Tootfinder

A look at the auction for the Oscars' TV rights, as sources say Netflix is out, NBCUniversal gains ground, and YouTube and ABC remain active bidders (Matt Donnelly/Variety)
https://variety.com/2025/film/news/oscars-tv-rights-nbcuniversal-a…

Oscar TV Rights Auction: NBCUniversal Leads Charge to Nab Telecast
NBCUniversal is in the lead to nab TV rights to the Oscars starting in 2029 as the Motion Picture Academy shops around for a new deal.

Prefrontal scaling of reward prediction error readout gates reinforcement-derived adaptive behavior in primates
Tian Sang, Yichun Huang, Fangwei Zhong, Miao Wang, Shiqi Yu, Jiahui Li, Yuanjing Feng, Yizhou Wang, Kwok Sze Chai, Ravi S. Menon, Meiyun Wang, Fang Fang, Zheng Wang
https://arxiv.org/abs/2512.09761 https://arxiv.org/pdf/2512.09761 https://arxiv.org/html/2512.09761
arXiv:2512.09761v1 Announce Type: new
Abstract: Reinforcement learning (RL) enables adaptive behavior across species via reward prediction errors (RPEs), but the neural origins of species-specific adaptability remain unknown. Integrating RL modeling, transcriptomics, and neuroimaging during reversal learning, we discovered convergent RPE signatures - shared monoaminergic/synaptic gene upregulation and neuroanatomical representations, yet humans outperformed macaques behaviorally. Single-trial decoding showed RPEs guided choices similarly in both species, but humans disproportionately recruited dorsal anterior cingulate (dACC) and dorsolateral prefrontal cortex (dlPFC). Cross-species alignment uncovered that macaque prefrontal circuits encode human-like optimal RPEs yet fail to translate them into action. Adaptability scaled not with RPE encoding fidelity, but with the areal extent of dACC/dlPFC recruitment governing RPE-to-action transformation. These findings resolve an evolutionary puzzle: behavioral performance gaps arise from executive cortical readout efficiency, not encoding capacity.
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RamPINN: Recovering Raman Spectra From Coherent Anti-Stokes Spectra Using Embedded Physics
Sai Karthikeya Vemuri, Adithya Ashok Chalain Valapil, Tim B\"uchner, Joachim Denzler
https://arxiv.org/abs/2510.06020

RamPINN: Recovering Raman Spectra From Coherent Anti-Stokes Spectra Using Embedded Physics
Transferring the recent advancements in deep learning into scientific disciplines is hindered by the lack of the required large-scale datasets for training. We argue that in these knowledge-rich domains, the established body of scientific theory provides reliable inductive biases in the form of governing physical laws. We address the ill-posed inverse problem of recovering Raman spectra from noisy Coherent Anti-Stokes Raman Scattering (CARS) measurements, as the true Raman signal here is suppre…

OF COURSE!
LLMs do not have any knowledge They only have words. When "introspecting" they are actually just word-salading that like they do everything else. They can't SAY that so they make up plausible-seeming explanations.
BECAUSE THAT IS ALL THEY DO, EVER. https://mastodon.social/@arstechni…

Ars Technica (@arstechnica@mastodon.social)
Attached: 1 image LLMs show a “highly unreliable” capacity to describe their own internal processes Anthropic finds some LLM "self-awareness," but "failures of introspection remain the norm." https://arstechnica.com/ai/2025/11/llms-show-a-highly-unreliable-capacity-to-describe-their-own-internal-processes/?utm_brand=arstechnica&utm_social-type=owned&utm_source=mastodon&utm_medium=social

On the Limits of Consensus under Dynamic Availability and Reconfiguration
Joachim Neu, Javier Nieto, Ling Ren
https://arxiv.org/abs/2510.03625 https://arxi…

On the Limits of Consensus under Dynamic Availability and Reconfiguration
Proof-of-stake blockchains require consensus protocols that support Dynamic Availability and Reconfiguration (so-called DAR setting), where the former means that the consensus protocol should remain live even if a large number of nodes temporarily crash, and the latter means it should be possible to change the set of operating nodes over time. State-of-the-art protocols for the DAR setting, such as Ethereum, Cardano's Ouroboros, or Snow White, require unrealistic additional assumptions, such as…

AUREXA-SE: Audio-Visual Unified Representation Exchange Architecture with Cross-Attention and Squeezeformer for Speech Enhancement
M. Sajid, Deepanshu Gupta, Yash Modi, Sanskriti Jain, Harshith Jai Surya Ganji, A. Rahaman, Harshvardhan Choudhary, Nasir Saleem, Amir Hussain, M. Tanveer
https://arxiv.org/abs/2510.05295

AUREXA-SE: Audio-Visual Unified Representation Exchange Architecture with Cross-Attention and Squeezeformer for Speech Enhancement
In this paper, we propose AUREXA-SE (Audio-Visual Unified Representation Exchange Architecture with Cross-Attention and Squeezeformer for Speech Enhancement), a progressive bimodal framework tailored for audio-visual speech enhancement (AVSE). AUREXA-SE jointly leverages raw audio waveforms and visual cues by employing a U-Net-based 1D convolutional encoder for audio and a Swin Transformer V2 for efficient and expressive visual feature extraction. Central to the architecture is a novel bidirect…

Crosslisted article(s) found for cs.MM. https://arxiv.org/list/cs.MM/new
[1/1]:
- AUREXA-SE: Audio-Visual Unified Representation Exchange Architecture with Cross-Attention and Squ...
Sajid, Gupta, Modi, Jain, Ganji, Rahaman, Choudhary, Saleem, Hussain, Tanveer