Tootfinder

From Capabilities to Performance: Evaluating Key Functional Properties of LLM Architectures in Penetration Testing
Lanxiao Huang, Daksh Dave, Ming Jin, Tyler Cody, Peter Beling
https://arxiv.org/abs/2509.14289

From Capabilities to Performance: Evaluating Key Functional Properties of LLM Architectures in Penetration Testing
Large language models (LLMs) are increasingly used to automate or augment penetration testing, but their effectiveness and reliability across attack phases remain unclear. We present a comprehensive evaluation of multiple LLM-based agents, from single-agent to modular designs, across realistic penetration testing scenarios, measuring empirical performance and recurring failure patterns. We also isolate the impact of five core functional capabilities via targeted augmentations: Global Context Me…

Quantum Reinforcement Learning-Guided Diffusion Model for Image Synthesis via Hybrid Quantum-Classical Generative Model Architectures
Chi-Sheng Chen, En-Jui Kuo
https://arxiv.org/abs/2509.14163

Quantum Reinforcement Learning-Guided Diffusion Model for Image Synthesis via Hybrid Quantum-Classical Generative Model Architectures
Diffusion models typically employ static or heuristic classifier-free guidance (CFG) schedules, which often fail to adapt across timesteps and noise conditions. In this work, we introduce a quantum reinforcement learning (QRL) controller that dynamically adjusts CFG at each denoising step. The controller adopts a hybrid quantum--classical actor--critic architecture: a shallow variational quantum circuit (VQC) with ring entanglement generates policy features, which are mapped by a compact multil…

Curriculum Multi-Task Self-Supervision Improves Lightweight Architectures for Onboard Satellite Hyperspectral Image Segmentation
Hugo Carlesso, Josiane Mothe, Radu Tudor Ionescu
https://arxiv.org/abs/2509.13229

Curriculum Multi-Task Self-Supervision Improves Lightweight Architectures for Onboard Satellite Hyperspectral Image Segmentation
Hyperspectral imaging (HSI) captures detailed spectral signatures across hundreds of contiguous bands per pixel, being indispensable for remote sensing applications such as land-cover classification, change detection, and environmental monitoring. Due to the high dimensionality of HSI data and the slow rate of data transfer in satellite-based systems, compact and efficient models are required to support onboard processing and minimize the transmission of redundant or low-value data, e.g. cloud-…

Exploring the Relationship between Brain Hemisphere States and Frequency Bands through Deep Learning Optimization Techniques
Robiul Islam, Dmitry I. Ignatov, Karl Kaberg, Roman Nabatchikov
https://arxiv.org/abs/2509.14078

Exploring the Relationship between Brain Hemisphere States and Frequency Bands through Deep Learning Optimization Techniques
This study investigates classifier performance across EEG frequency bands using various optimizers and evaluates efficient class prediction for the left and right hemispheres. Three neural network architectures - a deep dense network, a shallow three-layer network, and a convolutional neural network (CNN) - are implemented and compared using the TensorFlow and PyTorch frameworks. Results indicate that the Adagrad and RMSprop optimizers consistently perform well across different frequency bands,…

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Differential Privacy in Federated Learning: Mitigating Inference Attacks with Randomized Response
Ozer Ozturk, Busra Buyuktanir, Gozde Karatas Baydogmus, Kazim Yildiz
https://arxiv.org/abs/2509.13987

Differential Privacy in Federated Learning: Mitigating Inference Attacks with Randomized Response
Machine learning models used for distributed architectures consisting of servers and clients require large amounts of data to achieve high accuracy. Data obtained from clients are collected on a central server for model training. However, storing data on a central server raises concerns about security and privacy. To address this issue, a federated learning architecture has been proposed. In federated learning, each client trains a local model using its own data. The trained models are periodic…

Hierarchical Federated Learning for Crop Yield Prediction in Smart Agricultural Production Systems
Anas Abouaomar, Mohammed El hanjri, Abdellatif Kobbane, Anis Laouiti, Khalid Nafil
https://arxiv.org/abs/2510.12727

Hierarchical Federated Learning for Crop Yield Prediction in Smart Agricultural Production Systems
In this paper, we presents a novel hierarchical federated learning architecture specifically designed for smart agricultural production systems and crop yield prediction. Our approach introduces a seasonal subscription mechanism where farms join crop-specific clusters at the beginning of each agricultural season. The proposed three-layer architecture consists of individual smart farms at the client level, crop-specific aggregators at the middle layer, and a global model aggregator at the top le…

A Robust Modular Quantum Processor
Ramesh Bhandari
https://arxiv.org/abs/2509.13269 https://arxiv.org/pdf/2509.13269

A Robust Modular Quantum Processor
We explore the concept of redundancy of critical elements in a quantum computing architecture to circumvent disruption of quantum operations due to a failure of such an element, for example, from a catastrophic cosmic ray event. We illustrate this concept with reference to a recently proposed superconducting modular quantum architecture with a star-like configuration, which has a router at the center that enables superconducting qubit interactions across various modules. Regarding this router a…