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Norge trenger verken autoritære ledere eller formelle stater, for ekte frihet og folkestyre vokser fram når arbeiderklassen og lokale fellesskap tar styringen selv gjennom direkte handling og desentralisert selvstyre.
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SECNEURON: Reliable and Flexible Abuse Control in Local LLMs via Hybrid Neuron Encryption
Zhiqiang Wang, Haohua Du, Junyang Wang, Haifeng Sun, Kaiwen Guo, Haikuo Yu, Chao Liu, Xiang-Yang Li
https://arxiv.org/abs/2506.05242

SECNEURON: Reliable and Flexible Abuse Control in Local LLMs via Hybrid Neuron Encryption
Large language models (LLMs) with diverse capabilities are increasingly being deployed in local environments, presenting significant security and controllability challenges. These locally deployed LLMs operate outside the direct control of developers, rendering them more susceptible to abuse. Existing mitigation techniques mainly designed for cloud-based LLM services are frequently circumvented or ineffective in deployer-controlled environments. We propose SECNEURON, the first framework that se…

Reason-to-Recommend: Using Interaction-of-Thought Reasoning to Enhance LLM Recommendation
Keyu Zhao, Fengli Xu, Yong Li
https://arxiv.org/abs/2506.05069 ht…

Reason-to-Recommend: Using Interaction-of-Thought Reasoning to Enhance LLM Recommendation
Driven by advances in Large Language Models (LLMs), integrating them into recommendation tasks has gained interest due to their strong semantic understanding and prompt flexibility. Prior work encoded user-item interactions or metadata into prompts for recommendations. In parallel, LLM reasoning, boosted by test-time scaling and reinforcement learning, has excelled in fields like mathematics and code, where reasoning traces and correctness signals are clear, enabling high performance and interp…

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This https://arxiv.org/abs/2411.18065 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAR_…

FlexiBit: Fully Flexible Precision Bit-parallel Accelerator Architecture for Arbitrary Mixed Precision AI
Recent research has shown that large language models (LLMs) can utilize low-precision floating point (FP) quantization to deliver high efficiency while maintaining original model accuracy. In particular, recent works have shown the effectiveness of non-power-of-two precisions, such as FP6 and FP5, and diverse sensitivity to low-precision arithmetic of LLM layers, which motivates mixed precision arithmetic including non-power-of-two precisions in LLMs. Although low-precision algorithmically lead…

This https://arxiv.org/abs/1910.14012 has been replaced.
link: https://scholar.google.com/scholar?q=a

$\texttt{HEPfit}$: a Code for the Combination of Indirect and Direct Constraints on High Energy Physics Models
$\texttt{HEPfit}$ is a flexible open-source tool which, given the Standard Model or any of its extensions, allows to $\textit{i)}$ fit the model parameters to a given set of experimental observables; $\textit{ii)}$ obtain predictions for observables. $\texttt{HEPfit}$ can be used either in Monte Carlo mode, to perform a Bayesian Markov Chain Monte Carlo analysis of a given model, or as a library, to obtain predictions of observables for a given point in the parameter space of the model, allowin…

Distributionally Robust Optimization for Aerial Multi-access Edge Computing via Cooperation of UAVs and HAPs
Ziye Jia, Can Cui, Chao Dong, Qihui Wu, Zhuang Ling, Dusit Niyato, Zhu Han
https://arxiv.org/abs/2506.01972

Distributionally Robust Optimization for Aerial Multi-access Edge Computing via Cooperation of UAVs and HAPs
With an extensive increment of computation demands, the aerial multi-access edge computing (MEC), mainly based on unmanned aerial vehicles (UAVs) and high altitude platforms (HAPs), plays significant roles in future network scenarios. In detail, UAVs can be flexibly deployed, while HAPs are characterized with large capacity and stability. Hence, in this paper, we provide a hierarchical model composed of an HAP and multi-UAVs, to provide aerial MEC services. Moreover, considering the errors of c…

This https://arxiv.org/abs/2212.05053 has been replaced.
link: https://scholar.google.com/scholar?q=a

Joint Spectral Clustering in Multilayer Degree-Corrected Stochastic Blockmodels
Modern network datasets are often composed of multiple layers, either as different views, time-varying observations, or independent sample units, resulting in collections of networks over the same set of vertices but with potentially different connectivity patterns on each network. These data require models and methods that are flexible enough to capture local and global differences across the networks, while at the same time being parsimonious and tractable to yield computationally efficient a…

A Terminology and Quantitative Framework for Assessing the Habitability of Solar System and Extraterrestrial Worlds
Daniel Apai, Rory Barnes, Matthew M. Murphy, Tim Lichtenberg, Noah Tuchow, Regis Ferriere, Kevin Wagner, Antonin Affholder, Renu Malhotra, Baptiste Journaux, Allona Vazan, Ramses Ramirez, Abel Mendez, Stephen R. Kane, Veronica H. Klawender, NExSS Quantitative Habitability Science Working Group

A Terminology and Quantitative Framework for Assessing the Habitability of Solar System and Extraterrestrial Worlds
The search for extraterrestrial life in the Solar System and beyond is a key science driver in astrobiology, planetary science, and astrophysics. A critical step is the identification and characterization of potential habitats, both to guide the search and to interpret its results. However, a well-accepted, self-consistent, flexible, and quantitative terminology and method of assessment of habitability are lacking. Our paper fills this gap based on a three year-long study by the NExSS Quantitat…

Deep Learning Enhanced Multivariate GARCH
Haoyuan Wang, Chen Liu, Minh-Ngoc Tran, Chao Wang
https://arxiv.org/abs/2506.02796 https://…

Deep Learning Enhanced Multivariate GARCH
This paper introduces a novel multivariate volatility modeling framework, named Long Short-Term Memory enhanced BEKK (LSTM-BEKK), that integrates deep learning into multivariate GARCH processes. By combining the flexibility of recurrent neural networks with the econometric structure of BEKK models, our approach is designed to better capture nonlinear, dynamic, and high-dimensional dependence structures in financial return data. The proposed model addresses key limitations of traditional multiva…