5 papers
Improving LLM Agents with Reinforcement Learning on Cryptographic CTF Challenges
Lajos Muzsai, David Imolai, András Lukács
We present 'Random-Crypto', a procedurally generated cryptographic Capture The Flag (CTF) dataset designed to unlock the potential of Reinforcement Learning (RL) for LLM-based agen…
Enhancing pretraining efficiency for medical image segmentation via transferability metrics
Gábor Hidy, Bence Bakos, András Lukács
In medical image segmentation tasks, the scarcity of labeled training data poses a significant challenge when training deep neural networks. When using U-Net-style architectures, i…
: A Python package implementing Whittle's likelihood estimation of the Hurst exponent
Bálint Csanády, Lóránt Nagy, András Lukács
This paper presents , a Python package implementing Whittle's likelihood method for estimating the Hurst exponent in fractional Brownian motion (fBm). While the theor…
HackSynth: LLM Agent and Evaluation Framework for Autonomous Penetration Testing
Lajos Muzsai, David Imolai, András Lukács
We introduce HackSynth, a novel Large Language Model (LLM)-based agent capable of autonomous penetration testing. HackSynth's dual-module architecture includes a Planner and a Summ…
Parameter Estimation of Long Memory Stochastic Processes with Deep Neural Networks
Bálint Csanády, Lóránt Nagy, Dániel Boros +5
We present a purely deep neural network-based approach for estimating long memory parameters of time series models that incorporate the phenomenon of long-range dependence. Paramet…