Tootfinder

Top US general: will take time to achieve objectives in Iran (Reuters)
https://www.reuters.com/world/middle-east/top-us-general-will-take-time-achieve-objectives-iran-2026-03-02/
http://www.memeorandum.com/260302/p58#a260302p58

Trump leaves door open for boots on the ground in Iran (Cate Martel/The Hill)
https://thehill.com/newsletters/1230-report/5763061-trump-iran-timeline-objectives/
http://www.memeorandum.com/260302/p89#a260302p89

With the war in its fifth week, Trump and his aides have increasingly suggested he sees an opening for claiming to have achieved the military objectives in Iran and pulling out of the conflict.
Trump’s inclination to wind down the offensive comes as Iran has indicated it is not currently willing to engage in serious negotiations, believing Trump to be untrustworthy, and that it is in a strong position in the war, according to U.S. officials.
Earlier on Wednesday, Trump told Reut…

Iran War Live Updates: Trump to Speak to Nation Tonight on War

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/6]:
- Performance Asymmetry in Model-Based Reinforcement Learning
Jing Yu Lim, Rushi Shah, Zarif Ikram, Samson Yu, Haozhe Ma, Tze-Yun Leong, Dianbo Liu
https://arxiv.org/abs/2505.19698 https://mastoxiv.page/@arXiv_csLG_bot/114578810521008766
- Towards Robust Real-World Multivariate Time Series Forecasting: A Unified Framework for Dependenc...
Jinkwan Jang, Hyungjin Park, Jinmyeong Choi, Taesup Kim
https://arxiv.org/abs/2506.08660 https://mastoxiv.page/@arXiv_csLG_bot/114664238967892509
- Wasserstein Barycenter Soft Actor-Critic
Zahra Shahrooei, Ali Baheri
https://arxiv.org/abs/2506.10167 https://mastoxiv.page/@arXiv_csLG_bot/114675175949432731
- Foundation Models for Causal Inference via Prior-Data Fitted Networks
Yuchen Ma, Dennis Frauen, Emil Javurek, Stefan Feuerriegel
https://arxiv.org/abs/2506.10914 https://mastoxiv.page/@arXiv_csLG_bot/114675529854402158
- FREQuency ATTribution: benchmarking frequency-based occlusion for time series data
Dominique Mercier, Andreas Dengel, Sheraz Ahmed
https://arxiv.org/abs/2506.18481 https://mastoxiv.page/@arXiv_csLG_bot/114738421450807709
- Complexity-aware fine-tuning
Andrey Goncharov, Daniil Vyazhev, Petr Sychev, Edvard Khalafyan, Alexey Zaytsev
https://arxiv.org/abs/2506.21220 https://mastoxiv.page/@arXiv_csLG_bot/114754764750730849
- Transfer Learning in Infinite Width Feature Learning Networks
Clarissa Lauditi, Blake Bordelon, Cengiz Pehlevan
https://arxiv.org/abs/2507.04448 https://mastoxiv.page/@arXiv_csLG_bot/114818005803079705
- A hierarchy tree data structure for behavior-based user segment representation
Liu, Kang, Iyer, Malik, Li, Wang, Lu, Zhao, Wang, Liu, Liu, Liang, Yu
https://arxiv.org/abs/2508.01115 https://mastoxiv.page/@arXiv_csLG_bot/114975999992144374
- One-Step Flow Q-Learning: Addressing the Diffusion Policy Bottleneck in Offline Reinforcement Lea...
Thanh Nguyen, Chang D. Yoo
https://arxiv.org/abs/2508.13904 https://mastoxiv.page/@arXiv_csLG_bot/115060568241390847
- Uncertainty Propagation Networks for Neural Ordinary Differential Equations
Hadi Jahanshahi, Zheng H. Zhu
https://arxiv.org/abs/2508.16815 https://mastoxiv.page/@arXiv_csLG_bot/115094785677272005
- Learning Unified Representations from Heterogeneous Data for Robust Heart Rate Modeling
Zhengdong Huang, Zicheng Xie, Wentao Tian, Jingyu Liu, Lunhong Dong, Peng Yang
https://arxiv.org/abs/2508.21785 https://mastoxiv.page/@arXiv_csLG_bot/115128450608548173
- Monte Carlo Tree Diffusion with Multiple Experts for Protein Design
Liu, Cao, Jiang, Luo, Duan, Wang, Sosnick, Xu, Stevens
https://arxiv.org/abs/2509.15796 https://mastoxiv.page/@arXiv_csLG_bot/115247429156900905
- From Samples to Scenarios: A New Paradigm for Probabilistic Forecasting
Xilin Dai, Zhijian Xu, Wanxu Cai, Qiang Xu
https://arxiv.org/abs/2509.19975 https://mastoxiv.page/@arXiv_csLG_bot/115264498084813952
- Why High-rank Neural Networks Generalize?: An Algebraic Framework with RKHSs
Yuka Hashimoto, Sho Sonoda, Isao Ishikawa, Masahiro Ikeda
https://arxiv.org/abs/2509.21895 https://mastoxiv.page/@arXiv_csLG_bot/115287261047939306
- From Parameters to Behaviors: Unsupervised Compression of the Policy Space
Davide Tenedini, Riccardo Zamboni, Mirco Mutti, Marcello Restelli
https://arxiv.org/abs/2509.22566 https://mastoxiv.page/@arXiv_csLG_bot/115287379672141023
- RHYTHM: Reasoning with Hierarchical Temporal Tokenization for Human Mobility
Haoyu He, Haozheng Luo, Yan Chen, Qi R. Wang
https://arxiv.org/abs/2509.23115 https://mastoxiv.page/@arXiv_csLG_bot/115293273559547106
- Polychromic Objectives for Reinforcement Learning
Jubayer Ibn Hamid, Ifdita Hasan Orney, Ellen Xu, Chelsea Finn, Dorsa Sadigh
https://arxiv.org/abs/2509.25424 https://mastoxiv.page/@arXiv_csLG_bot/115298579764580635
- Recursive Self-Aggregation Unlocks Deep Thinking in Large Language Models
Siddarth Venkatraman, et al.
https://arxiv.org/abs/2509.26626 https://mastoxiv.page/@arXiv_csLG_bot/115298789487177431
- Cautious Weight Decay
Chen, Li, Liang, Su, Xie, Pierse, Liang, Lao, Liu
https://arxiv.org/abs/2510.12402 https://mastoxiv.page/@arXiv_csLG_bot/115377759317818093
- TeamFormer: Shallow Parallel Transformers with Progressive Approximation
Wei Wang, Xiao-Yong Wei, Qing Li
https://arxiv.org/abs/2510.15425 https://mastoxiv.page/@arXiv_csLG_bot/115405933861293858
- Latent-Augmented Discrete Diffusion Models
Dario Shariatian, Alain Durmus, Umut Simsekli, Stefano Peluchetti
https://arxiv.org/abs/2510.18114 https://mastoxiv.page/@arXiv_csLG_bot/115417332500265972
- Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease using Machine Learning Method...
Mary E. An, Paul Griffin, Jonathan G. Stine, Ramakrishna Balakrishnan, Soundar Kumara
https://arxiv.org/abs/2510.22293 https://mastoxiv.page/@arXiv_csLG_bot/115451746201804373
toXiv_bot_toot

Trump has never hidden his desire for domination
-- and the related willingness to have his followers engage in violence,
from the call to rough up people at his rallies
to the pardons of even the most brutal January 6 insurrectionists
Unlike with 20th century fascism, there is no attempt to promote or symbolically reward self-sacrifice
– it is just video game-style killing at a distance,
justified not with strategic objectives,
but with seemingly u…

Trump aides foresee Iran endgame divide: "Israel doesn't hate the chaos" (Axios)
https://www.axios.com/2026/03/18/israel-us-iran-war-objectives-trump-netanyahu
http://www.memeorandum.com/260318/p15#a260318p15

In recent weeks, the U.S. military has built up forces close to Iran, in what Donald Trump has referred to as an “armada.”
Trump is now deciding whether to take military action against Iran as soon as this weekend, officials said.
But unlike last June, when the United States joined Israel in attacking nuclear sites in Iran, the president’s objectives are less clear.
The "armada" includes the aircraft carrier Abraham Lincoln, which is accompanied by three warships …

Maps: Where the U.S. Is Building Up Military Force Near Iran
President Trump has not authorized military action in Iran, but the United States has built up its presence in the region in recent weeks. Now it’s sending even more firepower.

Trump's Iran War Strategy: Precision Strikes, Clear Objectives, and a Swift Return to Peace (Fred Fleitz/American Greatness)
https://amgreatness.com/2026/03/13/trumps-iran-war-strategy-precision-strikes-clear-objectives-and-a-swift-return-to-peace/
http://www.memeorandum.com/260313/p33#a260313p33

More than 3,100 anti-authoritarian protests are scheduled across the US and at least 15 other countries on Saturday.
All these events will take place under a single banner: No Kings.
Formally launched in June to fight back against Trump administrationpolicies, the No Kings movement has grown with astonishing speed
– its second and most recent mass protest in October drew an estimated 7 million participants.
Organizers expect Saturday’s events to be the biggest prote…

Mixed messaging from the White House comes about three weeks into the conflict which launched on Feb. 28.
Trump had promised the war would be swift.
On Friday evening, March 20, he said the country is "very close to meeting our objectives,"
including "never allowing Iran to get even close to Nuclear Capability."
But it remains unclear what exactly the White House needs to "complete its mission"
-- and whether that will involve depl…