Tootfinder

The Milky Way could have many more satellite galaxies than scientists have previously been able to predict or observe,
according to new research.
Cosmologists at Durham University used a new technique combining the highest-resolution supercomputer simulations that exist,
alongside novel mathematical modeling, to predict the existence of missing "orphan" galaxies.

The Milky Way could be teeming with more satellite galaxies than previously thought
The Milky Way could have many more satellite galaxies than scientists have previously been able to predict or observe, according to new research. Cosmologists at Durham University used a new technique combining the highest-resolution supercomputer simulations that exist, alongside novel mathematical modeling, to predict the existence of missing "orphan" galaxies.

[OT] Revealed: Apple is teaching its AI to adapt to the Trump era https://www.politico.eu/article/apple-teaching-artificial-intelligence-adapt-to-trump-era/ "updated guidelines on how the AI talks about diversity, equity and inclusion poli…

Apple is teaching its artificial intelligence to adapt to the Trump era
Apple policy documents enacted by a subcontractor show how the company shifted its approach to fine-tuning its AI in March, two months after the U.S. president was inaugurated.

Weather Alerts Metro #Vancouver -
Warnings
8:10 AM PDT Monday 11 August 2025
Heat Warning in effect for:
Metro Vancouver
This is a heat warning. Temperature are very high and there is a moderate risk to public health.
What: Temperatures reaching 33 degrees Celsius combined with overnight lows near 17 degrees Celsius.
Metro Vancouver (inland and eastern sections)…

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation
Bowen Zheng, Tianming Yang
https://arxiv.org/abs/2506.09376 https://

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation
Diffusion distillation is a widely used technique to reduce the sampling cost of diffusion models, yet it often requires extensive training, and the student performance tends to be degraded. Recent studies show that incorporating a GAN objective may alleviate these issues, yet the underlying mechanism remains unclear. In this work, we first identify a key limitation of distillation: mismatched step sizes and parameter numbers between the teacher and the student model lead them to converge to di…

Memo: Apple's March update to its AI training guidelines for data annotators marked DEI as a "controversial" topic and removed intolerance as "harmful" behavior (Océane Herrero/Politico)
https://www.politico.eu/article/apple-teac

Apple is teaching its artificial intelligence to adapt to the Trump era
Apple policy documents enacted by a subcontractor show how the company shifted its approach to fine-tuning its AI in March, two months after the U.S. president was inaugurated.

AntiDote: Bi-level Adversarial Training for Tamper-Resistant LLMs
Debdeep Sanyal, Manodeep Ray, Murari Mandal
https://arxiv.org/abs/2509.08000 https://arxi…

AntiDote: Bi-level Adversarial Training for Tamper-Resistant LLMs
The release of open-weight large language models (LLMs) creates a tension between advancing accessible research and preventing misuse, such as malicious fine-tuning to elicit harmful content. Current safety measures struggle to preserve the general capabilities of the LLM while resisting a determined adversary with full access to the model's weights and architecture, who can use full-parameter fine-tuning to erase existing safeguards. To address this, we introduce AntiDote, a bi-level optimizat…

Evolution from intralayer to interlayer superconductivity in a bilayer $t$-$J$ model
Yuan Yang, Xin Lu, Yuan Wan, Wei-Qiang Chen, Shou-Shu Gong
https://arxiv.org/abs/2507.07545

Evolution from intralayer to interlayer superconductivity in a bilayer $t$-$J$ model
Motivated by the bilayer cuprate superconductors and nickelate superconductor La$_3$Ni$_2$O$_7$, we investigate the evolution from intralayer to interlayer superconductivity based on a bilayer two-leg $t$-$J$-$J_{\bot}$ model, where $t$ is the in-plane electron hopping, $J$ is the in-plane spin interaction, and $J_{\bot}$ is the inter-plane spin interaction. By means of the density matrix renormalization group calculations, we obtain the quantum phase diagram of the system by tuning $J_{\bot}$ …

Whispers from the Early Universe: The Ringdown of Primordial Black Holes
Chen Yuan, Zhen Zhong, Qing-Guo Huang
https://arxiv.org/abs/2507.07665 https://

Whispers from the Early Universe: The Ringdown of Primordial Black Holes
We investigate the stochastic gravitational wave background (SGWB) generated by the ringdown phase of primordial black holes (PBHs) formed in the early universe. As the ringdown signal is independent of the PBH formation mechanism, the resulting SGWB offers a model-independent probe of PBHs. We numerically compute the ringdown waveform and derive the corresponding SGWB. We show that such a signal could be detected by future pulsar timing arrays (PTAs) for PBHs heavier than the solar mass. Addit…

Learning the Topic, Not the Language: How LLMs Classify Online Immigration Discourse Across Languages
Andrea Nasuto, Stefano Maria Iacus, Francisco Rowe, Devika Jain
https://arxiv.org/abs/2508.06435

Learning the Topic, Not the Language: How LLMs Classify Online Immigration Discourse Across Languages
Large language models (LLMs) are transforming social-science research by enabling scalable, precise analysis. Their adaptability raises the question of whether knowledge acquired through fine-tuning in a few languages can transfer to unseen languages that only appeared during pre-training. To examine this, we fine-tune lightweight LLaMA 3.2-3B models on monolingual, bilingual, or multilingual data sets to classify immigration-related tweets from X/Twitter across 13 languages, a domain character…

On the Effect of Instruction Tuning Loss on Generalization
Anwoy Chatterjee, H S V N S Kowndinya Renduchintala, Sumit Bhatia, Tanmoy Chakraborty
https://arxiv.org/abs/2507.07817

On the Effect of Instruction Tuning Loss on Generalization
Instruction Tuning has emerged as a pivotal post-training paradigm that enables pre-trained language models to better follow user instructions. Despite its significance, little attention has been given to optimizing the loss function used. A fundamental, yet often overlooked, question is whether the conventional auto-regressive objective - where loss is computed only on response tokens, excluding prompt tokens - is truly optimal for instruction tuning. In this work, we systematically investigat…