Tootfinder

scheise was ist pasirt
https://github.com/AasishPokhrel/repository/issues/1

Open repos & AI bots
#FederatedMemory
https://coar-repositories.org/news-updates/open-repositories-are-being-profoundly-impacted-by-ai-bots-and-other-crawlers-results-of-a-coar-survey/
and https://codeberg.org/forgejo/discussions/issues/339

Hooray, parallel downloading of repository metadata was merged for #dnf! 🥳
https://github.com/rpm-software-management/dnf5/issues/307#issuecomment-2…

Robust output stability and input-to-output stability based on output dissipation
Antoine Chaillet (IUF, L2S), Iasson Karafyllis (NTUA), Yuan Wang
https://arxiv.org/abs/2506.10479

Robust output stability and input-to-output stability based on output dissipation
LaSalle techniques to ensure the convergence of a given output usually fail at guaranteeing uniform convergence time, which induces robustness issues. Recent works have provided extra conditions under which a Lyapunov function that dissipates in terms of only the output guarantees this uniformity. In this paper, we extend these results to systems with inputs in order to establish either robust stability or input-to-output stability. In addition, we show that a recent relaxation of Barb__lat's l…

Calamus 30 A promise and gift to California
Well they can't all be winners. A tedious poem of Western fantasy. Appropriately aspirational for 1860 but not a particularly interesting nor unique sentiment. Also an unkept promise: Whitman never reached the west coast.
Looking for my gay reading, all I have is this:
I and robust love belong among you
I do like Whitman's lustiness, in this case his "robust love". We know a thing or two about that in California.

Mr. Seifert the science teacher addresses the class re: the rumors about him & Ms. Barker the 5th grade teacher.
▶️ Miss Barker Was NOT Ready for This... #shorts #repost #school

Robust copula estimation for one-shot devices with correlated failure modes
E. Castilla, P. J. Chocano
https://arxiv.org/abs/2506.10152 https://

Robust copula estimation for one-shot devices with correlated failure modes
This paper presents a robust method for estimating copula models to evaluate dependence between failure modes in one-shot devices-systems designed for single use and destroyed upon activation. Traditional approaches, such as maximum likelihood estimation (MLE), often produce unreliable results when faced with outliers or model misspecification. To overcome these limitations, we introduce a divergence-based estimation technique that enhances robustness and provides a more reliable characterizati…

Aucamp: An Underwater Camera-Based Multi-Robot Platform with Low-Cost, Distributed, and Robust Localization
Jisheng Xu, Ding Lin, Pangkit Fong, Chongrong Fang, Xiaoming Duan, Jianping He
https://arxiv.org/abs/2506.09876

Aucamp: An Underwater Camera-Based Multi-Robot Platform with Low-Cost, Distributed, and Robust Localization
This paper introduces an underwater multi-robot platform, named Aucamp, characterized by cost-effective monocular-camera-based sensing, distributed protocol and robust orientation control for localization. We utilize the clarity feature to measure the distance, present the monocular imaging model, and estimate the position of the target object. We achieve global positioning in our platform by designing a distributed update protocol. The distributed algorithm enables the perception process to si…

EXPEREPAIR: Dual-Memory Enhanced LLM-based Repository-Level Program Repair
Fangwen Mu, Junjie Wang, Lin Shi, Song Wang, Shoubin Li, Qing Wang
https://arxiv.org/abs/2506.10484

EXPEREPAIR: Dual-Memory Enhanced LLM-based Repository-Level Program Repair
Automatically repairing software issues remains a fundamental challenge at the intersection of software engineering and AI. Although recent advancements in Large Language Models (LLMs) have demonstrated potential for repository-level repair tasks, current methodologies exhibit two notable limitations: (1) they often address issues in isolation, neglecting to incorporate insights from previously resolved issues, and (2) they rely on static and rigid prompting strategies, which constrain their ab…

LLMs Cannot Reliably Judge (Yet?): A Comprehensive Assessment on the Robustness of LLM-as-a-Judge
Songze Li, Chuokun Xu, Jiaying Wang, Xueluan Gong, Chen Chen, Jirui Zhang, Jun Wang, Kwok-Yan Lam, Shouling Ji
https://arxiv.org/abs/2506.09443

LLMs Cannot Reliably Judge (Yet?): A Comprehensive Assessment on the Robustness of LLM-as-a-Judge
Large Language Models (LLMs) have demonstrated remarkable intelligence across various tasks, which has inspired the development and widespread adoption of LLM-as-a-Judge systems for automated model testing, such as red teaming and benchmarking. However, these systems are susceptible to adversarial attacks that can manipulate evaluation outcomes, raising concerns about their robustness and, consequently, their trustworthiness. Existing evaluation methods adopted by LLM-based judges are often pie…