Tootfinder

RoboLinker: A Diffusion-model-based Matching Clothing Generator Between Humans and Companion Robots
Jing Tang, Qing Xiao, Kunxu Du, Zaiqiao Ye
https://arxiv.org/abs/2508.01165 h…

RoboLinker: A Diffusion-model-based Matching Clothing Generator Between Humans and Companion Robots
We present RoboLinker, a generative design system that creates matching outfits for humans and their robots. Using a diffusion-based model, the system takes a robot image and a style prompt from users as input, and outputs a human outfit that visually complements the robot's attire. Through an interactive interface, users can refine the generated designs. We evaluate RoboLinker with both humanoid and pet-like robots, demonstrating its capacity to produce stylistically coherent and emotionally r…

A Reward-driven Automated Webshell Malicious-code Generator for Red-teaming
Yizhong Ding
https://arxiv.org/abs/2505.24252 https://arx…

A Reward-driven Automated Webshell Malicious-code Generator for Red-teaming
Frequent cyber-attacks have elevated WebShell exploitation and defense to a critical research focus within network security. However, there remains a significant shortage of publicly available, well-categorized malicious-code datasets organized by obfuscation method. Existing malicious-code generation methods, which primarily rely on prompt engineering, often suffer from limited diversity and high redundancy in the payloads they produce. To address these limitations, we propose \textbf{RAWG}, a…

Prompt Optimization and Evaluation for LLM Automated Red Teaming
Michael Freenor, Lauren Alvarez, Milton Leal, Lily Smith, Joel Garrett, Yelyzaveta Husieva, Madeline Woodruff, Ryan Miller, Erich Kummerfeld, Rafael Medeiros, Sander Schulhoff
https://arxiv.org/abs/2507.22133

Prompt Optimization and Evaluation for LLM Automated Red Teaming
Applications that use Large Language Models (LLMs) are becoming widespread, making the identification of system vulnerabilities increasingly important. Automated Red Teaming accelerates this effort by using an LLM to generate and execute attacks against target systems. Attack generators are evaluated using the Attack Success Rate (ASR) the sample mean calculated over the judgment of success for each attack. In this paper, we introduce a method for optimizing attack generator prompts that applie…

J/$\psi$ and $\Upsilon$(1S) production in jets at LHC energies
Lizardo Valencia Palomo
https://arxiv.org/abs/2506.15205 https://arxiv…

J/$ψ$ and $Υ$(1S) production in jets at LHC energies
Quarkonia production in hadronic collisions is far from being understood as none of the existing models can correctly describe the wealth of available data. In particular, LHCb and CMS experiments have reported that PYTHIA 8 cannot reproduce the prompt J/$ψ$ production in jets in proton-proton collisions at two different center of mass energies: the event generator predicts an important amount of the prompt J/$ψ$ to be produced isolated, opposite to the experimental data. This document demons…

This https://arxiv.org/abs/2502.00599 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_nuc…

Time Evolution of Prompt Gamma-Ray Emission in $^{252}$Cf(sf) and $^{233,235}$U($n$,f) Reactions
We investigate the time evolution of prompt fission $γ$ emission due to the presence of ns to ms isomers in the fragments produced in the neutron-induced fission of $^{233,235}$U and in the spontaneous fission of $^{252}$Cf. Calculations performed with the CGMF fission event generator are compared with recent experimental data on $^{252}$Cf(sf) and $^{235}$U($n$,f) obtained with the DANCE+NEUANCE setup at the Los Alamos Neutron Science Center. Of particular interest are the average $γ$-ray en…

Improving LLM Agents with Reinforcement Learning on Cryptographic CTF Challenges
Lajos Muzsai, David Imolai, Andr\'as Luk\'acs
https://arxiv.org/abs/2506.02048

Improving LLM Agents with Reinforcement Learning on Cryptographic CTF Challenges
Large Language Models (LLMs) still struggle with the structured reasoning and tool-assisted computation needed for problem solving in cybersecurity applications. In this work, we introduce "random-crypto", a cryptographic Capture-the-Flag (CTF) challenge generator framework that we use to fine-tune a tool-augmented Llama-3.1-8B with Guided Reinforcement Prompt Optimisation (GRPO), allowing the agent to iteratively write and execute Python inside an isolated REPL. GRPO yields a +53% absolute jum…