Tootfinder

Metropolitana V 🍎
城 V 🍎
📷 Pentax MX
🎞️ Ilford Pan 100
#filmphotography #Photography #blackandwhite

I keep coming back to the mirror dualities of the oppressed and oppressor under authoritarianism.
The oppressed is portrayed as both weak and godlike. The stereotypes are always some variation on sloth and incompetence, but yet somehow also a menace capable of destroying the "pure" society. To use the most relevant current example, Antifa being both little femme soy boys who would always get beat up by "real men" while also being an international terrorist organization on the brink of overthrowing the US government, the unarmed presence of whom makes the heavily armed agents of ICE flee for their lives. Antifa is both having absolutely no impact on ICE, and also having such an impact on ICE that the military needs to come in to protect them. The contradiction is obvious but never seems to occur to those who hold both to be true at the same time.
But few talk about the duality of the oppressor. The sovereign throughout history has always been both a ruler above the law, sometimes even the representative or incarnation of a divine force. Yet, this same superhuman/god-man is also a baby who needs constant care. This is absolutely a through line from the very earliest records of sovereign cults to modern cult leaders, CEOs, and Trump today. Power, for these people, is expressed both as the ability to force others to enact their will and in the ability to compel others to care for them. Can any of these "men" cook? Can they fix anything themselves? They are driven everywhere, cooked for all the time, constantly protected from danger. Kings are still dressed, at least for rituals. I could dissect masculinity here, but that's a whole thing.
It is as though the drive to care for our children, who must be taught to behave within acceptable norms, is hijacked by "leaders" who demand our care and attention... even at the expense of our literal children. And recently we've seen some of those very CEOs, with LLMs and return to office demands, show that their judgment is also little better than children, making decisions while pretending to understand a subject.
The oppressed are portrayed as both god-like and impotent and are, in fact, neither. Meanwhile the rulers portray themselves only as invulnerable and are, in fact, childish in their ability to survive without constant support. Their greatest fear from the collapse of society is figuring out how to make sure people keep taking care of them.
It just keeps rattling around in my head.
#USPol

Chronologically Consistent Generative AI
Songrun He, Linying Lv, Asaf Manela, Jimmy Wu
https://arxiv.org/abs/2510.11677 https://arxiv.org/pdf/2510.11677

Chronologically Consistent Generative AI
We introduce a family of chronologically consistent, instruction-following large language models to eliminate lookahead bias. Each model is trained only on data available before a clearly defined knowledge-cutoff date, ensuring strict temporal separation from any post-cutoff data. The resulting framework offers (i) a simple, conversational chat interface, (ii) fully open, fixed model weights that guarantee replicability, and (iii) a conservative lower bound on forecast accuracy, isolating the s…

MedicalOS: An LLM Agent based Operating System for Digital Healthcare
Jared Zhu, Junde Wu
https://arxiv.org/abs/2509.11507 https://arxiv.org/pdf/2509.11507…

MedicalOS: An LLM Agent based Operating System for Digital Healthcare
Decades' advances in digital health technologies, such as electronic health records, have largely streamlined routine clinical processes. Yet, most these systems are still hard to learn and use: Clinicians often face the burden of managing multiple tools, repeating manual actions for each patient, navigating complicated UI trees to locate functions, and spending significant time on administration instead of caring for patients. The recent rise of large language model (LLM) based agents demonstr…

MVP4D: Multi-View Portrait Video Diffusion for Animatable 4D Avatars
Felix Taubner, Ruihang Zhang, Mathieu Tuli, Sherwin Bahmani, David B. Lindell
https://arxiv.org/abs/2510.12785

MVP4D: Multi-View Portrait Video Diffusion for Animatable 4D Avatars
Digital human avatars aim to simulate the dynamic appearance of humans in virtual environments, enabling immersive experiences across gaming, film, virtual reality, and more. However, the conventional process for creating and animating photorealistic human avatars is expensive and time-consuming, requiring large camera capture rigs and significant manual effort from professional 3D artists. With the advent of capable image and video generation models, recent methods enable automatic rendering o…

Improving Decision Trees through the Lens of Parameterized Local Search
Juha Harviainen, Frank Sommer, Manuel Sorge
https://arxiv.org/abs/2510.12726 https://

Improving Decision Trees through the Lens of Parameterized Local Search
Algorithms for learning decision trees often include heuristic local-search operations such as (1) adjusting the threshold of a cut or (2) also exchanging the feature of that cut. We study minimizing the number of classification errors by performing a fixed number of a single type of these operations. Although we discover that the corresponding problems are NP-complete in general, we provide a comprehensive parameterized-complexity analysis with the aim of determining those properties of the pr…

GOAT: A Training Framework for Goal-Oriented Agent with Tools
Hyunji Min, Sangwon Jung, Junyoung Sung, Dosung Lee, Leekyeung Han, Paul Hongsuck Seo
https://arxiv.org/abs/2510.12218

GOAT: A Training Framework for Goal-Oriented Agent with Tools
Large language models (LLMs) have recently been extended beyond traditional text generation to serve as interactive agents capable of using external tools based on user intent. However, current LLM agents still show limited ability to handle goal-oriented queries, which require decomposing a high-level objective into multiple interdependent API calls with correct planning and execution. Current approaches mainly rely on zero-shot evaluation due to the absence of training data. While proprietary…

Towards Foundation Inference Models that Learn ODEs In-Context
Maximilian Mauel, Manuel Hinz, Patrick Seifner, David Berghaus, Ramses J. Sanchez
https://arxiv.org/abs/2510.12650

Towards Foundation Inference Models that Learn ODEs In-Context
Ordinary differential equations (ODEs) describe dynamical systems evolving deterministically in continuous time. Accurate data-driven modeling of systems as ODEs, a central problem across the natural sciences, remains challenging, especially if the data is sparse or noisy. We introduce FIM-ODE (Foundation Inference Model for ODEs), a pretrained neural model designed to estimate ODEs zero-shot (i.e., in context) from sparse and noisy observations. Trained on synthetic data, the model utilizes a …

Topological Signatures of ReLU Neural Network Activation Patterns
Vicente Bosca, Tatum Rask, Sunia Tanweer, Andrew R. Tawfeek, Branden Stone
https://arxiv.org/abs/2510.12700 htt…

Topological Signatures of ReLU Neural Network Activation Patterns
This paper explores the topological signatures of ReLU neural network activation patterns. We consider feedforward neural networks with ReLU activation functions and analyze the polytope decomposition of the feature space induced by the network. Mainly, we investigate how the Fiedler partition of the dual graph and show that it appears to correlate with the decision boundary -- in the case of binary classification. Additionally, we compute the homology of the cellular decomposition -- in a regr…