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Discovering functionally selective brain regions with a deep topographic multimodal model #AI #NeuroAI

Discovering Functionally Selective Brain Regions with a Deep Topographic Multimodal Model
Nearby neurons in cortex share similar response profiles, producing systematic spatial organization across sensory and cognitive systems. Recent topographic models reproduce aspects of this structure but remain unimodal and spatially constrain each layer separately, yielding fragmented maps that capture neither the contiguity of cortical processing streams nor their integration across modalities. We introduce Topo-Omni, a topographic multimodal model in which visual, auditory, and language/cogn…

Israel Counts the Ways That Netanyahu's Iran Strategy Failed (New York Times)
https://www.nytimes.com/2026/06/15/world/middleeast/israel-netanyahus-iran-strategy.html?unlocked_article_code=1.qVA.k1X9.84aqywB904jH&smid=url-share
http://www.memeorandum.com/260615/p45#a260615p45

A discrete Boltzmann model with state-dependent power-law relaxation time for nonequilibrium transport in compressible flows
Demei Li, Zhongyi He, Huilin Lai, Yanbiao Gan, Hailong Liu, Pengfei Lin
https://arxiv.org/abs/2605.18216 https://arxiv.org/pdf/2605.18216 https://arxiv.org/html/2605.18216
arXiv:2605.18216v1 Announce Type: new
Abstract: Thermodynamic nonequilibrium effects play a central role in momentum and energy transport in compressible flows. In conventional BGK kinetic models, the relaxation time $\tau$ is taken as a constant, which neglects the dependence of the relaxation process on local macroscopic states. To overcome this limitation, we develop a discrete Boltzmann model with a density- and temperature-dependent power-law relaxation time, termed DTRT-DBM, in which $\tau=\tau_0(\rho/\rho_0)^a(T/T_0)^b$. This formulation extends the discrete Boltzmann framework to flows with spatially varying nonequilibrium intensity. The model is validated by the Sod shock tube and by analytical solutions for viscous stress and heat flux, demonstrating accurate recovery of both macroscopic wave structures and nonequilibrium quantities across shock waves, rarefaction waves, and contact discontinuities. On this basis, phase diagrams of viscous stress and heat flux are constructed to examine how these quantities depend on the power-law exponents $a$ and $b$. The extrema of these quantities depend exponentially on the model parameters and exhibit regime-dependent behaviour. The roles of $a$ and $b$ are not symmetric: the nonequilibrium response is more sensitive to $a$ when density gradients dominate, but more sensitive to $b$ when temperature gradients dominate. Within the parameter range and flow configurations examined here, higher-order viscous stress increases the growth rate of the total viscous-stress extremum, whereas higher-order heat flux reduces the growth rate of the total heat-flux extremum. These results show that the proposed model can capture different higher-order nonequilibrium responses in compressible flows and provides a framework for the modelling and analysis of multiscale nonequilibrium processes.
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Anthropic has been trying to understand how large language models (LLMs) work for a few years now.
Anthropic isn’t the only one looking at this, but I think the company has made it part of its core mission more than most.
Anthropic’s CEO, Dario Amodei, has said we won’t be able to control LLMs fully unless we learn more about how they work. 
So this new research is very much in that context.
It goes deeper into the weird mechanisms inside LLMs than ever before.

What Anthropic’s latest AI discovery does—and doesn’t—show
The company says it has found a new window into how its models arrive at answers. We spoke with senior editor Will Douglas Heaven about it. 

Replaced article(s) found for physics.app-ph. https://arxiv.org/list/physics.app-ph/new
[1/1]:
- Dynamic sliding and rolling friction models for viscoelastic contact pairs
Luigi Romano

Japan commits up to $6.16B over five years to Noetra, a consortium led by SoftBank, Honda, NEC, and Sony, to develop a domestic AI foundation model by 2027 (Nikkei Asia)
https://asia.nikkei.com/business/techn

Japan backs SoftBank-led AI models with up to $6.2bn in chasing US, China
New company will focus on physical AI using data from Japanese companies

Enjoy the Java Geek Weekly #140, special first pneumonia ever edition 🤒
https://blog.frankel.ch/java-geek-weekly/140/

Too Good to Be True: A Study on Modern Automatic Speech Recognition for the Evaluation of Speech Enhancement
Danilo de Oliveira, Tal Peer, Timo Gerkmann
https://arxiv.org/abs/2605.12107 https://arxiv.org/pdf/2605.12107 https://arxiv.org/html/2605.12107
arXiv:2605.12107v1 Announce Type: new
Abstract: Speech enhancement (SE) systems are typically evaluated using a variety of instrumental metrics. The use of automatic speech recognition (ASR) systems to evaluate SE performance is common in literature, usually in terms of word error rate (WER). However, WER scores depend heavily on the choice of ASR system and text normalization pipeline. In this paper, we investigate how modern ASR models correlate with human recognition of enhanced speech. A listening experiment reveals that modern ASR models with large-scale noisy training and embedded language models correlate more with human WER than simpler ones, with a transducer model providing the most reliable transcriptions. Nevertheless, we also show that these models' robustness to noise and use of context can be uninformative to an acoustics-focused evaluation of enhancement performance.
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Meta launches Muse Image in Meta AI, Instagram, and WhatsApp, and previews Muse Video, the first media generation models from its Superintelligence Labs (Meta)
https://ai.meta.com/blog/introducing-muse-image-muse-video-msl/

Introducing Muse Image and Muse Video
Muse Image follows instructions faithfully, edits with precision, composes from multiple references, and draws on Instagram for social context. Muse Video delivers exceptional visual fidelity with native audio support.

Strikes early Wednesday destroyed what appears to be a drinking-water facility on Iran’s southern coast, near the Strait of Hormuz, according to an analysis by The New York Times.
Around the time of the strikes, the U.S. Central Command said in a post on X that it had conducted attacks near the strait “with precision munitions from U.S. Air Force and Navy fighter jets.”
It is unclear if the U.S. intentionally struck the water facilities, or knew what was in the buildings.

Analysis of Satellite Image and Videos Suggest Precision U.S. Strikes on Iranian Water Facility
It is unclear if the U.S. intentionally struck the facility or knew what it was. Deliberately targeting civilian infrastructure could constitute a war crime.