Tootfinder

🔸"In Trauma”: A key Trump adviser says a Trump administration will seek to make civil servants miserable in their jobs.
🔸Military: In private speeches, he laid out plans to use armed forces to quell any domestic “riots.”
🔸1776 and 1860: He likened the country’s moment to those fractious periods in American history.

“Put Them in Trauma”: Inside a Key MAGA Leader’s Plans for a New Trump Agenda
Private videos reveal Trump adviser Russ Vought’s “shadow” plans for using the military on protesters, defunding the EPA and villainizing civil servants.

Picture the human body. Zoom in on a single cell. It lives for a while, then splits or dies, as part of a community of cells that make up a particular tissue. This community lives together for many many cell-lifetimes, each performing their own favorite function and reproducing as much as necessary to maintain their community, consuming the essential resources they need and contributing back what they can so that the whole body can live for decades. Each community of cells is interdependent on the whole body, but also stable and sustainable over long periods of time.
Now imagine a cancer cell. It has lost its ability to harmonize with the whole and prioritize balance, instead consuming and reproducing as quickly as it can. As neighboring tissues start to die from its excess, it metastasizes, always spreading to new territory to fuel its unbalanced appetite. The inevitable result is death of the whole body, although through birth, that body can create a new fresh branch of tissues that may continue their stable existence free of cancer. Alternatively, radiation or chemotherapy might be able to kill off the cancer, at great cost to the other tissues, but permitting long-term survival.
To the cancer cell, the idea of decades-long survival of a tissue community is unbelievable. When your natural state is unbounded consumption, growth, and competition, the idea of interdependent cooperation (with tissues all around the body you're not even touching, no less) seems impossible, and the idea that a tissue might survive in a stable form for decades is ludicrous.
"Perhaps if conditions were bleak enough to perfectly balance incessant unrestrained growth against the depredations of a hostile environment it might be possible? I guess the past must have been horribly brutal, so that despite each tissue trying to grow as much as possible they each barely survived? Yes, a stable and sustainable population is probably only possible under conditions of perfectly extreme hardship, and in our current era of unfettered growth, we should rejoice that we live in much easier times!"
You can probably already see where I'm going with this metaphor, but did you know that there are human communities, alive today, that have been living sustainably for *tens, if not hundreds of thousands of years*?
#anarchy #colonialism #civilization
P.S. if you're someone who likes to think about past populations and historical population growth, I cannot recommend the (short, free) game Opera Omnia by Stephen Lavelle enough: https://www.increpare.com/2009/02/opera-omnia/

We found that ChatGPT made executives significantly more optimistic in their forecasts while peer discussions tended to encourage caution. Additionally, we found that the executives armed with ChatGPT made worse predictions, based on actual stock figures, than they had before they consulted the tool.”
But they'll still keep pushing it down our throats, because nobody wants to be first to get out of the bubble.

Research: Executives Who Used Gen AI Made Worse Predictions
In a recent experiment, nearly 300 executives and managers were shown recent stock prices for the chip-maker Nvidia and then asked to predict the stock’s price in a month’s time. Then, half the group was given the opportunity to ask questions of ChatGPT while the other half were allowed to consult with their peers about Nvidia’s stock. The executives who used ChatGPT became significantly more optimistic, confident, and produced worse forecasts than the group who discussed with their peers…

Hourly maps at ground level of Wildfire Smoke Fine Particulate Matter PM2.5, for regions and North America | Environment #Canada
https://weather.gc.ca/firework/firework_anim_e…

Revisiting Node Affinity Prediction in Temporal Graphs
Krishna Sri Ipsit Mantri, Or Feldman, Moshe Eliasof, Chaim Baskin
https://arxiv.org/abs/2510.06940 https://

Revisiting Node Affinity Prediction in Temporal Graphs
Node affinity prediction is a common task that is widely used in temporal graph learning with applications in social and financial networks, recommender systems, and more. Recent works have addressed this task by adapting state-of-the-art dynamic link property prediction models to node affinity prediction. However, simple heuristics, such as Persistent Forecast or Moving Average, outperform these models. In this work, we analyze the challenges in training current Temporal Graph Neural Networks …

What-If Analysis of Large Language Models: Explore the Game World Using Proactive Thinking
Yuan Sui, Yanming Zhang, Yi Liao, Yu Gu, Guohua Tang, Zhongqian Sun, Wei Yang, Bryan Hooi
https://arxiv.org/abs/2509.04791

What-If Analysis of Large Language Models: Explore the Game World Using Proactive Thinking
Large language models (LLMs) excel at processing information reactively but lack the ability to systemically explore hypothetical futures. They cannot ask, "what if we take this action? how will it affect the final outcome" and forecast its potential consequences before acting. This critical gap limits their utility in dynamic, high-stakes scenarios like strategic planning, risk assessment, and real-time decision making. To bridge this gap, we propose WiA-LLM, a new paradigm that equips LLMs wi…

ATOM: A Pretrained Neural Operator for Multitask Molecular Dynamics
Luke Thompson, Davy Guan, Dai Shi, Slade Matthews, Junbin Gao, Andi Han
https://arxiv.org/abs/2510.05482 http…

ATOM: A Pretrained Neural Operator for Multitask Molecular Dynamics
Molecular dynamics (MD) simulations underpin modern computational drug dis- covery, materials science, and biochemistry. Recent machine learning models provide high-fidelity MD predictions without the need to repeatedly solve quantum mechanical forces, enabling significant speedups over conventional pipelines. Yet many such methods typically enforce strict equivariance and rely on sequential rollouts, thus limiting their flexibility and simulation efficiency. They are also com- monly single-tas…

Scaling Law for Large-Scale Pre-Training Using Chaotic Time Series and Predictability in Financial Time Series
Yuki Takemoto
https://arxiv.org/abs/2509.04921 https://

Scaling Law for Large-Scale Pre-Training Using Chaotic Time Series and Predictability in Financial Time Series
Time series forecasting plays a critical role in decision-making processes across diverse fields including meteorology, traffic, electricity, economics, finance, and so on. Especially, predicting returns on financial instruments is a challenging problem. Some researchers have proposed time series foundation models applicable to various forecasting tasks. Simultaneously, based on the recognition that real-world time series exhibit chaotic properties, methods have been developed to artificially g…

Generative World Modelling for Humanoids: 1X World Model Challenge Technical Report
Riccardo Mereu, Aidan Scannell, Yuxin Hou, Yi Zhao, Aditya Jitta, Antonio Dominguez, Luigi Acerbi, Amos Storkey, Paul Chang
https://arxiv.org/abs/2510.07092

Generative World Modelling for Humanoids: 1X World Model Challenge Technical Report
World models are a powerful paradigm in AI and robotics, enabling agents to reason about the future by predicting visual observations or compact latent states. The 1X World Model Challenge introduces an open-source benchmark of real-world humanoid interaction, with two complementary tracks: sampling, focused on forecasting future image frames, and compression, focused on predicting future discrete latent codes. For the sampling track, we adapt the video generation foundation model Wan-2.2 TI2V-…

Deciphering Invariant Feature Decoupling in Source-free Time Series Forecasting with Proxy Denoising
Kangjia Yan, Chenxi Liu, Hao Miao, Xinle Wu, Yan Zhao, Chenjuan Guo, Bin Yang
https://arxiv.org/abs/2510.05589

Deciphering Invariant Feature Decoupling in Source-free Time Series Forecasting with Proxy Denoising
The proliferation of mobile devices generates a massive volume of time series across various domains, where effective time series forecasting enables a variety of real-world applications. This study focuses on a new problem of source-free domain adaptation for time series forecasting. It aims to adapt a pretrained model from sufficient source time series to the sparse target time series domain without access to the source data, embracing data protection regulations. To achieve this, we propose …