Tootfinder

Timestep-Compressed Attack on Spiking Neural Networks through Timestep-Level Backpropagation
Donghwa Kang, Doohyun Kim, Sang-Ki Ko, Jinkyu Lee, Hyeongboo Baek, Brent ByungHoon Kang
https://arxiv.org/abs/2508.13812

Timestep-Compressed Attack on Spiking Neural Networks through Timestep-Level Backpropagation
State-of-the-art (SOTA) gradient-based adversarial attacks on spiking neural networks (SNNs), which largely rely on extending FGSM and PGD frameworks, face a critical limitation: substantial attack latency from multi-timestep processing, rendering them infeasible for practical real-time applications. This inefficiency stems from their design as direct extensions of ANN paradigms, which fail to exploit key SNN properties. In this paper, we propose the timestep-compressed attack (TCA), a novel fr…

Quantization Meets Spikes: Lossless Conversion in the First Timestep via Polarity Multi-Spike Mapping
Hangming Zhang, Zheng Li, Qiang Yu
https://arxiv.org/abs/2508.14520 https:/…

Quantization Meets Spikes: Lossless Conversion in the First Timestep via Polarity Multi-Spike Mapping
Spiking neural networks (SNNs) offer advantages in computational efficiency via event-driven computing, compared to traditional artificial neural networks (ANNs). While direct training methods tackle the challenge of non-differentiable activation mechanisms in SNNs, they often suffer from high computational and energy costs during training. As a result, ANN-to-SNN conversion approach still remains a valuable and practical alternative. These conversion-based methods aim to leverage the discrete …

Sparse, Geometry- and Material-Aware Bases for Multilevel Elastodynamic Simulation
Ty Trusty, David I. W. Levin, Danny M. Kaufman
https://arxiv.org/abs/2508.13386 https://

Sparse, Geometry- and Material-Aware Bases for Multilevel Elastodynamic Simulation
We present a multi-level elastodynamics timestep solver for accelerating incremental potential contact (IPC) simulations. Our method retains the robustness of gold standard IPC in the face of intricate geometry, complex heterogeneous material distributions and high resolution input data without sacrificing visual fidelity (per-timestep relative displacement error of $\approx1\%$). The success of our method is enabled by a novel, sparse, geometry- and material-aware basis construction method whi…

SDSNN: A Single-Timestep Spiking Neural Network with Self-Dropping Neuron and Bayesian Optimization
Changqing Xu, Buxuan Song, Yi Liu, Xinfang Liao, Wenbin Zheng, Yintang Yang
https://arxiv.org/abs/2508.10913

SDSNN: A Single-Timestep Spiking Neural Network with Self-Dropping Neuron and Bayesian Optimization
Spiking Neural Networks (SNNs), as an emerging biologically inspired computational model, demonstrate significant energy efficiency advantages due to their event-driven information processing mechanism. Compared to traditional Artificial Neural Networks (ANNs), SNNs transmit information through discrete spike signals, which substantially reduces computational energy consumption through their sparse encoding approach. However, the multi-timestep computation model significantly increases inferenc…

NEURAL: An Elastic Neuromorphic Architecture with Hybrid Data-Event Execution and On-the-fly Attention Dataflow
Yuehai Chen, Farhad Merchant
https://arxiv.org/abs/2509.15036 htt…

NEURAL: An Elastic Neuromorphic Architecture with Hybrid Data-Event Execution and On-the-fly Attention Dataflow
Spiking neural networks (SNNs) have emerged as a promising alternative to artificial neural networks (ANNs), offering improved energy efficiency by leveraging sparse and event-driven computation. However, existing hardware implementations of SNNs still suffer from the inherent spike sparsity and multi-timestep execution, which significantly increase latency and reduce energy efficiency. This study presents NEURAL, a novel neuromorphic architecture based on a hybrid data-event execution paradigm…

A new Timestep Criterion for the Simulation of Immiscible Two-Phase Flow with IMPES Solvers
Dominik Burr, Stefan Rief, Konrad Steiner
https://arxiv.org/abs/2510.00577 https://…

A new Timestep Criterion for the Simulation of Immiscible Two-Phase Flow with IMPES Solvers
We present an iterative IMPES solver and a novel timestep criterion for the simulation of immiscible two-phase flow involving compressible fluid phases. The novel timestep criterion uses the Courant-Friedrichs-Lewy (CFL) condition and employs numerically computed velocity derivatives to adapt the timestep size, regardless of the dominant flow characteristics. The solver combined with this timestep criterion demonstrates both efficiency and robustness across a range of flow scenarios, including …

Another timestep of the #Pleiades #occultation by the #Moon, at 21:26 UTC - Merope had just emerged behind the dark side. Interestingly no stars whatsoever could be seen in 11 x 70 binoculars though the Moon - nice terminator - was a beauty. The Moon's elevation was 15°; 1/4 second at f/3.2 and ISO 200.

Compute Only 16 Tokens in One Timestep: Accelerating Diffusion Transformers with Cluster-Driven Feature Caching
Zhixin Zheng, Xinyu Wang, Chang Zou, Shaobo Wang, Linfeng Zhang
https://arxiv.org/abs/2509.10312

Compute Only 16 Tokens in One Timestep: Accelerating Diffusion Transformers with Cluster-Driven Feature Caching
Diffusion transformers have gained significant attention in recent years for their ability to generate high-quality images and videos, yet still suffer from a huge computational cost due to their iterative denoising process. Recently, feature caching has been introduced to accelerate diffusion transformers by caching the feature computation in previous timesteps and reusing it in the following timesteps, which leverage the temporal similarity of diffusion models while ignoring the similarity in…

OMGSR: You Only Need One Mid-timestep Guidance for Real-World Image Super-Resolution
Zhiqiang Wu, Zhaomang Sun, Tong Zhou, Bingtao Fu, Ji Cong, Yitong Dong, Huaqi Zhang, Xuan Tang, Mingsong Chen, Xian Wei
https://arxiv.org/abs/2508.08227

OMGSR: You Only Need One Mid-timestep Guidance for Real-World Image Super-Resolution
Denoising Diffusion Probabilistic Models (DDPM) and Flow Matching (FM) generative models show promising potential for one-step Real-World Image Super-Resolution (Real-ISR). Recent one-step Real-ISR models typically inject a Low-Quality (LQ) image latent distribution at the initial timestep. However, a fundamental gap exists between the LQ image latent distribution and the Gaussian noisy latent distribution, limiting the effective utilization of generative priors. We observe that the noisy laten…

On Fixed-Parameter Tractability of Weighted 0-1 Timed Matching Problem on Temporal Graphs
Rinku Kumar, Bodhisatwa Mazumdar, Subhrangsu Mandal
https://arxiv.org/abs/2508.10562 ht…

On Fixed-Parameter Tractability of Weighted 0-1 Timed Matching Problem on Temporal Graphs
Temporal graphs are introduced to model systems where the relationships among the entities of the system evolve over time. In this paper, we consider the temporal graphs where the edge set changes with time and all the changes are known a priori. The underlying graph of a temporal graph is a static graph consisting of all the vertices and edges that exist for at least one timestep in the temporal graph. The concept of 0-1 timed matching in temporal graphs was introduced by Mandal and Gupta [DAM…

Streaming Sequence-to-Sequence Learning with Delayed Streams Modeling
Neil Zeghidour, Eugene Kharitonov, Manu Orsini, V\'aclav Volhejn, Gabriel de Marmiesse, Edouard Grave, Patrick P\'erez, Laurent Mazar\'e, Alexandre D\'efossez
https://arxiv.org/abs/2509.08753

Streaming Sequence-to-Sequence Learning with Delayed Streams Modeling
We introduce Delayed Streams Modeling (DSM), a flexible formulation for streaming, multimodal sequence-to-sequence learning. Sequence-to-sequence generation is often cast in an offline manner, where the model consumes the complete input sequence before generating the first output timestep. Alternatively, streaming sequence-to-sequence rely on learning a policy for choosing when to advance on the input stream, or write to the output stream. DSM instead models already time-aligned streams with a …

And the the #Pleiades #occultation by the #Moon is already history in Bochum, Germany: a final timestep at 22:34 UTC, with the Moon having cleared all major stars of the cluster. Nice show that was - though only for the camera (which was a Panasonic DMC-FZ300 on a tripod, mostly at maximum zoom; this image 1/5 second at f/3.2 and ISO 200, with the Pleiades 25° up).

Replaced article(s) found for cs.SD. https://arxiv.org/list/cs.SD/new
[1/1]:
- TARO: Timestep-Adaptive Representation Alignment with Onset-Aware Conditioning for Synchronized V...
Tri Ton, Ji Woo Hong, Chang D. Yoo

Interactive Atmospheric Composition Emulation for Next-Generation Earth System Models
Seyed Mohammad Hassan Erfani, Kara Lamb, Susanne Bauer, Kostas Tsigaridis, Marcus van Lier-Walqui, Gavin Schmidt
https://arxiv.org/abs/2510.10654

Interactive Atmospheric Composition Emulation for Next-Generation Earth System Models
Interactive composition simulations in Earth System Models (ESMs) are computationally expensive as they transport numerous gaseous and aerosol tracers at each timestep. This limits higher-resolution transient climate simulations with current computational resources. ESMs like NASA GISS-ModelE3 (ModelE) often use pre-computed monthly-averaged atmospheric composition concentrations (Non-Interactive Tracers or NINT) to reduce computational costs. While NINT significantly cuts computations, it fail…

A Stable, Accurate and Well-Conditioned Time-Domain PMCHWT Formulation
Van Chien Le, Cedric Munger, Francesco P. Andriulli, Kristof Cools
https://arxiv.org/abs/2510.07989 https:…

A Stable, Accurate and Well-Conditioned Time-Domain PMCHWT Formulation
This paper introduces a new boundary element formulation for transient electromagnetic scattering by homogeneous dielectric objects based on the time-domain PMCHWT equation. To address dense-mesh breakdown, a multiplicative Calderon preconditioner utilizing a modified static electric field integral operator is employed. Large-timestep breakdown and late-time instability are simultaneously resolved by rescaling the Helmholtz components leveraging the quasi-Helmholtz projectors and using temporal…

Time-Aware One Step Diffusion Network for Real-World Image Super-Resolution
Tainyi Zhang, Zheng-Peng Duan, Peng-Tao Jiang, Bo Li, Ming-Ming Cheng, Chun-Le Guo, Chongyi Li
https://arxiv.org/abs/2508.16557

Time-Aware One Step Diffusion Network for Real-World Image Super-Resolution
Diffusion-based real-world image super-resolution (Real-ISR) methods have demonstrated impressive performance. To achieve efficient Real-ISR, many works employ Variational Score Distillation (VSD) to distill pre-trained stable-diffusion (SD) model for one-step SR with a fixed timestep. However, due to the different noise injection timesteps, the SD will perform different generative priors. Therefore, a fixed timestep is difficult for these methods to fully leverage the generative priors in SD, …

Realism Control One-step Diffusion for Real-World Image Super-Resolution
Zongliang Wu, Siming Zheng, Peng-Tao Jiang, Xin Yuan
https://arxiv.org/abs/2509.10122 https://

Realism Control One-step Diffusion for Real-World Image Super-Resolution
Pre-trained diffusion models have shown great potential in real-world image super-resolution (Real-ISR) tasks by enabling high-resolution reconstructions. While one-step diffusion (OSD) methods significantly improve efficiency compared to traditional multi-step approaches, they still have limitations in balancing fidelity and realism across diverse scenarios. Since the OSDs for SR are usually trained or distilled by a single timestep, they lack flexible control mechanisms to adaptively prioriti…

BTPG-max: Achieving Local Maximal Bidirectional Pairs for Bidirectional Temporal Plan Graphs
Yifan Su, Rishi Veerapaneni, Jiaoyang Li
https://arxiv.org/abs/2508.04849 https://…

BTPG-max: Achieving Local Maximal Bidirectional Pairs for Bidirectional Temporal Plan Graphs
Multi-Agent Path Finding (MAPF) requires computing collision-free paths for multiple agents in shared environment. Most MAPF planners assume that each agent reaches a specific location at a specific timestep, but this is infeasible to directly follow on real systems where delays often occur. To address collisions caused by agents deviating due to delays, the Temporal Plan Graph (TPG) was proposed, which converts a MAPF time dependent solution into a time independent set of inter-agent dependenc…

Spiffy: Multiplying Diffusion LLM Acceleration via Lossless Speculative Decoding
Sudhanshu Agrawal, Risheek Garrepalli, Raghavv Goel, Mingu Lee, Christopher Lott, Fatih Porikli
https://arxiv.org/abs/2509.18085

Spiffy: Multiplying Diffusion LLM Acceleration via Lossless Speculative Decoding
Diffusion LLMs (dLLMs) have recently emerged as a powerful alternative to autoregressive LLMs (AR-LLMs) with the potential to operate at significantly higher token generation rates. However, currently available open-source dLLMs often generate at much lower rates, typically decoding only a single token at every denoising timestep in order to maximize output quality. We present Spiffy, a speculative decoding algorithm that accelerates dLLM inference by $\mathbf{2.8{-}3.1\times}$ while provably p…

\`A Propos Strange Pulsations of Blue Massive Stars
Alfred Gautschy
https://arxiv.org/abs/2509.25892 https://arxiv.org/pdf/2509.25892

À Propos Strange Pulsations of Blue Massive Stars
The properties of radial nonlinear pulsations of massive blue stars are computed with the MESA software instrument in its dynamical mode. Pulsational instabilities could be computationally detected and followed if the evolutionary timestep was reduced to a fraction of the unfolding pulsation period. Stellar variability was recovered in regions on the HR plane that have been studied before and that are known to host LBVs and relatives. Mode properties are analyzed on the full stellar-evolution m…

UniTransfer: Video Concept Transfer via Progressive Spatial and Timestep Decomposition
Guojun Lei, Rong Zhang, Chi Wang, Tianhang Liu, Hong Li, Zhiyuan Ma, Weiwei Xu
https://arxiv.org/abs/2509.21086

UniTransfer: Video Concept Transfer via Progressive Spatial and Timestep Decomposition
We propose a novel architecture UniTransfer, which introduces both spatial and diffusion timestep decomposition in a progressive paradigm, achieving precise and controllable video concept transfer. Specifically, in terms of spatial decomposition, we decouple videos into three key components: the foreground subject, the background, and the motion flow. Building upon this decomposed formulation, we further introduce a dual-to-single-stream DiT-based architecture for supporting fine-grained contro…

Explicit Monotone Stable Super-Time-Stepping Methods for Finite Time Singularities
Zheng Tan, Tariq D. Aslam, Andrea L. Bertozzi
https://arxiv.org/abs/2507.17062 https://…

Explicit Monotone Stable Super-Time-Stepping Methods for Finite Time Singularities
We explore a novel way to numerically resolve the scaling behavior of finite-time singularities in solutions of nonlinear parabolic PDEs. The Runge--Kutta--Legendre (RKL) and Runge--Kutta--Gegenbauer (RKG) super-time-stepping methods were originally developed for nonlinear complex physics problems with diffusion. These are multi-stage single step second-order, forward-in-time methods with no implicit solves. The advantage is that the timestep size for stability scales with stage number $s$ as $…

Improved capabilities of the TurboGAP code for radiation induced cascade simulations: an illustration with silicon
Uttiyoarnab Saha, Ali Hamedani, Miguel A. Caro, Andrea E. Sand
https://arxiv.org/abs/2509.26199

Improved capabilities of the TurboGAP code for radiation induced cascade simulations: an illustration with silicon
TurboGAP is a software package designed for efficient molecular dynamics simulations using Gaussian Approximation Potential (GAP) machine-learning interatomic potentials (MLIP). In this work, we enhance the capabilities of TurboGAP for radiation damage simulations by implementing a two-temperature molecular dynamics model, based on electron density-dependent coupling of electronic and atomic subsystems. Additionally, we implement adaptive calculation of the timestep and grouping of atoms for ce…

Frame-Stacked Local Transformers For Efficient Multi-Codebook Speech Generation
Roy Fejgin, Paarth Neekhara, Xuesong Yang, Edresson Casanova, Ryan Langman Jaehyeon Kim, Subhankar Ghosh, Shehzeen Hussain, Jason Li
https://arxiv.org/abs/2509.19592

Frame-Stacked Local Transformers For Efficient Multi-Codebook Speech Generation
Speech generation models based on large language models (LLMs) typically operate on discrete acoustic codes, which differ fundamentally from text tokens due to their multicodebook structure. At each timestep, models must predict N codebook entries jointly, introducing dependencies that challenge simple parallel prediction approaches. Parallel prediction assumes independence among codebooks, yielding efficient decoding but often at the cost of reduced fidelity. To address this, hierarchical stra…

Heptapod: Language Modeling on Visual Signals
Yongxin Zhu, Jiawei Chen, Yuanzhe Chen, Zhuo Chen, Dongya Jia, Jian Cong, Xiaobin Zhuang, Yuping Wang, Yuxuan Wang
https://arxiv.org/abs/2510.06673

Heptapod: Language Modeling on Visual Signals
We introduce Heptapod, an image autoregressive model that adheres to the foundational principles of language modeling. Heptapod employs \textbf{causal attention}, \textbf{eliminates reliance on CFG}, and \textbf{eschews the trend of semantic tokenizers}. Our key innovation is \textit{next 2D distribution prediction}: a causal Transformer with reconstruction-focused visual tokenizer, learns to predict the distribution over the entire 2D spatial grid of images at each timestep. This learning obje…

SD3.5-Flash: Distribution-Guided Distillation of Generative Flows
Hmrishav Bandyopadhyay, Rahim Entezari, Jim Scott, Reshinth Adithyan, Yi-Zhe Song, Varun Jampani
https://arxiv.org/abs/2509.21318

SD3.5-Flash: Distribution-Guided Distillation of Generative Flows
We present SD3.5-Flash, an efficient few-step distillation framework that brings high-quality image generation to accessible consumer devices. Our approach distills computationally prohibitive rectified flow models through a reformulated distribution matching objective tailored specifically for few-step generation. We introduce two key innovations: "timestep sharing" to reduce gradient noise and "split-timestep fine-tuning" to improve prompt alignment. Combined with comprehensive pipeline optim…

Authentic Discrete Diffusion Model
Xiao Li, Jiaqi Zhang, Shuxiang Zhang, Tianshui Chen, Liang Lin, Guangrun Wang
https://arxiv.org/abs/2510.01047 https://a…

Authentic Discrete Diffusion Model
We propose an Authentic Discrete Diffusion (ADD) framework that fundamentally redefines prior pseudo-discrete approaches by preserving core diffusion characteristics directly in the one-hot space through a suite of coordinated mechanisms. Unlike conventional "pseudo" discrete diffusion (PDD) methods, ADD reformulates the diffusion input by directly using float-encoded one-hot class data, without relying on diffusing in the continuous latent spaces or masking policies. At its core, a timestep-co…

NeuCODEX: Edge-Cloud Co-Inference with Spike-Driven Compression and Dynamic Early-Exit
Maurf Hassan, Steven Davy, Muhammad Zawish, Owais Bin Zuber, Nouman Ashraf
https://arxiv.org/abs/2509.19156