Tootfinder

Scaling #Postgres
https://www.pgedge.com/blog/scaling-postgres

xLSTM Scaling Laws: Competitive Performance with Linear Time-Complexity
Maximilian Beck, Kajetan Schweighofer, Sebastian B\"ock, Sebastian Lehner, Sepp Hochreiter
https://arxiv.org/abs/2510.02228 …

xLSTM Scaling Laws: Competitive Performance with Linear Time-Complexity
Scaling laws play a central role in the success of Large Language Models (LLMs), enabling the prediction of model performance relative to compute budgets prior to training. While Transformers have been the dominant architecture, recent alternatives such as xLSTM offer linear complexity with respect to context length while remaining competitive in the billion-parameter regime. We conduct a comparative investigation on the scaling behavior of Transformers and xLSTM along the following lines, prov…

Generalized Parallel Scaling with Interdependent Generations
Harry Dong, David Brandfonbrener, Eryk Helenowski, Yun He, Mrinal Kumar, Han Fang, Yuejie Chi, Karthik Abinav Sankararaman
https://arxiv.org/abs/2510.01143

Generalized Parallel Scaling with Interdependent Generations
Parallel LLM inference scaling involves sampling a set of $N>1$ responses for a single input prompt. However, these $N$ parallel responses tend to be generated independently from each other, partitioning compute resources and leaving potentially useful information in one generation untapped by others. This is in contrast to response length scaling where past computation is used in all future steps. For higher quality responses and response sets, we propose Bridge to generate interdependent resp…

Parallel Scaling Law: Unveiling Reasoning Generalization through A Cross-Linguistic Perspective
Wen Yang, Junhong Wu, Chong Li, Chengqing Zong, Jiajun Zhang
https://arxiv.org/abs/2510.02272

Parallel Scaling Law: Unveiling Reasoning Generalization through A Cross-Linguistic Perspective
Recent advancements in Reinforcement Post-Training (RPT) have significantly enhanced the capabilities of Large Reasoning Models (LRMs), sparking increased interest in the generalization of RL-based reasoning. While existing work has primarily focused on investigating its generalization across tasks or modalities, this study proposes a novel cross-linguistic perspective to investigate reasoning generalization. This raises a crucial question: $\textit{Does the reasoning capability achieved from E…

Scaling Up: Lessons From The World's Best CEOs And Founders
Great Australian Pods Podcast Directory: https://www.greataustralianpods.com/scaling-up-lessons-from-the-worlds-best-ceos-and-founders/

An interview with Sam Altman and OpenAI President Greg Brockman on the tepid initial reception to GPT-5's launch, scaling, reinforcement learning, AGI, and more (Steven Levy/Wired)
https://www.wired.com/story/sam-altman-says-the-gpt-5-haters-got-it-all-wron…

Sam Altman Says the GPT-5 Haters Got It All Wrong
OpenAI's CEO explains that its large language model has been misunderstood—and that he's changed his attitude to AGI.

Entanglement Complexity in Many-body Systems from Positivity Scaling Laws
Anna O. Schouten, David A. Mazziotti
https://arxiv.org/abs/2509.02944 https://arx…

Entanglement Complexity in Many-body Systems from Positivity Scaling Laws
Area laws describe how entanglement entropy scales and thus provide important necessary conditions for efficient quantum many-body simulation, but they do not, by themselves, yield a direct measure of computational complexity. Here we introduce a complementary framework based on $p$-particle positivity conditions from reduced density matrix (RDM) theory. These conditions form a hierarchy of $N$-representability constraints for an RDM to correspond to a valid $N$-particle quantum system, becomin…

Scaling Legal AI: Benchmarking Mamba and Transformers for Statutory Classification and Case Law Retrieval
Anuraj Maurya
https://arxiv.org/abs/2509.00141 https://

Scaling Legal AI: Benchmarking Mamba and Transformers for Statutory Classification and Case Law Retrieval
The rapid growth of statutory corpora and judicial decisions requires scalable legal AI systems capable of classification and retrieval over extremely long contexts. Transformer-based architectures (e.g., Longformer, DeBERTa) dominate current legal NLP benchmarks but struggle with quadratic attention costs, limiting efficiency and scalability. In this work, we present the first comprehensive benchmarking of Mamba, a state-space model (SSM) with linear-time selective mechanisms, against leading …

Notes on Schauder estimates by scaling for elliptic PDEs in divergence form
Stefano Vita
https://arxiv.org/abs/2510.01765 https://arxiv.org/pdf/2510.01765

Notes on Schauder estimates by scaling for elliptic PDEs in divergence form
These are the notes of a part of the PhD course Regularity for free boundary problems and for elliptic PDEs, held in Pavia in the spring of 2025. The aim is to provide a comprehensive and self-contained treatment of classical interior and local Schauder estimates for second-order linear elliptic PDEs in divergence form via scaling in the spirit of Simon's work. The main techniques presented here are geometric in nature and were primarily developed in the study of geometric problems such as mini…

STM32H750 Trion bringup update: now have the MCU starting up, configuring LDO (no SMPS in this package) for VOS0 scaling level, initializing the PLLs for 450 MHz (nice convenient multiple of 25 MHz, I might consider 475 but that divides some stuff less well so we'll see... Fmax is 480), bringing up the UART, and logging some messages to stdout.
It's very similar to the H735 so most of the setup delay was just checking to see what was different (often nothing) as I went throug…

This guy sounds like a #crackpot but his wiki page suggests otherwise. I’d guess scaling the #desalinization and #purification would be a problem. I don’t know enough

On the chromatic number of random triangle-free graphs
Clayton Mizgerd, Will Perkins, Yuzhou Wang
https://arxiv.org/abs/2509.01376 https://arxiv.org/pdf/25…

On the chromatic number of random triangle-free graphs
We study the chromatic number of typical triangle-free graphs with $Θ\left( n^{3/2} (\log n)^{1/2} \right)$ edges and establish the width of the scaling window for the transitions from $χ= 3$ to $χ= 4$ and from $χ= 4$ to $χ= 5$. The transition from $3$- to $4$-colorability has scaling window of width $Θ(n^{4/3} (\log n)^{-1/3})$. To prove this, we show a high probability equivalence of the $3$-colorability of a random triangle-free graph at this density and the satisfiability of an inst…

Shape Happens: Automatic Feature Manifold Discovery in LLMs via Supervised Multi-Dimensional Scaling
Federico Tiblias, Irina Bigoulaeva, Jingcheng Niu, Simone Balloccu, Iryna Gurevych
https://arxiv.org/abs/2510.01025

Shape Happens: Automatic Feature Manifold Discovery in LLMs via Supervised Multi-Dimensional Scaling
The linear representation hypothesis states that language models (LMs) encode concepts as directions in their latent space, forming organized, multidimensional manifolds. Prior efforts focus on discovering specific geometries for specific features, and thus lack generalization. We introduce Supervised Multi-Dimensional Scaling (SMDS), a model-agnostic method to automatically discover feature manifolds. We apply SMDS to temporal reasoning as a case study, finding that different features form var…

Complex Scaling for the Junction of Semi-infinite Gratings
Fruzsina J. Agocs, Tristan Goodwill, Jeremy Hoskins
https://arxiv.org/abs/2509.02951 https://arx…

Complex Scaling for the Junction of Semi-infinite Gratings
We present and analyze an integral equation method for the scattering of a non-periodic source from a geometry consisting of two semi-infinite, periodic structures glued together in two dimensions. The two structures may involve a periodic wall, several layers of transmission surfaces with a shared period, or periodic sets of obstacles. This integral equation is posed on the infinite interface between the two periodic structures using kernels built out of the Green's function for each structure…

CodeChemist: Functional Knowledge Transfer for Low-Resource Code Generation via Test-Time Scaling
Kaixin Wang, Tianlin Li, Xiaoyu Zhang, Aishan Liu, Xianglong Liu, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, and Bin Shi
https://arxiv.org/abs/2510.00501

CodeChemist: Functional Knowledge Transfer for Low-Resource Code Generation via Test-Time Scaling
Code Large Language Models (CodeLLMs) are increasingly used in code generation tasks across a wide range of applications. However, their performance is often inconsistent across different programming languages (PLs), with low-resource PLs suffering the most due to limited training data. In this paper, we present CodeChemist, a novel and efficient framework for test-time scaling that enables functional knowledge transfer from high-resource to low-resource PLs using generated test cases. CodeChem…

Relaminarization of turbulent pipe flow induced by streamwise traveling wave wall transpiration and its scaling
Christian Bauer, Claus Wagner
https://arxiv.org/abs/2509.03115 ht…

Relaminarization of turbulent pipe flow induced by streamwise traveling wave wall transpiration and its scaling
In technical applications, more than 90\% of the energy required to pump the fluids through pipes is dissipated by turbulence near the wall. In this respect, streamwise traveling waves of wall blowing and suction have been used to relaminarize turbulent pipe flow at a low friction Reynolds number of $\mathrm{Re}_τ=110$, considerably reducing friction losses and energy consumption. Here, we demonstrate that streamwise traveling waves of wall blowing and suction applied to initially turbulent pi…

Go with Your Gut: Scaling Confidence for Autoregressive Image Generation
Harold Haodong Chen, Xianfeng Wu, Wen-Jie Shu, Rongjin Guo, Disen Lan, Harry Yang, Ying-Cong Chen
https://arxiv.org/abs/2509.26376

Go with Your Gut: Scaling Confidence for Autoregressive Image Generation
Test-time scaling (TTS) has demonstrated remarkable success in enhancing large language models, yet its application to next-token prediction (NTP) autoregressive (AR) image generation remains largely uncharted. Existing TTS approaches for visual AR (VAR), which rely on frequent partial decoding and external reward models, are ill-suited for NTP-based image generation due to the inherent incompleteness of intermediate decoding results. To bridge this gap, we introduce ScalingAR, the first TTS fr…

Hot-Ion Modes in Globus-M2 and Saturation of Energy Confinement Time Scaling in Spherical Tokamaks with Toroidal Magnetic Field of 1 T and Above
G. S. Kurskiev, V. B. Minaev, N. V. Sakharov, V. K. Gusev, Yu. V. Petrov, I. V. Miroshnikov, F. V. Chernyshev, N. N. Bakharev, E. O. Kiselev, A. Yu. Telnova, E. E. Tkachenko, N. S. Zhiltsov, Globus- M2 Team
https://

Hot-Ion Modes in Globus-M2 and Saturation of Energy Confinement Time Scaling in Spherical Tokamaks with Toroidal Magnetic Field of 1 T and Above
In a small spherical tokamak with minor radius of 0.22 m and toroidal magnetic field of 1 T, it is possible to heat ions of a sufficiently dense plasma to an extremely high temperature up to 50 million Kelvin. To do this, it is necessary to transfer a sufficiently large torque to stabilize ion-scale turbulence and achieve ion heat transport at neoclassical level reaching extremely low values of plasma collisionality. It is also necessary to ensure good thermal insulation of electrons, which is …

On sources to variabilities of simple cells in the primary visual cortex: A principled theory for the interaction between geometric image transformations and receptive field responses
Tony Lindeberg
https://arxiv.org/abs/2509.02139

On sources to variabilities of simple cells in the primary visual cortex: A principled theory for the interaction between geometric image transformations and receptive field responses
This paper gives an overview of a theory for modelling the interaction between geometric image transformations and receptive field responses for a visual observer that views objects and spatio-temporal events in the environment. This treatment is developed over combinations of (i) uniform spatial scaling transformations, (ii) spatial affine transformations, (iii) Galilean transformations and (iv) temporal scaling transformations. By postulating that the family of receptive fields should be co…

BroRL: Scaling Reinforcement Learning via Broadened Exploration
Jian Hu, Mingjie Liu, Ximing Lu, Fang Wu, Zaid Harchaoui, Shizhe Diao, Yejin Choi, Pavlo Molchanov, Jun Yang, Jan Kautz, Yi Dong
https://arxiv.org/abs/2510.01180

BroRL: Scaling Reinforcement Learning via Broadened Exploration
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a key ingredient for unlocking complex reasoning capabilities in large language models. Recent work ProRL has shown promise in scaling RL by increasing the number of training steps. However, performance plateaus after thousands of steps, with clear diminishing returns from allocating more computation to additional training. In this work, we investigate a complementary paradigm for scaling RL, BroR-Lincreasing the number of rol…

Material parameter influence on the expression of Solitary-Wave-Induced Surface Dilation
Eric Frizzell, Christine Hartzell
https://arxiv.org/abs/2509.03275 https://

Material parameter influence on the expression of Solitary-Wave-Induced Surface Dilation
We formulate a method for predicting peak particle forces in a Solitary Wave (SW) wavefront within a randomly filled 3D granular channel. The SW in our simulation are driven by a sustained impact originating in the bumpy floor of the channel. We show that, when generated in this manner, forces in the driven SW wavefront within the 3D assembly follow the same power law scaling on material properties and impact velocity as in a 1D chain. A simple scaling of the 1D forces matches results from simu…

The evolution of influence operations
from crude Russian troll farms to sophisticated AI systems using large language models;
the discovery of GoLaxy documents revealing a "Smart Propaganda System" that collects millions of data points daily, builds psychological profiles, and generates resilient personas;
the fundamental challenges of measuring effectiveness;
GoLaxy's ties to Chinese intelligence agencies;
operations targeting Hong Kong's…

Theory of Scaling Laws for In-Context Regression: Depth, Width, Context and Time
Blake Bordelon, Mary I. Letey, Cengiz Pehlevan
https://arxiv.org/abs/2510.01098 https://

Theory of Scaling Laws for In-Context Regression: Depth, Width, Context and Time
We study in-context learning (ICL) of linear regression in a deep linear self-attention model, characterizing how performance depends on various computational and statistical resources (width, depth, number of training steps, batch size and data per context). In a joint limit where data dimension, context length, and residual stream width scale proportionally, we analyze the limiting asymptotics for three ICL settings: (1) isotropic covariates and tasks (ISO), (2) fixed and structured covarianc…

Extreme dynamics and relaxation of quantum gases: A hydrodynamic approach
Ritwik Mukherjee, Abhishek Dhar, Manas Kulkarni, Samriddhi Sankar Ray
https://arxiv.org/abs/2509.00399 …

Extreme dynamics and relaxation of quantum gases: A hydrodynamic approach
The evolution of quantum gases, released from traps, are studied through hydrodynamics, both analytically and numerically, in one and two dimensions. In particular, we demonstrate the existence of long time self-similar solutions of the Euler equations, for the density and velocity fields, and derive the scaling exponents as well as the scaling functions. We find that the expanding gas develops a shock front and the size of the cloud grows in time as a powerlaw. We relate the associated exponen…

Rapid Scaling of Compositional Uncertainty from Sample to Population Levels
Yiran Wang, Martin Lysy, Audrey B\'eliveau
https://arxiv.org/abs/2510.00980 https://

Rapid Scaling of Compositional Uncertainty from Sample to Population Levels
Understanding population composition is essential across ecological, evolutionary, conservation, and resource management contexts. Modern methods such as genetic stock identification (GSI) estimate the proportion of individuals from each subpopulation using genetic data. Ideally, these estimates are obtained through mixture analysis, which captures both sampling and genetic uncertainty. However, historical datasets often rely on individual assignment methods that only account for sample-level u…

Scaling properties of nuclear parton distributions in short-range-correlation motivated two-component parametrization
Petja Paakkinen
https://arxiv.org/abs/2510.00252 https://…

Scaling properties of nuclear parton distributions in short-range-correlation motivated two-component parametrization
We provide some critical remarks on the recently proposed two-component parametrization of nuclear parton distribution functions, which was motivated by the apparent correlation between the nuclear modifications of structure functions and nucleon-nucleon short-range correlation phenomena. This parametrization, we show, is invariant under a rescaling transformation of the involved abundance coefficients, which means that the global normalization of these coefficients cannot be meaningfully deter…

From my LinkedIn post: “Telling your dev team to use AI coding tools is like telling your 2010 ops team to use AWS. They didn’t know how to code, they were ticket and click-it VMware people… developers who don’t have product management mindset or have never managed a dev team will fail by trying to micromanage the output of the tool rather than specifying the outcome of the product and managing the agent team to deliver that outcome.”

Back in 2010 when we first started using AWS to run Netflix, the AWS services were very incomplete, somewhat unreliable, had scaling issues, but were super fast to provision compared to filing… | Adrian Cockcroft
Back in 2010 when we first started using AWS to run Netflix, the AWS services were very incomplete, somewhat unreliable, had scaling issues, but were super fast to provision compared to filing tickets and waiting for data center ops teams to do what we need. We worked around the issues, built a new cloud native developer driven architecture and scaled Netflix at speed. At the time many people said we were crazy to run a large enterprise on cloud and would be back in datacenters when it failed. …

The High Cost of Keeping Warm: Characterizing Overhead in Serverless Autoscaling Policies
Leonid Kondrashov, Boxi Zhou, Hancheng Wang, Dmitrii Ustiugov
https://arxiv.org/abs/2509.03104

The High Cost of Keeping Warm: Characterizing Overhead in Serverless Autoscaling Policies
Serverless computing is transforming cloud application development, but the performance-cost trade-offs of control plane designs remain poorly understood due to a lack of open, cross-platform benchmarks and detailed system analyses. In this work, we address these gaps by designing a serverless system that approximates the scaling behaviors of commercial providers, including AWS Lambda and Google Cloud Run. We systematically compare the performance and cost-efficiency of both synchronous and asy…

Generalized Parallel Scaling with Interdependent Generations
Harry Dong, David Brandfonbrener, Eryk Helenowski, Yun He, Mrinal Kumar, Han Fang, Yuejie Chi, Karthik Abinav Sankararaman
https://arxiv.org/abs/2510.01143

Generalized Parallel Scaling with Interdependent Generations
Parallel LLM inference scaling involves sampling a set of $N>1$ responses for a single input prompt. However, these $N$ parallel responses tend to be generated independently from each other, partitioning compute resources and leaving potentially useful information in one generation untapped by others. This is in contrast to response length scaling where past computation is used in all future steps. For higher quality responses and response sets, we propose Bridge to generate interdependent resp…

Are neural scaling laws leading quantum chemistry astray?
Siwoo Lee, Adji Bousso Dieng
https://arxiv.org/abs/2509.26397 https://arxiv.org/pdf/2509.26397

Are neural scaling laws leading quantum chemistry astray?
Neural scaling laws are driving the machine learning community toward training ever-larger foundation models across domains, assuring high accuracy and transferable representations for extrapolative tasks. We test this promise in quantum chemistry by scaling model capacity and training data from quantum chemical calculations. As a generalization task, we evaluate the resulting models' predictions of the bond dissociation energy of neutral H$_2$, the simplest possible molecule. We find that, reg…

SecInfer: Preventing Prompt Injection via Inference-time Scaling
Yupei Liu, Yanting Wang, Yuqi Jia, Jinyuan Jia, Neil Zhenqiang Gong
https://arxiv.org/abs/2509.24967 https://

SecInfer: Preventing Prompt Injection via Inference-time Scaling
Prompt injection attacks pose a pervasive threat to the security of Large Language Models (LLMs). State-of-the-art prevention-based defenses typically rely on fine-tuning an LLM to enhance its security, but they achieve limited effectiveness against strong attacks. In this work, we propose \emph{SecInfer}, a novel defense against prompt injection attacks built on \emph{inference-time scaling}, an emerging paradigm that boosts LLM capability by allocating more compute resources for reasoning dur…

Fractal Rhetorics: Scaling Small and Subtle Feminist Rhetorical Actions
https://ift.tt/sLgBrbp
updated: Monday, November 3, 2025 - 3:29pmfull name / name of organization: Jessi Thomsen, Western…
via Input 4 RELCFP …

Solar Flare Ion Temperatures
Alexander J. B. Russell, Vanessa Polito, Paola Testa, Bart De Pontieu, Sergey A. Belov
https://arxiv.org/abs/2509.01237 https://

Solar Flare Ion Temperatures
This paper proposes that the ion temperature is several times the local electron temperature in the hot onset phase and at the above-the-loop region of solar flares. The paper considers: the evidence of spectral line Doppler widths ("non-thermal" broadening); evidence for "universal" ion and electron temperature increase scaling relations for magnetic reconnection in the solar wind, Earth's magnetopause, Earth's magnetotail and numerical simulations; and thermal equilibration times for onset an…

SMDS-based Rigid Body Localization
Niclas F\"uhrling, Giuseppe Abreu, David Gonz\'alez G., Osvaldo Gonsa
https://arxiv.org/abs/2509.01223 https://…

SMDS-based Rigid Body Localization
We consider a novel rigid body localization (RBL) method, based only on a set of measurements of the distances, as well as the angles between sensors of the vehicle to the anchor landmark points. A key point of the proposed method is to use a variation of the super multidimensional scaling (SMDS) algorithm, where only a minor part of the complex edge kernel is used, based on the available information, which in the case of RBL is anchor-to-anchor and target-to-target information. Simulation resu…

AudioRWKV: Efficient and Stable Bidirectional RWKV for Audio Pattern Recognition
Jiayu Xiong, Jun Xue, Jianlong Kwan, Jing Wang
https://arxiv.org/abs/2509.02167 https://

AudioRWKV: Efficient and Stable Bidirectional RWKV for Audio Pattern Recognition
Recently, Transformers (e.g., Audio Spectrogram Transformers, AST) and state-space models (e.g., Audio Mamba, AuM) have achieved remarkable progress in audio modeling. However, the O(L^2) computational complexity of the Transformer architecture hinders efficient long-sequence processing, while the Mamba architecture tends to become unstable when scaling parameters and data. To address these challenges, this paper proposes AudioRWKV (A-RWKV), a highly efficient and stable architecture for audio …

The non-perturbative sides of the Kardar-Parisi-Zhang equation
L\'eonie Canet
https://arxiv.org/abs/2509.01472 https://arxiv.org/pdf/2509.01472

The non-perturbative sides of the Kardar-Parisi-Zhang equation
The Kardar-Parisi-Zhang (KPZ) equation is a celebrated non-linear stochastic equation yielding non-equilibrium universal scaling. It exhibits notorious non-perturbative aspects. The KPZ fixed point is strong-coupling, all the more in $d>1$. Strikingly, another, even stronger-coupling fixed point of the KPZ equation, called inviscid Burgers fixed point, has been recently unveiled. These non-pertubative features can be theoretically accessed and studied in a controlled way in all dimensions using…

POKEMON: print optimization for kilo-fiber experiments using micro-optics and nanostructures
Megan Delamer, Suvrath Mahadevan, Chad Bender, Ceiwynn Longworth, Roger Angel, Joel Berkson, On To Sonja Choi, Kathleen Gehoski, Andy Monson, Chrisitan Schwab
https://arxiv.org/abs/2509.00181

POKEMON: print optimization for kilo-fiber experiments using micro-optics and nanostructures
The most pressing problems in modern astrophysics have often required the largest telescopes. With the cost scaling of mirror diameters, the field as a whole is faced with a challenge -- how to replicate or improve on the collecting area and sensitivity of the current generation of ELTs, which already boast 30 m class apertures and are multi-billion dollar facilities. One such approach is being pursued by the Large Fiber Array Spectroscopic Telescope (LFAST) -- a scalable array telescope. Each …

Exploring accuracy and uncertainty quantification in physics-informed neural networks for inferring microbial community dynamics
Pedro Fontanarrosa, Chris P Barnes
https://arxiv.org/abs/2509.02282

Exploring accuracy and uncertainty quantification in physics-informed neural networks for inferring microbial community dynamics
Physics-Informed Neural Networks (PINNs) have become a popular way to infer interpretable interaction parameters from noisy microbial time series, but practitioners face many tunable design choices (loss weights, regularisers, scaling, training schedules) with little guidance, and uncertainty is rarely quantified. We present a two-part study using a DeepXDE PINN for a six-species generalised Lotka-Volterra (gLV) model with a known step input and fixed, species-specific step amplitudes, under sy…

Cosmological constraints from the Planck cluster catalogue with DES shear profiles and Chandra observations
G. Aymerich, S. Grandis, M. Douspis, G. W. Pratt, L. Salvati, F. Andrade-Santos, S. Bocquet, M. Costanzi, W. R. Forman, C. Jones, M. Aguena, F. Andrade-Oliveira, D. Bacon, D. Brooks, D. L. Burke, J. Carretero, L. N. da Costa, M. E. da Silva Pereira, T. M. Davis, J. De Vicente, S. Desai, H. T. Diehl, P. Doel, S. Everett, B. Flaugher, J. Frieman, E. Gaztanaga, D. Gruen, G. Gutierre…

Cosmological constraints from the Planck cluster catalogue with DES shear profiles and Chandra observations
We present cosmological constraints from the Planck PSZ2 cosmological cluster sample, using weak-lensing shear profiles from Dark Energy Survey (DES) data and X-ray observations from the Chandra telescope for the mass calibration. We compute hydrostatic mass estimates for all clusters in the PSZ2 sample with a scaling relation between their Sunyaev-Zeldovich signal and X-ray derived hydrostatic mass, calibrated with the Chandra data. We introduce a method to correct these masses with a hydrosta…

VOTA: Parallelizing 6G-RAN Experimentation with Virtualized Over-The-Air Workloads
Chang Liu, T. D. Khoa Le, Rahul Saini, Kishor C. Joshi, George Exarchakos
https://arxiv.org/abs/2509.00130

VOTA: Parallelizing 6G-RAN Experimentation with Virtualized Over-The-Air Workloads
Testbed sharing, a practice in which different researchers concurrently develop independent use cases on top of the same testbed, is ubiquitous in wireless experimental research. Its key drawback is experimental inconvenience: one must delay experiments or tolerate compute and RF interference that harms experimental fidelity. In this paper, we propose \textbf{VOTA}, an open-source, software-only testbed scaling method that leverages real-time virtualization and frequency tuning to maximize para…

🧵 Scaling asyncio on Free-Threaded Python
#python

Scaling asyncio on Free-Threaded Python
A recap on the work done in Python 3.14 to enable asyncio to scale on the free-threaded build of CPython.

Dynamical interface above a hard wall and reflected SPDE on the half-line
Pierre Faug\`ere, Cyril Labb\'e
https://arxiv.org/abs/2509.03328 https://arxi…

Dynamical interface above a hard wall and reflected SPDE on the half-line
We consider a dynamical random interface on the infinite lattice $\mathbb{N}$ evolving according to a "corner flip" dynamic above a hard wall, with an additional pinning at the origin. We study the stationary fluctuations under a diffusive scaling and prove convergence in law towards the solution of an SPDE of Nualart-Pardoux's type, namely the Reflected Stochastic Heat Equation on the half-line. We also obtain that the law of the 3-dimensional Bessel process is an invariant measure for this SP…

FastVGGT: Training-Free Acceleration of Visual Geometry Transformer
You Shen, Zhipeng Zhang, Yansong Qu, Liujuan Cao
https://arxiv.org/abs/2509.02560 https://

FastVGGT: Training-Free Acceleration of Visual Geometry Transformer
Foundation models for 3D vision have recently demonstrated remarkable capabilities in 3D perception. However, scaling these models to long-sequence image inputs remains a significant challenge due to inference-time inefficiency. In this work, we present a detailed analysis of VGGT, a state-of-the-art feed-forward visual geometry model and identify its primary bottleneck. Visualization further reveals a token collapse phenomenon in the attention maps. Motivated by these findings, we explore the …

Competing Dirac masses in one dimension: Symmetry-enhanced pseudo-first-order transition and deconfined criticality
Manuel Weber
https://arxiv.org/abs/2509.02705 https://…

Competing Dirac masses in one dimension: Symmetry-enhanced pseudo-first-order transition and deconfined criticality
Emergent symmetries and slow crossover phenomena are central themes in quantum criticality and manifest themselves in the pseudocritical scaling experienced in the context of deconfined criticality. Here we discover its conceptual counterpart, i.e., a symmetry-enhanced pseudo-first-order transition. It emerges from a one-dimensional realization of deconfined criticality between charge- and bond-ordered states driven by competing Holstein and Su-Schrieffer-Heeger electron-phonon couplings, for w…

Scaling Fabric-Based Piezoresistive Sensor Arrays for Whole-Body Tactile Sensing
Curtis C. Johnson, Daniel Webb, David Hill, Marc D. Killpack
https://arxiv.org/abs/2508.20959 ht…

Scaling Fabric-Based Piezoresistive Sensor Arrays for Whole-Body Tactile Sensing
Scaling tactile sensing for robust whole-body manipulation is a significant challenge, often limited by wiring complexity, data throughput, and system reliability. This paper presents a complete architecture designed to overcome these barriers. Our approach pairs open-source, fabric-based sensors with custom readout electronics that reduce signal crosstalk to less than 3.3% through hardware-based mitigation. Critically, we introduce a novel, daisy-chained SPI bus topology that avoids the practi…

A profile of Jumia, hailed as the "Amazon of Africa", as CEO Francis Dufay tries to hit profitability by 2027 after scaling down its food delivery business (Aanu Adeoye/Financial Times)
https://www.ft.com/content/cb7319c9-3483-43f8-872c-b722ac849716
…

Scaling Equilibrium Propagation to Deeper Neural Network Architectures
Sankar Vinayak. E. P, Gopalakrishnan Srinivasan
https://arxiv.org/abs/2509.26003 https://

Scaling Equilibrium Propagation to Deeper Neural Network Architectures
Equilibrium propagation has been proposed as a biologically plausible alternative to the backpropagation algorithm. The local nature of gradient computations, combined with the use of convergent RNNs to reach equilibrium states, make this approach well-suited for implementation on neuromorphic hardware. However, previous studies on equilibrium propagation have been restricted to networks containing only dense layers or relatively small architectures with a few convolutional layers followed by a…

Interaction-limited conductivity of twisted bilayer graphene revealed by giant terahertz photoresistance
A. L. Shilov, M. Kravtsov, J. Covey, M. A. Kashchenko, O. Popova, X. Zhou, I. Yahniuk, T. Taniguchi, K. Watanabe, A. I. Berdyugin, Y. Wang, S. D. Ganichev, V. Perebeinos, D. A. Svintsov, A. Principi, K. S. Novoselov, D. L. Maslov, D. A. Bandurin
https://

Interaction-limited conductivity of twisted bilayer graphene revealed by giant terahertz photoresistance
Identifying the microscopic processes that limit conductivity is essential for understanding correlated and quantum-critical states in quantum materials. In twisted bilayer graphene (TBG) and other twist-controlled materials, the temperature dependence of metallic resistivity follows power-law scaling, with the exponent spanning a broad range, rendering standard transport measurements insufficient to unambiguously identify the dominant scattering processes and giving rise to competing interpret…

A universal compression theory: Lottery ticket hypothesis and superpolynomial scaling laws
Hong-Yi Wang, Di Luo, Tomaso Poggio, Isaac L. Chuang, Liu Ziyin
https://arxiv.org/abs/2510.00504

A universal compression theory: Lottery ticket hypothesis and superpolynomial scaling laws
When training large-scale models, the performance typically scales with the number of parameters and the dataset size according to a slow power law. A fundamental theoretical and practical question is whether comparable performance can be achieved with significantly smaller models and substantially less data. In this work, we provide a positive and constructive answer. We prove that a generic permutation-invariant function of $d$ objects can be asymptotically compressed into a function of $\ope…

Scaling of the Electrical Conductivity Spectra Reveals Distinct Transport Responses in A2SmTaO6 [A = Ba, Sr, Ca]
Saswata Halder, Binita Ghosh, T. P. Sinha
https://arxiv.org/abs/2508.21621

Scaling of the Electrical Conductivity Spectra Reveals Distinct Transport Responses in A2SmTaO6 [A = Ba, Sr, Ca]
Disorder plays an important role in materials science, influencing material behavior across different length scales. Imperfections like vacancies, atomic substitutions, lattice distortions, and microstructural inhomogeneities, disrupt ideal periodicity thereby altering physical properties. Analogous to spin-glass systems, electrical 'glassiness' arises when charge carriers confront disordered energy landscapes, leading to a broad range of relaxation times, especially in polycrystalline material…

Top-H Decoding: Adapting the Creativity and Coherence with Bounded Entropy in Text Generation
Erfan Baghaei Potraghloo, Seyedarmin Azizi, Souvik Kundu, Massoud Pedram
https://arxiv.org/abs/2509.02510

Top-H Decoding: Adapting the Creativity and Coherence with Bounded Entropy in Text Generation
Large language models (LLMs), despite their impressive performance across a wide range of tasks, often struggle to balance two competing objectives in open-ended text generation: fostering diversity and creativity while preserving logical coherence. Existing truncated sampling techniques, including temperature scaling, top-\$p\$ (nucleus) sampling, and min-\$p\$ sampling, aim to manage this trade-off. However, they exhibit limitations, particularly in the effective incorporation of the confiden…

Scaling Laws and Spectra of Shallow Neural Networks in the Feature Learning Regime
Leonardo Defilippis, Yizhou Xu, Julius Girardin, Emanuele Troiani, Vittorio Erba, Lenka Zdeborov\'a, Bruno Loureiro, Florent Krzakala
https://arxiv.org/abs/2509.24882

Scaling Laws and Spectra of Shallow Neural Networks in the Feature Learning Regime
Neural scaling laws underlie many of the recent advances in deep learning, yet their theoretical understanding remains largely confined to linear models. In this work, we present a systematic analysis of scaling laws for quadratic and diagonal neural networks in the feature learning regime. Leveraging connections with matrix compressed sensing and LASSO, we derive a detailed phase diagram for the scaling exponents of the excess risk as a function of sample complexity and weight decay. This anal…

Google has teased 60-second AI videos from Veo, which sounds impressive until you realize that requires not 10x, but 100x the energy of a 6-second clip.
Analysis confirming quadratic scaling of AI video footprint: https://www.linkedin.com/posts/jonipp…

VerilogMonkey: Exploring Parallel Scaling for Automated Verilog Code Generation with LLMs
Juxin Niu, Yuxin Du, Dan Niu, Xi Wang, Zhe Jiang, Nan Guan
https://arxiv.org/abs/2509.16246

VerilogMonkey: Exploring Parallel Scaling for Automated Verilog Code Generation with LLMs
We present VerilogMonkey, an empirical study of parallel scaling for the under-explored task of automated Verilog generation. Parallel scaling improves LLM performance by sampling many outputs in parallel. Across multiple benchmarks and mainstream LLMs, we find that scaling to hundreds of samples is cost-effective in both time and money and, even without any additional enhancements such as post-training or agentic methods, surpasses prior results on LLM-based Verilog generation. We further diss…

LLM-based Triplet Extraction for Automated Ontology Generation in Software Engineering Standards
Songhui Yue
https://arxiv.org/abs/2509.00140 https://arxiv…

LLM-based Triplet Extraction for Automated Ontology Generation in Software Engineering Standards
Ontologies have supported knowledge representation and whitebox reasoning for decades; thus, the automated ontology generation (AOG) plays a crucial role in scaling their use. Software engineering standards (SES) consist of long, unstructured text (with high noise) and paragraphs with domain-specific terms. In this setting, relation triple extraction (RTE), together with term extraction, constitutes the first stage toward AOG. This work proposes an open-source large language model (LLM)-assiste…

Theory for the spectral splitting exponent of exceptional points
Shu-Xuan Wang, Zhongbo Yan
https://arxiv.org/abs/2509.02174 https://arxiv.org/pdf/2509.021…

Theory for the spectral splitting exponent of exceptional points
Exceptional points (EPs), singularities in non-Hermitian systems where eigenvalues and eigenstates coalesce, exhibit a dramatically enhanced response to perturbations compared to Hermitian degeneracies. This makes them exceptional candidates for sensing applications. The spectral splitting of an $N$th-order EP scales with perturbation strength $ε$ over a wide range, from $ε$ to $ε^{1/N}$. Although the exact scaling exponent can be determined in principle by solving the characteristic equatio…

Shape Happens: Automatic Feature Manifold Discovery in LLMs via Supervised Multi-Dimensional Scaling
Federico Tiblias, Irina Bigoulaeva, Jingcheng Niu, Simone Balloccu, Iryna Gurevych
https://arxiv.org/abs/2510.01025

Shape Happens: Automatic Feature Manifold Discovery in LLMs via Supervised Multi-Dimensional Scaling
The linear representation hypothesis states that language models (LMs) encode concepts as directions in their latent space, forming organized, multidimensional manifolds. Prior efforts focus on discovering specific geometries for specific features, and thus lack generalization. We introduce Supervised Multi-Dimensional Scaling (SMDS), a model-agnostic method to automatically discover feature manifolds. We apply SMDS to temporal reasoning as a case study, finding that different features form var…

🚀 AI's Energy Crisis?
#AI is exploding, but scaling compute with more data & power is too slow and costly—data centers are building nuclear plants! We need smarter ways to turn energy into intelligence.
🧠 Nature-Inspired Innovation At #Extropic rethinks hardware: Probabilistic…

If AI chatbot companies truly had what they claim they have (arbitrary scaling human-level intelligence)—they would use it exclusively themselves, prompting it to come up with schemes to make money and execute them.
In reality these companies all lose money (in historically unprecedented amounts), to fuel a drug dealer-like approach by giving it away for free and hoping enough people get addicted to sycophantic chatbots; with the goal to charge exorbitant fees for it in the future.

Parallelizing Drug Discovery: HPC Pipelines for Alzheimer's Molecular Docking and Simulation
Paul Ruiz Alliata, Diana Rubaga, Daniel Kumlin, Alberto Puliga
https://arxiv.org/abs/2509.00937

Parallelizing Drug Discovery: HPC Pipelines for Alzheimer's Molecular Docking and Simulation
High-performance computing (HPC) is reshaping computational drug discovery by enabling large-scale, time-efficient molecular simulations. In this work, we explore HPC-driven pipelines for Alzheimer's disease drug discovery, focusing on virtual screening, molecular docking, and molecular dynamics simulations. We implemented a parallelised workflow using GROMACS with hybrid MPI-OpenMP strategies, benchmarking scaling performance across energy minimisation, equilibration, and production stages. Ad…

"Chart: Check out how fast China is shifting to renewables"
#China #Energy #Renewables

Chart: Check out how fast China is shifting to renewables
China still emits more planet-warming pollution than any other country, but its furious build-out of wind and solar means the nation is scaling back on…

LOBE-GS: Load-Balanced and Efficient 3D Gaussian Splatting for Large-Scale Scene Reconstruction
Sheng-Hsiang Hung, Ting-Yu Yen, Wei-Fang Sun, Simon See, Shih-Hsuan Hung, Hung-Kuo Chu
https://arxiv.org/abs/2510.01767

LOBE-GS: Load-Balanced and Efficient 3D Gaussian Splatting for Large-Scale Scene Reconstruction
3D Gaussian Splatting (3DGS) has established itself as an efficient representation for real-time, high-fidelity 3D scene reconstruction. However, scaling 3DGS to large and unbounded scenes such as city blocks remains difficult. Existing divide-and-conquer methods alleviate memory pressure by partitioning the scene into blocks, but introduce new bottlenecks: (i) partitions suffer from severe load imbalance since uniform or heuristic splits do not reflect actual computational demands, and (ii) co…

Singly heavy tetraquark resonant states with multiple strange quarks
Xin-He Zheng, Yao Ma, Shi-Lin Zhu
https://arxiv.org/abs/2510.01505 https://arxiv.org/p…

Singly heavy tetraquark resonant states with multiple strange quarks
We systematically investigate the S-wave singly heavy tetraquark systems containing two or three strange quarks, $Qs\bar{s}\bar{s}$, $Qn\bar{s}\bar{s}$ and $Qs\bar{s}\bar{n}\left( Q=c,b,n=u,d \right) $, within the constituent quark potential model. We solve the four-body Schrödinger equation using the Gaussian expansion method (GEM) and identify resonances via the complex scaling method (CSM). There are no bound states below the lowest two-meson thresholds. We obtain several compact resonances…

The Nearest-Neighbor Derivative Process: Modeling Spatial Rates of Change in Massive Datasets
Jiawen Chen, Aritra Halder, Yun Li, Sudipto Banerjee, Didong Li
https://arxiv.org/abs/2509.02752

The Nearest-Neighbor Derivative Process: Modeling Spatial Rates of Change in Massive Datasets
Gaussian processes (GPs) are instrumental in modeling spatial processes, offering precise interpolation and prediction capabilities across fields such as environmental science and biology. Recently, there has been growing interest in extending GPs to infer spatial derivatives, which are vital for analyzing spatial dynamics and detecting subtle changes in data patterns. Despite their utility, traditional GPs suffer from computational inefficiencies, due to the cubic scaling with the number of sp…

Boundary Renormalization Group Flow of Entanglement Entropy at a (2 1)-Dimensional Quantum Critical Point
Zhiyan Wang, Zhe Wang, Yi-Ming Ding, Zenan Liu, Zheng Yan, Long Zhang
https://arxiv.org/abs/2509.02044

Boundary Renormalization Group Flow of Entanglement Entropy at a (2+1)-Dimensional Quantum Critical Point
We investigate the second order Rényi entanglement entropy at the quantum critical point of spin-1/2 antiferromagnetic Heisenberg model on a columnar dimerized square lattice. The universal constant $γ$ in the area-law scaling $S_{2}(\ell) = α\ell - γ$ is found to be sensitive to the entangling surface configurations, with $γ_{\text{sp}} > 0$ for strong-bond-cut (special) surfaces and $γ_{\text{ord}} < 0$ for weak-bond-cut (ordinary) surfaces, which is attributed to the distinct conformal…

Correlation estimates for Brownian particles with singular interactions
Mitia Duerinckx, Pierre-Emmanuel Jabin
https://arxiv.org/abs/2510.01507 https://arx…

Correlation estimates for Brownian particles with singular interactions
We study particle systems with singular pairwise interactions and non-vanishing diffusion in the mean-field scaling. A classical approach to describing corrections to mean-field behavior is through the analysis of correlation functions. For bounded interactions, the optimal estimates on correlations are well known: the $m$-particle correlation function is $G_{N,m}=O(N^{1-m})$ for all $m$. Such estimates, however, have remained out of reach for more singular interactions. In this work, we develo…

Self-similar kinetics for gravitational Bose-Einstein condensation
A. S. Dmitriev, D. G. Levkov, A. G. Panin, I. I. Tkachev
https://arxiv.org/abs/2509.02694 https://

Self-similar kinetics for gravitational Bose-Einstein condensation
We study an overpopulated gas of gravitationally interacting bosons surrounding a droplet of Bose-Einstein condensate - Bose star. We argue that kinetic evolution of this gas approaches with time a self-similar attractor solution to the kinetic equation. If the scale symmetry of the equation is broken by external conditions, the attractor solution exists, remains approximately self-similar, but has slowly drifting scaling dimension. The latter new regime of adiabatic self-similarity can determi…

LatentEvolve: Self-Evolving Test-Time Scaling in Latent Space
Guibin Zhang, Fanci Meng, Guancheng Wan, Zherui Li, Kun Wang, Zhenfei Yin, Lei Bai, Shuicheng Yan
https://arxiv.org/abs/2509.24771

LatentEvolve: Self-Evolving Test-Time Scaling in Latent Space
Test-time Scaling (TTS) has been demonstrated to significantly enhance the reasoning capabilities of Large Language Models (LLMs) during the inference phase without altering model parameters. However, existing TTS methods are largely independent, implying that LLMs have not yet evolved to progressively learn how to scale more effectively. With the objective of evolving LLMs to learn ``how to scale test-time computation,'' we propose LatentEvolve, a self-evolving latent TTS framework inspired by…

Scaling rules for multiphase flow in vertical Venturis
Meng-Ke Zhan, Cheng-Gang Xie, Jian-Jun Shu
https://arxiv.org/abs/2509.23786 https://arxiv.org/pdf/25…

Scaling rules for multiphase flow in vertical Venturis
Employing a suitable scaling rule in gas-liquid flow can produce dynamically comparable results, which helps in the development of flow models applicable to a wide range of flow conditions and reduces the carbon footprint; however, matching all dimensionless numbers in gas-liquid flow is a challenge. This study uses a computational fluid dynamics approach to identify key dimensionless numbers that can produce dimensionally equivalent results under a variety of flow conditions in the vertical Ve…

An Efficient Deep Template Matching and In-Plane Pose Estimation Method via Template-Aware Dynamic Convolution
Ke Jia, Ji Zhou, Hanxin Li, Zhigan Zhou, Haojie Chu, Xiaojie Li
https://arxiv.org/abs/2510.01678

An Efficient Deep Template Matching and In-Plane Pose Estimation Method via Template-Aware Dynamic Convolution
In industrial inspection and component alignment tasks, template matching requires efficient estimation of a target's position and geometric state (rotation and scaling) under complex backgrounds to support precise downstream operations. Traditional methods rely on exhaustive enumeration of angles and scales, leading to low efficiency under compound transformations. Meanwhile, most deep learning-based approaches only estimate similarity scores without explicitly modeling geometric pose, making …

Recursive Self-Aggregation Unlocks Deep Thinking in Large Language Models
Siddarth Venkatraman, Vineet Jain, Sarthak Mittal, Vedant Shah, Johan Obando-Ceron, Yoshua Bengio, Brian R. Bartoldson, Bhavya Kailkhura, Guillaume Lajoie, Glen Berseth, Nikolay Malkin, Moksh Jain
https://arxiv.org/abs/2509.26626…

Recursive Self-Aggregation Unlocks Deep Thinking in Large Language Models
Test-time scaling methods improve the capabilities of large language models (LLMs) by increasing the amount of compute used during inference to make a prediction. Inference-time compute can be scaled in parallel by choosing among multiple independent solutions or sequentially through self-refinement. We propose Recursive Self-Aggregation (RSA), a test-time scaling method inspired by evolutionary methods that combines the benefits of both parallel and sequential scaling. Each step of RSA refines…

Scaling Accessibility Education: Reflections from a Workshop Targeting CS Educators and Software Professionals
P D Parthasarathy, Anshu M Mittal, Swaroop Joshi
https://arxiv.org/abs/2509.22759

Scaling Accessibility Education: Reflections from a Workshop Targeting CS Educators and Software Professionals
Despite growing global attention to digital accessibility, research from India highlights a significant gap in accessibility training for both computing educators and software professionals. To address this need, we designed and conducted an experiential workshop aimed at building foundational capacity in accessibility practices among 77 participants, including computer science (CS) faculty and industry practitioners. The one-day workshop combined hands-on activities, tool demonstrations, and c…

Crosslisted article(s) found for physics.chem-ph. https://arxiv.org/list/physics.chem-ph/new
[1/1]:
- Entanglement Complexity in Many-body Systems from Positivity Scaling Laws
Anna O. Schouten, David A. Mazziotti

Geometry-induced criticality in $p$-adic scaling limits of random walks
Rahul Rajkumar, David Weisbart
https://arxiv.org/abs/2509.24234 https://arxiv.org/p…

Geometry-induced criticality in $p$-adic scaling limits of random walks
An anisotropy parameter $h$ in $(0,1]$ induces on $\mathbb{Q}_p^2$ a duality-compatible, two-scale filtration that collapses to one scale at the right endpoint. This filtration defines shell-uniform transition laws for hierarchical random walks on a discrete group whose scaling limits are Lévy processes on $\mathbb{Q}_p^2$. The diffusion constants of the coordinate processes jump at the right endpoint, even though the radial jump law depends continuously on $h$. This instance of geometry-induc…

Metadata-Guided Adaptable Frequency Scaling across Heterogeneous Applications and Devices
Jinqi Yan, Fang He, Qianlong Sang, Bifeng Tong, Peng Sun, Yili Gong, Chuang Hu, Dazhao Cheng
https://arxiv.org/abs/2509.22707

Metadata-Guided Adaptable Frequency Scaling across Heterogeneous Applications and Devices
Dynamic Voltage and Frequency Scaling is essential for enhancing energy efficiency in mobile platforms. However, traditional heuristic-based governors are increasingly inadequate for managing the complexity of heterogeneous System-on-Chip designs and diverse application workloads. Although reinforcement learning approaches offer improved performance, their poor generalization capability and reliance on extensive retraining for each hardware and application combination leads to significant deplo…

MGM-Omni: Scaling Omni LLMs to Personalized Long-Horizon Speech
Chengyao Wang, Zhisheng Zhong, Bohao Peng, Senqiao Yang, Yuqi Liu, Haokun Gui, Bin Xia, Jingyao Li, Bei Yu, Jiaya Jia
https://arxiv.org/abs/2509.25131

MGM-Omni: Scaling Omni LLMs to Personalized Long-Horizon Speech
We present MGM-Omni, a unified Omni LLM for omni-modal understanding and expressive, long-horizon speech generation. Unlike cascaded pipelines that isolate speech synthesis, MGM-Omni adopts a "brain-mouth" design with a dual-track, token-based architecture that cleanly decouples multimodal reasoning from real-time speech generation. This design enables efficient cross-modal interaction and low-latency, streaming speech generation. For understanding, a unified training strategy coupled with a du…

Computing Linear Combinations of $\varphi$-Function Actions for Exponential Integrators
Awad H. Al-Mohy
https://arxiv.org/abs/2509.26475 https://arxiv.org/…

Computing Linear Combinations of $φ$-Function Actions for Exponential Integrators
We propose a matrix-free algorithm for evaluating linear combinations of $φ$-function actions, $w_i := \sum_{j=0}^{p} α_i^{\,j}\,φ_j(t_i A)v_j$ for $i=1\colon r$, arising in exponential integrators. The method combines the scaling and recovering method with a truncated Taylor series, choosing a spectral shift and a scaling parameter by minimizing a power-based objective of the shifted operator. Accuracy is user-controlled and ultimately limited by the working precision. The algorithm decoupl…

Scaling Spoken Language Models with Syllabic Speech Tokenization
Nicholas Lee, Cheol Jun Cho, Alan W Black, Gopala K. Anumanchipalli
https://arxiv.org/abs/2509.26634 https://

Scaling Spoken Language Models with Syllabic Speech Tokenization
Spoken language models (SLMs) typically discretize speech into high-frame-rate tokens extracted from SSL speech models. As the most successful LMs are based on the Transformer architecture, processing these long token streams with self-attention is expensive, as attention scales quadratically with sequence length. A recent SSL work introduces acoustic tokenization of speech at the syllable level, which is more interpretable and potentially more scalable with significant compression in token len…

Sharp unconditional well-posedness of the 2-$d$ periodic cubic hyperbolic nonlinear Schr\"odinger equation
Engin Ba\c{s}ako\u{g}lu, Tadahiro Oh, Yuzhao Wang
https://arxiv.org/abs/2509.01650

Sharp unconditional well-posedness of the 2-$d$ periodic cubic hyperbolic nonlinear Schrödinger equation
We study semilinear local well-posedness of the two-dimensional periodic cubic hyperbolic nonlinear Schrödinger equation (HNLS) in Fourier-Lebesgue spaces. By employing the Fourier restriction norm method, we first establish sharp semilinear local well-posedness of HNLS in Fourier-Lebesgue spaces (modulo the endpoint case), including almost scaling-critical Fourier-Lebesgue spaces. Then, by adapting the normal form approach, developed by the second author with Guo and Kwon (2013) and by the se…

SIRI: Scaling Iterative Reinforcement Learning with Interleaved Compression
Haoming Wen, Yushi Bai, Juanzi Li, Jie Tang
https://arxiv.org/abs/2509.25176 https://

SIRI: Scaling Iterative Reinforcement Learning with Interleaved Compression
We introduce SIRI, Scaling Iterative Reinforcement Learning with Interleaved Compression, a simple yet effective RL approach for Large Reasoning Models (LRMs) that enables more efficient and accurate reasoning. Existing studies have observed repetitive thinking patterns in LRMs, and attempts to reduce them often come at the cost of performance. In this paper, we show that this trade-off can be overcome through a training regime that iteratively alternates between compressing and expanding the r…

Scaling LLM Test-Time Compute with Mobile NPU on Smartphones
Zixu Hao, Jianyu Wei, Tuowei Wang, Minxing Huang, Huiqiang Jiang, Shiqi Jiang, Ting Cao, Ju Ren
https://arxiv.org/abs/2509.23324

Scaling LLM Test-Time Compute with Mobile NPU on Smartphones
Deploying Large Language Models (LLMs) on mobile devices faces the challenge of insufficient performance in smaller models and excessive resource consumption in larger ones. This paper highlights that mobile Neural Processing Units (NPUs) have underutilized computational resources, particularly their matrix multiplication units, during typical LLM inference. To leverage this wasted compute capacity, we propose applying parallel test-time scaling techniques on mobile NPUs to enhance the performa…

Cosmic domain walls on a lattice: illusive effects of initial conditions
I. Dankovsky, S. Ramazanov, E. Babichev, D. Gorbunov, A. Vikman
https://arxiv.org/abs/2509.25367 https:/…

Cosmic domain walls on a lattice: illusive effects of initial conditions
Evolution of cosmic domain walls (DWs) settles to the scaling solution, which is often assumed to be independent of initial conditions. However, lattice simulations performed in this work reveal a clear dependence of the scaling DW area on the initial configuration of the sourcing scalar field, specifically, its infrared (IR) properties. Namely, the DW area grows as one suppresses IR modes in the initial scalar field spectrum. This growth is saturated, when the area parameter $ξ$ commonly used…

Cambridge, UK-based Paragraf, which makes graphene-based electronics using standard semiconductor processes, raised a $55M Series C to scale its manufacturing (Tamara Djurickovic/Tech.eu)
https://tech.eu/2025/08/25/paragraf-closes-55m-series-c-funding-round/…

Paragraf closes $55M Series C funding round
Paragraf, the UK-based company leading the way in mass-producing graphene-based electronics using industry-standard semiconductor processes, has completed a $55 million Series C funding round. The funding will speed up the scaling of Paragraf’s manufacturing capabilities and increase production capacity, paving the way for graphene electronics to enter mass markets.Paragraf’s technology fits smoothly into the existing semiconductor ecosystem, offering customers ready-to-use solutions…

Scaling with Collapse: Efficient and Predictable Training of LLM Families
Shane Bergsma, Bin Claire Zhang, Nolan Dey, Shaheer Muhammad, Gurpreet Gosal, Joel Hestness
https://arxiv.org/abs/2509.25087

Scaling with Collapse: Efficient and Predictable Training of LLM Families
Effective LLM training relies on *consistency*, meaning that key quantities -- such as final losses and optimal hyperparameters -- scale predictably across model sizes. Qiu et al. (2025) recently showed that this consistency extends beyond scalars: whole training loss curves can *collapse* onto a universal trajectory after a simple normalization. What remains unclear is whether this phenomenon holds for LLM families trained under *practical scaling recipes*, where width, depth, learning rate, b…

Conditional Multidimensional Scaling with Incomplete Conditioning Data
Anh Tuan Bui
https://arxiv.org/abs/2509.16627 https://arxiv.org/pdf/2509.16627

Conditional Multidimensional Scaling with Incomplete Conditioning Data
Conditional multidimensional scaling seeks for a low-dimensional configuration from pairwise dissimilarities, in the presence of other known features. By taking advantage of available data of the known features, conditional multidimensional scaling improves the estimation quality of the low-dimensional configuration and simplifies knowledge discovery tasks. However, existing conditional multidimensional scaling methods require full data of the known features, which may not be always attainable …

Breakdown of Kolmogorov Scaling and Modified Energy Transfer in Bubble-Laden Turbulence
Andrea Montessori, Marco Lauricella, Aritra Mukherjee, Luca Brandt
https://arxiv.org/abs/2509.22324

Breakdown of Kolmogorov Scaling and Modified Energy Transfer in Bubble-Laden Turbulence
We investigate the effect of a dispersed bubble phase on forced homogeneous and isotropic turbulence using high-resolution high-performance simulations based on the lattice Boltzmann method. While the classical Kolmogorov energy cascade is largely preserved when considering the system as a whole, a phase-specific analysis reveals striking deviations from the classical turbulence scaling. In particular, the gas phase exhibits significant departures from Kolmogorov's predictions, whereas the cont…

Symmetry of concentration and scaling for self-bounding functions
George Crowley, I\~naki Esnaola
https://arxiv.org/abs/2509.22375 https://arxiv.org/pdf/25…

Symmetry of concentration and scaling for self-bounding functions
We prove generalised concentration inequalities for a class of scaled self-bounding functions of independent random variables, referred to as ${(M,a,b)}$ self-bounding. The scaling refers to the fact that the component-wise difference is upper bounded by an arbitrary positive real number $M$ instead of the case $M=1$ previously considered in the literature. Using the entropy method, we derive symmetric bounds for both the upper and lower tails, and study the tightness of the proposed bounds. Ou…

Scaling Generalist Data-Analytic Agents
Shuofei Qiao, Yanqiu Zhao, Zhisong Qiu, Xiaobin Wang, Jintian Zhang, Zhao Bin, Ningyu Zhang, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen
https://arxiv.org/abs/2509.25084

Scaling Generalist Data-Analytic Agents
Data-analytic agents are emerging as a key catalyst for automated scientific discovery and for the vision of Innovating AI. Current approaches, however, rely heavily on prompt engineering over proprietary models, while open-source models struggle to face diverse-format, large-scale data files and long-horizon, multi-step reasoning that real-world analytics demands. This paper introduces DataMind, a scalable data synthesis and agent training recipe designed to build generalist data-analytic agen…

Redesigning GROMACS Halo Exchange: Improving Strong Scaling with GPU-initiated NVSHMEM
Mahesh Doijade, Andrey Alekseenko, Ania Brown, Alan Gray, Szil\'ard P\'all
https://arxiv.org/abs/2509.21527

Redesigning GROMACS Halo Exchange: Improving Strong Scaling with GPU-initiated NVSHMEM
Improving time-to-solution in molecular dynamics simulations often requires strong scaling due to fixed-sized problems. GROMACS is highly latency-sensitive, with peak iteration rates in the sub-millisecond, making scalability on heterogeneous supercomputers challenging. MPI's CPU-centric nature introduces additional latencies on GPU-resident applications' critical path, hindering GPU utilization and scalability. To address these limitations, we present an NVSHMEM-based GPU kernel-initiated rede…

Quantifying non-equilibrium pressure-gradient turbulent boundary layers through a symmetry-based framework
Wei-Tao Bi, Ke-Xin Zheng, Jun Chen, Zhen-Su She
https://arxiv.org/abs/2508.21447

Quantifying non-equilibrium pressure-gradient turbulent boundary layers through a symmetry-based framework
This study establishes a symmetry-based framework to quantify non-equilibrium processes in complex pressure gradient (PG) turbulent boundary layers (TBLs), using a Lie-group-informed dilation-symmetry-breaking formalism. We derive a universal multilayer defect scaling law for the evolution of total shear stress (TSS). The law shows that gradually varying adverse pressure gradients (APGs) break the dilation symmetry in the two-layer defect scaling of equilibrium TSS, leading to three-layer TSS s…

Despite hype and optimistic projections, the humanoid robot industry faces hurdles, from battery life and design to limited demand for large-scale deployments (Evan Ackerman/IEEE Spectrum)
https://spectrum.ieee.org/humanoid-robot-scaling

Humanoid Robots: The Scaling Challenge
​It takes more than building a humanoid robot to build a humanoid robot product.

VideoChat-R1.5: Visual Test-Time Scaling to Reinforce Multimodal Reasoning by Iterative Perception
Ziang Yan, Xinhao Li, Yinan He, Zhengrong Yue, Xiangyu Zeng, Yali Wang, Yu Qiao, Limin Wang, Yi Wang
https://arxiv.org/abs/2509.21100

VideoChat-R1.5: Visual Test-Time Scaling to Reinforce Multimodal Reasoning by Iterative Perception
Inducing reasoning in multimodal large language models (MLLMs) is critical for achieving human-level perception and understanding. Existing methods mainly leverage LLM reasoning to analyze parsed visuals, often limited by static perception stages. This paper introduces Visual Test-Time Scaling (VTTS), a novel approach to enhance MLLMs' reasoning via iterative perception during inference. VTTS mimics humans' hierarchical attention by progressively refining focus on high-confidence spatio-tempora…

Can Structured Templates Facilitate LLMs in Tackling Harder Tasks? : An Exploration of Scaling Laws by Difficulty
Zhichao Yang, Zhaoxin Fan, Gen Li, Yuanze Hu, Xinyu Wang, Ye Qiu, Xin Wang, Yifan Sun, Wenjun Wu
https://arxiv.org/abs/2508.19069

Can Structured Templates Facilitate LLMs in Tackling Harder Tasks? : An Exploration of Scaling Laws by Difficulty
Structured, procedural reasoning is essential for Large Language Models (LLMs), especially in mathematics. While post-training methods have improved LLM performance, they still fall short in capturing deep procedural logic on complex tasks. To tackle the issue, in this paper, we first investigate this limitation and uncover a novel finding: a Scaling Law by Difficulty, which reveals that model performance follows a U-shaped curve with respect to training data complexity -- excessive low-difficu…

Investigating Test-Time Scaling with Reranking for Machine Translation
Shaomu Tan, Ryosuke Mitani, Ritvik Choudhary, Toshiyuki Sekiya
https://arxiv.org/abs/2509.19020 https://…

Input Time Scaling
Rapheal Huang (Yuming), Weilong Guo
https://arxiv.org/abs/2508.13654 https://arxiv.org/pdf/2508.13654

Input Time Scaling
Current Large Language Models (LLMs) are usually post-trained on large-scale carefully curated datasets (data & training scaling) and doing reasoning in test time (inference time scaling). In this work, we present a new scaling paradigm, Input Time Scaling, to complement previous scaling methods by putting resources on queries (input time). During training and testing, we combine meta-knowledge from LLMs to refine inputs with different strategies. We also find a new phenomenon, training-testing…

Mitigating Strategy-Selection Bias in Reasoning for More Effective Test-Time Scaling
Zongqian Wu, Baoduo Xu, Tianyu Li, Zhu Sun, Xiaofeng Zhu, Lei Feng
https://arxiv.org/abs/2509.17905

Mitigating Strategy-Selection Bias in Reasoning for More Effective Test-Time Scaling
Test-time scaling (TTS) has been shown to improve the performance of large language models (LLMs) by sampling and aggregating diverse reasoning paths. However, existing research has overlooked a critical issue: selection bias of reasoning strategies during scaling. Specifically, when generating reasoning processes, LLMs tend to follow certain strategies (e.g., algebraic solutions for math problems) while neglecting other valid alternatives (e.g., geometric solutions), resulting in insufficient …

Logical Reasoning with Outcome Reward Models for Test-Time Scaling
Ramya Keerthy Thatikonda, Wray Buntine, Ehsan Shareghi
https://arxiv.org/abs/2508.19903 https://

Logical Reasoning with Outcome Reward Models for Test-Time Scaling
Logical reasoning is a critical benchmark for evaluating the capabilities of large language models (LLMs), as it reflects their ability to derive valid conclusions from given premises. While the combination of test-time scaling with dedicated outcome or process reward models has opened up new avenues to enhance LLMs performance in complex reasoning tasks, this space is under-explored in deductive logical reasoning. We present a set of Outcome Reward Models (ORMs) for deductive reasoning. To tra…

Closed-form $\ell_r$ norm scaling with data for overparameterized linear regression and diagonal linear networks under $\ell_p$ bias
Shuofeng Zhang, Ard Louis
https://arxiv.org/abs/2509.21181

Closed-form $\ell_r$ norm scaling with data for overparameterized linear regression and diagonal linear networks under $\ell_p$ bias
For overparameterized linear regression with isotropic Gaussian design and minimum-$\ell_p$ interpolator $p\in(1,2]$, we give a unified, high-probability characterization for the scaling of the family of parameter norms $ \\{ \lVert \widehat{w_p} \rVert_r \\}_{r \in [1,p]} $ with sample size. We solve this basic, but unresolved question through a simple dual-ray analysis, which reveals a competition between a signal *spike* and a *bulk* of null coordinates in $X^\top Y$, yielding closed-form …

ScaleDiff: Scaling Difficult Problems for Advanced Mathematical Reasoning
Qizhi Pei, Zhuoshi Pan, Honglin Lin, Xin Gao, Yu Li, Zinan Tang, Conghui He, Rui Yan, Lijun Wu
https://arxiv.org/abs/2509.21070

ScaleDiff: Scaling Difficult Problems for Advanced Mathematical Reasoning
Large Reasoning Models (LRMs) have shown impressive capabilities in complex problem-solving, often benefiting from training on difficult mathematical problems that stimulate intricate reasoning. Recent efforts have explored automated synthesis of mathematical problems by prompting proprietary models or large-scale open-source models from seed data or inherent mathematical concepts. However, scaling up these methods remains challenging due to their high computational/API cost, complexity of prom…

MIRAGE: Scaling Test-Time Inference with Parallel Graph-Retrieval-Augmented Reasoning Chains
Kaiwen Wei, Rui Shan, Dongsheng Zou, Jianzhong Yang, Bi Zhao, Junnan Zhu, Jiang Zhong
https://arxiv.org/abs/2508.18260

MIRAGE: Scaling Test-Time Inference with Parallel Graph-Retrieval-Augmented Reasoning Chains
Large reasoning models (LRMs) have shown significant progress in test-time scaling through chain-of-thought prompting. Current approaches like search-o1 integrate retrieval augmented generation (RAG) into multi-step reasoning processes but rely on a single, linear reasoning chain while incorporating unstructured textual information in a flat, context-agnostic manner. As a result, these approaches can lead to error accumulation throughout the reasoning chain, which significantly limits its effec…