Tootfinder

PRISM: Distributed Inference for Foundation Models at Edge
Muhammad Azlan Qazi, Alexandros Iosifidis, Qi Zhang
https://arxiv.org/abs/2507.12145 https://

PRISM: Distributed Inference for Foundation Models at Edge
Foundation models (FMs) have achieved remarkable success across a wide range of applications, from image classification to natural langurage processing, but pose significant challenges for deployment at edge. This has sparked growing interest in developing practical and efficient strategies for bringing foundation models to edge environments. In this work, we propose PRISM, a communication-efficient and compute-aware strategy for distributed Transformer inference on edge devices. Our method lev…

The Novo Nordisk Foundation and the Danish government plan to invest €80M to acquire and run a Microsoft-powered quantum computer, set to operate in early 2027 (Sanne Wass/Bloomberg)
https://www.bloomberg.com/news/articles/20…

Novo Nordisk Owner Buys Microsoft Quantum Computer With Denmark
The foundation behind Novo Nordisk A/S and the Danish government will invest €80 million ($93 million) in a Microsoft-powered quantum computer to boost technological development in the Nordic region.

Generative Representational Learning of Foundation Models for Recommendation
Zheli Zhou, Chenxu Zhu, Jianghao Lin, Bo Chen, Ruiming Tang, Weinan Zhang, Yong Yu
https://arxiv.org/abs/2506.11999

Generative Representational Learning of Foundation Models for Recommendation
Developing a single foundation model with the capability to excel across diverse tasks has been a long-standing objective in the field of artificial intelligence. As the wave of general-purpose foundation models sweeps across various domains, their influence has significantly extended to the field of recommendation systems. While recent efforts have explored recommendation foundation models for various generative tasks, they often overlook crucial embedding tasks and struggle with the complexit…

Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis
Yuan Gao, Mattia Piccinini, Yuchen Zhang, Dingrui Wang, Korbinian Moller, Roberto Brusnicki, Baha Zarrouki, Alessio Gambi, Jan Frederik Totz, Kai Storms, Steven Peters, Andrea Stocco, Bassam Alrifaee, Marco Pavone, Johannes Betz
https://

Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis
For autonomous vehicles, safe navigation in complex environments depends on handling a broad range of diverse and rare driving scenarios. Simulation- and scenario-based testing have emerged as key approaches to development and validation of autonomous driving systems. Traditional scenario generation relies on rule-based systems, knowledge-driven models, and data-driven synthesis, often producing limited diversity and unrealistic safety-critical cases. With the emergence of foundation models, wh…

RefModel: Detecting Refactorings using Foundation Models
Pedro Sim\~oes, Rohit Gheyi, Rian Melo, Jonhnanthan Oliveira, M\'arcio Ribeiro, Wesley K. G. Assun\c{c}\~ao
https://arxiv.org/abs/2507.11346

RefModel: Detecting Refactorings using Foundation Models
Refactoring is a common software engineering practice that improves code quality without altering program behavior. Although tools like ReExtractor+, RefactoringMiner, and RefDiff have been developed to detect refactorings automatically, they rely on complex rule definitions and static analysis, making them difficult to extend and generalize to other programming languages. In this paper, we investigate the viability of using foundation models for refactoring detection, implemented in a tool nam…

Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration?
Hanxue Gu, Yaqian Chen, Nicholas Konz, Qihang Li, Maciej A. Mazurowski
https://arxiv.org/abs/2507.11569

Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration?
Foundation models, pre-trained on large image datasets and capable of capturing rich feature representations, have recently shown potential for zero-shot image registration. However, their performance has mostly been tested in the context of rigid or less complex structures, such as the brain or abdominal organs, and it remains unclear whether these models can handle more challenging, deformable anatomy. Breast MRI registration is particularly difficult due to significant anatomical variation b…

Mediaocean's ad verification division partners with the Internet Watch Foundation, which identifies and works to remove websites containing CSAM (Joanna Gerber/AdExchanger)
https://www.adexchanger.com/technology/mediao…

Mediaocean Partners With The Internet Watch Foundation To Report CSAM Content
Protected by Mediaocean is partnering with the Internet Watch Foundation to crack down on CSAM and ensure children's online safety.

The Strategic Imperative for Healthcare Organizations to Build Proprietary Foundation Models
Naresh Tiwari
https://arxiv.org/abs/2506.11412 https://…

The Strategic Imperative for Healthcare Organizations to Build Proprietary Foundation Models
This paper presents a comprehensive analysis of the strategic imperative for healthcare organizations to develop proprietary foundation models rather than relying exclusively on commercial alternatives. We examine four fundamental considerations driving this imperative: the domain-specific requirements of healthcare data representation, critical data sovereignty and governance considerations unique to healthcare, strategic competitive advantages afforded by proprietary AI infrastructure, and th…

Foundation Models for Logistics: Toward Certifiable, Conversational Planning Interfaces
Yunhao Yang, Neel P. Bhatt, Christian Ellis, Alvaro Velasquez, Zhangyang Wang, Ufuk Topcu
https://arxiv.org/abs/2507.11352

Foundation Models for Logistics: Toward Certifiable, Conversational Planning Interfaces
Logistics operators, from battlefield coordinators rerouting airlifts ahead of a storm to warehouse managers juggling late trucks, often face life-critical decisions that demand both domain expertise and rapid and continuous replanning. While popular methods like integer programming yield logistics plans that satisfy user-defined logical constraints, they are slow and assume an idealized mathematical model of the environment that does not account for uncertainty. On the other hand, large langua…

Towards Depth Foundation Model: Recent Trends in Vision-Based Depth Estimation
Zhen Xu, Hongyu Zhou, Sida Peng, Haotong Lin, Haoyu Guo, Jiahao Shao, Peishan Yang, Qinglin Yang, Sheng Miao, Xingyi He, Yifan Wang, Yue Wang, Ruizhen Hu, Yiyi Liao, Xiaowei Zhou, Hujun Bao
https://arxiv.org/abs/2507.11540

Towards Depth Foundation Model: Recent Trends in Vision-Based Depth Estimation
Depth estimation is a fundamental task in 3D computer vision, crucial for applications such as 3D reconstruction, free-viewpoint rendering, robotics, autonomous driving, and AR/VR technologies. Traditional methods relying on hardware sensors like LiDAR are often limited by high costs, low resolution, and environmental sensitivity, limiting their applicability in real-world scenarios. Recent advances in vision-based methods offer a promising alternative, yet they face challenges in generalizatio…

Foundation Models for Brain Signals: A Critical Review of Current Progress and Future Directions
Gayal Kuruppu, Neeraj Wagh, Yogatheesan Varatharajah
https://arxiv.org/abs/2507.11783

Foundation Models for Brain Signals: A Critical Review of Current Progress and Future Directions
Patterns of electrical brain activity recorded via electroencephalography (EEG) offer immense value for scientific and clinical investigations. The inability of supervised EEG encoders to learn robust EEG patterns and their over-reliance on expensive signal annotations have sparked a transition towards general-purpose self-supervised EEG encoders, i.e., EEG foundation models (EEG-FMs), for robust and scalable EEG feature extraction. However, the real-world readiness of early EEG-FMs and the rub…

The Gates Foundation, Ballmer Group, and others form NextLadder to spend $1B over 15 years to develop AI tools for public defenders, parole officers, and more (Thalia Beaty/Associated Press)
https://apnews.com/article/artificial-

Next Woman Up: Hannah McNair, VP of the Houston Texans Foundation https://www.nfl.com/news/next-woman-up-hannah-mcnair-vp-of-the-houston-texans-foundation

heise | Booster für Xcode und Swift: Das war auf der WWDC 2025 neu für Developer
Entwickler dürfen sich über Apples evolutionäre Fortschritte freuen, doch der große KI-Wurf fehlt. Ein Blick auf Foundation Models, Liquid Glass und mehr.

Meanwhile, the Israeli genocide in Gaza against Palestinians continues...
"Israel kills 59 Palestinians in Gaza, many while trying to get aid
Thirteen starving Palestinians are killed as they tried to get food near the Netzarim Corridor and in Rafah’s al-Mawasi area."

Flexible Personalized Split Federated Learning for On-Device Fine-Tuning of Foundation Models
Tianjun Yuan, Jiaxiang Geng, Pengchao Han, Xianhao Chen, Bing Luo
https://arxiv.org/abs/2508.10349

Flexible Personalized Split Federated Learning for On-Device Fine-Tuning of Foundation Models
Fine-tuning foundation models is critical for superior performance on personalized downstream tasks, compared to using pre-trained models. Collaborative learning can leverage local clients' datasets for fine-tuning, but limited client data and heterogeneous data distributions hinder effective collaboration. To address the challenge, we propose a flexible personalized federated learning paradigm that enables clients to engage in collaborative learning while maintaining personalized objectives. G…

AnalogSeeker: An Open-source Foundation Language Model for Analog Circuit Design
Zihao Chen, Ji Zhuang, Jinyi Shen, Xiaoyue Ke, Xinyi Yang, Mingjie Zhou, Zhuoyao Du, Xu Yan, Zhouyang Wu, Zhenyu Xu, Jiangli Huang, Li Shang, Xuan Zeng, Fan Yang
https://arxiv.org/abs/2508.10409

AnalogSeeker: An Open-source Foundation Language Model for Analog Circuit Design
In this paper, we propose AnalogSeeker, an effort toward an open-source foundation language model for analog circuit design, with the aim of integrating domain knowledge and giving design assistance. To overcome the scarcity of data in this field, we employ a corpus collection strategy based on the domain knowledge framework of analog circuits. High-quality, accessible textbooks across relevant subfields are systematically curated and cleaned into a textual domain corpus. To address the complex…

Multimodal Modeling of CRISPR-Cas12 Activity Using Foundation Models and Chromatin Accessibility Data
Azim Dehghani Amirabad, Yanfei Zhang, Artem Moskalev, Sowmya Rajesh, Tommaso Mansi, Shuwei Li, Mangal Prakash, Rui Liao
https://arxiv.org/abs/2506.11182

Multimodal Modeling of CRISPR-Cas12 Activity Using Foundation Models and Chromatin Accessibility Data
Predicting guide RNA (gRNA) activity is critical for effective CRISPR-Cas12 genome editing but remains challenging due to limited data, variation across protospacer adjacent motifs (PAMs-short sequence requirements for Cas binding), and reliance on large-scale training. We investigate whether pre-trained biological foundation model originally trained on transcriptomic data can improve gRNA activity estimation even without domain-specific pre-training. Using embeddings from existing RNA foundati…

CytoSAE: Interpretable Cell Embeddings for Hematology
Muhammed Furkan Dasdelen, Hyesu Lim, Michele Buck, Katharina S. G\"otze, Carsten Marr, Steffen Schneider
https://arxiv.org/abs/2507.12464

CytoSAE: Interpretable Cell Embeddings for Hematology
Sparse autoencoders (SAEs) emerged as a promising tool for mechanistic interpretability of transformer-based foundation models. Very recently, SAEs were also adopted for the visual domain, enabling the discovery of visual concepts and their patch-wise attribution to tokens in the transformer model. While a growing number of foundation models emerged for medical imaging, tools for explaining their inferences are still lacking. In this work, we show the applicability of SAEs for hematology. We pr…

The Foundation Cracks: A Comprehensive Study on Bugs and Testing Practices in LLM Libraries
Weipeng Jiang, Xiaoyu Zhang, Xiaofei Xie, Jiongchi Yu, Yuhan Zhi, Shiqing Ma, Chao Shen
https://arxiv.org/abs/2506.12320

The Foundation Cracks: A Comprehensive Study on Bugs and Testing Practices in LLM Libraries
Large Language Model (LLM) libraries have emerged as the foundational infrastructure powering today's AI revolution, serving as the backbone for LLM deployment, inference optimization, fine-tuning, and production serving across diverse applications. Despite their critical role in the LLM ecosystem, these libraries face frequent quality issues and bugs that threaten the reliability of AI systems built upon them. To address this knowledge gap, we present the first comprehensive empirical investig…

Wikipedia: Test von KI-generierten Artikelzusammenfassungen vorerst gestoppt
Die Wikimedia Foundation wollte die Wirkung von KI-generierten Zusammenfassungen über Wikipedia-Artikeln testen. Die Community hat das erst einmal verhindert.

Employee of Dutch Butterfly Foundation @… gets death threat for sharing research findings on pesticides in protected nature areas. Foundation removes report from its website.
https://nos.nl/l/2578369

Medewerker De Vlinderstichting bedreigd na delen resultaten pesticidenonderzoek
De Vlinderstichting heeft uit veiligheidsoverwegingen en op verzoek van de medewerker besloten het onderzoek van de website te verwijderen.

Focusing on Students, not Machines: Grounded Question Generation and Automated Answer Grading
G\'er\^ome Meyer, Philip Breuer
https://arxiv.org/abs/2506.12066

Focusing on Students, not Machines: Grounded Question Generation and Automated Answer Grading
Digital technologies are increasingly used in education to reduce the workload of teachers and students. However, creating open-ended study or examination questions and grading their answers is still a tedious task. This thesis presents the foundation for a system that generates questions grounded in class materials and automatically grades student answers. It introduces a sophisticated method for chunking documents with a visual layout, specifically targeting PDF documents. This method enhance…

MRI-CORE: A Foundation Model for Magnetic Resonance Imaging
Haoyu Dong, Yuwen Chen, Hanxue Gu, Nicholas Konz, Yaqian Chen, Qihang Li, Maciej A. Mazurowski
https://arxiv.org/abs/2506.12186

MRI-CORE: A Foundation Model for Magnetic Resonance Imaging
The widespread use of Magnetic Resonance Imaging (MRI) and the rise of deep learning have enabled the development of powerful predictive models for a wide range of diagnostic tasks in MRI, such as image classification or object segmentation. However, training models for specific new tasks often requires large amounts of labeled data, which is difficult to obtain due to high annotation costs and data privacy concerns. To circumvent this issue, we introduce MRI-CORE (MRI COmprehensive Representat…

The #GiantMagellanTelescope received official approval from the National Science Foundation (NSF) confirming that the observatory will advance into its Major Facilities Final Design Phase, one of the final steps before becoming eligible for federal construction funding: https://astro.arizona.edu/news/giant-magellan-telescope-advances-national-science-foundation-final-design-phase

Superluminal Quantum Reference Frames
Amrapali Sen, Matthias Salzger, {\L}ukasz Rudnicki
https://arxiv.org/abs/2506.11787 https://arx…

Superluminal Quantum Reference Frames
While particles cannot travel faster than the speed of light, nor can information, this assumption has over the years been frequently questioned. Most recently, it has been argued [New J. Phys. 22, 033038 (2020)] that in a world with superluminal observers local determinism is impossible, linking the two pillars of physics-quantum theory and relativity-suggesting that the latter serves as the foundation for the former. Motivated by this approach, in this work, we extend the framework of quantum…

Game Theory Meets LLM and Agentic AI: Reimagining Cybersecurity for the Age of Intelligent Threats
Quanyan Zhu
https://arxiv.org/abs/2507.10621 https://

Game Theory Meets LLM and Agentic AI: Reimagining Cybersecurity for the Age of Intelligent Threats
Protecting cyberspace requires not only advanced tools but also a shift in how we reason about threats, trust, and autonomy. Traditional cybersecurity methods rely on manual responses and brittle heuristics. To build proactive and intelligent defense systems, we need integrated theoretical frameworks and software tools. Game theory provides a rigorous foundation for modeling adversarial behavior, designing strategic defenses, and enabling trust in autonomous systems. Meanwhile, software tools p…

Pre-trained Transformer-models using chronic invasive electrophysiology for symptom decoding without patient-individual training
Timon Merk, Saeed Salehi, Richard M. Koehler, Qiming Cui, Maria Olaru, Amelia Hahn, Nicole R. Provenza, Simon Little, Reza Abbasi-Asl, Phil A. Starr, Wolf-Julian Neumann
https://arxiv.org/abs/2508.10160

Pre-trained Transformer-models using chronic invasive electrophysiology for symptom decoding without patient-individual training
Neural decoding of pathological and physiological states can enable patient-individualized closed-loop neuromodulation therapy. Recent advances in pre-trained large-scale foundation models offer the potential for generalized state estimation without patient-individual training. Here we present a foundation model trained on chronic longitudinal deep brain stimulation recordings spanning over 24 days. Adhering to long time-scale symptom fluctuations, we highlight the extended context window of 30…

https://stadt-bremerhaven.de/linux-foundation-startet-eigenen-paketmanager-fuer-wordpress-plugins/

Linux Foundation startet eigenen Paketmanager für WordPress-Plugins
Die Linux Foundation hat mit FAIR einen neuen Paketmanager für WordPress-Plugins, Themes und Tools vorgestellt. Der neue FAIR-Paketmanager ...

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model
Ganghui Ru, Jieying Wang, Jiahao Zhao, Yulun Wu, Yi Yu, Nannan Jiang, Wei Wang, Wei Li
https://arxiv.org/abs/2508.09790

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model
Beat tracking is a widely researched topic in music information retrieval. However, current beat tracking methods face challenges due to the scarcity of labeled data, which limits their ability to generalize across diverse musical styles and accurately capture complex rhythmic structures. To overcome these challenges, we propose a novel beat tracking paradigm BeatFM, which introduces a pre-trained music foundation model and leverages its rich semantic knowledge to improve beat tracking performa…

An augmented Lagrangian method for strongly regular minimizers in a class of convex composite optimization problems
Chengjing Wang, Peipei Tang
https://arxiv.org/abs/2507.12040

An augmented Lagrangian method for strongly regular minimizers in a class of convex composite optimization problems
In this paper, we study a class of convex composite optimization problems. We begin by characterizing the equivalence between the primal/dual strong second-order sufficient condition and the dual/primal nondegeneracy condition. Building on this foundation, we derive a specific set of equivalent conditions for the perturbation analysis of the problem. Furthermore, we employ the augmented Lagrangian method (ALM) to solve the problem and provide theoretical guarantees for its performance. Specific…

»Erfolgreicher Widerstand – Wikipedia stoppt geplante KI-Zusammenfassungen:
Eigentlich wollte die Wikimedia Foundation - vorerst testweise - KI-generierte Zusammenfassungen in Wikipedia-Artikel integrieren. Doch diese Pläne sind jetzt überraschend schnell wieder ad acta gelegt worden. Was ist da passiert?«
Bei @… war Widerspruch schon immer vorhanden a…

Erfolgreicher Widerstand: Wikipedia stoppt geplante KI-Zusammenfassungen - t3n – digital pioneers
Eigentlich wollte die Wikimedia Foundation – vorerst testweise – KI-generierte Zusammenfassungen in Wikipedia-Artikel integrieren. Doch diese Pläne sind jetzt überraschend schnell wieder ad acta gelegt worden. Was ist da passiert? Manchem Fan erscheint Wikipedia wie der letzte Ort im World Wide Web, an dem es weder Werbung noch Bezahlschranken und auch keine KI-Integration gibt. Genau das scheinen […]

Не ОРИ — комиссия по разработке XMPP оштрафована за отказ сотрудничать с властями РФ
http://roskomsvoboda.org/ru/post/russia-fines-xmpp-foundation-for-not-being-info-distributor/

Не ОРИ — комиссия по разработке XMPP оштрафована за отказ сотрудничать с властями РФ
Независимая некоммерческая организация XMPP Standards Foundation оштрафована за неуплату административного штрафа, возложенного на неё за повторный отказ исполнять обязанности организатора распространения информации (ОРИ).

An Intelligent Infrastructure as a Foundation for Modern Science
Satrajit S. Ghosh
https://arxiv.org/abs/2508.10051 https://arxiv.org/pdf/2508.10051…

An Intelligent Infrastructure as a Foundation for Modern Science
Infrastructure shapes societies and scientific discovery. Traditional scientific infrastructure, often static and fragmented, leads to issues like data silos, lack of interoperability and reproducibility, and unsustainable short-lived solutions. Our current technical inability and social reticence to connect and coordinate scientific research and engineering leads to inefficiencies and impedes progress. With AI technologies changing how we interact with the world around us, there is an opportun…

Extension Research of Principal Bundle Constraint System Theory on Ricci-flat K\"ahler Manifolds
Dongzhe Zheng
https://arxiv.org/abs/2506.11593 https:…

Extension Research of Principal Bundle Constraint System Theory on Ricci-flat Kähler Manifolds
This paper extends the geometric mechanics theory of constraint systems on principal bundles from the flat connection case to the general situation with non-zero curvature. Based on the theoretical foundation of compatible pairs under strong transversality conditions and principal bundle constraint systems, we systematically study the behavior of compatible pair theory in non-flat geometry within the context of Ricci-flat Kähler manifolds. Through rigorous mathematical analysis, we prove that …

Machine-learning inference of stellar properties using integrated photometric and spectroscopic data
Ilay Kamai, Alex M. Bronstein, Hagai B. Perets
https://arxiv.org/abs/2507.10666

Machine-learning inference of stellar properties using integrated photometric and spectroscopic data
Stellar astrophysics relies on diverse observational modalities-primarily photometric light curves and spectroscopic data-from which fundamental stellar properties are inferred. While machine learning (ML) has advanced analysis within individual modalities, the complementary information encoded across modalities remains largely underexploited. We present DESA (Dual Embedding model for Stellar Astrophysics), a novel multi-modal foundation model that integrates light curves and spectra to learn a…

Cutting rules for non-relativistic dark matter in solids based on Kohn-Sham orbitals
Zheng-Liang Liang, Fawei Zheng
https://arxiv.org/abs/2507.11033 https:…

Cutting rules for non-relativistic dark matter in solids based on Kohn-Sham orbitals
The Cutkosky cutting rules establish a direct connection between the imaginary parts of loop amplitudes and physical observables such as decay rates and cross sections, providing heuristic insights into the underlying processes. This work lays a robust theoretical foundation for the application of cutting rules in solid-state systems involving instantaneous dark matter (DM)-electron Yukawa interaction as well as the Coulomb potential. The cutting rules are formulated using the single-electron w…

oh shit #foundation in back.
#tv

Ooooo….
https://www.linuxfoundation.org/press/linux-foundation-announces-the-fair-package-manager-project-for-open-source-content-management-system-stability

Linux Foundation Announces the FAIR Package Manager Project for Open Source Content Management System Stability
Linux Foundation announces FAIR Package Manager project, creating simplicity, security and consistency for the WordPress ecosystem

SharpLink Gaming, a Nasdaq-listed online gambling marketing company, becomes the largest corporate ether holder with 280,706 ETH, surpassing Ethereum Foundation (Naga Avan-Nomayo/The Block)
https://www.theblock.co/post/362693/sharplink-amasse…

As far as FOSS goes, you might consider me to be the Mule of it.
I'll put that idea in your head.
Just saying.
#foundation

It's #NoKings day!
🚫👑🚫👑🚫👑🚫
Be loud. Be heard. Be safe.
⸻
EFF Protest Guide:
•⁠📱⁠https://mastodon.social/@eff/114678037068775207
Know Your Rights:
•⁠🚨⁠…

#KINutzen #Retröt
Das #DKFZ entwickelt mit dem „Human Radiome Project“ ein KI-Foundation-Model, das Millionen radiologische

DKFZ entwickelt KI-Foundation-Model für die Radiologie
Forscher des DKFZ arbeiten am Human Radiome Project und entwickeln ein KI-Foundation-Model, das radiologische Diagnosen verbessern soll.

Replaced article(s) found for q-bio.QM. https://arxiv.org/list/q-bio.QM/new
[1/1]:
- Test-time Adaptation for Foundation Medical Segmentation Model without Parametric Updates
Kecheng Chen, Xinyu Luo, Tiexin Qin, Jie Liu, Hui Liu, Victor Ho Fun Lee, Hong Yan, Haoliang Li
…

Trump nominates chief economist at rightwing Heritage Foundation
to lead Bureau of Labor Statistics
The nomination comes after Trump fired the BLS commissioner
Erika McEntarfer earlier this month
after the release of a weak jobs report.
Antoni has previously voiced concerns about revisions to the jobs data at BLS.
“There are better ways to collect, process, and disseminate data
– that is the task for the next BLS commissioner,
and only consiste…

La mortalité associée Š la fumée des incendies de forêt pourrait être sous-estimée de 93 %, selon des résultats de recherche publiés dans The Lancet Planetary Health.
#changementclimatique #feuxdeforêt

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective
Simone Brivio, Nicola Rares Franco
https://arxiv.org/abs/2506.11641 https://

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective
Deep autoencoders have become a fundamental tool in various machine learning applications, ranging from dimensionality reduction and reduced order modeling of partial differential equations to anomaly detection and neural machine translation. Despite their empirical success, a solid theoretical foundation for their expressiveness remains elusive, particularly when compared to classical projection-based techniques. In this work, we aim to take a step forward in this direction by presenting a com…

A Tale of Two Mobile Generations: 5G-Advanced and 6G in 3GPP Release 20
Xingqin Lin
https://arxiv.org/abs/2506.11828 https://arxiv.or…

A Tale of Two Mobile Generations: 5G-Advanced and 6G in 3GPP Release 20
As the telecommunications industry stands at the crossroads between the fifth generation (5G) and sixth generation (6G) of mobile communications, the 3rd generation partnership project (3GPP) Release 20 emerges as a pivotal point of transition. By striking a balance between enhancing 5G-Advanced capabilities and setting the stage for 6G, Release 20 provides the crucial foundation upon which future mobile communication standards and deployments will be built. This article examines these dual obj…

Phase-field modelling of cohesive fracture. Part I: $\Gamma$-convergence results
Roberto Alessi, Francesco Colasanto, Matteo Focardi
https://arxiv.org/abs/2507.12169

Phase-field modelling of cohesive fracture. Part I: $Γ$-convergence results
The main aim of this three-part work is to provide a unified consistent framework for the phase-field modeling of cohesive fracture. In this first paper we establish the mathematical foundation of a cohesive phase-field model by proving a $Γ$-convergence result in a one-dimensional setting. Specifically, we consider a broad class of phase-field energies, encompassing different models present in the literature, thereby both extending the results in \cite{ContiFocardiIurlano2016} and providing…

as a transit advocate, I would like to be able to embrace Scott Wiener as a champion for transit funding, but unfortunately he has also regularly attacked and defamed pro-Palestine activists. his actions have contributed to a neo-McCarthyist atmosphere around this issue that was immediately seized upon by Trump on coming back to power

Doctor Says UCSF Fired Her Due to Pressure From Wiener, Dillers Over Pro-Palestinian Speech
CA State Senator Scott Wiener and UCSF’s largest donor, the Diller Foundation, are named in new allegations by Dr. Rupa Marya, prominent physician, activist, and musician, who is suing UCSF over her recent termination stemming from public criticism of Israel.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis
Zuzanna Skorniewska, Bartlomiej W. Papiez
https://arxiv.org/abs/2506.11753

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis
The adoption of neural network models in medical imaging has been constrained by strict privacy regulations, limited data availability, high acquisition costs, and demographic biases. Deep generative models offer a promising solution by generating synthetic data that bypasses privacy concerns and addresses fairness by producing samples for under-represented groups. However, unlike natural images, medical imaging requires validation not only for fidelity (e.g., Fréchet Inception Score) but also…

Beyond Decoherence: Control the Collective Quantum Dynamics of Quasi Particles in Topological Interface
Fatemeh Davoodi
https://arxiv.org/abs/2506.12805 ht…

Beyond Decoherence: Control the Collective Quantum Dynamics of Quasi Particles in Topological Interface
Long lived coherent quasiparticles are a promising foundation for novel quantum technologies, where maintaining quantum coherence is crucial. Decoherence, driven by finite emitter lifetimes, remains a central challenge in quantum computing. Here, we control the dynamics of spatially separated quantum emitters via preserving their phase information by introducing a topological waveguide as a robust chiral reservoir. Incoherent quantum emitters randomly positioned near a perturbed honeycomb plasm…

Henry Chu named interim curator of the Nieman Foundation at Harvard once Ann Marie Lipinski steps down on July 1 after 14 years leading the institution (Nieman Foundation)
https://nieman.harvard.edu/henry-chu-named-int…

Henry Chu named interim curator of the Nieman Foundation for Journalism at Harvard - Nieman Foundation
Henry Chu has been named interim curator of the Nieman Foundation for Journalism at Harvard University. Currently Nieman’s deputy curator, Chu will start his new role when Curator Ann Marie Lipinski steps down on July 1 after 14 years as head of the foundation. Chu has played a key role at Nieman, strengthening the foundation’s publications, overseeing […]

Replaced article(s) found for q-bio.BM. https://arxiv.org/list/q-bio.BM/new
[1/1]:
- Multi-view biomedical foundation models for molecule-target and property prediction
Parthasarathy Suryanarayanan, et al.

Community Report from the 2025 SNOLAB Future Projects Workshop
M. D. Diamond, P. Abbamonte, A. Arvanitaki, D. M. Asner, D. Balut, D. Baxter, C. Blanco, D. Boreham, M. Boulay, B. Broerman, T. Brunner, E. Caden, A. Chavarria, M. Chen, J. P. Davis, A. Drlica-Wagner, J. Estrada, N. Fatemighomi, J. Foster, D. Freedman, C. Gao, J. Hall, S. Hall, W. Halperin, M. Hirschel, N. Hoch, Z. Hong, A. Ianni, C. Jillings, D. Johnson, Y. Kahn, C. B. Krauss, T. Laframboise, M. Lai, M. R. Lapointe, B. Lil…

Community Report from the 2025 SNOLAB Future Projects Workshop
SNOLAB hosts a biannual Future Projects Workshop (FPW) with the goal of encouraging future project stakeholders to present ideas, concepts, and needs for experiments or programs that could one day be hosted at SNOLAB. The 2025 FPW was held in the larger context of a 15-year planning exercise requested by the Canada Foundation for Innovation. This report collects input from the community, including both contributions to the workshop and contributions that could not be scheduled in the workshop b…

The Jets make quiet strides, Buccaneers are legit contenders https://www.nytimes.com/athletic/6500348/2025/07/17/jets-offseason-recap-buccaneers-2025-preview-nfl-scoop-city/

RadioDiff-3D: A 3D$\times$3D Radio Map Dataset and Generative Diffusion Based Benchmark for 6G Environment-Aware Communication
Xiucheng Wang, Qiming Zhang, Nan Cheng, Junting Chen, Zezhong Zhang, Zan Li, Shuguang Cui, Xuemin Shen
https://arxiv.org/abs/2507.12166

RadioDiff-3D: A 3D$\times$3D Radio Map Dataset and Generative Diffusion Based Benchmark for 6G Environment-Aware Communication
Radio maps (RMs) serve as a critical foundation for enabling environment-aware wireless communication, as they provide the spatial distribution of wireless channel characteristics. Despite recent progress in RM construction using data-driven approaches, most existing methods focus solely on pathloss prediction in a fixed 2D plane, neglecting key parameters such as direction of arrival (DoA), time of arrival (ToA), and vertical spatial variations. Such a limitation is primarily due to the relian…

https://www.nbcnews.com/health/womens-health/gates-foundation-commits-25-billion-ignored-womens-health-rcna223003

Distillation of atomistic foundation models across architectures and chemical domains
John L. A. Gardner, Daniel F. Thomas du Toit, Chiheb Ben Mahmoud, Zo\'e Faure Beaulieu, Veronika Juraskova, Laura-Bianca Pa\c{s}ca, Louise A. M. Rosset, Fernanda Duarte, Fausto Martelli, Chris J. Pickard, Volker L. Deringer
https://arxiv.or…

Distillation of atomistic foundation models across architectures and chemical domains
Machine-learned interatomic potentials have transformed computational research in the physical sciences. Recent atomistic `foundation' models have changed the field yet again: trained on many different chemical elements and domains, these potentials are widely applicable, but comparably slow and resource-intensive to run. Here we show how distillation via synthetic data can be used to cheaply transfer knowledge from atomistic foundation models to a range of different architectures, unlocking mu…

Repeat after me: The Linux Foundation are a bunch of Big Tech bellends. Don’t listen to a single thing they say.
#FOSS

A bit of good news!   our #PurdueFortWayne 🐘 department collaborates with Indiana University and #IUI on an annual conference series, which has been supported by the National Science Foundation for approx. 10 years.  We have just received notice that this year's grant has been approved at all…

DeepFleet: Multi-Agent Foundation Models for Mobile Robots
Ameya Agaskar, Sriram Siva, William Pickering, Kyle O'Brien, Charles Kekeh, Ang Li, Brianna Gallo Sarker, Alicia Chua, Mayur Nemade, Charun Thattai, Jiaming Di, Isaac Iyengar, Ramya Dharoor, Dino Kirouani, Jimmy Erskine, Tamir Hegazy, Scott Niekum, Usman A. Khan, Federico Pecora, Joseph W. Durham
ht…

DeepFleet: Multi-Agent Foundation Models for Mobile Robots
We introduce DeepFleet, a suite of foundation models designed to support coordination and planning for large-scale mobile robot fleets. These models are trained on fleet movement data, including robot positions, goals, and interactions, from hundreds of thousands of robots in Amazon warehouses worldwide. DeepFleet consists of four architectures that each embody a distinct inductive bias and collectively explore key points in the design space for multi-agent foundation models: the robot-centric …

#Marfan conference in Atlanta. It's been a busy summer!
Probably a…

HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data
Hiren Madhu, Jo\~ao Felipe Rocha, Tinglin Huang, Siddharth Viswanath, Smita Krishnaswamy, Rex Ying
https://arxiv.org/abs/2506.11152

HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data
Single-cell transcriptomics has become a great source for data-driven insights into biology, enabling the use of advanced deep learning methods to understand cellular heterogeneity and transcriptional regulation at the single-cell level. With the advent of spatial transcriptomics data we have the promise of learning about cells within a tissue context as it provides both spatial coordinates and transcriptomic readouts. However, existing models either ignore spatial resolution or the gene regula…

Foundation EGI, an "engineering general intelligence" startup that builds custom LLMs for engineering tasks, raised a $23M Series A led by Translink Capital (Duncan Riley/SiliconANGLE)
https://siliconangle.com/2025/07/10/fo

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/5]:
Foundation Models for Anomaly Detection: Vision and Challenges
https://

Waterfilling at the Edge: Optimal Percentile Resource Allocation via Risk-Averse Reduction
Gokberk Yaylali, Ahmad Ali Khan, Dionysios S. Kalogerias
https://arxiv.org/abs/2507.10838

Waterfilling at the Edge: Optimal Percentile Resource Allocation via Risk-Averse Reduction
We address deterministic resource allocation in point-to-point multi-terminal AWGN channels without inter-terminal interference, with particular focus on optimizing quantile transmission rates for cell-edge terminal service. Classical utility-based approaches -- such as minimum rate, sumrate, and proportional fairness -- are either overconservative, or inappropriate, or do not provide a rigorous and/or interpretable foundation for fair rate optimization at the edge. To overcome these challenges…

Model Context Protocol (MCP) at First Glance: Studying the Security and Maintainability of MCP Servers
Mohammed Mehedi Hasan, Hao Li, Emad Fallahzadeh, Bram Adams, Ahmed E. Hassan
https://arxiv.org/abs/2506.13538

Model Context Protocol (MCP) at First Glance: Studying the Security and Maintainability of MCP Servers
Although Foundation Models (FMs), such as GPT-4, are increasingly used in domains like finance and software engineering, reliance on textual interfaces limits these models' real-world interaction. To address this, FM providers introduced tool calling-triggering a proliferation of frameworks with distinct tool interfaces. In late 2024, Anthropic introduced the Model Context Protocol (MCP) to standardize this tool ecosystem, which has become the de facto standard with over eight million weekly SD…

RedDino: A foundation model for red blood cell analysis
Luca Zedda, Andrea Loddo, Cecilia Di Ruberto, Carsten Marr
https://arxiv.org/abs/2508.08180 https://

RedDino: A foundation model for red blood cell analysis
Red blood cells (RBCs) are essential to human health, and their precise morphological analysis is important for diagnosing hematological disorders. Despite the promise of foundation models in medical diagnostics, comprehensive AI solutions for RBC analysis remain scarce. We present RedDino, a self-supervised foundation model designed for RBC image analysis. RedDino uses an RBC-specific adaptation of the DINOv2 self-supervised learning framework and is trained on a curated dataset of 1.25 millio…

Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[2/2]:
- On the Need for a Statistical Foundation in Scenario-Based Testing of Autonomous Vehicles
Xingyu Zhao, Robab Aghazadeh-Chakherlou, Chih-Hong Cheng, Peter Popov, Lorenzo Strigini

The move to Windows 11 isn’t just about security updates.
It increases dependence on Microsoft through aggressive cloud integration,
forcing users to adopt Microsoft accounts and services.
It also leads to higher costs due to subscription and licensing models,
and reduces control over how your computer works and how your data is managed.
Furthermore, new hardware requirements will render millions of perfectly good PCs obsolete.
This is a turning point.

The end of Windows 10 is approaching, so it's time to consider Linux and LibreOffice - The Document Foundation Blog
The Document Foundation and LibreOffice support the international campaign @endof10 https://endof10.org/ The countdown has begun. On 14 October 2025, Microsoft will end support for Windows 10. This will leave millions of users and organisations with a difficult choice: should they upgrade to Windows 11, or completely rethink their work environment? The good news? You don’t […]

The Orlando Sentinel got a cease-and-desist from Florida's DCF that claims its reporter coerced families for a story on a foundation tied to Gov. DeSantis' wife (Mike Schneider/Associated Press)
https://apnews.com/article/orlando-sen

Florida agency tells newspaper to halt reporting angle on foundation associated with governor's wife
Florida’s child welfare agency has sent a letter to a newspaper telling it to “cease and desist” its reporting on foster families for a story about a nonprofit associated with Gov. Ron DeSantis’ wife. The foundation is the subject of an investigation. The Orlando Sentinel received the letter on Friday from the Department of Children and Families. The letter claims the newspaper’s Tallahassee reporter intimidated foster families who had received money from the Hope Florida Foundation w…

SToFM: a Multi-scale Foundation Model for Spatial Transcriptomics
Suyuan Zhao, Yizhen Luo, Ganbo Yang, Yan Zhong, Hao Zhou, Zaiqing Nie
https://arxiv.org/abs/2507.11588

SToFM: a Multi-scale Foundation Model for Spatial Transcriptomics
Spatial Transcriptomics (ST) technologies provide biologists with rich insights into single-cell biology by preserving spatial context of cells. Building foundational models for ST can significantly enhance the analysis of vast and complex data sources, unlocking new perspectives on the intricacies of biological tissues. However, modeling ST data is inherently challenging due to the need to extract multi-scale information from tissue slices containing vast numbers of cells. This process require…

Enhanced THz third-harmonic generation in graphene via hot-carriers in a topological cavity
Spyros Doukas (Department of Physics, National and Kapodistrian University of Athens, Athens, Greece), Ioannis Katsantonis (Department of Physics, National and Kapodistrian University of Athens, Athens, Greece, Foundation of Research and Technology Hellas, Institute of Electronic Structure and Laser, 71110, Heraklion, Greece), Thomas Koschny (Ames National Laboratory and Department of Physics an…

Enhanced THz third-harmonic generation in graphene via hot-carriers in a topological cavity
Graphene is a promising material for nonlinear THz applications owing to its high third-order susceptibility and tunable optical properties. Its strong nonlinear response, driven by free-carrier thermodynamics, facilitates efficient third-harmonic generation (THG), which can be further enhanced using resonant metasurfaces and plasmonic structures. In this study, we use a platform that integrates graphene ribbons at the interface of topological photonic crystals designed to boost THG at low THz …

This https://arxiv.org/abs/2506.05394 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Attacking Attention of Foundation Models Disrupts Downstream Tasks
Foundation models represent the most prominent and recent paradigm shift in artificial intelligence.Foundation models are large models, trained on broad data that deliver high accuracy in many downstream tasks, often without fine-tuning. For this reason, models such as CLIP , DINO or Vision Transfomers (ViT), are becoming the bedrock of many industrial AI-powered applications. However, the reliance on pre-trained foundation models also introduces significant security concerns, as these models a…

Apple announces the Foundation Models framework, giving developers direct access to its on-device AI models for the first time (Tim Hardwick/MacRumors)
https://www.macrumors.com/2025/06/09/foundation-models-framework/

Apple Announces Foundation Models Framework for Developers to Leverage AI
Apple at WWDC today announced Foundation Models Framework, a new API allowing third-party developers to leverage the large language models at the...

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/5]:
- Multi-view biomedical foundation models for molecule-target and property prediction
Parthasarathy Suryanarayanan, et al.

Making Qwen3 Think in Korean with Reinforcement Learning
Jungyup Lee, Jemin Kim, Sang Park, SeungJae Lee
https://arxiv.org/abs/2508.10355 https://arxiv.org…

Making Qwen3 Think in Korean with Reinforcement Learning
We present a two-stage fine-tuning approach to make the large language model Qwen3 14B "think" natively in Korean. In the first stage, supervised fine-tuning (SFT) on a high-quality Korean reasoning dataset establishes a strong foundation in Korean logical reasoning, yielding notable improvements in Korean-language tasks and even some gains in general reasoning ability. In the second stage, we employ reinforcement learning with a customized Group Relative Policy Optimization (GRPO) algorithm to…

On the Derivation of Equations of Motion from Symmetries in Quantum-Mechanical Systems via Heisenberg's Uncertainty
Enrique Casanova, Jos\'e Rojas, Melvin Arias
https://arxiv.org/abs/2508.10661

On the Derivation of Equations of Motion from Symmetries in Quantum-Mechanical Systems via Heisenberg's Uncertainty
We propose the construction of equations of motion based on symmetries in quantum-mechanical systems, using Heisenberg's uncertainty principle as a minimal foundation. From canonical operators, two spaces of conjugate operators are constructed, along with a third space derived from the former, which includes the ``Symmetry-Dilation'' operator. When this operator commutes with the main equation of motion, it defines the set of observables compatible with a complete basis of operators (symmetry g…

Raptor: Scalable Train-Free Embeddings for 3D Medical Volumes Leveraging Pretrained 2D Foundation Models
Ulzee An, Moonseong Jeong, Simon A. Lee, Aditya Gorla, Yuzhe Yang, Sriram Sankararaman
https://arxiv.org/abs/2507.08254

Raptor: Scalable Train-Free Embeddings for 3D Medical Volumes Leveraging Pretrained 2D Foundation Models
Current challenges in developing foundational models for volumetric imaging data, such as magnetic resonance imaging (MRI), stem from the computational complexity of training state-of-the-art architectures in high dimensions and curating sufficiently large datasets of volumes. To address these challenges, we introduce Raptor (Random Planar Tensor Reduction), a train-free method for generating semantically rich embeddings for volumetric data. Raptor leverages a frozen 2D foundation model, pretra…

Beyond Overconfidence: Foundation Models Redefine Calibration in Deep Neural Networks
Achim Hekler, Lukas Kuhn, Florian Buettner
https://arxiv.org/abs/2506.09593

Beyond Overconfidence: Foundation Models Redefine Calibration in Deep Neural Networks
Reliable uncertainty calibration is essential for safely deploying deep neural networks in high-stakes applications. Deep neural networks are known to exhibit systematic overconfidence, especially under distribution shifts. Although foundation models such as ConvNeXt, EVA and BEiT have demonstrated significant improvements in predictive performance, their calibration properties remain underexplored. This paper presents a comprehensive investigation into the calibration behavior of foundation mo…

“The Maha report is essentially a distraction from the real causes of poor health,”
said Elizabeth Jacobs, professor emerita at the University of Arizona and a founding member of Defend Public Health.
“This administration does not want to address things like poverty and education and access to healthcare.
Instead, they’re distracting the public with information on solutions to problems that don’t actually exist.
When the foundation of your policy is not evidence-based…

Brain Network Analysis Based on Fine-tuned Self-supervised Model for Brain Disease Diagnosis
Yifei Tang, Hongjie Jiang, Changhong Jing, Hieu Pham, Shuqiang Wang
https://arxiv.org/abs/2506.11671

Brain Network Analysis Based on Fine-tuned Self-supervised Model for Brain Disease Diagnosis
Functional brain network analysis has become an indispensable tool for brain disease analysis. It is profoundly impacted by deep learning methods, which can characterize complex connections between ROIs. However, the research on foundation models of brain network is limited and constrained to a single dimension, which restricts their extensive application in neuroscience. In this study, we propose a fine-tuned brain network model for brain disease diagnosis. It expands brain region representati…

Analytic Task Scheduler: Recursive Least Squares Based Method for Continual Learning in Embodied Foundation Models
Lipei Xie, Yingxin Li, Huiping Zhuang
https://arxiv.org/abs/2506.09623

Analytic Task Scheduler: Recursive Least Squares Based Method for Continual Learning in Embodied Foundation Models
Embodied foundation models are crucial for Artificial Intelligence (AI) interacting with the physical world by integrating multi-modal inputs, such as proprioception, vision and language, to understand human intentions and generate actions to control robots. While these models demonstrate strong generalization and few-shot learning capabilities, they face significant challenges in continually acquiring new skills without forgetting previously learned skills, a problem known as catastrophic forg…

From Diagnosis to Improvement: Probing Spatio-Physical Reasoning in Vision Language Models
Tiancheng Han, Yunfei Gao, Yong Li, Wuzhou Yu, Qiaosheng Zhang, Wenqi Shao
https://arxiv.org/abs/2508.10770

From Diagnosis to Improvement: Probing Spatio-Physical Reasoning in Vision Language Models
Spatio-physical reasoning, a foundation capability for understanding the real physics world, is a critical step towards building robust world models. While recent vision language models (VLMs) have shown remarkable progress in specialized domains like multimodal mathematics and pure spatial understanding, their capability for spatio-physical reasoning remains largely unexplored. This paper provides a comprehensive diagnostic analysis of mainstream VLMs, revealing that current models perform ina…

An interview with US Press Freedom Tracker Senior Reporter Stephanie Sugars on documenting nearly 62,000 anti-press social posts by Trump over the past 10 years (Liam Scott/Freedom of the Press Foundation)
http://freedom.press/issues/the-repor

The reporter documenting 10 years of Trump’s anti-media posts
Since Trump’s first term as president, Stephanie Sugars has managed an extensive database that documents each and every anti-media post from Trump.

The Marin Community Foundation sold 13.4M Figma shares for $440M in the IPO; source: the foundation had received the shares from Figma co-founder Evan Wallace (Allie Garfinkle/Fortune)
https://fortune.com/2025/08/02/figma-ipo-surpris…

Figma IPO’s surprise winner is a charity with 13 million shares—and a famous backstory that sparked a bitter feud over an oil fortune decades ago
The Marin Community Foundation netted $440 million as the IPO's largest selling shareholder. The philanthropic organization is itself the product of a previous generation of big business—and a famous legal fight for control of the money.

A generation of scientific talent is at the brink of being lost to overseas competitors by the Trump administration’s dismantling of the National Science Foundation (NSF), with unprecedented political interference at the agency jeopardizing the future of US industries and economic growth, according to a Guardian investigation.
The gold standard peer-reviewed process used by the NSF to support cutting-edge, high-impact science is being undermined by the chaotic cuts to staff, programs a…

Integrating Vision Foundation Models with Reinforcement Learning for Enhanced Object Interaction
Ahmad Farooq, Kamran Iqbal
https://arxiv.org/abs/2508.05838 https://

Integrating Vision Foundation Models with Reinforcement Learning for Enhanced Object Interaction
This paper presents a novel approach that integrates vision foundation models with reinforcement learning to enhance object interaction capabilities in simulated environments. By combining the Segment Anything Model (SAM) and YOLOv5 with a Proximal Policy Optimization (PPO) agent operating in the AI2-THOR simulation environment, we enable the agent to perceive and interact with objects more effectively. Our comprehensive experiments, conducted across four diverse indoor kitchen settings, demons…

Integrating Pathology Foundation Models and Spatial Transcriptomics for Cellular Decomposition from Histology Images
Yutong Sun, Sichen Zhu, Peng Qiu
https://arxiv.org/abs/2507.07013

Integrating Pathology Foundation Models and Spatial Transcriptomics for Cellular Decomposition from Histology Images
The rapid development of digital pathology and modern deep learning has facilitated the emergence of pathology foundation models that are expected to solve general pathology problems under various disease conditions in one unified model, with or without fine-tuning. In parallel, spatial transcriptomics has emerged as a transformative technology that enables the profiling of gene expression on hematoxylin and eosin (H&E) stained histology images. Spatial transcriptomics unlocks the unprecedented…

Polygon co-founder Sandeep Nailwal is named CEO of the Polygon Foundation after the departure of Mihailo Bjelic, and unveils changes to streamline the nonprofit (Daniel Kuhn/The Block)
https://www.theblock.co/post/35…

Foundation Models in Medical Imaging -- A Review and Outlook
Vivien van Veldhuizen, Vanessa Botha, Chunyao Lu, Melis Erdal Cesur, Kevin Groot Lipman, Edwin D. de Jong, Hugo Horlings, Cl\'arisa Sanchez, Cees Snoek, Ritse Mann, Eric Marcus, Jonas Teuwen
https://arxiv.org/abs/2506.09095…

Foundation Models in Medical Imaging -- A Review and Outlook
Foundation models (FMs) are changing the way medical images are analyzed by learning from large collections of unlabeled data. Instead of relying on manually annotated examples, FMs are pre-trained to learn general-purpose visual features that can later be adapted to specific clinical tasks with little additional supervision. In this review, we examine how FMs are being developed and applied in pathology, radiology, and ophthalmology, drawing on evidence from over 150 studies. We explain the co…

AnimateAnyMesh: A Feed-Forward 4D Foundation Model for Text-Driven Universal Mesh Animation
Zijie Wu, Chaohui Yu, Fan Wang, Xiang Bai
https://arxiv.org/abs/2506.09982

AnimateAnyMesh: A Feed-Forward 4D Foundation Model for Text-Driven Universal Mesh Animation
Recent advances in 4D content generation have attracted increasing attention, yet creating high-quality animated 3D models remains challenging due to the complexity of modeling spatio-temporal distributions and the scarcity of 4D training data. In this paper, we present AnimateAnyMesh, the first feed-forward framework that enables efficient text-driven animation of arbitrary 3D meshes. Our approach leverages a novel DyMeshVAE architecture that effectively compresses and reconstructs dynamic mes…

Tissue Concepts v2: a Supervised Foundation Model for whole slide images
Till Nicke, Daniela Scharcherer, Jan Raphael Sch\"afer, Natalia Artysh, Antje Prasse, Andr\'e Homeyer, Andrea Schenk, Henning H\"ofener, Johannes Lotz
https://arxiv.org/abs/2507.05742

Tissue Concepts v2: a Supervised Foundation Model for whole slide images
Foundation models (FMs) are transforming the field of computational pathology by offering new approaches to analyzing histopathology images. Typically relying on weeks of training on large databases, the creation of FMs is a resource-intensive process in many ways. In this paper, we introduce the extension of our supervised foundation model, Tissue Concepts, to whole slide images, called Tissue Concepts v2 (TCv2), a supervised foundation model for whole slide images to address the issue above. …

Improving Robustness of Foundation Models in Domain Adaptation with Soup-Adapters
Marco Roschkowski
https://arxiv.org/abs/2507.05807 https://

Improving Robustness of Foundation Models in Domain Adaptation with Soup-Adapters
In this paper, we tackle two fundamental problems in few-shot domain adaptation of foundation models. First, hyperparameter tuning is often impractical due to the lack of large validation datasets. Second, model robustness under distribution shifts where test time data deviates slightly from training distributions, remains a concern. We show that by training multiple independent adapters and averaging their outputs, the new model has a higher performance and is more robust to distribution shift…

The Linux Foundation unveils the Agent2Agent project, with founding members including Google Cloud, AWS, and Microsoft; Google donated the protocol spec and SDK (Google Developers Blog)
https://developers.googleblog.com/en/google-cloud-donates-a2…

Google Cloud donates A2A to Linux Foundation- Google Developers Blog
Explore how Google, Amazon, and Cisco form the Agent2Agent Foundation under the Linux Foundation to drive AI innovation via interoperability as an industry standard.

This https://arxiv.org/abs/2506.01933 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models
Spatial intelligence, encompassing 3D reconstruction, perception, and reasoning, is fundamental to applications such as robotics, aerial imaging, and extended reality. A key enabler is the real-time, accurate estimation of core 3D attributes (camera parameters, point clouds, depth maps, and 3D point tracks) from unstructured or streaming imagery. Inspired by the success of large foundation models in language and 2D vision, a new class of end-to-end 3D geometric foundation models (GFMs) has emer…

TerraFM: A Scalable Foundation Model for Unified Multisensor Earth Observation
Muhammad Sohail Danish, Muhammad Akhtar Munir, Syed Roshaan Ali Shah, Muhammad Haris Khan, Rao Muhammad Anwer, Jorma Laaksonen, Fahad Shahbaz Khan, Salman Khan
https://arxiv.org/abs/2506.06281

TerraFM: A Scalable Foundation Model for Unified Multisensor Earth Observation
Modern Earth observation (EO) increasingly leverages deep learning to harness the scale and diversity of satellite imagery across sensors and regions. While recent foundation models have demonstrated promising generalization across EO tasks, many remain limited by the scale, geographical coverage, and spectral diversity of their training data, factors critical for learning globally transferable representations. In this work, we introduce TerraFM, a scalable self-supervised learning model that l…