Tootfinder

Higher Gauge Flow Models
Alexander Strunk, Roland Assam
https://arxiv.org/abs/2507.16334 https://arxiv.org/pdf/2507.16334

Higher Gauge Flow Models
This paper introduces Higher Gauge Flow Models, a novel class of Generative Flow Models. Building upon ordinary Gauge Flow Models (arXiv:2507.13414), these Higher Gauge Flow Models leverage an L$_{\infty}$-algebra, effectively extending the Lie Algebra. This expansion allows for the integration of the higher geometry and higher symmetries associated with higher groups into the framework of Generative Flow Models. Experimental evaluation on a Gaussian Mixture Model dataset revealed substantial p…

How much of my children's future is AI going to burn up? That depends on how much we feed the hype beast. *That* is why "don't use AI at all without mentioning the drawbacks & a very good reason" is my stance (and I'm an AI researcher, technically).
Local models that run on your laptop: acceptable if produced by ethical means (including data sourcing & compensation for data filtering) & training costs are mitigated. Are such models way worse than the huge datacenter-scale models? Yes, for now. Deal with it.
ChatGPT, Claude, Copilot, even DeepSeek: get out. You're feeding the beast that is consuming my kids' future. Heck, even talking up these models or about how "everyone is using them so it's okay" or about "they're not going away" I'd feeding the beast even if you don't touch them.
I wish it weren't like this, because the capabilities of the big models are cool even once you cut past the hype.
#AI

Sources: Apple's team working on AI models wanted to release several as open source, Craig Federighi disagreed, largely concerned about public perception issues (The Information)
https://www.theinformation.com/articles/apple-losing-ground-ai-talent-wa…

Why Apple Is Losing Ground in the AI Talent War (It’s Not Just Money)
Earlier this year, the Apple team working on the company’s artificial intelligence models wanted to release several of them as open-source software. Doing so would have shown Apple’s technical progress in AI while also harnessing the help of outside researchers to improve the models. Still, it ...

CM2LoD3: Reconstructing LoD3 Building Models Using Semantic Conflict Maps
Franz Hanke, Antonia Bieringer, Olaf Wysocki, Boris Jutzi
https://arxiv.org/abs/2508.15672 https://

CM2LoD3: Reconstructing LoD3 Building Models Using Semantic Conflict Maps
Detailed 3D building models are crucial for urban planning, digital twins, and disaster management applications. While Level of Detail 1 (LoD)1 and LoD2 building models are widely available, they lack detailed facade elements essential for advanced urban analysis. In contrast, LoD3 models address this limitation by incorporating facade elements such as windows, doors, and underpasses. However, their generation has traditionally required manual modeling, making large-scale adoption challenging. …

Dr. Boot: Bootstrapping Program Synthesis Language Models to Perform Repairing
Noah van der Vleuten
https://arxiv.org/abs/2507.15889 https://

Dr. Boot: Bootstrapping Program Synthesis Language Models to Perform Repairing
Language models for program synthesis are usually trained and evaluated on programming competition datasets (MBPP, APPS). However, these datasets are limited in size and quality, while these language models are extremely data hungry. Additionally, the language models have a misaligned program synthesis process compared to humans. While humans iteratively develop code with the help of a compiler, most program synthesis models currently produce code in one go. To solve these issues, we introduce …

Cooling Matters: Benchmarking Large Language Models and Vision-Language Models on Liquid-Cooled Versus Air-Cooled H100 GPU Systems
Imran Latif, Muhammad Ali Shafique, Hayat Ullah, Alex C. Newkirk, Xi Yu, Arslan Munir
https://arxiv.org/abs/2507.16781

Cooling Matters: Benchmarking Large Language Models and Vision-Language Models on Liquid-Cooled Versus Air-Cooled H100 GPU Systems
The unprecedented growth in artificial intelligence (AI) workloads, recently dominated by large language models (LLMs) and vision-language models (VLMs), has intensified power and cooling demands in data centers. This study benchmarks LLMs and VLMs on two HGX nodes, each with 8x NVIDIA H100 graphics processing units (GPUs), using liquid and air cooling. Leveraging GPU Burn, Weights and Biases, and IPMItool, we collect detailed thermal, power, and computation data. Results show that the liquid-c…

CLEAR-3K: Assessing Causal Explanatory Capabilities in Language Models
Naiming Liu, Richard Baraniuk, Shashank Sonkar
https://arxiv.org/abs/2506.17180 http…

CLEAR-3K: Assessing Causal Explanatory Capabilities in Language Models
We introduce CLEAR-3K, a dataset of 3,000 assertion-reasoning questions designed to evaluate whether language models can determine if one statement causally explains another. Each question present an assertion-reason pair and challenge language models to distinguish between semantic relatedness and genuine causal explanatory relationships. Through comprehensive evaluation of 21 state-of-the-art language models (ranging from 0.5B to 72B parameters), we identify two fundamental findings. First, l…

SustainDiffusion: Optimising the Social and Environmental Sustainability of Stable Diffusion Models
Giordano d'Aloisio, Tosin Fadahunsi, Jay Choy, Rebecca Moussa, Federica Sarro
https://arxiv.org/abs/2507.15663

SustainDiffusion: Optimising the Social and Environmental Sustainability of Stable Diffusion Models
Background: Text-to-image generation models are widely used across numerous domains. Among these models, Stable Diffusion (SD) - an open-source text-to-image generation model - has become the most popular, producing over 12 billion images annually. However, the widespread use of these models raises concerns regarding their social and environmental sustainability. Aims: To reduce the harm that SD models may have on society and the environment, we introduce SustainDiffusion, a search-based appr…

DeepThink3D: Enhancing Large Language Models with Programmatic Reasoning in Complex 3D Situated Reasoning Tasks
Jiayi Song, Rui Wan, Lipeng Ma, Weidong Yang, Qingyuan Zhou, Yixuan Li, Ben Fei
https://arxiv.org/abs/2508.15548

DeepThink3D: Enhancing Large Language Models with Programmatic Reasoning in Complex 3D Situated Reasoning Tasks
This work enhances the ability of large language models (LLMs) to perform complex reasoning in 3D scenes. Recent work has addressed the 3D situated reasoning task by invoking tool usage through large language models. Large language models call tools via APIs and integrate the generated programs through a chain of thought to solve problems based on the program results. However, due to the simplicity of the questions in the dataset, the generated program reasoning chains are relatively short. To …

Anthropic's test of 16 top AI models from OpenAI and others found that, in some cases, they resorted to malicious behavior to avoid replacement or achieve goals (Ina Fried/Axios)
https://www.axios.com/2025/06/20/ai-models-deceive-steal-blackmail-an…

Top AI models will lie, cheat and steal to reach goals, Anthropic finds
The research used hypothetical scenarios, but those situations could get real fast.