Tootfinder

3 out of 7 recommended news stories are about Trump failing.
I would be happier about it if it did not imply Putin was winning. This is bad for everyone, but especially Ukraine.
Trump’s ego, fealty, and idiocy is going to destroy the world if Europe, the Global South, and other democratic nations don’t take the reins from the USA.
#russiaukrainewar #russia #ukraine

🤯 Been working for hours and missed the newest eruption 🌋🇮🇸
#Iceland

Gossprungan hætt að lengjast - RÚV.is
Eldgos hófst á Sundhnúksgígaröðinni á Reykjanesskaga klukkan 03:54.Þetta er níunda gosið á þessum slóðum og það tólfta á Reykjanesskaga frá því að hrinan hófst með gosi við Fagradalsfjall í mars 2021.Kvikuhlaup hófst rétt fyrir miðnætti með ákafri jarðskjálftahrinu.Búið er að rýma Bláa lónið og tjaldsvæðið í Grindavík.Hættustig almannavarna hefur verið virkjað og hættumat fyrir svæðið hefur verið uppfært.Gossprungan er tæplega tveggja kíl…

Rich Lyons,
the University of California, Berkeley, chancellor,
challenged US House Republicanson Tuesday
as they questioned Lyons and leaders of Georgetown University and the City University of New York
in the latest hearing on antisemitism in higher education.
The committee accused the schools of failing to respond adequately to allegations of bias or discrimination,
however the university leaders said that disciplinary action had been taken where approp…

Google announces Gemma 3 270M, a compact model designed for task-specific fine-tuning with strong capabilities in instruction following and text structuring (Google Developers Blog)
https://developers.googleblog.com/en/introducing-gemma-3-270m/

Introducing Gemma 3 270M: The compact model for hyper-efficient AI- Google Developers Blog
Explore Gemma 3 270M, a compact, energy-efficient AI model for task-specific fine-tuning, offering strong instruction-following and production-ready quantization.

How Many Instructions Can LLMs Follow at Once?
Daniel Jaroslawicz, Brendan Whiting, Parth Shah, Karime Maamari
https://arxiv.org/abs/2507.11538 https://

How Many Instructions Can LLMs Follow at Once?
Production-grade LLM systems require robust adherence to dozens or even hundreds of instructions simultaneously. However, the instruction-following capabilities of LLMs at high instruction densities have not yet been characterized, as existing benchmarks only evaluate models on tasks with a single or few instructions. We introduce IFScale, a simple benchmark of 500 keyword-inclusion instructions for a business report writing task to measure how instruction-following performance degrades as inst…

Influence of Platinum Thin Films on the Photophysical and Quantum Properties of Near-Surface NV Centers
Joachim P. Leibold (Department of Chemistry, School of Natural Sciences, Technical University of Munich, Garching, Germany), Lina M. Todenhagen (Walter Schottky Institute, Technical University of Munich, Garching, Germany), Matthias Althammer (Walther-Mei{\ss}ner-Institute, Bavarian Academy of Sciences and Humanities, Garching, Germany), Nikhita Khera (Department of Physics and Resea…

Influence of Platinum Thin Films on the Photophysical and Quantum Properties of Near-Surface NV Centers
Nitrogen-vacancy (NV) centers in diamond are optically addressable spin defects with great potential for nanoscale quantum sensing. A key application of NV centers is the detection of external spins at the diamond surface. Among metals, platinum thin films - widely used in spintronics, catalysis and electrochemistry - provide a particularly interesting system for such studies. However, the interaction between NV centers and metals is known to affect their quantum sensing capabilities. In this w…

MagicMirror: A Large-Scale Dataset and Benchmark for Fine-Grained Artifacts Assessment in Text-to-Image Generation
Jia Wang, Jie Hu, Xiaoqi Ma, Hanghang Ma, Yanbing Zeng, Xiaoming Wei
https://arxiv.org/abs/2509.10260

MagicMirror: A Large-Scale Dataset and Benchmark for Fine-Grained Artifacts Assessment in Text-to-Image Generation
Text-to-image (T2I) generation has achieved remarkable progress in instruction following and aesthetics. However, a persistent challenge is the prevalence of physical artifacts, such as anatomical and structural flaws, which severely degrade perceptual quality and limit application. Given the diversity and complexity of these artifacts, a systematic and fine-grained evaluation framework is required, which is lacking in current benchmarks. To fill this gap, we introduce MagicMirror, a comprehens…

TabStruct: Measuring Structural Fidelity of Tabular Data
Xiangjian Jiang, Nikola Simidjievski, Mateja Jamnik
https://arxiv.org/abs/2509.11950 https://arxiv…

TabStruct: Measuring Structural Fidelity of Tabular Data
Evaluating tabular generators remains a challenging problem, as the unique causal structural prior of heterogeneous tabular data does not lend itself to intuitive human inspection. Recent work has introduced structural fidelity as a tabular-specific evaluation dimension to assess whether synthetic data complies with the causal structures of real data. However, existing benchmarks often neglect the interplay between structural fidelity and conventional evaluation dimensions, thus failing to prov…

ReconVLA: Reconstructive Vision-Language-Action Model as Effective Robot Perceiver
Wenxuan Song, Ziyang Zhou, Han Zhao, Jiayi Chen, Pengxiang Ding, Haodong Yan, Yuxin Huang, Feilong Tang, Donglin Wang, Haoang Li
https://arxiv.org/abs/2508.10333

ReconVLA: Reconstructive Vision-Language-Action Model as Effective Robot Perceiver
Recent advances in Vision-Language-Action (VLA) models have enabled robotic agents to integrate multimodal understanding with action execution. However, our empirical analysis reveals that current VLAs struggle to allocate visual attention to target regions. Instead, visual attention is always dispersed. To guide the visual attention grounding on the correct target, we propose ReconVLA, a reconstructive VLA model with an implicit grounding paradigm. Conditioned on the model's visual outputs, a …

Precise Attribute Intensity Control in Large Language Models via Targeted Representation Editing
Rongzhi Zhang, Liqin Ye, Yuzhao Heng, Xiang Chen, Tong Yu, Lingkai Kong, Sudheer Chava, Chao Zhang
https://arxiv.org/abs/2510.12121

Precise Attribute Intensity Control in Large Language Models via Targeted Representation Editing
Precise attribute intensity control--generating Large Language Model (LLM) outputs with specific, user-defined attribute intensities--is crucial for AI systems adaptable to diverse user expectations. Current LLM alignment methods, however, typically provide only directional or open-ended guidance, failing to reliably achieve exact attribute intensities. We address this limitation with three key designs: (1) reformulating precise attribute intensity control as a target-reaching problem, rather t…