Tootfinder

Raubkopien für KI-Training: Meta bekommt recht, die Klagenden einen Rüffel
Für das KI-Training hat Meta Schattenbibliotheken heruntergeladen. Trotzdem sind die Autoren und Autorinnen vor Gericht gescheitert – wegen mehrerer Fehler.

Anti-Phishing Training Does Not Work: A Large-Scale Empirical Assessment of Multi-Modal Training Grounded in the NIST Phish Scale
Andrew T. Rozema, James C. Davis
https://arxiv.org/abs/2506.19899

Anti-Phishing Training Does Not Work: A Large-Scale Empirical Assessment of Multi-Modal Training Grounded in the NIST Phish Scale
Social engineering attacks using email, commonly known as phishing, are a critical cybersecurity threat. Phishing attacks often lead to operational incidents and data breaches. As a result, many organizations allocate a substantial portion of their cybersecurity budgets to phishing awareness training, driven in part by compliance requirements. However, the effectiveness of this training remains in dispute. Empirical evidence of training (in)effectiveness is essential for evidence-based cybersec…

A US judge rules that Meta's use of books to train AI is protected by fair use but says his opinion is more a reflection of the plaintiffs' poor arguments (Isaiah Poritz/Bloomberg Law)
https://news.bloomberglaw.com/legal-op

Meta Beats Copyright Suit From Authors Over AI Training on Books
Meta Platforms Inc. escaped a first-of-its-kind copyright lawsuit from a group of authors who alleged the tech giant hoovered up millions of copyrighted books without permission to train its generative AI model called Llama.

Let‘s train an #AI which will output copyrighted work if asked to.
Judge Alsup: Training AI On Copyrighted Works? Fair Use. Building Pirate Libraries? Not So Much https://www.…

2025 Dallas Cowboys Training Camp: Schedule, dates plus offseason moves analysis https://www.foxsports.com/stories/nfl/2025-dallas-cowboys-training-camp-schedule-dates-plus-offseason-moves-analysis

Weniger Daten fürs Training: Gemini für Android soll mehr Privatsphäre achten
Google macht sich bereit für das Ende des Google Assistant. Der Konzern reduziert im Zuge dessen die Datensammelei bei Basisfunktionen von Gemini auf Android.

A US judge rules that Meta's use of books to train AI is protected by fair use but says his opinion is more a reflection of the plaintiffs' poor arguments (Isaiah Poritz/Bloomberg Law)
https://news.bloomberglaw.com/legal-op

Meta Beats Copyright Suit From Authors Over AI Training on Books
Meta Platforms Inc. escaped a first-of-its-kind copyright lawsuit from a group of authors who alleged the tech giant hoovered up millions of copyrighted books without permission to train its generative AI model called Llama.

A US judge has said that Meta’s use of copyrighted books to train its AI models constitutes "fair use" under US copyright law. It follows a similar judgement about Anthropic earlier this week, and will come as disappointment to authors and other creators looking for compensation in what they see as use of their work without permission.

Meta’s AI training on copyrighted content is ‘fair use’, US judge says
But creators could have grounds for complaint in future cases they can prove market dilution and unfair competition, judge adds.

Microsoft Sued By Authors Over Use of Books in AI Training - Slashdot
https://news.slashdot.org/story/25/06/25/2137220/microsoft-sued-by-authors-over-use-of-books-in-ai-training?utm_source=rss1.0mainlinkanon&utm_medium=feed

Raiders Mailbag: Assessing the roster with training camp one month away https://www.raiders.com/news/mailbag/raiders-mailbag-assessing-the-roster-with-training-camp-one-month-away

OctoThinker: Mid-training Incentivizes Reinforcement Learning Scaling
Zengzhi Wang, Fan Zhou, Xuefeng Li, Pengfei Liu
https://arxiv.org/abs/2506.20512 http…

OctoThinker: Mid-training Incentivizes Reinforcement Learning Scaling
Different base language model families, such as Llama and Qwen, exhibit divergent behaviors during post-training with reinforcement learning (RL), especially on reasoning-intensive tasks. What makes a base language model suitable for reinforcement learning? Gaining deeper insight into this question is essential for developing RL-scalable foundation models of the next generation. In this work, we investigate how mid-training strategies shape RL dynamics, focusing on two representative model fami…

Kein Verbot vorgesehen: Creative Commons arbeitet an Lizenzen fürs KI-Training
Creative Commons will dafür sorgen, dass Verantwortliche für Datensätze signalisieren können, was sie dafür erwarten, wenn damit ein KI-Modell trainiert wird.

Urheberrecht: KI-Training mit Büchern ist erlaubt – wenn man sie kauft
https://www.zeit.de/digital/internet/2025-06/urheberrecht-buecher-meta-anthropic-newsletter-kuenstliche-intelligenz

2025 Dallas Cowboys training camp will be filled with questions https://insidethestar.com/2025-dallas-cowboys-training-camp-will-be-filled-with-questions

We're recruiting on our MSc Sociology and Data Analytics for next September at the University of Limerick @…

This MSc offers in depth knowledge of sociological analysis, research… | Faculty of Arts, Humanities and Social Sciences, University of Limerick
This MSc offers in depth knowledge of sociological analysis, research methods and social science analytics - including high level training in data, data science, and data mining, and a range of sequenced topics that include crime, law, and deviance, culture and media, health and well-being, political analysis and social movements, and stratification and social inequality. The combination of high level training in research methods and robust exposure to core topics in Sociology and the Social …

E-FreeM2: Efficient Training-Free Multi-Scale and Cross-Modal News Verification via MLLMs
Van-Hoang Phan, Long-Khanh Pham, Dang Vu, Anh-Duy Tran, Minh-Son Dao
https://arxiv.org/abs/2506.20944

E-FreeM2: Efficient Training-Free Multi-Scale and Cross-Modal News Verification via MLLMs
The rapid spread of misinformation in mobile and wireless networks presents critical security challenges. This study introduces a training-free, retrieval-based multimodal fact verification system that leverages pretrained vision-language models and large language models for credibility assessment. By dynamically retrieving and cross-referencing trusted data sources, our approach mitigates vulnerabilities of traditional training-based models, such as adversarial attacks and data poisoning. Addi…

Raiders Mailbag: Assessing the roster with training camp one month away https://www.raiders.com/news/mailbag/raiders-mailbag-assessing-the-roster-with-training-camp-one-month-away

This comes down to the cyberlibertarian roots of most digital movements (thing Archive.org, EFF, EDRI etc.): To them "open" is a value in itself and any political values are read as "restrictions" or "regulation" or "lack of freedom".
https://indieweb.social/@jaredw…

Creative Commons debuts CC Signals, a framework that will allow dataset holders to detail how their content can or cannot be reused for training AI models (Sarah Perez/TechCrunch)
https://techcrunch.com/2025/06/25/creative…

Creative Commons debuts CC signals, a framework for an open AI ecosystem | TechCrunch
CC signals will allow dataset holders to detail how their content can or cannot be reused by machines, as in the case of training AI models.

Smart Cuts: Enhance Active Learning for Vulnerability Detection by Pruning Bad Seeds
Xiang Lan, Tim Menzies, Bowen Xu
https://arxiv.org/abs/2506.20444 http…

Smart Cuts: Enhance Active Learning for Vulnerability Detection by Pruning Bad Seeds
Vulnerability detection is crucial for identifying security weaknesses in software systems. However, the effectiveness of machine learning models in this domain is often hindered by low-quality training datasets, which contain noisy, mislabeled, or imbalanced samples. This paper proposes a novel dataset maps-empowered approach that systematically identifies and mitigates hard-to-learn outliers, referred to as "bad seeds", to improve model training efficiency. Our approach can categorize trainin…

Anthony Richardson (shoulder) expects to be ready for Colts training camp in July https://www.nfl.com/news/anthony-richardson-shoulder-expects-to-be-ready-for-colts-training-camp-in-july

Cowboy Roundup: Top training camp battles, Linebacker roster bubble https://www.si.com/nfl/cowboys/news/dallas-cowboys-top-training-camp-battles-linebacker-roster-bubble

Instance Space Analysis for the Quadratic Assignment Problem
Jeffrey Christiansen (ARC Training Centre in Optimisation Technologies, Integrated Methodologies and Applications), Kate Smith-Miles (ARC Training Centre in Optimisation Technologies, Integrated Methodologies and Applications)
https://arxiv.org/abs/2506.20172

Instance Space Analysis for the Quadratic Assignment Problem
For any optimisation problem where diverse algorithmic approaches are available, the task of predicting algorithm performance and selecting the algorithm most likely to perform well on a given instance holds great practical interest. However, if our test instances do not adequately represent the potential problem space, we may be misled about the strengths and weaknesses of an algorithm. In this paper we consider algorithm prediction and selection for the Quadratic Assignment Problem (QAP). We …

I thought that police recruits had to take a basic training course that included the very basics principles of law. Like:
"Every person (not just every US citizen, not just every white US citizen, not just every rich white US citizen, not just every billionaire white US citizen) is to be presumed innocent by the government until proved guilty of a crime." 🧵‍>

Creative Commons debuts CC Signals, a framework that will allow dataset holders to detail how their content can or cannot be reused for training AI models (Sarah Perez/TechCrunch)
https://techcrunch.com/2025/06/25/creative…

Creative Commons debuts CC signals, a framework for an open AI ecosystem | TechCrunch
CC signals will allow dataset holders to detail how their content can or cannot be reused by machines, as in the case of training AI models.

Programming by Backprop: LLMs Acquire Reusable Algorithmic Abstractions During Code Training
Jonathan Cook, Silvia Sapora, Arash Ahmadian, Akbir Khan, Tim Rocktaschel, Jakob Foerster, Laura Ruis
https://arxiv.org/abs/2506.18777

Programming by Backprop: LLMs Acquire Reusable Algorithmic Abstractions During Code Training
Training large language models (LLMs) on source code significantly enhances their general-purpose reasoning abilities, but the mechanisms underlying this generalisation are poorly understood. In this paper, we propose Programming by Backprop (PBB) as a potential driver of this effect - teaching a model to evaluate a program for inputs by training on its source code alone, without ever seeing I/O examples. To explore this idea, we finetune LLMs on two sets of programs representing simple maths p…

angry sales guy: hey, what gives. I was reading the minutes yesterday and you recorded `and was written for sales (if they can read..?)`.. that's not professional.
me: well, I write all the customer facing stuff.
asg: ... so?
me: and I write all of the training material for all teams downstream of engineering
asg: .. and?
me: so what I write is arguably the most important, most exciting content that engineering produces.
asg: Look, I don't see-

https://techcrunch.com/2025/06/17/mastodon-updates-its-terms-to-prohibit-ai-model-training/
Mastodon updates its terms to prohibit AI model training

Mastodon updates its terms to prohibit AI model training | TechCrunch
Days after Elon Musk-owned X updated its terms to explicitly prohibit AI model training, decentralized social network Mastodon updated its own rules to bar any kind of model training, as well.

Mastodon updates its terms to prohibit AI model training
https://techcrunch.com/2025/06/17/mastodon-updates-its-terms-to-prohibit-ai-model-training/

Mastodon updates its terms to prohibit AI model training | TechCrunch
Days after Elon Musk-owned X updated its terms to explicitly prohibit AI model training, decentralized social network Mastodon updated its own rules to bar any kind of model training, as well.

Judge Rules Training AI on Authors' Books Is Legal But Pirating Them Is Not https://www.404media.co/judge-rules-training-ai-on-authors-books-is-legal-but-pirating-them-is-not/

Fellirium – Spring Equinox
#ambient

Fast Training-free Perceptual Image Compression
Ziran Zhu, Tongda Xu, Minye Huang, Dailan He, Xingtong Ge, Xinjie Zhang, Ling Li, Yan Wang
https://arxiv.org/abs/2506.16102

Fast Training-free Perceptual Image Compression
Training-free perceptual image codec adopt pre-trained unconditional generative model during decoding to avoid training new conditional generative model. However, they heavily rely on diffusion inversion or sample communication, which take 1 min to intractable amount of time to decode a single image. In this paper, we propose a training-free algorithm that improves the perceptual quality of any existing codec with theoretical guarantee. We further propose different implementations for optimal p…

Large-Scale Training Data Attribution for Music Generative Models via Unlearning
Woosung Choi, Junghyun Koo, Kin Wai Cheuk, Joan Serr\`a, Marco A. Mart\'inez-Ram\'irez, Yukara Ikemiya, Naoki Murata, Yuhta Takida, Wei-Hsiang Liao, Yuki Mitsufuji
https://arxiv.org/abs/2506.18312

Large-Scale Training Data Attribution for Music Generative Models via Unlearning
This paper explores the use of unlearning methods for training data attribution (TDA) in music generative models trained on large-scale datasets. TDA aims to identify which specific training data points contributed to the generation of a particular output from a specific model. This is crucial in the context of AI-generated music, where proper recognition and credit for original artists are generally overlooked. By enabling white-box attribution, our work supports a fairer system for acknowledg…

GenHSI: Controllable Generation of Human-Scene Interaction Videos
Zekun Li, Rui Zhou, Rahul Sajnani, Xiaoyan Cong, Daniel Ritchie, Srinath Sridhar
https://arxiv.org/abs/2506.19840

GenHSI: Controllable Generation of Human-Scene Interaction Videos
Large-scale pre-trained video diffusion models have exhibited remarkable capabilities in diverse video generation. However, existing solutions face several challenges in using these models to generate long movie-like videos with rich human-object interactions that include unrealistic human-scene interaction, lack of subject identity preservation, and require expensive training. We propose GenHSI, a training-free method for controllable generation of long human-scene interaction videos (HSI). Ta…

Critical Anatomy-Preserving & Terrain-Augmenting Navigation (CAPTAiN): Application to Laminectomy Surgical Education
Jonathan Wang, Hisashi Ishida, David Usevitch, Kesavan Venkatesh, Yi Wang, Mehran Armand, Rachel Bronheim, Amit Jain, Adnan Munawar
https://arxiv.org/abs/2506.20496

Critical Anatomy-Preserving & Terrain-Augmenting Navigation (CAPTAiN): Application to Laminectomy Surgical Education
Surgical training remains a crucial milestone in modern medicine, with procedures such as laminectomy exemplifying the high risks involved. Laminectomy drilling requires precise manual control to mill bony tissue while preserving spinal segment integrity and avoiding breaches in the dura: the protective membrane surrounding the spinal cord. Despite unintended tears occurring in up to 11.3% of cases, no assistive tools are currently utilized to reduce this risk. Variability in patient anatomy fu…

This https://arxiv.org/abs/2505.09551 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Fast Learning in Quantitative Finance with Extreme Learning Machine
This paper demonstrates that a broad class of problems in quantitative finance, including those previously addressed using deep neural networks, can be efficiently solved using single-layer neural networks without iterative gradient-based training, namely extreme learning machine (ELM). ELM utilizes a single-layer network with randomly initialized hidden nodes and analytically computed output weights obtained via convex optimization, enabling rapid training and inference. Both supervised and un…

TrainVerify: Equivalence-Based Verification for Distributed LLM Training
Yunchi Lu, Youshan Miao, Cheng Tan, Peng Huang, Yi Zhu, Xian Zhang, Fan Yang
https://arxiv.org/abs/2506.15961

TrainVerify: Equivalence-Based Verification for Distributed LLM Training
Training large language models (LLMs) at scale requires parallel execution across thousands of devices, incurring enormous computational costs. Yet, these costly distributed trainings are rarely verified, leaving them prone to silent errors and potentially wasting millions of GPU hours. We introduce TrainVerify, a system for verifiable distributed training of LLMs. Given a deep learning model's logical specification as the ground truth, TrainVerify formally verifies that a distributed parallel …

Toward a Global Regime for Compute Governance: Building the Pause Button
Ananthi Al Ramiah, Raymond Koopmanschap, Josh Thorsteinson, Sadruddin Khan, Jim Zhou, Shafira Noh, Joep Meindertsma, Farhan Shafiq
https://arxiv.org/abs/2506.20530

Toward a Global Regime for Compute Governance: Building the Pause Button
As AI capabilities rapidly advance, the risk of catastrophic harm from large-scale training runs is growing. Yet the compute infrastructure that enables such development remains largely unregulated. This paper proposes a concrete framework for a global "Compute Pause Button": a governance system designed to prevent dangerously powerful AI systems from being trained by restricting access to computational resources. We identify three key intervention points -- technical, traceability, and regulat…

POLAR: A Pessimistic Model-based Policy Learning Algorithm for Dynamic Treatment Regimes
Ruijia Zhang, Zhengling Qi, Yue Wu, Xiangyu Zhang, Yanxun Xu
https://arxiv.org/abs/2506.20406

POLAR: A Pessimistic Model-based Policy Learning Algorithm for Dynamic Treatment Regimes
Dynamic treatment regimes (DTRs) provide a principled framework for optimizing sequential decision-making in domains where decisions must adapt over time in response to individual trajectories, such as healthcare, education, and digital interventions. However, existing statistical methods often rely on strong positivity assumptions and lack robustness under partial data coverage, while offline reinforcement learning approaches typically focus on average training performance, lack statistical gu…

Gibt es irgendwo eine Landkarte der regionalen, nationalen und europäischen Initiativen zum Aufbau von #FDM-Kompetenzen? Sitze gerade in einem Vortrag zu https://www.skills4eosc.eu/. Dann gibt es ja noch vers…

Unidentified and Confounded? Understanding Two-Tower Models for Unbiased Learning to Rank
Philipp Hager, Onno Zoeter, Maarten de Rijke
https://arxiv.org/abs/2506.20501

Unidentified and Confounded? Understanding Two-Tower Models for Unbiased Learning to Rank
Additive two-tower models are popular learning-to-rank methods for handling biased user feedback in industry settings. Recent studies, however, report a concerning phenomenon: training two-tower models on clicks collected by well-performing production systems leads to decreased ranking performance. This paper investigates two recent explanations for this observation: confounding effects from logging policies and model identifiability issues. We theoretically analyze the identifiability conditio…

OLALa: Online Learned Adaptive Lattice Codes for Heterogeneous Federated Learning
Natalie Lang, Maya Simhi, Nir Shlezinger
https://arxiv.org/abs/2506.20297

OLALa: Online Learned Adaptive Lattice Codes for Heterogeneous Federated Learning
Federated learning (FL) enables collaborative training across distributed clients without sharing raw data, often at the cost of substantial communication overhead induced by transmitting high-dimensional model updates. This overhead can be alleviated by having the clients quantize their model updates, with dithered lattice quantizers identified as an attractive scheme due to its structural simplicity and convergence-preserving properties. However, existing lattice-based FL schemes typically re…

Pre-training Time Series Models with Stock Data Customization
Mengyu Wang, Tiejun Ma, Shay B. Cohen
https://arxiv.org/abs/2506.16746 https://

Pre-training Time Series Models with Stock Data Customization
Stock selection, which aims to predict stock prices and identify the most profitable ones, is a crucial task in finance. While existing methods primarily focus on developing model structures and building graphs for improved selection, pre-training strategies remain underexplored in this domain. Current stock series pre-training follows methods from other areas without adapting to the unique characteristics of financial data, particularly overlooking stock-specific contextual information and the…

Official 2025 Cowboys training camp calendar, dates revealed https://www.dallascowboys.com/news/official-2025-cowboys-training-camp-calendar-dates-revealed

How online fandom communities are advocating against AI, including protesting companies scraping fanfic content for AI training and opposing AI-generated fanfic (Decca Muldowney/The Verge)
https://www.theverge.com/ai-artificial-intelligence/688640/fanficti…

Fanfiction writers battle AI, one scrape at a time
‘It is a gift. They stole it without my permission.’

IRANIAN PROGRAM DEBATED AT M.I.T.
https://www.nytimes.com/1975/04/27/archives/iranian-program-debated-at-mit-training-of-atom-scientists-called.html?u…

IRANIAN PROGRAM DEBATED AT M.I.T.
Special Iranian Govt program to train nuclear engineers has stirred controversy on MIT campus; program, for which Iran is paying $1.4-billion, will place up to 54 masters degree candidates in MIT nuclear engineering dept; venture is only 1 of series of programs in which Iran is investing its oil wealth in setting up special arrangements with US universities, including Harvard and Columbia; MIT students argue that program will lead to proliferation of nuclear weapons and contend that Shah of Ira…

The Raiders Will be Much More Entertaining This Season https://www.si.com/nfl/raiders/las-vegas-training-camp-chip-kelly-geno-smith-ashton-jeanty

🕸️ A weird phrase is plaguing scientific papers – and we traced it back to a glitch in AI training data (https://theconversation.com/a-weird-phrase-is-plaguing-scientific-papers-and-we-traced-it-back-to-a-glitch-in-ai-training-data-254463).

Mittwoch: Anthropic mit Copyright-Teilerfolg, Google Earth bietet Rückblicke
Gericht zu KI-Training mit Büchern Google Earth mit alten Bildern Entlassungen bei Microsoft Bill Gates gegen Gesundheitskrise Transparenz bei Glasfaser

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It's crunch time in Congress
- Trump's budget, Iran, & oligarch money in politics.
Join the Virtual Lobby Training this Sun 6/22
at 7pm ET (4pm PT).
Our Revolution's DC team is facilitating virtual lobby visits with Capitol Hill offices
and we need your help.
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Our Revolution Virtual Lobby Training
Our Revolution Virtual Lobby Training Your elected officials in Congress need to hear from you to build support for progressive policies and make democracy work for the many! Come learn about Our Revolution’s virtual lobby model, how you can get involved, and opportunities to join your neighbors to meet with your Senators and Representatives. Event Details: Date: Sunday, June 22, 2025 Time: 7pm ET (4pm PT) Location: Virtual Training - Zoom Link

2025 Dallas Cowboys training camp schedule: Full list of official dates https://www.si.com/nfl/cowboys/news/2025-dallas-cowboys-training-camp-schedule-full-list-of-official-dates

Anthony Richardson injury update: Colts QB optimistic about training camp return after missing minicamp

https://www.cbssports.com/nfl/news/anthony

A group of authors sue Microsoft in a NY federal court, claiming the company used nearly 200,000 pirated books without permission to train its Megatron AI model (Blake Brittain/Reuters)
https://www.reuters.com/sustainability/boa

ReCode: Updating Code API Knowledge with Reinforcement Learning
Haoze Wu, Yunzhi Yao, Wenhao Yu, Huajun Chen, Ningyu Zhang
https://arxiv.org/abs/2506.20495

ReCode: Updating Code API Knowledge with Reinforcement Learning
Large Language Models (LLMs) exhibit remarkable code generation capabilities but falter when adapting to frequent updates in external library APIs. This critical limitation, stemming from reliance on outdated API knowledge from their training data, even with access to current documentation, impedes reliable code generation in dynamic environments. To tackle this issue, we propose ReCode (rule-based Reinforcement learning for Code Update), a novel framework that mimics human programmer adaptatio…

2025 Dallas Cowboys training camp schedule: Full list of official dates https://www.si.com/nfl/cowboys/news/2025-dallas-cowboys-training-camp-schedule-full-list-of-official-dates

A group of authors sue Microsoft in a NY federal court, claiming the company used nearly 200,000 pirated books without permission to train its Megatron AI model (Blake Brittain/Reuters)
https://www.reuters.com/sustainability/boa

Blameless Users in a Clean Room: Defining Copyright Protection for Generative Models
Aloni Cohen
https://arxiv.org/abs/2506.19881 https://

Blameless Users in a Clean Room: Defining Copyright Protection for Generative Models
Are there any conditions under which a generative model's outputs are guaranteed not to infringe the copyrights of its training data? This is the question of "provable copyright protection" first posed by Vyas, Kakade, and Barak (ICML 2023). They define near access-freeness (NAF) and propose it as sufficient for protection. This paper revisits the question and establishes new foundations for provable copyright protection -- foundations that are firmer both technically and legally. First, we sho…

Evolutionary Gait Reconfiguration in Damaged Legged Robots
Sahand Farghdani, Robin Chhabra
https://arxiv.org/abs/2506.19968 https://a…

Evolutionary Gait Reconfiguration in Damaged Legged Robots
Multi-legged robots deployed in complex missions are susceptible to physical damage in their legs, impairing task performance and potentially compromising mission success. This letter presents a rapid, training-free damage recovery algorithm for legged robots subject to partial or complete loss of functional legs. The proposed method first stabilizes locomotion by generating a new gait sequence and subsequently optimally reconfigures leg gaits via a developed differential evolution algorithm to…

WattsOnAI: Measuring, Analyzing, and Visualizing Energy and Carbon Footprint of AI Workloads
Hongzhen Huang, Kunming Zhang, Hanlong Liao, Kui Wu, Guoming Tang
https://arxiv.org/abs/2506.20535

WattsOnAI: Measuring, Analyzing, and Visualizing Energy and Carbon Footprint of AI Workloads
The rapid advancement of AI, particularly large language models (LLMs), has raised significant concerns about the energy use and carbon emissions associated with model training and inference. However, existing tools for measuring and reporting such impacts are often fragmented, lacking systematic metric integration and offering limited support for correlation analysis among them. This paper presents WattsOnAI, a comprehensive software toolkit for the measurement, analysis, and visualization of …

Frequency-Weighted Training Losses for Phoneme-Level DNN-based Speech Enhancement
Nasser-Eddine Monir, Paul Magron, Romain Serizel
https://arxiv.org/abs/2506.18714

Frequency-Weighted Training Losses for Phoneme-Level DNN-based Speech Enhancement
Recent advances in deep learning have significantly improved multichannel speech enhancement algorithms, yet conventional training loss functions such as the scale-invariant signal-to-distortion ratio (SDR) may fail to preserve fine-grained spectral cues essential for phoneme intelligibility. In this work, we propose perceptually-informed variants of the SDR loss, formulated in the time-frequency domain and modulated by frequency-dependent weighting schemes. These weights are designed to emphas…

VoxelOpt: Voxel-Adaptive Message Passing for Discrete Optimization in Deformable Abdominal CT Registration
Hang Zhang, Yuxi Zhang, Jiazheng Wang, Xiang Chen, Renjiu Hu, Xin Tian, Gaolei Li, Min Liu
https://arxiv.org/abs/2506.19975

VoxelOpt: Voxel-Adaptive Message Passing for Discrete Optimization in Deformable Abdominal CT Registration
Recent developments in neural networks have improved deformable image registration (DIR) by amortizing iterative optimization, enabling fast and accurate DIR results. However, learning-based methods often face challenges with limited training data, large deformations, and tend to underperform compared to iterative approaches when label supervision is unavailable. While iterative methods can achieve higher accuracy in such scenarios, they are considerably slower than learning-based methods. To a…

Raiders offensive depth chart following minicamp and heading into training camp https://raiderswire.usatoday.com/story/sports/nfl/raiders/2025/06/24/raiders-offensive-depth-chart-in-mi…

Developing Artificial Mechanics Intuitions from Extremely Small Data
Jingruo Peng, Shuze Zhu
https://arxiv.org/abs/2506.20148 https://

Developing Artificial Mechanics Intuitions from Extremely Small Data
Humans can possess good mechanics intuitions by learning from a few examples, which leads to the question of how to develop artificial mechanics intuitions that can be learned from small data, as we are eagerly entering the era of artificial intelligence. We propose in this Letter the sample-switchable training method, which successfully develops highly-accurate artificial mechanics intuitions that can master brachistochrone problem, catenary problem, and large nonlinear deformation problem of …

DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation
Shansan Gong, Ruixiang Zhang, Huangjie Zheng, Jiatao Gu, Navdeep Jaitly, Lingpeng Kong, Yizhe Zhang
https://arxiv.org/abs/2506.20639

DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation
Diffusion large language models (dLLMs) are compelling alternatives to autoregressive (AR) models because their denoising models operate over the entire sequence. The global planning and iterative refinement features of dLLMs are particularly useful for code generation. However, current training and inference mechanisms for dLLMs in coding are still under-explored. To demystify the decoding behavior of dLLMs and unlock their potential for coding, we systematically investigate their denoising pr…

Kölner Urteil: Meta darf auch sensible Daten fürs KI-Training verwenden
Wenn Nutzer heikle Informationen wie Gesundheitsdaten in sozialen Netzwerken teilen, können die Betreiber diese direkt fürs Anlernen von KI-Systemen einsetzen.

Hierarchical Reinforcement Learning and Value Optimization for Challenging Quadruped Locomotion
Jeremiah Coholich, Muhammad Ali Murtaza, Seth Hutchinson, Zsolt Kira
https://arxiv.org/abs/2506.20036

Hierarchical Reinforcement Learning and Value Optimization for Challenging Quadruped Locomotion
We propose a novel hierarchical reinforcement learning framework for quadruped locomotion over challenging terrain. Our approach incorporates a two-layer hierarchy in which a high-level policy (HLP) selects optimal goals for a low-level policy (LLP). The LLP is trained using an on-policy actor-critic RL algorithm and is given footstep placements as goals. We propose an HLP that does not require any additional training or environment samples and instead operates via an online optimization proces…

Raiders offensive depth chart following minicamp and heading into training camp https://raiderswire.usatoday.com/story/sports/nfl/raiders/2025/06/24/raiders-offensive-depth-chart-in-mi…

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation
Yunsung Chung, Yunbei Zhang, Nassir Marrouche, Jihun Hamm
https://arxiv.org/abs/2506.19360

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation
Advances in generative models have transformed the field of synthetic image generation for privacy-preserving data synthesis (PPDS). However, the field lacks a comprehensive survey and comparison of synthetic image generation methods across diverse settings. In particular, when we generate synthetic images for the purpose of training a classifier, there is a pipeline of generation-sampling-classification which takes private training as input and outputs the final classifier of interest. In this…

NFL announces training camp reporting dates, locations for all 32 teams for 2025 season https://www.nfl.com/news/nfl-training-camp-reporting-dates-locations-all-32-teams-2025-season

Selecting N-lowest scores for training MOS prediction models
Yuto Kondo, Hirokazu Kameoka, Kou Tanaka, Takuhiro Kaneko
https://arxiv.org/abs/2506.18326 htt…

Selecting N-lowest scores for training MOS prediction models
The automatic speech quality assessment (SQA) has been extensively studied to predict the speech quality without time-consuming questionnaires. Recently, neural-based SQA models have been actively developed for speech samples produced by text-to-speech or voice conversion, with a primary focus on training mean opinion score (MOS) prediction models. The quality of each speech sample may not be consistent across the entire duration, and it remains unclear which segments of the speech receive the …

NeRF-based CBCT Reconstruction needs Normalization and Initialization
Zhuowei Xu, Han Li, Dai Sun, Zhicheng Li, Yujia Li, Qingpeng Kong, Zhiwei Cheng, Nassir Navab, S. Kevin Zhou
https://arxiv.org/abs/2506.19742

NeRF-based CBCT Reconstruction needs Normalization and Initialization
Cone Beam Computed Tomography (CBCT) is widely used in medical imaging. However, the limited number and intensity of X-ray projections make reconstruction an ill-posed problem with severe artifacts. NeRF-based methods have achieved great success in this task. However, they suffer from a local-global training mismatch between their two key components: the hash encoder and the neural network. Specifically, in each training step, only a subset of the hash encoder's parameters is used (local sparse…

Mastodon updates its terms to prohibit AI model training starting July 1, and enforces a new age limit of 16 for users globally (Ivan Mehta/TechCrunch)
https://techcrunch.com/2025/06/17/mastodon-updates-its-terms-to-prohibit-ai-model-tra…

Mastodon updates its terms to prohibit AI model training | TechCrunch
Days after Elon Musk-owned X updated its terms to explicitly prohibit AI model training, decentralized social network Mastodon updated its own rules to bar any kind of model training, as well.

Mastodon updates its terms to prohibit AI model training starting July 1, and enforces a new age limit of 16 for users globally (Ivan Mehta/TechCrunch)
https://techcrunch.com/2025/06/17/mastodon-updates-its-terms-to-prohibit-ai-model-tra…

Mastodon updates its terms to prohibit AI model training | TechCrunch
Days after Elon Musk-owned X updated its terms to explicitly prohibit AI model training, decentralized social network Mastodon updated its own rules to bar any kind of model training, as well.

AALC: Large Language Model Efficient Reasoning via Adaptive Accuracy-Length Control
Ruosen Li, Ziming Luo, Quan Zhang, Ruochen Li, Ben Zhou, Ali Payani, Xinya Du
https://arxiv.org/abs/2506.20160

AALC: Large Language Model Efficient Reasoning via Adaptive Accuracy-Length Control
Large reasoning models (LRMs) achieve impressive reasoning capabilities by generating lengthy chain-of-thoughts, but this "overthinking" incurs high latency and cost without commensurate accuracy gains. In this work, we introduce AALC, a lightweight, accuracy-aware length reward integrated into reinforcement learning that dynamically balances correctness and brevity during training. By incorporating validation accuracy into the reward and employing a smooth, dynamically scheduled length penalty…

Cowboys tendršn pršctica conjunta con Rams en Training Camp https://www.dallascowboys.com/news/cowboys-tendran-practica-conjunta-con-rams-en-training-camp

Consensus-Driven Uncertainty for Robotic Grasping based on RGB Perception
Eric C. Joyce, Qianwen Zhao, Nathaniel Burgdorfer, Long Wang, Philippos Mordohai
https://arxiv.org/abs/2506.20045

Consensus-Driven Uncertainty for Robotic Grasping based on RGB Perception
Deep object pose estimators are notoriously overconfident. A grasping agent that both estimates the 6-DoF pose of a target object and predicts the uncertainty of its own estimate could avoid task failure by choosing not to act under high uncertainty. Even though object pose estimation improves and uncertainty quantification research continues to make strides, few studies have connected them to the downstream task of robotic grasping. We propose a method for training lightweight, deep networks t…

CultureMERT: Continual Pre-Training for Cross-Cultural Music Representation Learning
Angelos-Nikolaos Kanatas, Charilaos Papaioannou, Alexandros Potamianos
https://arxiv.org/abs/2506.17818

CultureMERT: Continual Pre-Training for Cross-Cultural Music Representation Learning
Recent advances in music foundation models have improved audio representation learning, yet their effectiveness across diverse musical traditions remains limited. We introduce CultureMERT-95M, a multi-culturally adapted foundation model developed to enhance cross-cultural music representation learning and understanding. To achieve this, we propose a two-stage continual pre-training strategy that integrates learning rate re-warming and re-decaying, enabling stable adaptation even with limited co…

Former Browns coach downplays Shedeur Sanders' speeding citations as QB battle extends to training camp

https://www.cbssports.com/nfl/news/form…

Scale AI used Google Docs to track work for customers like Google, Meta, and xAI, and left confidential AI training documents accessible to anyone with the link (Business Insider)
https://africa.businessinsider.com/new

Scale AI exposed sensitive data about clients like Meta and xAI in public Google Docs, BI finds
As Scale AI seeks to reassure customers that their data is secure following Metas $14

Cowboys release full Oxnard schedule, including open practice dates https://cowboyswire.usatoday.com/story/sports/nfl/cowboys/2025/06/25/cowboys-2025-oxnard-training-camp-schedule-open-pract…

DUMB and DUMBer: Is Adversarial Training Worth It in the Real World?
Francesco Marchiori, Marco Alecci, Luca Pajola, Mauro Conti
https://arxiv.org/abs/2506.18516

DUMB and DUMBer: Is Adversarial Training Worth It in the Real World?
Adversarial examples are small and often imperceptible perturbations crafted to fool machine learning models. These attacks seriously threaten the reliability of deep neural networks, especially in security-sensitive domains. Evasion attacks, a form of adversarial attack where input is modified at test time to cause misclassification, are particularly insidious due to their transferability: adversarial examples crafted against one model often fool other models as well. This property, known as a…

Robust Training with Data Augmentation for Medical Imaging Classification
Josu\'e Mart\'inez-Mart\'inez, Olivia Brown, Mostafa Karami, Sheida Nabavi
https://arxiv.org/abs/2506.17133

Robust Training with Data Augmentation for Medical Imaging Classification
Deep neural networks are increasingly being used to detect and diagnose medical conditions using medical imaging. Despite their utility, these models are highly vulnerable to adversarial attacks and distribution shifts, which can affect diagnostic reliability and undermine trust among healthcare professionals. In this study, we propose a robust training algorithm with data augmentation (RTDA) to mitigate these vulnerabilities in medical image classification. We benchmark classifier robustness a…

Rethinking LLM Training through Information Geometry and Quantum Metrics
Riccardo Di Sipio
https://arxiv.org/abs/2506.15830 https://a…

Rethinking LLM Training through Information Geometry and Quantum Metrics
Optimization in large language models (LLMs) unfolds over high-dimensional parameter spaces with non-Euclidean structure. Information geometry frames this landscape using the Fisher information metric, enabling more principled learning via natural gradient descent. Though often impractical, this geometric lens clarifies phenomena such as sharp minima, generalization, and observed scaling laws. We argue that curvature-aware approaches deepen our understanding of LLM training. Finally, we specula…

Sources: Google is using its expansive library of YouTube videos to train Gemini and Veo 3; Google says it only uses a subset of videos for the training (Zach Vallese/CNBC)
https://www.cnbc.com/2025/06/19/google-youtube-ai-training-veo-3.html
…

Google is using YouTube videos to train its AI video generator
Google is using its expansive library of YouTube videos to train its AI models, including Gemini and the Veo 3 video and audio generator, CNBC has learned.

Sources: Google is using its expansive library of YouTube videos to train Gemini and Veo 3; Google says it only uses a subset of videos for the training (Zach Vallese/CNBC)
https://www.cnbc.com/2025/06/19/google-youtube-ai-training-veo-3.html
…

Google is using YouTube videos to train its AI video generator
Google is using its expansive library of YouTube videos to train its AI models, including Gemini and the Veo 3 video and audio generator, CNBC has learned.

Replaced article(s) found for cs.SD. https://arxiv.org/list/cs.SD/new
[1/1]:
- SLEEPING-DISCO 9M: A large-scale pre-training dataset for generative music modeling
Tawsif Ahmed, Andrej Radonjic, Gollam Rabby

Rashee Rice will be 'ready to go' for Chiefs training camp: What does his return mean for Kansas City?

https://www.cbssports.com/nfl/news/rashe…

Cowboys tendršn pršctica conjunta con Rams en Training Camp https://www.dallascowboys.com/news/cowboys-tendran-practica-conjunta-con-rams-en-training-camp

KI-Training: Britisches Oberhaus standhaft gegen Freigabe geschützter Werke
Die britische Regierung will geschützte Werke fürs KI-Training freigeben, wer nicht will, soll widersprechen. Viermal stimmte das Oberhaus nun dagegen.

Cowboys 53-man pre-training camp projection: Does Diggs make it? 2nd rounder cut? https://cowboyswire.usatoday.com/story/sports/nfl/cowboys/2025/06/23/cowboys-53-man-pre-training-camp-projec…

Giants not worried about Nabers' nagging toe https://www.espn.com/nfl/story/_/id/45539349/giants-expect-malik-nabers-ready-training-camp

Cowboys' Day 3 pick reaching last-chance status as 2025 training camp rolls around https://cowboyswire.usatoday.com/story/sports/nfl/cowboys/2025/06/25/dallas-cowboys-player-profile-no-79-ot-matt-…

A US judge rules Anthropic use of copyrighted books to train AI was fair use, but its storage of pirated books in a "central library" used for training was not (Olivia Sophie Rafferty/ai fray)
https://aifra…

Pic 6: Getting to know the new vets https://www.dallascowboys.com/news/pic-6-getting-to-know-the-new-vets-kaiir-elam

Pic 6: Getting to know the new vets https://www.dallascowboys.com/news/pic-6-getting-to-know-the-new-vets-kaiir-elam

Pic 6: Getting to know the new vets https://www.dallascowboys.com/news/pic-6-getting-to-know-the-new-vets-kenneth-murray

Cowboys, Micah Parsons contract extension epected 'in coming weeks' https://www.si.com/nfl/cowboys/news/dallas-cowboys-micah-parsons-contract-extension-epected-coming-weeks

2025 Dallas Cowboys Training Camp: Dates, Schedule, Location https://www.foxsports.com/stories/nfl/2025-dallas-cowboys-training-camp-dates-schedule-location

Dez Bryant has succinct response after efforts to crown son prematurely https://cowboyswire.usatoday.com/story/sports/nfl/cowboys/2025/06/23/dez-bryant-social-media-son-video-training/84317727007/

2025 Dallas Cowboys training camp dates and schedule https://www.si.com/nfl/cowboys/news/2025-dallas-cowboys-training-camp-dates-schedule