Tootfinder

Text (1/2):
Mark S. Zaid & Abbe David Lowell
Counsel for CDC Director Susan Monarez
August 27, 2025 - 7:03 pm ET
First it was independent advisory committees and career experts. Then it was the dismissal of seasoned scientists. Now, Secretary Kennedy and HHS have set their sights on weaponizing public health for political gain and putting millions of American lives at risk.

Dr. Lucky Tran :verified: (@luckytran@med-mastodon.com)
Attached: 1 image Wow the CDC director is now saying she was not officially fired and is refusing to resign.

RoboChemist: Long-Horizon and Safety-Compliant Robotic Chemical Experimentation
Zongzheng Zhang, Chenghao Yue, Haobo Xu, Minwen Liao, Xianglin Qi, Huan-ang Gao, Ziwei Wang, Hao Zhao
https://arxiv.org/abs/2509.08820

RoboChemist: Long-Horizon and Safety-Compliant Robotic Chemical Experimentation
Robotic chemists promise to both liberate human experts from repetitive tasks and accelerate scientific discovery, yet remain in their infancy. Chemical experiments involve long-horizon procedures over hazardous and deformable substances, where success requires not only task completion but also strict compliance with experimental norms. To address these challenges, we propose \textit{RoboChemist}, a dual-loop framework that integrates Vision-Language Models (VLMs) with Vision-Language-Action (V…

Re4: Scientific Computing Agent with Rewriting, Resolution, Review and Revision
Ao Cheng, Lei Zhang, Guowei He
https://arxiv.org/abs/2508.20729 https://arx…

Re4: Scientific Computing Agent with Rewriting, Resolution, Review and Revision
Large language models (LLMs) serve as an active and promising field of generative artificial intelligence and have demonstrated abilities to perform complex tasks in multiple domains, including mathematical and scientific reasoning. In this work, we construct a novel agent framework for solving representative problems in scientific computing. The proposed agent, incorporating a "rewriting-resolution-review-revision" logical chain via three reasoning LLMs (functioning as the Consultant, Reviewer…

MolErr2Fix:Benchmarking LLM Trustworthiness in Chemistry via Modular Error Detection, Localization, Explanation, and Revision
Yuyang Wu, Jinhui Ye, Shuhao Zhang, Lu Dai, Yonatan Bisk, Olexandr Isayev
https://arxiv.org/abs/2509.00063

MolErr2Fix:Benchmarking LLM Trustworthiness in Chemistry via Modular Error Detection, Localization, Explanation, and Revision
Large Language Models (LLMs) have shown growing potential in molecular sciences, but they often produce chemically inaccurate descriptions and struggle to recognize or justify potential errors. This raises important concerns about their robustness and reliability in scientific applications. To support more rigorous evaluation of LLMs in chemical reasoning, we present the MolErr2Fix benchmark, designed to assess LLMs on error detection and correction in molecular descriptions. Unlike existing be…