Tootfinder

AutoMLGen: Navigating Fine-Grained Optimization for Coding Agents
Shangheng Du, Xiangchao Yan, Dengyang Jiang, Jiakang Yuan, Yusong Hu, Xin Li, Liang He, Bo Zhang, Lei Bai
https://arxiv.org/abs/2510.08511

AutoMLGen: Navigating Fine-Grained Optimization for Coding Agents
Large language models (LLMs) have shown impressive performance in general programming tasks. However, in Machine Learning Engineering (MLE) scenarios such as AutoML and Kaggle competitions, achieving high performance depends heavily on expert intervention and repeated adjustments rather than simply generating correct code. When applied directly to these tasks, LLMs often lack fine-grained domain priors, and existing MLE approaches that use linear or tree-structured searches limit knowledge tran…

Time-relaxation structure-preserving explicit low-regularity integrators for the nonlinear Schr\"odinger equation
Hang Li, Xicui Li, Katharina Schratz, Bin Wang
https://arxiv.org/abs/2510.02963

Time-relaxation structure-preserving explicit low-regularity integrators for the nonlinear Schrödinger equation
We propose and rigorously analyze a novel family of explicit low-regularity exponential integrators for the nonlinear Schrödinger (NLS) equation, based on a time-relaxation framework. The methods combine a resonance-based scheme for the twisted variable with a dynamically adjusted relaxation parameter that guarantees exact mass conservation. Unlike existing symmetric or structure-preserving low-regularity integrators, which are typically implicit and computationally expensive, the proposed met…

OpenGL GPU-Based Rowhammer Attack (Work in Progress)
Antoine Plin, Fr\'ed\'eric Fauberteau, Nga Nguyen
https://arxiv.org/abs/2509.19959 https://arx…

OpenGL GPU-Based Rowhammer Attack (Work in Progress)
Rowhammer attacks have emerged as a significant threat to modern DRAM-based memory systems, leveraging frequent memory accesses to induce bit flips in adjacent memory cells. This work-in-progress paper presents an adaptive, many-sided Rowhammer attack utilizing GPU compute shaders to systematically achieve high-frequency memory access patterns. Our approach employs statistical distributions to optimize row targeting and avoid current mitigations. The methodology involves initializing memory wit…

Anatomy-DT: A Cross-Diffusion Digital Twin for Anatomical Evolution
Moinak Bhattacharya, Gagandeep Singh, Prateek Prasanna
https://arxiv.org/abs/2509.25280 https://

Anatomy-DT: A Cross-Diffusion Digital Twin for Anatomical Evolution
Accurately modeling the spatiotemporal evolution of tumor morphology from baseline imaging is a pre-requisite for developing digital twin frameworks that can simulate disease progression and treatment response. Most existing approaches primarily characterize tumor growth while neglecting the concomitant alterations in adjacent anatomical structures. In reality, tumor evolution is highly non-linear and heterogeneous, shaped not only by therapeutic interventions but also by its spatial context an…