Tootfinder

I was looking into the #DANDI archive, following a post of @… that mentioned it as a possibility for #Neuroscience data storage.. It looks like it could be useful i…

Robust Recovery and Control of Cyber-physical Discrete Event Systems under Actuator Attacks
Samuel Oliveira, Mostafa Tavakkoli Anbarani, Gregory Beal, Ilya Kovalenko, Marcelo Teixeira, Andr\'e B. Leal, R\^omulo Meira-G\'oes
https://arxiv.org/abs/2510.11405

Robust Recovery and Control of Cyber-physical Discrete Event Systems under Actuator Attacks
Critical real-world applications strongly rely on Cyber-physical systems (CPS), but their dependence on communication networks introduces significant security risks, as attackers can exploit vulnerabilities to compromise their integrity and availability. This work explores the topic of cybersecurity in the context of CPS modeled as discrete event systems (DES), focusing on recovery strategies following the detection of cyberattacks. Specifically, we address actuator enablement attacks and propo…

Jakobi Meyers ready to play Week 1 for Raiders following trade request: 'I asked, they said no' https://www.nfl.com/news/jakobi-meyers-ready-to-play-week-1-for-raiders-following-trade-request-i-asked-they-said-no…

Time-To-Inconsistency: A Survival Analysis of Large Language Model Robustness to Adversarial Attacks
Yubo Li, Ramayya Krishnan, Rema Padman
https://arxiv.org/abs/2510.02712 http…

Time-To-Inconsistency: A Survival Analysis of Large Language Model Robustness to Adversarial Attacks
Large Language Models (LLMs) have revolutionized conversational AI, yet their robustness in extended multi-turn dialogues remains poorly understood. Existing evaluation frameworks focus on static benchmarks and single-turn assessments, failing to capture the temporal dynamics of conversational degradation that characterize real-world interactions. In this work, we present the first comprehensive survival analysis of conversational AI robustness, analyzing 36,951 conversation turns across 9 stat…

Robust and Adaptive Spectral Method for Representation Multi-Task Learning with Contamination
Yian Huang, Yang Feng, Zhiliang Ying
https://arxiv.org/abs/2509.06575 https://

Robust and Adaptive Spectral Method for Representation Multi-Task Learning with Contamination
Representation-based multi-task learning (MTL) improves efficiency by learning a shared structure across tasks, but its practical application is often hindered by contamination, outliers, or adversarial tasks. Most existing methods and theories assume a clean or near-clean setting, failing when contamination is significant. This paper tackles representation MTL with an unknown and potentially large contamination proportion, while also allowing for heterogeneity among inlier tasks. We introduce …

SparkUI-Parser: Enhancing GUI Perception with Robust Grounding and Parsing
Hongyi Jing, Jiafu Chen, Chen Rao, Ziqiang Dang, Jiajie Teng, Tianyi Chu, Juncheng Mo, Shuo Fang, Huaizhong Lin, Rui Lv, Chenguang Ma, Lei Zhao
https://arxiv.org/abs/2509.04908

SparkUI-Parser: Enhancing GUI Perception with Robust Grounding and Parsing
The existing Multimodal Large Language Models (MLLMs) for GUI perception have made great progress. However, the following challenges still exist in prior methods: 1) They model discrete coordinates based on text autoregressive mechanism, which results in lower grounding accuracy and slower inference speed. 2) They can only locate predefined sets of elements and are not capable of parsing the entire interface, which hampers the broad application and support for downstream tasks. To address the a…

Revisiting Cases 2 and 11 of the Map Color Theorem
Timothy Sun
https://arxiv.org/abs/2509.06407 https://arxiv.org/pdf/2509.06407

Revisiting Cases 2 and 11 of the Map Color Theorem
In 1968, Ringel and Youngs solved the remaining cases of the orientable Map Color Theorem by finding genus embeddings of the complete graphs $K_n$, for sufficiently large $n \equiv 2, 8, 11 \pmod{12}$. Following the approach previously explored by the author for $n \equiv 8 \pmod{12}$, we aim to streamline their constructions for $n \equiv 2, 11 \pmod{12}$ by finding families of current graphs with simpler patterns for the arc labelings.

Mean dimension and rate-distortion function revisited
Rui Yang
https://arxiv.org/abs/2510.08051 https://arxiv.org/pdf/2510.08051

Mean dimension and rate-distortion function revisited
Around the mean dimensions and rate-distortion functions, using some tools from local entropy theory this paper establishes the following main results: $(1)$ We prove that for non-ergodic measures associated with almost sure processes, the mean Rényi information dimension coincides with the information dimension rate. This answers a question posed by Gutman and Śpiewak (in Around the variational principle for metric mean dimension, \emph{Studia Math.} \textbf{261}(2021) 345-360). $(2)$ We…

DACIP-RC: Domain Adaptive Continual Instruction Pre-Training via Reading Comprehension on Business Conversations
Elena Khasanova, Harsh Saini, Md Tahmid Rahman Laskar, Xue-Yong Fu, Cheng Chen, Shashi Bhushan TN
https://arxiv.org/abs/2510.08152

DACIP-RC: Domain Adaptive Continual Instruction Pre-Training via Reading Comprehension on Business Conversations
The rapid advancements in Large Language Models (LLMs) have enabled their adoption in real-world industrial scenarios for various natural language processing tasks. However, the high inference cost of large-scale LLMs makes their deployment impractical, necessitating the use of smaller models. Despite their efficiency, smaller LLMs lack robust zero-shot instruction-following capabilities across diverse domains, limiting their adaptability to dynamic user requirements. Traditional fine-tuning ap…