Tootfinder

Error chaining in JavaScript: cleaner debugging with Error.cause
Error handling in JavaScript has always felt a bit chaotic. Throwing errors is easy, but tracing them back to the root cause? Not so much. That’s where the cause property comes in.
🧑‍💻 https://all…

Error chaining in JavaScript: cleaner debugging with Error.cause - Matt Smith
Use JavaScript's 'cause' property to chain errors, preserve context, and simplify debugging. Cleaner stack traces, better test assertions.

Authentic Discrete Diffusion Model
Xiao Li, Jiaqi Zhang, Shuxiang Zhang, Tianshui Chen, Liang Lin, Guangrun Wang
https://arxiv.org/abs/2510.01047 https://a…

Authentic Discrete Diffusion Model
We propose an Authentic Discrete Diffusion (ADD) framework that fundamentally redefines prior pseudo-discrete approaches by preserving core diffusion characteristics directly in the one-hot space through a suite of coordinated mechanisms. Unlike conventional "pseudo" discrete diffusion (PDD) methods, ADD reformulates the diffusion input by directly using float-encoded one-hot class data, without relying on diffusing in the continuous latent spaces or masking policies. At its core, a timestep-co…

Syntax-Guided Diffusion Language Models with User-Integrated Personalization
Ruqian Zhang, Yijiao Zhang, Juan Shen, Zhongyi Zhu, Annie Qu
https://arxiv.org/abs/2510.01028 https:…

Syntax-Guided Diffusion Language Models with User-Integrated Personalization
Large language models have made revolutionary progress in generating human-like text, yet their outputs often tend to be generic, exhibiting insufficient structural diversity, which limits personalized expression. Recent advances in diffusion models have opened new opportunities for improving language generation beyond the limitations of autoregressive paradigms. In this work, we propose a syntax-guided diffusion language model that integrates structural supervision and personalized conditionin…

Temporal Score Rescaling for Temperature Sampling in Diffusion and Flow Models
Yanbo Xu, Yu Wu, Sungjae Park, Zhizhuo Zhou, Shubham Tulsiani
https://arxiv.org/abs/2510.01184 htt…

Temporal Score Rescaling for Temperature Sampling in Diffusion and Flow Models
We present a mechanism to steer the sampling diversity of denoising diffusion and flow matching models, allowing users to sample from a sharper or broader distribution than the training distribution. We build on the observation that these models leverage (learned) score functions of noisy data distributions for sampling and show that rescaling these allows one to effectively control a `local' sampling temperature. Notably, this approach does not require any finetuning or alterations to training…

🇺🇦 Auf radioeins läuft...
Joy Division:
🎵 Disorder
#NowPlaying #JoyDivision
https://josephleeharris.bandcamp.com/track/joy-division-disorder
https://open.spotify.com/track/2WEw8oFZiANQzWdGElO3Mf

Lecture notes: Biological propagation via reaction-diffusion equations with nonlocal diffusion and free boundary
Yihong Du
https://arxiv.org/abs/2510.00710 https://

Lecture notes: Biological propagation via reaction-diffusion equations with nonlocal diffusion and free boundary
These notes are based on the lectures given in a mini-course at VIASM (Vietnam Institute for Advanced Study in Mathematics) 2025 Summer School. They give a brief account of the theory (with detailed proofs) for propagation governed by a nonlocal reaction-diffusion model with free boundaries in one space dimension. The main part is concerned with a KPP reaction term, though the basic results on the existence and uniqueness of solutions as well as on the comparison principles are for more general…

On This World AIDS Day, The U.S. Declines To Participate (Dave Wessner/Forbes)
https://www.forbes.com/sites/davewessner/2025/12/01/on-this-world-aids-day-the-us-declines-to-participate/
http://www.memeorandum.com/251201/p47#a251201p47

#Version Your #Cache Keys to Survive Rolling Deployments
https://blog.devgenius.io/versio…

Compose Your Policies! Improving Diffusion-based or Flow-based Robot Policies via Test-time Distribution-level Composition
Jiahang Cao, Yize Huang, Hanzhong Guo, Rui Zhang, Mu Nan, Weijian Mai, Jiaxu Wang, Hao Cheng, Jingkai Sun, Gang Han, Wen Zhao, Qiang Zhang, Yijie Guo, Qihao Zheng, Chunfeng Song, Xiao Li, Ping Luo, Andrew F. Luo
https://

Compose Your Policies! Improving Diffusion-based or Flow-based Robot Policies via Test-time Distribution-level Composition
Diffusion-based models for robotic control, including vision-language-action (VLA) and vision-action (VA) policies, have demonstrated significant capabilities. Yet their advancement is constrained by the high cost of acquiring large-scale interaction datasets. This work introduces an alternative paradigm for enhancing policy performance without additional model training. Perhaps surprisingly, we demonstrate that the composed policies can exceed the performance of either parent policy. Our contr…

Secure and reversible face anonymization with diffusion models
Pol Labarbarie, Vincent Itier, William Puech
https://arxiv.org/abs/2510.01031 https://arxiv.…

Secure and reversible face anonymization with diffusion models
Face images processed by computer vision algorithms contain sensitive personal information that malicious actors can capture without consent. These privacy and security risks highlight the need for effective face anonymization methods. Current methods struggle to propose a good trade-off between a secure scheme with high-quality image generation and reversibility for later person authentication. Diffusion-based approaches produce high-quality anonymized images but lack the secret key mechanism …