Tootfinder

Just saw this:
#AI can mean a lot of things these days, but lots of the popular meanings imply a bevy of harms that I definitely wouldn't feel are worth a cute fish game. In fact, these harms are so acute that even "just" playing into the AI hype becomes its own kind of harm (it's similar to blockchain in that way).
@… noticed that the authors claim the code base is 80% AI generated, which is a red flag because people with sound moral compasses wouldn't be using AI to "help" write code in the first place. The authors aren't by some miracle people who couldn't build this app without help, in case that influences your thinking about it: they have the skills to write the code themselves, although it likely would have taken longer (but also been better).
I was more interested in the fish-classification AI, and how much it might be dependent on datacenters. Thankfully, a quick glance at the code confirms they're using ONNX and running a self-trained neural network on your device. While the exponentially-increasing energy & water demands of datacenters to support billion-parameter models are a real concern, this is not that. Even a non-AI game can burn a lot of cycles on someone's phone, and I don't think there's anything to complain about energy-wise if we're just using cycles on the end user's device as long as we're not having them keep it on for hours crunching numbers like blockchain stuff does. Running whatever stuff locally while the user is playing a game is a negligible environmental concern, unlike, say, calling out to ChatGPT where you're directly feeding datacenter demand. Since they claimed to have trained the network themselves, and since it's actually totally reasonable to make your own dataset for this and get good-enough-for-a-silly-game results with just a few hundred examples, I don't have any ethical objections to the data sourcing or training processes either. Hooray! This is finally an example of "ethical use of neutral networks" that I can hold up as an example of what people should be doing instead of the BS they are doing.
But wait... Remember what I said about feeding the AI hype being its own form of harm? Yeah, between using AI tools for coding and calling their classifier "AI" in a way that makes it seem like the same kind of thing as ChatGPT et al., they're leaning into the hype rather than helping restrain it. And that means they're causing harm. Big AI companies can point to them and say "look AI enables cute things you like" when AI didn't actually enable it. So I'm feeling meh about this cute game and won't be sharing it aside from this post. If you love the cute fish, you don't really have to feel bad for playing with it, but I'd feel bad for advertising it without a disclaimer.

Time-Aware One Step Diffusion Network for Real-World Image Super-Resolution
Tainyi Zhang, Zheng-Peng Duan, Peng-Tao Jiang, Bo Li, Ming-Ming Cheng, Chun-Le Guo, Chongyi Li
https://arxiv.org/abs/2508.16557

Time-Aware One Step Diffusion Network for Real-World Image Super-Resolution
Diffusion-based real-world image super-resolution (Real-ISR) methods have demonstrated impressive performance. To achieve efficient Real-ISR, many works employ Variational Score Distillation (VSD) to distill pre-trained stable-diffusion (SD) model for one-step SR with a fixed timestep. However, due to the different noise injection timesteps, the SD will perform different generative priors. Therefore, a fixed timestep is difficult for these methods to fully leverage the generative priors in SD, …

Full-spectrum modeling of mobile gamma-ray spectrometry systems in scattering media
David Breitenmoser, Alberto Stabilini, Sabine Mayer
https://arxiv.org/abs/2506.17820

Full-spectrum modeling of mobile gamma-ray spectrometry systems in scattering media
Mobile gamma-ray spectrometry (MGRS) systems are essential for localizing, identifying, and quantifying gamma-ray sources in complex environments. Full-spectrum template matching offers the highest accuracy and sensitivity for these tasks but is limited by the computational cost of generating the required spectral templates. Here, we present a generalized full-spectrum modeling framework for MGRS systems in scattering media, enabling near real-time template generation through dynamic, anisotrop…

TransLight: Image-Guided Customized Lighting Control with Generative Decoupling
Zongming Li, Lianghui Zhu, Haocheng Shen, Longjin Ran, Wenyu Liu, Xinggang Wang
https://arxiv.org/abs/2508.14814

TransLight: Image-Guided Customized Lighting Control with Generative Decoupling
Most existing illumination-editing approaches fail to simultaneously provide customized control of light effects and preserve content integrity. This makes them less effective for practical lighting stylization requirements, especially in the challenging task of transferring complex light effects from a reference image to a user-specified target image. To address this problem, we propose TransLight, a novel framework that enables high-fidelity and high-freedom transfer of light effects. Extract…

Development and testing of integrated readout electronics for next generation SiSeRO (Single electron Sensitive Read Out) devices
Tanmoy Chattopadhyay, Haley R. Stueber, Abigail Y. Pan, Sven Herrmann, Peter Orel, Kevan Donlon, Steven W. Allen, Marshall W. Bautz, Michael Cooper, Catherine E. Grant, Beverly LaMarr, Christopher Leitz, Andrew Malonis, Eric D. Miller, R. Glenn Morris, Gregory Prigozhin, Ilya Prigozhin, Artem Poliszczuk, Keith Warner, Daniel R. Wilkins

Development and testing of integrated readout electronics for next generation SiSeRO (Single electron Sensitive Read Out) devices
The first generation of Single electron Sensitive Read Out (SiSeRO) amplifiers, employed as on-chip charge detectors for charge-coupled devices (CCDs) have demonstrated excellent noise and spectral performance: a responsivity of around 800 pA per electron, an equivalent noise charge (ENC) of 3.2 electrons root mean square (RMS), and a full width half maximum (FWHM) energy resolution of 130 eV at 5.9 keV for a readout speed of 625 Kpixel/s. Repetitive Non Destructive Readout (RNDR) has also been…

A Fast, Reliable, and Secure Programming Language for LLM Agents with Code Actions
Stephen Mell, Botong Zhang, David Mell, Shuo Li, Ramya Ramalingam, Nathan Yu, Steve Zdancewic, Osbert Bastani
https://arxiv.org/abs/2506.12202

A Fast, Reliable, and Secure Programming Language for LLM Agents with Code Actions
Modern large language models (LLMs) are often deployed as agents, calling external tools adaptively to solve tasks. Rather than directly calling tools, it can be more effective for LLMs to write code to perform the tool calls, enabling them to automatically generate complex control flow such as conditionals and loops. Such code actions are typically provided as Python code, since LLMs are quite proficient at it; however, Python may not be the ideal language due to limited built-in support for p…

Tree-Based Text Retrieval via Hierarchical Clustering in RAGFrameworks: Application on Taiwanese Regulations
Chia-Heng Yu, Yen-Lung Tsai
https://arxiv.org/abs/2506.13607

Tree-Based Text Retrieval via Hierarchical Clustering in RAGFrameworks: Application on Taiwanese Regulations
Traditional Retrieval-Augmented Generation (RAG) systems employ brute-force inner product search to retrieve the top-k most similar documents, then combined with the user query and passed to a language model. This allows the model to access external knowledge and reduce hallucinations. However, selecting an appropriate k value remains a significant challenge in practical applications: a small k may fail to retrieve sufficient information, while a large k can introduce excessive and irrelevant c…

NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale
NextStep Team, Chunrui Han, Guopeng Li, Jingwei Wu, Quan Sun, Yan Cai, Yuang Peng, Zheng Ge, Deyu Zhou, Haomiao Tang, Hongyu Zhou, Kenkun Liu, Ailin Huang, Bin Wang, Changxin Miao, Deshan Sun, En Yu, Fukun Yin, Gang Yu, Hao Nie, Haoran Lv, Hanpeng Hu, Jia Wang, Jian Zhou, Jianjian Sun, Kaijun Tan, Kang An, Kangheng Lin, Liang Zhao, Mei Chen, Peng Xing, Rui Wang, Shiyu Liu, Shutao Xia, Tianhao You, Wei Ji…

NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale
Prevailing autoregressive (AR) models for text-to-image generation either rely on heavy, computationally-intensive diffusion models to process continuous image tokens, or employ vector quantization (VQ) to obtain discrete tokens with quantization loss. In this paper, we push the autoregressive paradigm forward with NextStep-1, a 14B autoregressive model paired with a 157M flow matching head, training on discrete text tokens and continuous image tokens with next-token prediction objectives. Next…

Reward Models Enable Scalable Code Verification by Trading Accuracy for Throughput
Gabriel Orlanski, Nicholas Roberts, Aws Albarghouthi, Frederic Sala
https://arxiv.org/abs/2506.10056

Reward Models Enable Scalable Code Verification by Trading Accuracy for Throughput
The standard paradigm for solving coding tasks via large language models (LLMs) is to generate-then-rank programs, where the latter step uses a verifier in the ranking process. The growing consensus is that a comprehensive verifier (e.g., a full test suite) should be prioritized over an outcome reward model (ORM) whenever possible, with little consideration given to the trade-offs involved. We aim to challenge this assumption by systematically exploring the tradeoff between speed and accuracy. …

TTS-CtrlNet: Time varying emotion aligned text-to-speech generation with ControlNet
Jaeseok Jeong, Yuna Lee, Mingi Kwon, Youngjung Uh
https://arxiv.org/abs/2507.04349

TTS-CtrlNet: Time varying emotion aligned text-to-speech generation with ControlNet
Recent advances in text-to-speech (TTS) have enabled natural speech synthesis, but fine-grained, time-varying emotion control remains challenging. Existing methods often allow only utterance-level control and require full model fine-tuning with a large emotion speech dataset, which can degrade performance. Inspired by adding conditional control to the existing model in ControlNet (Zhang et al, 2023), we propose the first ControlNet-based approach for controllable flow-matching TTS (TTS-CtrlNet)…