv.org/abs/2507.11002

A scalable quantum-neural hybrid variational algorithm for ground state estimation
We propose the unitary variational quantum-neural hybrid eigensolver (U-VQNHE), which improves upon the original VQNHE by enforcing unitary neural transformations. The non-unitary nature of VQNHE causes normalization issues and divergence of the loss function during training, leading to exponential scaling of measurement overhead with qubit number. U-VQNHE resolves these issues, significantly reduces required measurements, and retains improved accuracy and stability over standard variational qu…

Human-robot collaborative transport personalization via Dynamic Movement Primitives and velocity scaling
Paolo Franceschi, Andrea Bussolan, Vincenzo Pomponi, Oliver Avram, Stefano Baraldo, Anna Valente
https://arxiv.org/abs/2506.09697

Human-robot collaborative transport personalization via Dynamic Movement Primitives and velocity scaling
Nowadays, industries are showing a growing interest in human-robot collaboration, particularly for shared tasks. This requires intelligent strategies to plan a robot's motions, considering both task constraints and human-specific factors such as height and movement preferences. This work introduces a novel approach to generate personalized trajectories using Dynamic Movement Primitives (DMPs), enhanced with real-time velocity scaling based on human feedback. The method was rigorously tested in …

Break-up of an active chiral fluid
Luke Neville, Jens Eggers, Tanniemola B. Liverpool
https://arxiv.org/abs/2506.10534 https://arxiv.…

Break-up of an active chiral fluid
We consider the non-linear dynamics governing the break-up of a two-dimensional strip of active chiral fluid. We observe that the strip thickness goes to zero at the pinch off points as a power law in finite time. Using slender body theory combined with a scaling analysis, we identify a new class of scaling exponents and scaling functions characterizing the speed and shape of break-up in these systems. The scaling analysis is in excellent agreement with direct numerical simulations of the hydro…

Semi-empirical constraints on the HI mass function of star-forming galaxies and $\Omega_{\rm HI}$ at $z\sim 0.37$ from interferometric surveys
Francesco Sinigaglia, Alessandro Bianchetti, Giulia Rodighiero, Lucio Mayer, Miroslava Dessauges-Zavadsky, Ed Elson, Mattia Vaccari, Matt J. Jarvis
https://arxiv.org/abs/2506.11280…

Semi-empirical constraints on the HI mass function of star-forming galaxies and $Ω_{\rm HI}$ at $z\sim 0.37$ from interferometric surveys
The HI mass function is a crucial tool to understand the evolution of the HI content in galaxies over cosmic times and to constrain both the baryon cycle in galaxy evolution and the reionization history of the Universe. We aim to derive semi-empirical constraints at $z\sim 0.37$ by combining literature results on the stellar mass function from optical surveys with recent findings on the $M_{\rm HI}-M_\star$ scaling relation derived via spectral stacking analysis applied to 21-cm line interferom…

On surface energies in scaling laws for singular perturbation problems for martensitic phase transitions
Angkana R\"uland, Camillo Tissot, Antonio Tribuzio, Christian Zillinger
https://arxiv.org/abs/2507.06773 https://arxiv.org/pdf/2507.06773 https://arxiv.org/html/2507.06773
arXiv:2507.06773v1 Announce Type: new
Abstract: The objective of this article is to compare different surface energies for multi-well singular perturbation problems associated with martensitic phase transformations involving higher order laminates. We deduce scaling laws in the singular perturbation parameter which are robust in the choice of the surface energy (e.g., diffuse, sharp, an interpolation thereof or discrete). Furthermore, we show that these scaling laws do not require the presence of isotropic surface energies but that generically also highly anisotropic surface energies yield the same scaling results. More precisely, the presence of essentially generic partial directional derivatives in the regularization terms suffices to produce the same scaling behaviour as in the isotropic setting. The only sensitive directional dependences are directly linked to the lamination directions of the well structure -- and even for these only the ``inner-most'' lamination direction is of significance in determining the scaling law. In view of experimental applications, this shows that also for higher-order laminates, the precise structure of the surface energies -- which is often very difficult to determine experimentally -- does not have a crucial impact on the scaling behaviour of the investigated structures but only enters when considering finer properties.
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On the Scaling of Robustness and Effectiveness in Dense Retrieval
Yu-An Liu, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Yixing Fan, Xueqi Cheng
https://arxiv.org/abs/2505.24279 …

On the Scaling of Robustness and Effectiveness in Dense Retrieval
Robustness and Effectiveness are critical aspects of developing dense retrieval models for real-world applications. It is known that there is a trade-off between the two. Recent work has addressed scaling laws of effectiveness in dense retrieval, revealing a power-law relationship between effectiveness and the size of models and data. Does robustness follow scaling laws too? If so, can scaling improve both robustness and effectiveness together, or do they remain locked in a trade-off? To answ…

Voter model on heterogeneous directed networks
Luca Avena, Federico Capannoli, Rajat Subhra Hazra, Diego Garlaschelli
https://arxiv.org/abs/2506.12169 http…

Voter model on heterogeneous directed networks
We investigate the consensus dynamics of the voter model on large random graphs with heterogeneous and directed features, focusing in particular on networks with power-law degree distributions. By extending recent results on sparse directed graphs, we derive exact first-order asymptotics for the expected consensus time in directed configuration models with i.i.d. Pareto-distributed in- and out-degrees. For any tail exponent α>0, we derive the mean consensus time scaling depending on the networ…

Counting sums of two powers
Anand Patel
https://arxiv.org/abs/2507.08337 https://arxiv.org/pdf/2507.08337

Counting sums of two powers
We generalize two well-known enumerative facts. The first, due to Clebsch, says that a general binary sextic form is expressible as the sum of a cube and a square in 40 different ways. The second, due to Zariski and later Vakil, states that a general pencil of binary 12-ics contains exactly 3762 cubes plus squares, ignoring scaling.

Radius valley scaling among low mass stars with TESS
Harshitha M. Parashivamurthy, Gijs D. Mulders
https://arxiv.org/abs/2507.07181 https://

Radius valley scaling among low mass stars with TESS
The Transiting Exoplanet Survey Satellite (TESS) has been highly successful in detecting planets in close orbits around low-mass stars, particularly M dwarfs. This presents a valuable opportunity to conduct detailed population studies to understand how these planets depend on the properties of their host stars. The previously observed radius valley in Sun-like stars has not been unambiguously detected among M dwarfs, and how its properties varies when compared with more massive stars remains un…

Isolated attosecond spatio-temporal optical vortices: Interplay between the topological charge and orbital angular momentum scaling in high harmonic generation
Rodrigo Martin-Hernandez, Luis Plaja, Carlos Hernandez-Garcia, Miguel A. Porras
https://arxiv.org/abs/2506.07465

Isolated attosecond spatio-temporal optical vortices: Interplay between the topological charge and orbital angular momentum scaling in high harmonic generation
The propagation properties and the nature of the transverse orbital angular momentum (t-OAM) of spatiotemporal optical vortices (STOVs) open new scenarios in high-harmonic generation (HHG), where the richness of the topological charge and OAM up-conversion are exposed. Through advanced numerical simulations, we demonstrate that HHG driven by spatio-spectral optical vortices produces far-field, extreme-ultraviolet STOV harmonics with non-scaling topological charge, i.e., with the same topologica…

From Bjorken Scaling to Scaling Violations
Giorgio Parisi
https://arxiv.org/abs/2506.03383 https://arxiv.org/pdf/2506.03383

From Bjorken Scaling to Scaling Violations
This paper traces the historical and conceptual journey from Bjorken scaling to the discovery of scaling violations in deep inelastic scattering, culminating in the development of Quantum Chromodynamics (QCD). Beginning with the challenges faced by early strong interaction theories in the 1950s, we explore the emergence of agnostic approaches such as the bootstrap philosophy and current algebra, which sought to describe hadronic phenomena without relying on specific field theories. The pivotal …

Replaced article(s) found for physics.comp-ph. https://arxiv.org/list/physics.comp-ph/new
[1/1]:
Error Analysis and Parallel Scaling Study of A Parareal Parallel-in-Time Integration Algorithm fo...

Discrete Element Parameter Calibration of Livestock Salt Based on Particle Scaling
Lulu Nie, Baoqin Wen, Jingbin Li, Shufeng Li, Yali Li, Zhaokun Zhang, Zhiyuan Wang, Zhihao Fan
https://arxiv.org/abs/2506.03786

Discrete Element Parameter Calibration of Livestock Salt Based on Particle Scaling
In order to obtain accurate contact parameters for the discrete element simulation of salt particles used in animal husbandry, the principle of particle contact scaling and dimensional analysis were used for particle scaling. Firstly, the Plackett Burman experiment was used to screen the parameters that significantly affect the angle of repose: salt salt rolling friction coefficient, salt salt recovery coefficient, and salt steel rolling friction coefficient. Considering the influence of other …

Robust Scaling in Human Brain Dynamics Despite Latent Variables and Limited Sampling Distortions
Rub\'en Calvo, Carles Martorell, Adri\'an Roig, Miguel A. Mu\~noz
https://arxiv.org/abs/2506.03640

Robust Scaling in Human Brain Dynamics Despite Latent Variables and Limited Sampling Distortions
The idea that information-processing systems operate near criticality to enhance computational performance is supported by scaling signatures in brain activity. However, external signals raise the question of whether this behavior is intrinsic or input-driven. We show that autocorrelated inputs and temporal resolution influence observed scaling exponents in simple neural models. We also demonstrate analytically that under subsampling, non-critical systems driven by independent autocorrelated si…

Hilbert subspace imprint: a new mechanism for non-thermalization
Hui Yu, Jiangping Hu, Shi-Xin Zhang
https://arxiv.org/abs/2506.11922 https://

Hilbert subspace imprint: a new mechanism for non-thermalization
The search for non-ergodic mechanisms in quantum many-body systems has become a frontier area of research in non-equilibrium physics. In this Letter, we introduce Hilbert subspace imprint (HSI)-a novel mechanism that enables evasion of thermalization and bridges the gap between quantum many-body scars (QMBS) and Hilbert space fragmentation (HSF). HSI manifests when initial states overlap exclusively with a polynomial scaling (with system size) set of eigenstates. We demonstrate this phenomenon …

HarMoEny: Efficient Multi-GPU Inference of MoE Models
Zachary Douchet, Rishi Sharma, Martijn de Vos, Rafael Pires, Anne-Marie Kermarrec, Oana Balmau
https://arxiv.org/abs/2506.12417

HarMoEny: Efficient Multi-GPU Inference of MoE Models
Mixture-of-Experts (MoE) models offer computational efficiency during inference by activating only a subset of specialized experts for a given input. This enables efficient model scaling on multi-GPU systems that use expert parallelism without compromising performance. However, load imbalance among experts and GPUs introduces waiting times, which can significantly increase inference latency. To address this challenge, we propose HarMoEny, a novel solution to address MoE load imbalance through t…

Unreal is all you need: Multimodal ISAC Data Simulation with Only One Engine
Kongwu Huang, Shiyi Mu, Jun Jiang, Yuan Gao, Shugong Xu
https://arxiv.org/abs/2507.08716

Unreal is all you need: Multimodal ISAC Data Simulation with Only One Engine
Scaling laws have achieved success in LLM and foundation models. To explore their potential in ISAC research, we propose Great-X. This single-engine multimodal data twin platform reconstructs the ray-tracing computation of Sionna within Unreal Engine and is deeply integrated with autonomous driving tools. This enables efficient and synchronized simulation of multimodal data, including CSI, RGB, Radar, and LiDAR. Based on this platform, we construct an open-source, large-scale, low-altitude UAV …

This https://arxiv.org/abs/2501.03341 has been replaced.
initial toot: https://mastoxiv.page/@…

Dynamic scaling theory for a field quench near the Kasteleyn transition in spin ice
We present a dynamic scaling theory to describe relaxation dynamics following a magnetic-field quench near an unconventional phase transition in the magnetic material spin ice. Starting from a microscopic model, we derive an effective description for the critical dynamics in terms of the seeding and growth of string excitations, and use this to find scaling forms in terms of time, reduced temperature and monopole fugacity. We confirm the predictions of scaling theory using Monte Carlo simulatio…

This https://arxiv.org/abs/2505.10981 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Rethinking the Role of Prompting Strategies in LLM Test-Time Scaling: A Perspective of Probability Theory
Recently, scaling test-time compute on Large Language Models (LLM) has garnered wide attention. However, there has been limited investigation of how various reasoning prompting strategies perform as scaling. In this paper, we focus on a standard and realistic scaling setting: majority voting. We systematically conduct experiments on 6 LLMs $\times$ 8 prompting strategies $\times$ 6 benchmarks. Experiment results consistently show that as the sampling time and computational overhead increase, co…

Electron Heating in Hypersonic Flows: A New Thermodynamically Consistent Model
Felipe Martin Rodriguez Fuentes, Bernard Parent
https://arxiv.org/abs/2506.11457

Electron Heating in Hypersonic Flows: A New Thermodynamically Consistent Model
Accurate prediction of electron temperature ($T_{\rm e}$) in hypersonic non-equilibrium flows is critical, yet hampered by inadequate models for electron heating from vibrationally excited nitrogen. Prior models often relied on ad-hoc scaling or flawed applications of detailed balance that failed to ensure the convergence of electron and vibrational temperatures ($T_{\rm v}$) at thermal equilibrium. This paper introduces a novel, thermodynamically consistent electron heating model derived rigor…

This https://arxiv.org/abs/2409.09685 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

On the critical finite-size gap scaling for frustration-free Hamiltonians
We prove that the critical finite-size gap scaling for frustration-free Hamiltonians is of inverse-square type. The result covers general graphs embedded in $\mathbb R^D$ and general finite-range interactions without requiring assumptions about the ground state correlations. Therefore, the inverse-square critical gap scaling is a robust, universal property of finite-range frustration-free Hamiltonians. This places further limits on their ability to produce conformal field theories in the contin…

Cyclic Data Streaming on GPUs for Short Range Stencils Applied to Molecular Dynamics
Martin Rose, Simon Homes, Lukas Ramsperger, Jose Gracia, Christoph Niethammer, Jadran Vrabec
https://arxiv.org/abs/2507.11289

Cyclic Data Streaming on GPUs for Short Range Stencils Applied to Molecular Dynamics
In the quest for highest performance in scientific computing, we present a novel framework that relies on high-bandwidth communication between GPUs in a compute cluster. The framework offers linear scaling of performance for explicit algorithms that is only limited by the size of the dataset and the number of GPUs. Slices of the dataset propagate in a ring of processes (GPUs) from one GPU, where they are processed, to the next, which results in a parallel-in-time parallelization. The user of th…

Dynamic scaling of growing interfaces
Pierre Le Doussal
https://arxiv.org/abs/2507.08341 https://arxiv.org/pdf/2507.08341

Dynamic scaling of growing interfaces
We give a brief overview of the seminal paper which introduced the Kardar-Parisi-Zhang equation as a paradigmatic model for random growth in 1986. We describe some of the developments to which it gave rise in mathematics and physics over the years, and some examples of applications.

Kibble-Zurek dynamical scaling hypothesis in the Google analog-digital quantum simulator of the $XX$ model
Yintai Zhang, Francis A. Bayocboc Jr., Jacek Dziarmaga
https://arxiv.org/abs/2506.10771

Kibble-Zurek dynamical scaling hypothesis in the Google analog-digital quantum simulator of the $XX$ model
The state-of-the-art tensor networks are employed to simulate the Hamiltonian ramp in the analog-digital quantum simulation of the quantum phase transition to the quasi-long-range ordered phase of the 2D square-lattice $XX$ model [Nature 638, 79 (2025)]. We focus on the quantum Kibble-Zurek (KZ) mechanism near the quantum critical point. Using the infinite projected entangled pair state (iPEPS), we simulate an infinite lattice and demonstrate the KZ scaling hypothesis for the $XX$ correlations …

This https://arxiv.org/abs/2505.24009 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Diversity of Transformer Layers: One Aspect of Parameter Scaling Laws
Transformers deliver outstanding performance across a wide range of tasks and are now a dominant backbone architecture for large language models (LLMs). Their task-solving performance is improved by increasing parameter size, as shown in the recent studies on parameter scaling laws. Although recent mechanistic-interpretability studies have deepened our understanding of the internal behavior of Transformers by analyzing their residual stream, the relationship between these internal mechanisms an…

Admissibility of Stein Shrinkage for Batch Normalization in the Presence of Adversarial Attacks
Sofia Ivolgina, P. Thomas Fletcher, Baba C. Vemuri
https://arxiv.org/abs/2507.08261

Admissibility of Stein Shrinkage for Batch Normalization in the Presence of Adversarial Attacks
Batch normalization (BN) is a ubiquitous operation in deep neural networks used primarily to achieve stability and regularization during network training. BN involves feature map centering and scaling using sample means and variances, respectively. Since these statistics are being estimated across the feature maps within a batch, this problem is ideally suited for the application of Stein's shrinkage estimation, which leads to a better, in the mean-squared-error sense, estimate of the mean and …

The impact of applying black hole-host galaxy scaling relations to large galaxy populations
Maggie C. Huber (University of Colorado Boulder), Joseph Simon (University of Colorado Boulder), Julia M. Comerford (University of Colorado Boulder)
https://arxiv.org/abs/2506.08102

The impact of applying black hole-host galaxy scaling relations to large galaxy populations
Supermassive black holes (SMBHs) with dynamically measured masses have shown empirical correlations with host galaxy properties. These correlations are often the only method available to estimate SMBH masses and gather statistics for large galaxy populations across a range of redshifts, even though the scaling relations themselves are derived from a small subset of nearby galaxies. Depending on the scaling relation used, estimated SMBH masses can vary significantly. The most widely used scaling…

Emergent dynamical scaling in the inviscid limit of 3D stochastic Navier-Stokes equation with thermal noise
Liubov Gosteva, Marc Brachet, L\'eonie Canet
https://arxiv.org/abs/2507.05811

Emergent dynamical scaling in the inviscid limit of 3D stochastic Navier-Stokes equation with thermal noise
Spectrally truncated three-dimensional (3D) Euler flows reach an absolute equilibrium. Yet they show non-trivial temporal correlations, similar to the ones of turbulent flows. In this work, we investigate Navier-Stokes equation in equilibrium conditions, both at finite viscosity and in the inviscid limit. We determine the space-time velocity correlations, using functional renormalisation group and direct numerical simulations. We characterise the crossover in the dynamical scaling from $τ\sim …

Hybrid scaling mechanism of critical behavior in the overlapping critical regions of classical and quantum Yang-Lee edge singularities
Yue-Mei Sun, Wen-Jing Yu, Xin-Yu Wang, Liang-Jun Zhai
https://arxiv.org/abs/2506.00919

Hybrid scaling mechanism of critical behavior in the overlapping critical regions of classical and quantum Yang-Lee edge singularities
Recently, the study of scaling behavior in Yang-Lee edge singularities (YLES) has attracted sustained attention. However, the scaling mechanism for the overlapping critical region between classical and quantum YLES remains unclear. In this work, we investigate this question, and a hybrid scaling mechanism is introduced to characterize the scaling behavior in the overlapping regions. The hybrid scaling mechanism asserts that in the overlapping region the scaling behavior can be described by the …

Replaced article(s) found for cs.IR. https://arxiv.org/list/cs.IR/new
[1/1]:
- Drowning in Documents: Consequences of Scaling Reranker Inference
Mathew Jacob, Erik Lindgren, Matei Zaharia, Michael Carbin, Omar Khattab, Andrew Drozdov

On the regularization property of Levenberg-Marquardt method with Singular Scaling for nonlinear inverse problems
Rafaela Filippozzi, Everton Boos, Douglas S. Gon\c{c}alves, Fermin S. V. Baz\'an
https://arxiv.org/abs/2506.00190

On the regularization property of Levenberg-Marquardt method with Singular Scaling for nonlinear inverse problems
Recently, in Applied Mathematics and Computation 474 (2024) 128688, a Levenberg-Marquardt method (LMM) with Singular Scaling was analyzed and successfully applied in parameter estimation problems in heat conduction where the use of a particular singular scaling matrix (semi-norm regularizer) provided approximate solutions of better quality than those of the classic LMM. Here we propose a regularization framework for the Levenberg-Marquardt method with Singular Scaling (LMMSS) applied to nonline…

Video-RTS: Rethinking Reinforcement Learning and Test-Time Scaling for Efficient and Enhanced Video Reasoning
Ziyang Wang, Jaehong Yoon, Shoubin Yu, Md Mohaiminul Islam, Gedas Bertasius, Mohit Bansal
https://arxiv.org/abs/2507.06485

Video-RTS: Rethinking Reinforcement Learning and Test-Time Scaling for Efficient and Enhanced Video Reasoning
Despite advances in reinforcement learning (RL)-based video reasoning with large language models (LLMs), data collection and finetuning remain significant challenges. These methods often rely on large-scale supervised fine-tuning (SFT) with extensive video data and long Chain-of-Thought (CoT) annotations, making them costly and hard to scale. To address this, we present Video-RTS, a new approach to improve video reasoning capability with drastically improved data efficiency by combining data-ef…

Radical scaling: beyond our feet and fingers
Marc-Antoine Fardin, Mathieu Hautefeuille, Vivek Sharma
https://arxiv.org/abs/2507.02631 https://

Radical scaling: beyond our feet and fingers
Scaling laws arise and are eulogized across disciplines from natural to social sciences for providing pithy, quantitative, `scale-free', and `universal' power law relationships between two variables. On a log-log plot, the power laws display as straight lines, with a slope set by the exponent of the scaling law. In practice, a scaling relationship works only for a limited range, bookended by crossovers to other scaling laws. Leading with Taylor's oft-cited scaling law for the blast radius of an…

Unified Scaling Laws for Compressed Representations
Andrei Panferov, Alexandra Volkova, Ionut-Vlad Modoranu, Vage Egiazarian, Mher Safaryan, Dan Alistarh
https://arxiv.org/abs/2506.01863

Unified Scaling Laws for Compressed Representations
Scaling laws have shaped recent advances in machine learning by enabling predictable scaling of model performance based on model size, computation, and data volume. Concurrently, the rise in computational cost for AI has motivated model compression techniques, notably quantization and sparsification, which have emerged to mitigate the steep computational demands associated with large-scale training and inference. This paper investigates the interplay between scaling laws and compression formats…

ScaleRTL: Scaling LLMs with Reasoning Data and Test-Time Compute for Accurate RTL Code Generation
Chenhui Deng, Yun-Da Tsai, Guan-Ting Liu, Zhongzhi Yu, Haoxing Ren
https://arxiv.org/abs/2506.05566

ScaleRTL: Scaling LLMs with Reasoning Data and Test-Time Compute for Accurate RTL Code Generation
Recent advances in large language models (LLMs) have enabled near-human performance on software coding benchmarks, but their effectiveness in RTL code generation remains limited due to the scarcity of high-quality training data. While prior efforts have fine-tuned LLMs for RTL tasks, they do not fundamentally overcome the data bottleneck and lack support for test-time scaling due to their non-reasoning nature. In this work, we introduce ScaleRTL, the first reasoning LLM for RTL coding that scal…

Multi-agent Reinforcement Learning-based In-place Scaling Engine for Edge-cloud Systems
Jovan Prodanov, Bla\v{z} Bertalani\v{c}, Carolina Fortuna, Shih-Kai Chou, Matja\v{z} Branko Juri\v{c}, Ramon Sanchez-Iborra, Jernej Hribar
https://arxiv.org/abs/2507.07671

Multi-agent Reinforcement Learning-based In-place Scaling Engine for Edge-cloud Systems
Modern edge-cloud systems face challenges in efficiently scaling resources to handle dynamic and unpredictable workloads. Traditional scaling approaches typically rely on static thresholds and predefined rules, which are often inadequate for optimizing resource utilization and maintaining performance in distributed and dynamic environments. This inefficiency hinders the adaptability and performance required in edge-cloud infrastructures, which can only be achieved through the newly proposed in-…

Adaptive Termination for Multi-round Parallel Reasoning: An Universal Semantic Entropy-Guided Framework
Zenan Xu, Zexuan Qiu, Guanhua Huang, Kun Li, Siheng Li, Chenchen Zhang, Kejiao Li, Qi Yi, Yuhao Jiang, Bo Zhou, Fengzong Lian, Zhanhui Kang
https://arxiv.org/abs/2507.06829

Adaptive Termination for Multi-round Parallel Reasoning: An Universal Semantic Entropy-Guided Framework
Recent advances in large language models (LLMs) have accelerated progress toward artificial general intelligence, with inference-time scaling emerging as a key technique. Contemporary approaches leverage either sequential reasoning (iteratively extending chains of thought) or parallel reasoning (generating multiple solutions simultaneously) to scale inference. However, both paradigms face fundamental limitations: sequential scaling typically relies on arbitrary token budgets for termination, le…

Is Diversity All You Need for Scalable Robotic Manipulation?
Modi Shi, Li Chen, Jin Chen, Yuxiang Lu, Chiming Liu, Guanghui Ren, Ping Luo, Di Huang, Maoqing Yao, Hongyang Li
https://arxiv.org/abs/2507.06219

Is Diversity All You Need for Scalable Robotic Manipulation?
Data scaling has driven remarkable success in foundation models for Natural Language Processing (NLP) and Computer Vision (CV), yet the principles of effective data scaling in robotic manipulation remain insufficiently understood. In this work, we investigate the nuanced role of data diversity in robot learning by examining three critical dimensions-task (what to do), embodiment (which robot to use), and expert (who demonstrates)-challenging the conventional intuition of "more diverse is better…

STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis
Jiatao Gu, Tianrong Chen, David Berthelot, Huangjie Zheng, Yuyang Wang, Ruixiang Zhang, Laurent Dinh, Miguel Angel Bautista, Josh Susskind, Shuangfei Zhai
https://arxiv.org/abs/2506.06276

STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis
We present STARFlow, a scalable generative model based on normalizing flows that achieves strong performance in high-resolution image synthesis. The core of STARFlow is Transformer Autoregressive Flow (TARFlow), which combines the expressive power of normalizing flows with the structured modeling capabilities of Autoregressive Transformers. We first establish the theoretical universality of TARFlow for modeling continuous distributions. Building on this foundation, we introduce several key arch…

Decay and Strichartz estimates for critical electromagnetic wave equations on conic manifolds
Qiuye Jia, Junyong Zhang
https://arxiv.org/abs/2506.09635 htt…

Decay and Strichartz estimates for critical electromagnetic wave equations on conic manifolds
We establish the decay and Strichartz estimates for the wave equation with large scaling-critical electromagnetic potentials on a conical singular space $(X,g)$ with dimension $n\geq3$, where the metric $g=dr^2+r^2 h$ and $X=C(Y)=(0,\infty)\times Y$ is a product cone over the closed Riemannian manifold $(Y,h)$ with metric $h$. The decay assumption on the magnetic potentials is scaling critical and includes the decay of Coulomb type. The main technical innovation lies in proving localized pointw…

This https://arxiv.org/abs/2503.18941 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Exploring Training and Inference Scaling Laws in Generative Retrieval
Generative retrieval reformulates retrieval as an autoregressive generation task, where large language models (LLMs) generate target documents directly from a query. As a novel paradigm, the mechanisms that underpin its performance and scalability remain largely unexplored. We systematically investigate training and inference scaling laws in generative retrieval, exploring how model size, training data scale, and inference-time compute jointly influence performance. We propose a novel evaluatio…

Scaling of Structure and Dynamics in Molecular Liquids: Insights from Pressure Experiments and Molecular Dynamics
Erik L{\o}rup
https://arxiv.org/abs/2507.06242

Scaling of Structure and Dynamics in Molecular Liquids: Insights from Pressure Experiments and Molecular Dynamics
The overall goal of this thesis is to investigate the connection between the dynamics and structure of molecular glass formers, by testing different scaling laws for both. The inspiration for this work is the Isomorph theory because it predicts a connection between structure and dynamics. The fundamental prediction of the Isomorph Theory is that there exist lines in the phase diagram where both structure and dynamics are invariant when presented in reduced units. The prediction of constant dyna…

On the commutator scaling in Hamiltonian simulation with multi-product formulas
Kaoru Mizuta
https://arxiv.org/abs/2507.06557 https://

On the commutator scaling in Hamiltonian simulation with multi-product formulas
A multi-product formula (MPF) is a promising approach for Hamiltonian simulation efficiently both in the system size $N$ and the inverse allowable error $1/\varepsilon$ by combining Trotterization and the linear combination of unitaries (LCU). It achieves poly-logarithmic cost in $1/\varepsilon$ like LCU [G. H. Low, V. Kliuchnikov, N. Wiebe, arXiv:1907.11679 (2019)]. The efficiency in $N$ is expected to come from the commutator scaling in Trotterization, and this appears to be confirmed by the …

Unveiling the different scaling regimes of the one-dimensional Kardar-Parisi-Zhang--Burgers equation using the functional renormalisation group
Liubov Gosteva, Nicol\'as Wschebor, L\'eonie Canet
https://arxiv.org/abs/2506.03937

Unveiling the different scaling regimes of the one-dimensional Kardar-Parisi-Zhang--Burgers equation using the functional renormalisation group
The Kardar-Parisi-Zhang (KPZ) equation is a celebrated non-linear stochastic equation featuring non-equilibrium scaling. Although in one dimension, its statistical properties are very well understood, a new scaling regime has been reported in recent numerical simulations. This new regime is characterised by a dynamical exponent $z=1$, markedly different from the expected one $z=3/2$ for the KPZ universality class, and it emerges when approaching the inviscid limit. The origin of this scaling ha…

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library
Weixun Wang, Shaopan Xiong, Gengru Chen, Wei Gao, Sheng Guo, Yancheng He, Ju Huang, Jiaheng Liu, Zhendong Li, Xiaoyang Li, Zichen Liu, Haizhou Zhao, Dakai An, Lunxi Cao, Qiyang Cao, Wanxi Deng, Feilei Du, Yiliang Gu, Jiahe Li, Xiang Li, Mingjie Liu, Yijia Luo, Zihe Liu, Yadao Wang, Pei Wang, Tianyuan Wu, Yanan Wu, Yuheng Zhao, Shuaibing Zhao, Jin Yang, Siran Yang, Yingshui Tan, …

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library
We introduce ROLL, an efficient, scalable, and user-friendly library designed for Reinforcement Learning Optimization for Large-scale Learning. ROLL caters to three primary user groups: tech pioneers aiming for cost-effective, fault-tolerant large-scale training, developers requiring flexible control over training workflows, and researchers seeking agile experimentation. ROLL is built upon several key modules to serve these user groups effectively. First, a single-controller architecture combin…

Scaling the glassy dynamics of active particles: Tunable fragility and reentrance
Puneet Pareek, Peter Sollich, Saroj Kumar Nandi, Ludovic Berthier
https://arxiv.org/abs/2506.09589

Scaling the glassy dynamics of active particles: Tunable fragility and reentrance
Understanding the influence of activity on dense amorphous assemblies is crucial for biological processes such as wound healing, embryogenesis, or cancer progression. Here, we study the effect of self-propulsion forces of amplitude $f_0$ and persistence time $τ_p$ in dense assemblies of soft repulsive particles, a model system that interpolates between particulate active matter and biological tissues. We identify the fluid and glass phases of the three-dimensional phase diagram obtained by var…

Finite-size scaling of percolation on scale-free networks
Xuewei Zhao, Liwenying Yang, Dan Peng, Run-Ran Liu, Ming Li
https://arxiv.org/abs/2507.05998 http…

Finite-size scaling of percolation on scale-free networks
Critical phenomena on scale-free networks with a degree distribution $p_k \sim k^{-λ}$ exhibit rich finite-size effects due to its structural heterogeneity. We systematically study the finite-size scaling of percolation and identify two distinct crossover routes to mean-field behavior: one controlled by the degree exponent $λ$, the other by the degree cutoff $K \sim V^κ$, where $V$ is the system size and $κ\in [0,1]$ is the cutoff exponent. Increasing $λ$ or decreasing $κ$ suppresses hete…

Scaling Portfolio Diversification with Quantum Circuit Cutting Techniques
Vicente P. Soloviev, Antonio M\'arquez Romero, Josh Kirsopp, Michal Krompiec
https://arxiv.org/abs/2506.08947

Scaling Portfolio Diversification with Quantum Circuit Cutting Techniques
Quantum Approximate Optimization Algorithms (QAOA) have demonstrated a strong potential in addressing graph-based optimization problems. However, the execution of large-scale quantum circuits remains constrained by the limitations of current quantum hardware. In this work, we introduce QuantCut, an automatic framework for circuit cutting that enables efficient execution of large quantum circuits by decomposing entangling two-qubit gates into manageable sub-circuits. Specifically, we focus on ga…

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO
Jonas Svedas, Hannah Watson, Nathan Laubeuf, Diksha Moolchandani, Abubakr Nada, Arjun Singh, Dwaipayan Biswas, James Myers, Debjyoti Bhattacharjee
https://arxiv.org/abs/2506.09275

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO
Distributed deep neural networks (DNNs) have become a cornerstone for scaling machine learning to meet the demands of increasingly complex applications. However, the rapid growth in model complexity far outpaces CMOS technology scaling, making sustainable and efficient system design a critical challenge. Addressing this requires coordinated co-design across software, hardware, and technology layers. Due to the prohibitive cost and complexity of deploying full-scale training systems, simulators …

On the Impact of Classical and Quantum Communication Networks Upon Modular Quantum Computing Architecture System Performance
Pau Escofet, Abhijit Das, Sahar Ben Rached, Santiago Rodrigo, Jordi Domingo, Fabio Sebastiano, Masoud Babaie, Batuhan Keskin, Edoardo Charbon, Peter Haring Bol\'ivar, Maurizio Palesi, Elena Blokhina, Bogdan Staszewski, Avishek Nag, Artur Garcia-S\'aez, Sergi Abadal, Eduard Alarc\'on, Carmen G. Almud\'ever

On the Impact of Classical and Quantum Communication Networks Upon Modular Quantum Computing Architecture System Performance
Modular architectures are a promising approach to scaling quantum computers beyond the limits of monolithic designs. However, non-local communications between different quantum processors might significantly impact overall system performance. In this work, we investigate the role of the network infrastructure in modular quantum computing architectures, focusing on coherence loss due to communication constraints. We analyze the impact of classical network latency on quantum teleportation and ide…

KIS-S: A GPU-Aware Kubernetes Inference Simulator with RL-Based Auto-Scaling
Guilin Zhang, Wulan Guo, Ziqi Tan, Qiang Guan, Hailong Jiang
https://arxiv.org/abs/2507.07932

KIS-S: A GPU-Aware Kubernetes Inference Simulator with RL-Based Auto-Scaling
Autoscaling GPU inference workloads in Kubernetes remains challenging due to the reactive and threshold-based nature of default mechanisms such as the Horizontal Pod Autoscaler (HPA), which struggle under dynamic and bursty traffic patterns and lack integration with GPU-level metrics. We present KIS-S, a unified framework that combines KISim, a GPU-aware Kubernetes Inference Simulator, with KIScaler, a Proximal Policy Optimization (PPO)-based autoscaler. KIScaler learns latency-aware and resour…

Resilient Auto-Scaling of Microservice Architectures with Efficient Resource Management
Hussain Ahmad, Christoph Treude, Markus Wagner, Claudia Szabo
https://arxiv.org/abs/2506.05693

Resilient Auto-Scaling of Microservice Architectures with Efficient Resource Management
Horizontal Pod Auto-scalers (HPAs) are crucial for managing resource allocation in microservice architectures to handle fluctuating workloads. However, traditional HPAs fail to address resource disruptions caused by faults, cyberattacks, maintenance, and other operational challenges. These disruptions result in resource wastage, service unavailability, and HPA performance degradation. To address these challenges, we extend our prior work on Smart HPA and propose SecureSmart HPA, which offers re…