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@Mediagazer@mstdn.social
2026-02-27 20:21:03

Memo: another CBS News producer leaves, after 46 years, and says, "We've been told to aim our reporting at a particular part of the political spectrum" (Jeremy Barr/The Guardian)
theguardian.com/media/2026/feb

@arXiv_csLG_bot@mastoxiv.page
2026-02-25 16:07:58

Replaced article(s) found for cs.LG. arxiv.org/list/cs.LG/new
[3/6]:
- Towards Scalable Oversight via Partitioned Human Supervision
Ren Yin, Takashi Ishida, Masashi Sugiyama
arxiv.org/abs/2510.22500 mastoxiv.page/@arXiv_csLG_bot/
- ContextPilot: Fast Long-Context Inference via Context Reuse
Yinsicheng Jiang, Yeqi Huang, Liang Cheng, Cheng Deng, Xuan Sun, Luo Mai
arxiv.org/abs/2511.03475 mastoxiv.page/@arXiv_csLG_bot/
- Metabolomic Biomarker Discovery for ADHD Diagnosis Using Interpretable Machine Learning
Nabil Belacel, Mohamed Rachid Boulassel
arxiv.org/abs/2601.11283 mastoxiv.page/@arXiv_csLG_bot/
- PhysE-Inv: A Physics-Encoded Inverse Modeling approach for Arctic Snow Depth Prediction
Akila Sampath, Vandana Janeja, Jianwu Wang
arxiv.org/abs/2601.17074
- SAGE-5GC: Security-Aware Guidelines for Evaluating Anomaly Detection in the 5G Core Network
Cristian Manca, Christian Scano, Giorgio Piras, Fabio Brau, Maura Pintor, Battista Biggio
arxiv.org/abs/2602.03596
- LORE: Jointly Learning the Intrinsic Dimensionality and Relative Similarity Structure From Ordina...
Anand, Helbling, Davenport, Berman, Alagapan, Rozell
arxiv.org/abs/2602.04192
- Towards Robust Scaling Laws for Optimizers
Alexandra Volkova, Mher Safaryan, Christoph H. Lampert, Dan Alistarh
arxiv.org/abs/2602.07712 mastoxiv.page/@arXiv_csLG_bot/
- Do We Need Adam? Surprisingly Strong and Sparse Reinforcement Learning with SGD in LLMs
Sagnik Mukherjee, Lifan Yuan, Pavan Jayasinha, Dilek Hakkani-T\"ur, Hao Peng
arxiv.org/abs/2602.07729 mastoxiv.page/@arXiv_csLG_bot/
- AceGRPO: Adaptive Curriculum Enhanced Group Relative Policy Optimization for Autonomous Machine L...
Yuzhu Cai, Zexi Liu, Xinyu Zhu, Cheng Wang, Siheng Chen
arxiv.org/abs/2602.07906 mastoxiv.page/@arXiv_csLG_bot/
- VESPO: Variational Sequence-Level Soft Policy Optimization for Stable Off-Policy LLM Training
Guobin Shen, Chenxiao Zhao, Xiang Cheng, Lei Huang, Xing Yu
arxiv.org/abs/2602.10693 mastoxiv.page/@arXiv_csLG_bot/
- KBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models
Zukang Xu, Zhixiong Zhao, Xing Hu, Zhixuan Chen, Dawei Yang
arxiv.org/abs/2602.11184 mastoxiv.page/@arXiv_csLG_bot/
- MUSE: Multi-Tenant Model Serving With Seamless Model Updates
Correia, Ferreira, Martins, Bento, Guerreiro, Pereira, Gomes, Bono, Ferreira, Bizarro
arxiv.org/abs/2602.11776 mastoxiv.page/@arXiv_csLG_bot/
- Pawsterior: Variational Flow Matching for Structured Simulation-Based Inference
Jorge Carrasco-Pollo, Floor Eijkelboom, Jan-Willem van de Meent
arxiv.org/abs/2602.13813 mastoxiv.page/@arXiv_csLG_bot/
- Silent Inconsistency in Data-Parallel Full Fine-Tuning: Diagnosing Worker-Level Optimization Misa...
Hong Li, Zhen Zhou, Honggang Zhang, Yuping Luo, Xinyue Wang, Han Gong, Zhiyuan Liu
arxiv.org/abs/2602.14462 mastoxiv.page/@arXiv_csLG_bot/
- Divine Benevolence is an $x^2$: GLUs scale asymptotically faster than MLPs
Alejandro Francisco Queiruga
arxiv.org/abs/2602.14495 mastoxiv.page/@arXiv_csLG_bot/
- \"UberWeb: Insights from Multilingual Curation for a 20-Trillion-Token Dataset
DatologyAI, et al.
arxiv.org/abs/2602.15210 mastoxiv.page/@arXiv_csLG_bot/
- GLM-5: from Vibe Coding to Agentic Engineering
GLM-5-Team, et al.
arxiv.org/abs/2602.15763 mastoxiv.page/@arXiv_csLG_bot/
- Anatomy of Capability Emergence: Scale-Invariant Representation Collapse and Top-Down Reorganizat...
Jayadev Billa
arxiv.org/abs/2602.15997 mastoxiv.page/@arXiv_csLG_bot/
- AI-CARE: Carbon-Aware Reporting Evaluation Metric for AI Models
KC Santosh, Srikanth Baride, Rodrigue Rizk
arxiv.org/abs/2602.16042 mastoxiv.page/@arXiv_csLG_bot/
- Beyond Message Passing: A Symbolic Alternative for Expressive and Interpretable Graph Learning
Chuqin Geng, Li Zhang, Haolin Ye, Ziyu Zhao, Yuhe Jiang, Tara Saba, Xinyu Wang, Xujie Si
arxiv.org/abs/2602.16947 mastoxiv.page/@arXiv_csLG_bot/
toXiv_bot_toot

@annsev@troet.cafe
2025-12-03 14:15:08

“Media Offenders” – this term should not refer to journalists who anger Trump with their reporting, but to the Darth #Trump administration itself.
It constantly attacks #media coverage, disparages and threatens media professionals.
Trump and his accomplices are the real “Media Offenders.”
There is…