Tootfinder

The 2025 Web Almanac mistook me.
I did *not* say LLMs provide better image descriptions. I cited SeeingAI and Be My Eyes as tools for undescribed IRL uses.
I said LLM-generated captions could be better than craptions. I mentioned abstracts / reading-level changes, which could be summaries?
But “better” image descriptions is right out.

Mira Murati's Thinking Machines Lab makes Tinker, its API for fine-tuning language models, generally available, adds support for Kimi K2 Thinking, and more (Thinking Machines Lab)
https://thinkingmachines.ai/blog/tinker-general-availability/

Tinker: General Availability and Vision Input
Today we are announcing four updates to Tinker: No more waitlist New reasoning model: Kimi K2 Thinking New inference interface that is compatible with the OpenAI API Vision input support with Qwen3-VL General availability The waitlist is over! Everybody can use Tinker now; sign up here to get started. See the Tinker homepage for available models and pricing, and check out the Tinker cookbook for code examples. More reasoning with Kimi K2 Thinking Users can now fine-tune Kimi K2 Thinking on Ti…

from my link log —
By the power of grayscale!
https://zserge.com/posts/grayskull/
saved 2025-11-06 https://dotat.at/:/IWFHW.html

Part of why #Trump has always been so hard to pin down politically is that he was always representing highly conflicting interests. Now, as that eats him alive, the GOP is fracturing in to two main groups: the Pinochet/Franco wing and the Hitler wing.
The Pinochet/Franco wing (let's call them PF) are lead by Vance. PF are also a coalition with some competing interests, but basically it's evangelical leaders, Opus Dei (fascist catholics), tech fascists (Yarvinites), pharma, and the other normal big republican donors. They support Israel, some because apartheid is extremely profitable and some because they support the genocide of Palestinian in order to bring the end of the world. They are split between extremely antisemitic evangelicals and Zionists, wanting similar things for completely different reasons. PF wants strong immigration enforcement because it lets them exploit immigrants, they don't want actual ethnic cleansing (just the constant threat). They want H1B visas because they want to a precarious tech work force. They want to end tariffs because they support free trade and don't actually care about things being made here.
The Hitler wing are lead by Nick Fuentes. I think they're a more unified group, but they're going to try to pull together a coalition that I don't think can really work. They're against Israel because they believe in some bat shit antisemitic conspiracy theory (which they are trying to inject along side legitimate criticism of Israel). They are focused on release of the #EpsteinFiles because they believe that it shows that Epstein worked for Mossad. They don't think that the ICE raids are going far enough, they oppose H1Bs because they are racists. They want a full ethnic cleansing of the US where everyone who isn't "white" is either enslaved for menial labor, deported, or dead. But they're also critical of big business (partially because of conspiracy theories but also) because they think their best option is to push for a white socialism (red/brown alliance).
Both of them want to sink Trump because they see him as standing in the way of their objectives. Both see #Epstein as an opportunity. Both of them have absolutely terrifying visions of authoritarian dictatorships, but they're different dictatorships.with opposing interests. Even within these there may be opportunities to fracture these more.
While these fractures decrease the likelihood of either group getting enough people together, their vision is more clear and thus more likely to succeed if they can make that happen. Now is absolutely *not* the time to just enjoy the collapse, we need to keep up or accelerate anti-fascist efforts to avoid repeating some of the mistakes of history.
Edit:
I should not that this isn't *totally* original analysis. I'll link a video later when I have time to find it.
Here it is:
#USPol

Sources: Nvidia's DGX Cloud is merging with the engineering unit, pivoting from selling cloud services to enterprises to supporting internal AI development (The Information)
https://www.theinformation.com/articles/nvidia-restructures-clou…

Nvidia Restructures Cloud Team After Retreating From AWS Competition
More than two years after Nvidia CEO Jensen Huang shared his vision to develop a cloud service that could rival Amazon Web Services, he has backed away from competing with such firms and last week reorganized his company’s cloud team, according to several people with knowledge of the ...

Exploiting ID-Text Complementarity via Ensembling for Sequential Recommendation
Liam Collins, Bhuvesh Kumar, Clark Mingxuan Ju, Tong Zhao, Donald Loveland, Leonardo Neves, Neil Shah
https://arxiv.org/abs/2512.17820 https://arxiv.org/pdf/2512.17820 https://arxiv.org/html/2512.17820
arXiv:2512.17820v1 Announce Type: new
Abstract: Modern Sequential Recommendation (SR) models commonly utilize modality features to represent items, motivated in large part by recent advancements in language and vision modeling. To do so, several works completely replace ID embeddings with modality embeddings, claiming that modality embeddings render ID embeddings unnecessary because they can match or even exceed ID embedding performance. On the other hand, many works jointly utilize ID and modality features, but posit that complex fusion strategies, such as multi-stage training and/or intricate alignment architectures, are necessary for this joint utilization. However, underlying both these lines of work is a lack of understanding of the complementarity of ID and modality features. In this work, we address this gap by studying the complementarity of ID- and text-based SR models. We show that these models do learn complementary signals, meaning that either should provide performance gain when used properly alongside the other. Motivated by this, we propose a new SR method that preserves ID-text complementarity through independent model training, then harnesses it through a simple ensembling strategy. Despite this method's simplicity, we show it outperforms several competitive SR baselines, implying that both ID and text features are necessary to achieve state-of-the-art SR performance but complex fusion architectures are not.
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Study: brain-computer interface company Science's PRIMA device, which includes a retinal implant and special glasses, restored vision in some blind patients (Antonio Regalado/MIT Technology Review)
https://www.technologyreview.com/2025/10/2

This retina implant lets people with vision loss do a crossword puzzle
Competition to deploy commercial brain-computer interfaces is heating up.

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/3]:
- Sharp Structure-Agnostic Lower Bounds for General Functional Estimation
Jikai Jin, Vasilis Syrgkanis
https://arxiv.org/abs/2512.17341 https://mastoxiv.page/@arXiv_statML_bot/115762312049963700
- Timely Information Updating for Mobile Devices Without and With ML Advice
Yu-Pin Hsu, Yi-Hsuan Tseng
https://arxiv.org/abs/2512.17381 https://mastoxiv.page/@arXiv_csNI_bot/115762180316858485
- SWE-Bench : A Framework for the Scalable Generation of Software Engineering Benchmarks from Open...
Wang, Ramalho, Celestino, Pham, Liu, Sinha, Portillo, Osunwa, Maduekwe
https://arxiv.org/abs/2512.17419 https://mastoxiv.page/@arXiv_csSE_bot/115762487015279852
- Perfect reconstruction of sparse signals using nonconvexity control and one-step RSB message passing
Xiaosi Gu, Ayaka Sakata, Tomoyuki Obuchi
https://arxiv.org/abs/2512.17426 https://mastoxiv.page/@arXiv_statML_bot/115762346108219997
- MULTIAQUA: A multimodal maritime dataset and robust training strategies for multimodal semantic s...
Jon Muhovi\v{c}, Janez Per\v{s}
https://arxiv.org/abs/2512.17450 https://mastoxiv.page/@arXiv_csCV_bot/115762717053353674
- When Data Quality Issues Collide: A Large-Scale Empirical Study of Co-Occurring Data Quality Issu...
Emmanuel Charleson Dapaah, Jens Grabowski
https://arxiv.org/abs/2512.17460 https://mastoxiv.page/@arXiv_csSE_bot/115762500123147574
- Behavioural Effects of Agentic Messaging: A Case Study on a Financial Service Application
Olivier Jeunen, Schaun Wheeler
https://arxiv.org/abs/2512.17462 https://mastoxiv.page/@arXiv_csIR_bot/115762430673347625
- Linear Attention for Joint Power Optimization and User-Centric Clustering in Cell-Free Networks
Irched Chafaa, Giacomo Bacci, Luca Sanguinetti
https://arxiv.org/abs/2512.17466 https://mastoxiv.page/@arXiv_eessSY_bot/115762336277179643
- Translating the Rashomon Effect to Sequential Decision-Making Tasks
Dennis Gross, J{\o}rn Eirik Betten, Helge Spieker
https://arxiv.org/abs/2512.17470 https://mastoxiv.page/@arXiv_csAI_bot/115762556506696539
- Alternating Direction Method of Multipliers for Nonlinear Matrix Decompositions
Atharva Awari, Nicolas Gillis, Arnaud Vandaele
https://arxiv.org/abs/2512.17473 https://mastoxiv.page/@arXiv_eessSP_bot/115762580078964235
- TwinSegNet: A Digital Twin-Enabled Federated Learning Framework for Brain Tumor Analysis
Almustapha A. Wakili, Adamu Hussaini, Abubakar A. Musa, Woosub Jung, Wei Yu
https://arxiv.org/abs/2512.17488 https://mastoxiv.page/@arXiv_csCV_bot/115762726884307901
- Resource-efficient medical image classification for edge devices
Mahsa Lavaei, Zahra Abadi, Salar Beigzad, Alireza Maleki
https://arxiv.org/abs/2512.17515 https://mastoxiv.page/@arXiv_eessIV_bot/115762459510336799
- PathBench-MIL: A Comprehensive AutoML and Benchmarking Framework for Multiple Instance Learning i...
Brussee, Valkema, Weijer, Doeleman, Schrader, Kers
https://arxiv.org/abs/2512.17517 https://mastoxiv.page/@arXiv_csCV_bot/115762741957639051
- HydroGym: A Reinforcement Learning Platform for Fluid Dynamics
Christian Lagemann, et al.
https://arxiv.org/abs/2512.17534 https://mastoxiv.page/@arXiv_physicsfludyn_bot/115762391350754768
- When De-noising Hurts: A Systematic Study of Speech Enhancement Effects on Modern Medical ASR Sys...
Chondhekar, Murukuri, Vasani, Goyal, Badami, Rana, SN, Pandia, Katiyar, Jagadeesh, Gulati
https://arxiv.org/abs/2512.17562 https://mastoxiv.page/@arXiv_csSD_bot/115762423443170715
- Enabling Disaggregated Multi-Stage MLLM Inference via GPU-Internal Scheduling and Resource Sharing
Lingxiao Zhao, Haoran Zhou, Yuezhi Che, Dazhao Cheng
https://arxiv.org/abs/2512.17574 https://mastoxiv.page/@arXiv_csDC_bot/115762425409322293
- SkinGenBench: Generative Model and Preprocessing Effects for Synthetic Dermoscopic Augmentation i...
N. A. Adarsh Pritam, Jeba Shiney O, Sanyam Jain
https://arxiv.org/abs/2512.17585 https://mastoxiv.page/@arXiv_eessIV_bot/115762479150695610
- MAD-OOD: A Deep Learning Cluster-Driven Framework for an Out-of-Distribution Malware Detection an...
Tosin Ige, Christopher Kiekintveld, Aritran Piplai, Asif Rahman, Olukunle Kolade, Sasidhar Kunapuli
https://arxiv.org/abs/2512.17594 https://mastoxiv.page/@arXiv_csCR_bot/115762509298207765
- Confidence-Credibility Aware Weighted Ensembles of Small LLMs Outperform Large LLMs in Emotion De...
Menna Elgabry, Ali Hamdi
https://arxiv.org/abs/2512.17630 https://mastoxiv.page/@arXiv_csCL_bot/115762575512981257
- Generative Multi-Objective Bayesian Optimization with Scalable Batch Evaluations for Sample-Effic...
Madhav R. Muthyala, Farshud Sorourifar, Tianhong Tan, You Peng, Joel A. Paulson
https://arxiv.org/abs/2512.17659 https://mastoxiv.page/@arXiv_statML_bot/115762554519447500
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