Tootfinder

Again I'm confused at the human urge to pretend something is way more automated than it really is. Examples:
- Robot 'chef' that only automates the very least time-consuming part of cooking, serving no purpose but to slow things down: https://post.lurk.org/@jonty@chaos.soc

Aesthetic Experience and Educational Value in Co-creating Art with Generative AI: Evidence from a Survey of Young Learners
Chengyuan Zhang, Suzhe Xu
https://arxiv.org/abs/2509.10576

Aesthetic Experience and Educational Value in Co-creating Art with Generative AI: Evidence from a Survey of Young Learners
This study investigates the aesthetic experience and educational value of collaborative artmaking with generative artificial intelligence (AI) among young learners and art students. Based on a survey of 112 participants, we examine how human creators renegotiate their roles, how conventional notions of originality are challenged, how the creative process is transformed, and how aesthetic judgment is formed in human--AI co-creation. Empirically, participants generally view AI as a partner that s…

GAR: Generative Adversarial Reinforcement Learning for Formal Theorem Proving
Ruida Wang, Jiarui Yao, Rui Pan, Shizhe Diao, Tong Zhang
https://arxiv.org/abs/2510.11769 https://

GAR: Generative Adversarial Reinforcement Learning for Formal Theorem Proving
Solving math problems through verifiable languages such as Lean has significantly impacted both the mathematics and computer science communities. Current state-of-the-art models are often trained with expensive online Reinforcement Learning (RL) or expert iteration. However, these approaches rely on fixed problem sets, which causes inefficient training and limits the model to tackle complex problems. To overcome these limitations, we propose GAR: Generative Adversarial Reinforcement learning, a…

I see that with a lot of criticism of generative "AI"—people state that obviously it's completely unreliable and untrustworthy for _their domain of expertise_ but they'll somehow gladly use it for other stuff.
I believe this cognitive dissonance has to do with how the chatbots pretend to be humans and trick us to assume agency when there is none.
Anyway, as I said otherwise it's great, you should read it: https://theoatmeal.com/comics/ai_art

A New Hybrid Model of Generative Adversarial Network and You Only Look Once Algorithm for Automatic License-Plate Recognition
Behnoud Shafiezadeh, Amir Mashmool, Farshad Eshghi, Manoochehr Kelarestaghi
https://arxiv.org/abs/2509.06868

A New Hybrid Model of Generative Adversarial Network and You Only Look Once Algorithm for Automatic License-Plate Recognition
Automatic License-Plate Recognition (ALPR) plays a pivotal role in Intelligent Transportation Systems (ITS) as a fundamental element of Smart Cities. However, due to its high variability, ALPR faces challenging issues more efficiently addressed by deep learning techniques. In this paper, a selective Generative Adversarial Network (GAN) is proposed for deblurring in the preprocessing step, coupled with the state-of-the-art You-Only-Look-Once (YOLO)v5 object detection architectures for License-Pl…

Here's a great webcomic essay on generative model ("#AI") art by @…
https://the…

A cartoonist's review of AI art - The Oatmeal
This is a comic about AI art.

Testing chatbots on the creation of encoders for audio conditioned image generation
Jorge E. Le\'on, Miguel Carrasco
https://arxiv.org/abs/2509.09717 https://

Testing chatbots on the creation of encoders for audio conditioned image generation
On one hand, recent advances in chatbots has led to a rising popularity in using these models for coding tasks. On the other hand, modern generative image models primarily rely on text encoders to translate semantic concepts into visual representations, even when there is clear evidence that audio can be employed as input as well. Given the previous, in this work, we explore whether state-of-the-art conversational agents can design effective audio encoders to replace the CLIP text encoder from …

Siracusa@mastodon.social It looks like the BBC is using AI to generate its own headlines now … Looks https://www.bbc.co.uk/mediacentre/2025/articles/bbc-to-launch-new-generative-ai-pilots-to-support-news-production/

BBC to launch new Generative AI pilots to support news production

More releases today: New version of https://thi.ng/genart-api, an art-platform-independent API for browser-based generative art.
The new version (v0.28.0) addresses some recent (type) changes in TypeScript and updates the WebAssembly bindings to support the latest Zig 0.15.1 (just like releases of some of my o…

thi.ng/genart-api

Exploring Opportunities to Support Novice Visual Artists' Inspiration and Ideation with Generative AI
Cindy Peng, Alice Qian, Linghao Jin, Jieneng Chen, Evans Xu Han, Paul Pu Liang, Hong Shen, Haiyi Zhu, Jane Hsieh
https://arxiv.org/abs/2509.24167

Exploring Opportunities to Support Novice Visual Artists' Inspiration and Ideation with Generative AI
Recent generative AI advances present new possibilities for supporting visual art creation, but how such promise might assist novice artists during early-stage processes requires investigation. How novices adopt or resist these tools can shift the relationship between the art community and generative systems. We interviewed 13 artists to uncover needs in key dimensions during early stages of creation: (1) quicker and better access to references, (2) visualizations of reference combinations, (3)…

"How a Teaching-Focused Institution Approaches AI Literacy"
https://katinamagazine.org/content/article/future-of-work/2025/ai-literacy-at-a-teaching-focused-institution
"At the Institute of Inner-City …

The Impact of Artificial Intelligence on Traditional Art Forms: A Disruption or Enhancement
Viswa Chaitanya Marella, Sai Teja Erukude, Suhasnadh Reddy Veluru
https://arxiv.org/abs/2509.07029

The Impact of Artificial Intelligence on Traditional Art Forms: A Disruption or Enhancement
The introduction of Artificial Intelligence (AI) into the domains of traditional art (visual arts, performing arts, and crafts) has sparked a complicated discussion about whether this might be an agent of disruption or an enhancement of our traditional art forms. This paper looks at the duality of AI, exploring the ways that recent technologies like Generative Adversarial Networks and Diffusion Models, and text-to-image generators are changing the fields of painting, sculpture, calligraphy, dan…

🍮 Wissen zum Nachtisch: 🍨
Immer mehr Menschen sehen sich im beruflichen Umfeld genötigt, mit generativer #KI zu arbeiten.
Besonders Großunternehmen „überrollen“ damit ihre #Mitarbeiter. Es wird eine Art

Generative KI für Einsteiger - Praxisnahe Prompts für Privat und Beruf – einfach erklärt
Buchempfehlungen zu Klimawandel, Nachhaltigkeit, Umweltschutz und Klimaanpassung

Uncertainty-Aware Tabular Prediction: Evaluating VBLL-Enhanced TabPFN in Safety-Critical Medical Data
Madhushan Ramalingam
https://arxiv.org/abs/2509.10048 https://

Uncertainty-Aware Tabular Prediction: Evaluating VBLL-Enhanced TabPFN in Safety-Critical Medical Data
Predictive models are being increasingly used across a wide range of domains, including safety-critical applications such as medical diagnosis and criminal justice. Reliable uncertainty estimation is a crucial task in such settings. Tabular Prior-data Fitted Network (TabPFN) is a recently proposed machine learning foundation model for tabular dataset, which uses a generative transformer architecture. Variational Bayesian Last Layers (VBLL) is a state-of-the-art lightweight variational formulati…

From Data to Rewards: a Bilevel Optimization Perspective on Maximum Likelihood Estimation
Abdelhakim Benechehab, Gabriel Singer, Corentin L\'eger, Youssef Attia El Hili, Giuseppe Paolo, Albert Thomas, Maurizio Filippone, Bal\'azs K\'egl
https://arxiv.org/abs/2510.07624

From Data to Rewards: a Bilevel Optimization Perspective on Maximum Likelihood Estimation
Generative models form the backbone of modern machine learning, underpinning state-of-the-art systems in text, vision, and multimodal applications. While Maximum Likelihood Estimation has traditionally served as the dominant training paradigm, recent work have highlighted its limitations, particularly in generalization and susceptibility to catastrophic forgetting compared to Reinforcement Learning techniques, such as Policy Gradient methods. However, these approaches depend on explicit reward …

MorphGen: Controllable and Morphologically Plausible Generative Cell-Imaging
Berker Demirel, Marco Fumero, Theofanis Karaletsos, Francesco Locatello
https://arxiv.org/abs/2510.01298

MorphGen: Controllable and Morphologically Plausible Generative Cell-Imaging
Simulating in silico cellular responses to interventions is a promising direction to accelerate high-content image-based assays, critical for advancing drug discovery and gene editing. To support this, we introduce MorphGen, a state-of-the-art diffusion-based generative model for fluorescent microscopy that enables controllable generation across multiple cell types and perturbations. To capture biologically meaningful patterns consistent with known cellular morphologies, MorphGen is trained wit…

Machines in the Crowd? Measuring the Footprint of Machine-Generated Text on Reddit
Lucio La Cava, Luca Maria Aiello, Andrea Tagarelli
https://arxiv.org/abs/2510.07226 https://…

Machines in the Crowd? Measuring the Footprint of Machine-Generated Text on Reddit
Generative Artificial Intelligence is reshaping online communication by enabling large-scale production of Machine-Generated Text (MGT) at low cost. While its presence is rapidly growing across the Web, little is known about how MGT integrates into social media environments. In this paper, we present the first large-scale characterization of MGT on Reddit. Using a state-of-the-art statistical method for detection of MGT, we analyze over two years of activity (2022-2024) across 51 subreddits rep…

Machine Learning Assisted Parameter-Space Searches for Lensed Gravitational Waves
Giulia Campailla, Marco Raveri, Wayne Hu, Jose Mar\'ia Ezquiaga
https://arxiv.org/abs/2509.06901

Machine Learning Assisted Parameter-Space Searches for Lensed Gravitational Waves
When a gravitational wave encounters a massive object along the line of sight, repeated copies of the original signal may be produced due to gravitational lensing. In this paper, we develop a series of new machine-learning based statistical methods to identify promising strong lensing candidates in gravitational wave catalogs. We employ state-of-the-art normalizing flow generative models to perform statistical calculations on the posterior distributions of gravitational wave events that would o…

(Yet another) 1-minute excerpt of an #Umbra variation, one of my recent algorithmic/generative realtime animation art pieces, constantly evolving and exhibited exclusively on Layer.
Very much love this color theme, especially after ~15 seconds... 🤌
(See above hashtag for other clips)
Made with ❤️ and my opensource

Adversarial Attacks on Audio Deepfake Detection: A Benchmark and Comparative Study
Kutub Uddin, Muhammad Umar Farooq, Awais Khan, Khalid Mahmood Malik
https://arxiv.org/abs/2509.07132

Adversarial Attacks on Audio Deepfake Detection: A Benchmark and Comparative Study
The widespread use of generative AI has shown remarkable success in producing highly realistic deepfakes, posing a serious threat to various voice biometric applications, including speaker verification, voice biometrics, audio conferencing, and criminal investigations. To counteract this, several state-of-the-art (SoTA) audio deepfake detection (ADD) methods have been proposed to identify generative AI signatures to distinguish between real and deepfake audio. However, the effectiveness of thes…

🍮 Wissen zum Nachtisch: 🍨
Immer mehr Menschen sehen sich im beruflichen Umfeld genötigt, mit generativer #KI zu arbeiten.
Besonders Großunternehmen „überrollen“ damit ihre #Mitarbeiter. Es wird eine Art

Generative KI für Einsteiger - Praxisnahe Prompts für Privat und Beruf – einfach erklärt
Buchempfehlungen zu Klimawandel, Nachhaltigkeit, Umweltschutz und Klimaanpassung

FuXi-TC: A generative framework integrating deep learning and physics-based models for improved tropical cyclone forecasts
Shan Guo, Lei Chen, Zeyi Niu, Ziyao Sun, Xinyan Zhang, Yangyang Zhao, Xiaohui Zhong, Hao Li
https://arxiv.org/abs/2508.16168

FuXi-TC: A generative framework integrating deep learning and physics-based models for improved tropical cyclone forecasts
Tropical cyclones (TCs) are among the most devastating natural hazards, yet their intensity remains notoriously difficult to predict. Numerical weather prediction (NWP) models are constrained by both computational demands and intrinsic predictability, while state-of-the-art deep learning-based weather forecasting models tend to underestimate TC intensity due to biases in reanalysis-based training data. Here, we present FuXi-TC, a diffusion-based generative forecasting framework that combines th…

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization
Th\'eophane Vallaeys, Jakob Verbeek, Matthieu Cord
https://arxiv.org/abs/2510.04961 https://

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization
Tokenizers are a key component of state-of-the-art generative image models, extracting the most important features from the signal while reducing data dimension and redundancy. Most current tokenizers are based on KL-regularized variational autoencoders (KL-VAE), trained with reconstruction, perceptual and adversarial losses. Diffusion decoders have been proposed as a more principled alternative to model the distribution over images conditioned on the latent. However, matching the performance o…

SALAD-VAE: Semantic Audio Compression with Language-Audio Distillation
Sebastian Braun, Hannes Gamper, Dimitra Emmanouilidou
https://arxiv.org/abs/2510.07592 https://

SALAD-VAE: Semantic Audio Compression with Language-Audio Distillation
Modern generative and multimodal models increasingly rely on compact latent representations that trade and balance semantic richness with high-fidelity reconstruction. We introduce SALAD-VAE, a continuous and highly compact semantic Audio Variational Autoencoder, which operates in the frequency domain and achieves state-of-the-art compression with very low latent frame rate (7.8 Hz) while surfacing semantic structure and producing high audio quality. We enhance the standard VAE semantic losses …

Hyperparameters are all you need: Using five-step inference for an original diffusion model to generate images comparable to the latest distillation model
Zilai Li
https://arxiv.org/abs/2510.02390

Hyperparameters are all you need: Using five-step inference for an original diffusion model to generate images comparable to the latest distillation model
The diffusion model is a state-of-the-art generative model that generates an image by applying a neural network iteratively. Moreover, this generation process is regarded as an algorithm solving an ordinary differential equation or a stochastic differential equation. Based on the analysis of the truncation error of the diffusion ODE and SDE, our study proposes a training-free algorithm that generates high-quality 512 x 512 and 1024 x 1024 images in eight steps, with flexible guidance scales. To…

Overclocking Electrostatic Generative Models
Daniil Shlenskii, Alexander Korotin
https://arxiv.org/abs/2509.22454 https://arxiv.org/pdf/2509.22454

Overclocking Electrostatic Generative Models
Electrostatic generative models such as PFGM++ have recently emerged as a powerful framework, achieving state-of-the-art performance in image synthesis. PFGM++ operates in an extended data space with auxiliary dimensionality $D$, recovering the diffusion model framework as $D\to\infty$, while yielding superior empirical results for finite $D$. Like diffusion models, PFGM++ relies on expensive ODE simulations to generate samples, making it computationally costly. To address this, we propose Inve…

Another 1-minute excerpt of an #Umbra variation, one of my recent algorithmic/generative realtime animation art pieces, constantly evolving and exhibited exclusively on Layer.
(See above hashtag for other clips)
Made with ❤️ and my opensource https://th…

🍮 Wissen zum Nachtisch: 🍨
Immer mehr Menschen sehen sich im beruflichen Umfeld genötigt, mit generativer #KI zu arbeiten.
Besonders Großunternehmen „überrollen“ damit ihre #Mitarbeiter. Es wird eine Art

Generative KI für Einsteiger - Praxisnahe Prompts für Privat und Beruf – einfach erklärt
Buchempfehlungen zu Klimawandel, Nachhaltigkeit, Umweltschutz und Klimaanpassung

Transport Based Mean Flows for Generative Modeling
Elaheh Akbari, Ping He, Ahmadreza Moradipari, Yikun Bai, Soheil Kolouri
https://arxiv.org/abs/2509.22592 https://

Transport Based Mean Flows for Generative Modeling
Flow-matching generative models have emerged as a powerful paradigm for continuous data generation, achieving state-of-the-art results across domains such as images, 3D shapes, and point clouds. Despite their success, these models suffer from slow inference due to the requirement of numerous sequential sampling steps. Recent work has sought to accelerate inference by reducing the number of sampling steps. In particular, Mean Flows offer a one-step generation approach that delivers substantial s…

Distribution estimation via Flow Matching with Lipschitz guarantees
Lea Kunkel
https://arxiv.org/abs/2509.02337 https://arxiv.org/pdf/2509.02337

Distribution estimation via Flow Matching with Lipschitz guarantees
Flow Matching, a promising approach in generative modeling, has recently gained popularity. Relying on ordinary differential equations, it offers a simple and flexible alternative to diffusion models, which are currently the state-of-the-art. Despite its empirical success, the mathematical understanding of its statistical power so far is very limited. This is largely due to the sensitivity of theoretical bounds to the Lipschitz constant of the vector field which drives the ODE. In this work, we…

Flexibility-Conditioned Protein Structure Design with Flow Matching
Vsevolod Viliuga, Leif Seute, Nicolas Wolf, Simon Wagner, Arne Elofsson, Jan St\"uhmer, Frauke Gr\"ater
https://arxiv.org/abs/2508.18211

Flexibility-Conditioned Protein Structure Design with Flow Matching
Recent advances in geometric deep learning and generative modeling have enabled the design of novel proteins with a wide range of desired properties. However, current state-of-the-art approaches are typically restricted to generating proteins with only static target properties, such as motifs and symmetries. In this work, we take a step towards overcoming this limitation by proposing a framework to condition structure generation on flexibility, which is crucial for key functionalities such as c…

Been working on some additions/adjustments for #Umbra, one of my recent algorithmic/generative art pieces, currently shown exclusively on Layer.
Here's a 3 minute segment (of an infinite realtime animation). Give it time to develop/change! Fullscreen playback probably best...
Made with

DiffQ: Unified Parameter Initialization for Variational Quantum Algorithms via Diffusion Models
Chi Zhang, Mengxin Zheng, Qian Lou, Fan Chen
https://arxiv.org/abs/2509.17324 htt…

DiffQ: Unified Parameter Initialization for Variational Quantum Algorithms via Diffusion Models
Variational Quantum Algorithms (VQAs) are widely used in the noisy intermediate-scale quantum (NISQ) era, but their trainability and performance depend critically on initialization parameters that shape the optimization landscape. Existing machine learning-based initializers achieve state-of-the-art results yet remain constrained to single-task domains and small datasets of only hundreds of samples. We address these limitations by reformulating VQA parameter initialization as a generative model…

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement
Shrishti Saha Shetu, Emanu\"el A. P. Habets, Andreas Brendel
https://arxiv.org/abs/2508.20859

Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement
Generative speech enhancement methods based on generative adversarial networks (GANs) and diffusion models have shown promising results in various speech enhancement tasks. However, their performance in very low signal-to-noise ratio (SNR) scenarios remains under-explored and limited, as these conditions pose significant challenges to both discriminative and generative state-of-the-art methods. To address this, we propose a method that leverages latent features extracted from discriminative spe…

Bias beyond Borders: Global Inequalities in AI-Generated Music
Ahmet Solak, Florian Gr\"otschla, Luca A. Lanzend\"orfer, Roger Wattenhofer
https://arxiv.org/abs/2510.01963

Bias beyond Borders: Global Inequalities in AI-Generated Music
While recent years have seen remarkable progress in music generation models, research on their biases across countries, languages, cultures, and musical genres remains underexplored. This gap is compounded by the lack of datasets and benchmarks that capture the global diversity of music. To address these challenges, we introduce GlobalDISCO, a large-scale dataset consisting of 73k music tracks generated by state-of-the-art commercial generative music models, along with paired links to 93k refer…

Shortcut Flow Matching for Speech Enhancement: Step-Invariant flows via single stage training
Naisong Zhou, Saisamarth Rajesh Phaye, Milos Cernak, Tijana Stojkovic, Andy Pearce, Andrea Cavallaro, Andy Harper
https://arxiv.org/abs/2509.21522

Shortcut Flow Matching for Speech Enhancement: Step-Invariant flows via single stage training
Diffusion-based generative models have achieved state-of-the-art performance for perceptual quality in speech enhancement (SE). However, their iterative nature requires numerous Neural Function Evaluations (NFEs), posing a challenge for real-time applications. On the contrary, flow matching offers a more efficient alternative by learning a direct vector field, enabling high-quality synthesis in just a few steps using deterministic ordinary differential equation~(ODE) solvers. We thus introduce …

Saddle Hierarchy in Dense Associative Memory
Robin Th\'eriault, Daniele Tantari
https://arxiv.org/abs/2508.19151 https://arxiv.org/pdf/2508.19151

Saddle Hierarchy in Dense Associative Memory
Dense associative memory (DAM) models have been attracting renewed attention since they were shown to be robust to adversarial examples and closely related to state-of-the-art machine learning paradigms, such as the attention mechanisms in transformers and generative diffusion models. We study a DAM built upon a three-layer Boltzmann machine with Potts hidden units, which represent data clusters and classes. Through a statistical mechanics analysis, we derive saddle-point equations that charact…

Attentive AV-FusionNet: Audio-Visual Quality Prediction with Hybrid Attention
Ina Salaj, Arijit Biswas
https://arxiv.org/abs/2509.16994 https://arxiv.org/p…

Attentive AV-FusionNet: Audio-Visual Quality Prediction with Hybrid Attention
We introduce a novel deep learning-based audio-visual quality (AVQ) prediction model that leverages internal features from state-of-the-art unimodal predictors. Unlike prior approaches that rely on simple fusion strategies, our model employs a hybrid representation that combines learned Generative Machine Listener (GML) audio features with hand-crafted Video Multimethod Assessment Fusion (VMAF) video features. Attention mechanisms capture cross-modal interactions and intra-modal relationships, …