Tootfinder

The bitter lesson of misuse detection
Hadrien Mariaccia, Charbel-Rapha\"el Segerie, Diego Dorn
https://arxiv.org/abs/2507.06282 https://

The bitter lesson of misuse detection
Prior work on jailbreak detection has established the importance of adversarial robustness for LLMs but has largely focused on the model ability to resist adversarial inputs and to output safe content, rather than the effectiveness of external supervision systems. The only public and independent benchmark of these guardrails to date evaluates a narrow set of supervisors on limited scenarios. Consequently, no comprehensive public benchmark yet verifies how well supervision systems from the marke…

Pan-Cancer mitotic figures detection and domain generalization: MIDOG 2025 Challenge
Zhuoyan Shen, Esther B\"ar, Maria Hawkins, Konstantin Br\"autigam, Charles-Antoine Collins-Fekete
https://arxiv.org/abs/2509.02585

Pan-Cancer mitotic figures detection and domain generalization: MIDOG 2025 Challenge
This report details our submission to the Mitotic Domain Generalization (MIDOG) 2025 challenge, which addresses the critical task of mitotic figure detection in histopathology for cancer prognostication. Following the "Bitter Lesson"\cite{sutton2019bitterlesson} principle that emphasizes data scale over algorithmic novelty, we have publicly released two new datasets to bolster training data for both conventional \cite{Shen2024framework} and atypical mitoses \cite{shen_2025_16780587}. Besides, w…