Tootfinder

People keep making the same mistake, again and again and again and again forever, of thinking that it is syntax that makes software development hard.
Oh honey.
Re this from @mathaetaes:
https://infosec.exchange/@mathaetaes/114656764053846137
(P.S. Visual coding is actually really cool, and IMO an underexplored PL design space — but is very much coding, and very much tricky for the same reasons as any other kind of coding.)

We've been studying the cognitive science of *diagrams*, and have recently put out a new (very) lightweight programming language and tool for diagramming models in formal methods. Check it out here!
https://blog.brownplt.org/2025/06/09/copeanddrag.html

Reusing Trajectories in Policy Gradients Enables Fast Convergence
Alessandro Montenegro, Federico Mansutti, Marco Mussi, Matteo Papini, Alberto Maria Metelli
https://arxiv.org/abs/2506.06178

Reusing Trajectories in Policy Gradients Enables Fast Convergence
Policy gradient (PG) methods are a class of effective reinforcement learning algorithms, particularly when dealing with continuous control problems. These methods learn the parameters of parametric policies via stochastic gradient ascent, typically using on-policy trajectory data to estimate the policy gradient. However, such reliance on fresh data makes them sample-inefficient. Indeed, vanilla PG methods require $O(ε^{-2})$ trajectories to reach an $ε$-approximate stationary point. A common …

Interpretation Meets Safety: A Survey on Interpretation Methods and Tools for Improving LLM Safety
Seongmin Lee, Aeree Cho, Grace C. Kim, ShengYun Peng, Mansi Phute, Duen Horng Chau
https://arxiv.org/abs/2506.05451

Interpretation Meets Safety: A Survey on Interpretation Methods and Tools for Improving LLM Safety
As large language models (LLMs) see wider real-world use, understanding and mitigating their unsafe behaviors is critical. Interpretation techniques can reveal causes of unsafe outputs and guide safety, but such connections with safety are often overlooked in prior surveys. We present the first survey that bridges this gap, introducing a unified framework that connects safety-focused interpretation methods, the safety enhancements they inform, and the tools that operationalize them. Our novel t…

Performance Impact of Containerized METADOCK 2 on Heterogeneous Platforms
Antonio Jes\'us Banegas-Luna, Baldomero Imbern\'on Tudela, Carlos Mart\'inez-Cort\'es, Jos\'e Mar\'ia Cecilia, Horacio P\'erez-S\'anchez
https://arxiv.org/abs/2506.06450

Performance Impact of Containerized METADOCK 2 on Heterogeneous Platforms
Virtual screening (VS) is a computationally intensive process crucial for drug discovery, often requiring significant resources to analyze large chemical libraries and predict ligand-protein interactions. This study evaluates the performance impact of containerization on METADOCK 2, a high-throughput docking software when deployed on heterogeneous high-performance computing (HPC) platforms. By testing three containerization technologies - Docker, Singularity, and Apptainer - across varying CPU …

This https://arxiv.org/abs/2504.13151 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

MIB: A Mechanistic Interpretability Benchmark
How can we know whether new mechanistic interpretability methods achieve real improvements? In pursuit of lasting evaluation standards, we propose MIB, a Mechanistic Interpretability Benchmark, with two tracks spanning four tasks and five models. MIB favors methods that precisely and concisely recover relevant causal pathways or causal variables in neural language models. The circuit localization track compares methods that locate the model components - and connections between them - most impor…

Leveraging Network Methods for Hub-like Microservice Detection
Alexander Bakhtin, Matteo Esposito, Valentina Lenarduzzi, Davide Taibi
https://arxiv.org/abs/2506.07683

Leveraging Network Methods for Hub-like Microservice Detection
Context: Microservice Architecture is a popular architectural paradigm that facilitates flexibility by decomposing applications into small, independently deployable services. Catalogs of architectural anti-patterns have been proposed to highlight the negative aspects of flawed microservice design. In particular, the Hub-like anti-pattern lacks an unambiguous definition and detection method. Aim: In this work, we aim to find a robust detection approach for the Hub-like microservice anti-pattern …

Synthetic Tabular Data: Methods, Attacks and Defenses
Graham Cormode, Samuel Maddock, Enayat Ullah, Shripad Gade
https://arxiv.org/abs/2506.06108 https://

Synthetic Tabular Data: Methods, Attacks and Defenses
Synthetic data is often positioned as a solution to replace sensitive fixed-size datasets with a source of unlimited matching data, freed from privacy concerns. There has been much progress in synthetic data generation over the last decade, leveraging corresponding advances in machine learning and data analytics. In this survey, we cover the key developments and the main concepts in tabular synthetic data generation, including paradigms based on probabilistic graphical models and on deep learni…

This https://arxiv.org/abs/2505.19038 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Turb-L1: Achieving Long-term Turbulence Tracing By Tackling Spectral Bias
Accurately predicting the long-term evolution of turbulence is crucial for advancing scientific understanding and optimizing engineering applications. However, existing deep learning methods face significant bottlenecks in long-term autoregressive prediction, which exhibit excessive smoothing and fail to accurately track complex fluid dynamics. Our extensive experimental and spectral analysis of prevailing methods provides an interpretable explanation for this shortcoming, identifying Spectral …

Unisoma: A Unified Transformer-based Solver for Multi-Solid Systems
Shilong Tao, Zhe Feng, Haonan Sun, Zhanxing Zhu, Yunhuai Liu
https://arxiv.org/abs/2506.06021

Unisoma: A Unified Transformer-based Solver for Multi-Solid Systems
Multi-solid systems are foundational to a wide range of real-world applications, yet modeling their complex interactions remains challenging. Existing deep learning methods predominantly rely on implicit modeling, where the factors influencing solid deformation are not explicitly represented but are instead indirectly learned. However, as the number of solids increases, these methods struggle to accurately capture intricate physical interactions. In this paper, we introduce a novel explicit mod…