Tootfinder

Why LLM Safety Guardrails Collapse After Fine-tuning: A Similarity Analysis Between Alignment and Fine-tuning Datasets
Lei Hsiung, Tianyu Pang, Yung-Chen Tang, Linyue Song, Tsung-Yi Ho, Pin-Yu Chen, Yaoqing Yang
https://arxiv.org/abs/2506.05346

Why LLM Safety Guardrails Collapse After Fine-tuning: A Similarity Analysis Between Alignment and Fine-tuning Datasets
Recent advancements in large language models (LLMs) have underscored their vulnerability to safety alignment jailbreaks, particularly when subjected to downstream fine-tuning. However, existing mitigation strategies primarily focus on reactively addressing jailbreak incidents after safety guardrails have been compromised, removing harmful gradients during fine-tuning, or continuously reinforcing safety alignment throughout fine-tuning. As such, they tend to overlook a critical upstream factor: …

Buyer With Ties to Chinese Communist Party Got V.I.P. Treatment at Trump Crypto Dinner (New York Times)
https://www.nytimes.com/2025/06/06/us/politics/trump-crypto-dinner-china-he-tianying.html
http://www.memeorandum.com/250606/p144#a250606p144

The Chinese Pulsar Timing Array data release I. Single pulsar noise analysis
Siyuan Chen, Heng Xu, Yanjun Guo, Bojun Wang, R. Nicolas Caballero, Jinchen Jiang, Jiangwei Xu, Zihan Xue, Kejia Lee, Jianping Yuan, Yonghua Xu, Jingbo Wang, Longfei Hao, Jintao Luo, Jinlin Han, Peng Jiang, Zhiqiang Shen, Min Wang, Na Wang, Renxin Xu, Xiangping Wu, Lei Qian, Xin Guan, Menglin Huang, Chun Sun, Yan Zhu

The Chinese Pulsar Timing Array data release I. Single pulsar noise analysis
The Chinese Pulsar Timing Array (CPTA) has collected observations from 57 millisecond pulsars using the Five-hundred-meter Aperture Spherical Radio Telescope (FAST) for close to three years, for the purpose of searching for gravitational waves (GWs). To robustly search for ultra-low-frequency GWs, pulsar timing arrays (PTAs) need to use models to describe the noise from the individual pulsars. We report on the results from the single pulsar noise analysis of the CPTA data release I (DR1). Conve…

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning
Hasin Us Sami, Swapneel Sen, Amit K. Roy-Chowdhury, Srikanth V. Krishnamurthy, Basak Guler
https://arxiv.org/abs/2506.04453

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning
Federated learning (FL) allows multiple data-owners to collaboratively train machine learning models by exchanging local gradients, while keeping their private data on-device. To simultaneously enhance privacy and training efficiency, recently parameter-efficient fine-tuning (PEFT) of large-scale pretrained models has gained substantial attention in FL. While keeping a pretrained (backbone) model frozen, each user fine-tunes only a few lightweight modules to be used in conjunction, to fit speci…

Precise Timing Analysis of Four Magnetic Cataclysmic Variables with TESS
Srinivas M Rao, Jeewan C Pandey, Nikita Rawat, Arti Joshi, Ajay Kumar Singh
https://arxiv.org/abs/2506.04371

Precise Timing Analysis of Four Magnetic Cataclysmic Variables with TESS
We analysed high time-resolution optical photometric data from the Transiting Exoplanet Survey Satellite (TESS) to study the timing behaviour of four intermediate polar-like objects, namely, V1460 Her, 1RXS J045707.4+452751, Swift J0958.0-4208, and V842 Cen. In the case of V1460 Her, we refined the measurement of its orbital period. Long-term observations suggest a gradual decrease in the orbital period of V1460 Her, and the stable light curve during the TESS observations indicates its quiescen…

ROSGuard: A Bandwidth Regulation Mechanism for ROS2-based Applications
Jon Altonaga Puente, Enrico Mezzetti, Irune Agirre Troncoso, Jaume Abella Ferrer, Francisco J. Cazorla Almeida
https://arxiv.org/abs/2506.04640

ROSGuard: A Bandwidth Regulation Mechanism for ROS2-based Applications
Multicore timing interference, arising when multiple requests contend for the same shared hardware resources, is a primary concern for timing verification and validation of time-critical applications. Bandwidth control and regulation approaches have been proposed in the literature as an effective method to monitor and limit the impact of timing interference at run time. These approaches seek for fine-grained control of the bandwidth consumption (at the microsecond level) to meet stringent timin…

Teaming in the AI Era: AI-Augmented Frameworks for Forming, Simulating, and Optimizing Human Teams
Mohammed Almutairi
https://arxiv.org/abs/2506.05265 http…

Teaming in the AI Era: AI-Augmented Frameworks for Forming, Simulating, and Optimizing Human Teams
Effective teamwork is essential across diverse domains. During the team formation stage, a key challenge is forming teams that effectively balance user preferences with task objectives to enhance overall team satisfaction. In the team performing stage, maintaining cohesion and engagement is critical for sustaining high team performance. However, existing computational tools and algorithms for team optimization often rely on static data inputs, narrow algorithmic objectives, or solutions tailore…

VisCoder: Fine-Tuning LLMs for Executable Python Visualization Code Generation
Yuansheng Ni, Ping Nie, Kai Zou, Xiang Yue, Wenhu Chen
https://arxiv.org/abs/2506.03930

VisCoder: Fine-Tuning LLMs for Executable Python Visualization Code Generation
Large language models (LLMs) often struggle with visualization tasks like plotting diagrams, charts, where success depends on both code correctness and visual semantics. Existing instruction-tuning datasets lack execution-grounded supervision and offer limited support for iterative code correction, resulting in fragile and unreliable plot generation. We present VisCode-200K, a large-scale instruction tuning dataset for Python-based visualization and self-correction. It contains over 200K exampl…

Para los regalones mšs metaleros también tenemos lanzamientos musicales. «God Of Angels Trust» de los daneses Volbeat.
#Volbeat

Learning to Diagnose Privately: DP-Powered LLMs for Radiology Report Classification
Payel Bhattacharjee, Fengwei Tian, Ravi Tandon, Joseph Lo, Heidi Hanson, Geoffrey Rubin, Nirav Merchant, John Gounley
https://arxiv.org/abs/2506.04450

Learning to Diagnose Privately: DP-Powered LLMs for Radiology Report Classification
Purpose: This study proposes a framework for fine-tuning large language models (LLMs) with differential privacy (DP) to perform multi-abnormality classification on radiology report text. By injecting calibrated noise during fine-tuning, the framework seeks to mitigate the privacy risks associated with sensitive patient data and protect against data leakage while maintaining classification performance. Materials and Methods: We used 50,232 radiology reports from the publicly available MIMIC-CXR …