Tootfinder

NASIM: Revealing the low surface brightness Universe from legacy VISTA data
Elham Saremi, Ignacio Trujillo, Mohammad Akhlaghi, Zohreh Ghaffari, Johan H. Knapen, Manda Banerji, Helmut Dannerbauer, S\'ebastien Comer\'on
https://arxiv.org/abs/2508.02780

NASIM: Revealing the low surface brightness Universe from legacy VISTA data
Near-infrared imaging is a powerful technique in observational astronomy, but the bright background, primarily from the Earthś atmosphere, makes the detection of faint features particularly challenging. To recover low surface brightness (LSB) structures in such data, we present NASIM (Near-infrared Automated low Surface brightness reduction In Maneage), a fully automated and reproducible data reduction pipeline optimised for VISTA/VIRCAM observations. NASIM builds on GNU Astronomy Utilities (G…

LLaSO: A Foundational Framework for Reproducible Research in Large Language and Speech Model
Yirong Sun, Yizhong Geng, Peidong Wei, Yanjun Chen, Jinghan Yang, Rongfei Chen, Wei Zhang, Xiaoyu Shen
https://arxiv.org/abs/2508.15418

LLaSO: A Foundational Framework for Reproducible Research in Large Language and Speech Model
The development of Large Speech-Language Models (LSLMs) has been slowed by fragmented architectures and a lack of transparency, hindering the systematic comparison and reproducibility of research. Unlike in the vision-language domain, the LSLM field suffers from the common practice of releasing model weights without their corresponding training data and configurations. To address these critical gaps, we introduce LLaSO, the first fully open, end-to-end framework for large-scale speech-language …

A Reproducible, Scalable Pipeline for Synthesizing Autoregressive Model Literature
Faruk Alpay, Bugra Kilictas, Hamdi Alakkad
https://arxiv.org/abs/2508.04612 https://

A Reproducible, Scalable Pipeline for Synthesizing Autoregressive Model Literature
The accelerating pace of research on autoregressive generative models has produced thousands of papers, making manual literature surveys and reproduction studies increasingly impractical. We present a fully open-source, reproducible pipeline that automatically retrieves candidate documents from public repositories, filters them for relevance, extracts metadata, hyper-parameters and reported results, clusters topics, produces retrieval-augmented summaries and generates containerised scripts for …

Replaced article(s) found for stat.CO. https://arxiv.org/list/stat.CO/new
[1/1]:
- Introducing 'inrep': an R package that facilitates fully reproducible research workfows for surve...
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