Tootfinder

The lattices $\textbf m\times\textbf 2$ and $\textbf m\times\textbf 3$ are not Schur positive
David G. L. Wang, K. Zhang
https://arxiv.org/abs/2510.03116 https://

The lattices $\textbf m\times\textbf 2$ and $\textbf m\times\textbf 3$ are not Schur positive
We prove that the lattices $\textbf m\times\textbf 2$ and $\textbf m\times\textbf 3$ are not Schur positive for $m\ge 8$. This confirms a conjecture of Li, Qiu, Yang, and Zhang, as an extension of counterexamples to a comment of Stanley on the universal Schur positivity of distributive lattices. Our main tools include Pieri's rules, and Wang and Wang's combinatorial formula for computing any Schur coefficient of the chromatic symmetric function of a graph in terms of special ribbon tabloids. We…

Judge calls for Justice Dept. whistleblower to testify in migrant planes case (Steve Thompson/Washington Post)
https://www.washingtonpost.com/politics/2025/12/08/boasberg-ruling-whistleblower-testify/
http://www.memeorandum.com/251208/p131#a251208p131

THE MILITARY OFFICER CORPS DEFIED TRUMP ON SEPTEMBER 30th; IT WILL NOT ABANDON THE AMERICAN PEOPLE TO A DICTATORSHIP; IT WILL OPPOSE IT WITH THEIR LIVES AND SACRED HONOR https://thewestpointhistoryprofessor.substack.com/p/the-military-officer-corps-defied

THE MILITARY OFFICER CORPS DEFIED TRUMP ON SEPTEMBER 30th; IT WILL NOT ABANDON THE AMERICAN PEOPLE TO A DICTATORSHIP; IT WILL OPPOSE IT WITH THEIR LIVES AND SACRED HONOR
THE THREE PRECEDENTS WHERE THE U,S ARMY DEFIED A SITTING PRESIDENT AND AGGRESSIVELY ACTED TO DEFEND THE CONSTITUTIONAL GOVERNMENT. WE DISCUSS THE FIRST EXAMPLE TODAY. BUT FIRST A SIDEBAR.

Provably Mitigating Corruption, Overoptimization, and Verbosity Simultaneously in Offline and Online RLHF/DPO Alignment
Ziyi Chen, Junyi Li, Peiran Yu, Heng Huang
https://arxiv.org/abs/2510.05526

Provably Mitigating Corruption, Overoptimization, and Verbosity Simultaneously in Offline and Online RLHF/DPO Alignment
Reinforcement learning from human feedback (RLHF) and direct preference optimization (DPO) are important techniques to align large language models (LLM) with human preference. However, the quality of RLHF and DPO training is seriously compromised by \textit{\textbf{C}orrupted} preference, reward \textit{\textbf{O}veroptimization}, and bias towards \textit{\textbf{V}erbosity}. To our knowledge, most existing works tackle only one of these important issues, and the few other works require much co…

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<https://www.freshports.org/textproc/py-pdftotext/#pkg-plist> "There is no configure plist information for this port."…

Free business idea, a service to print and frame thoughtful on-topic replies on social media.
On second thought probably not very viable.

This is quite a thoughtful takedown of 'AI', especially in art. It helps characters/express something most of us feel but can't quite explain: https://theoatmeal.com/comics/ai_art

TESTIFY. Yao Ming was legendary. So fun to watch. Take it from Cuttino: Once he understood the assignment, Yao dunking on everyone & even blocking Shaq.
▶️ Cuttino Mobley How Yao Ming Ruined Our Summer
https://youtube.com/shorts/D1DUE9gylM0?si=mk_X4TG-D8tDwIs2<…

"The man said he did not know Gisèle Pelicot had been drugged. Her ex-husband, who admitted drugging and raping her and inviting strangers to join him, testified that the man knew."
Ex-Husband Contradicts a Man Appealing Conviction in Pelicot Rape Case - The New York Times
https://www.nytimes.com/2025/10/07/world/europe/gisele-pelicot-rape-trial-appeal-france.html

(Token-Level) \textbf{InfoRMIA}: Stronger Membership Inference and Memorization Assessment for LLMs
Jiashu Tao, Reza Shokri
https://arxiv.org/abs/2510.05582 https://

(Token-Level) \textbf{InfoRMIA}: Stronger Membership Inference and Memorization Assessment for LLMs
Machine learning models are known to leak sensitive information, as they inevitably memorize (parts of) their training data. More alarmingly, large language models (LLMs) are now trained on nearly all available data, which amplifies the magnitude of information leakage and raises serious privacy risks. Hence, it is more crucial than ever to quantify privacy risk before the release of LLMs. The standard method to quantify privacy is via membership inference attacks, where the state-of-the-art ap…