Tootfinder

Spotify says founder Daniel Ek will become Executive Chairman effective January 1, 2026, replaced by CPTO Gustav Söderström and CBO Alex Norström as co-CEOs (Spotify)
https://newsroom.spotify.com/2025-09-30/spotify-announcement-daniel-ek-executive-c…

Spotify Announces Leadership Evolution: Daniel Ek to Become Executive Chairman, Alex Norström and Gustav Söderström to Become Co-CEOs in January 2026 — Spotify
The following press release went live on Business Wire earlier today. You can also find more detail here in Founder & CEO Daniel Ek’s letter to employees. Stockholm and New York — 30 September, 2025 Today, Spotify (NYSE: SPOT) announced that Founder and Chief Executive Officer Daniel Ek will transition to the role of Executive...

Spotify says founder Daniel Ek will become Executive Chairman effective January 1, 2026, replaced by CPTO Gustav Söderström and CBO Alex Norström as co-CEOs (Spotify)
https://newsroom.spotify.com/2025-09-30/spotify-announcement-daniel-ek-executive-c…

Spotify Announces Leadership Evolution: Daniel Ek to Become Executive Chairman, Alex Norström and Gustav Söderström to Become Co-CEOs in January 2026 — Spotify
The following press release went live on Business Wire earlier today. You can also find more detail here in Founder & CEO Daniel Ek’s letter to employees. Stockholm and New York — 30 September, 2025 Today, Spotify (NYSE: SPOT) announced that Founder and Chief Executive Officer Daniel Ek will transition to the role of Executive...

Stable Cinemetrics : Structured Taxonomy and Evaluation for Professional Video Generation
Agneet Chatterjee, Rahim Entezari, Maksym Zhuravinskyi, Maksim Lapin, Reshinth Adithyan, Amit Raj, Chitta Baral, Yezhou Yang, Varun Jampani
https://arxiv.org/abs/2509.26555

Stable Cinemetrics : Structured Taxonomy and Evaluation for Professional Video Generation
Recent advances in video generation have enabled high-fidelity video synthesis from user provided prompts. However, existing models and benchmarks fail to capture the complexity and requirements of professional video generation. Towards that goal, we introduce Stable Cinemetrics, a structured evaluation framework that formalizes filmmaking controls into four disentangled, hierarchical taxonomies: Setup, Event, Lighting, and Camera. Together, these taxonomies define 76 fine-grained control nodes…

Interpretable Evaluation of AI-Generated Content with Language-Grounded Sparse Encoders
Yiming Tang, Arash Lagzian, Srinivas Anumasa, Qiran Zou, Trang Nguyen, Ehsan Adeli, Ching-Yu Cheng, Yilun Du, Dianbo Liu
https://arxiv.org/abs/2508.18236

Interpretable Evaluation of AI-Generated Content with Language-Grounded Sparse Encoders
While the quality of AI-generated contents, such as synthetic images, has become remarkably high, current evaluation metrics provide only coarse-grained assessments, failing to identify specific strengths and weaknesses that researchers and practitioners need for model selection and development, further limiting the scientific understanding and commercial deployment of these generative models. To address this, we introduce Language-Grounded Sparse Encoders (LanSE), a novel architecture that cre…

Skyrmion-Antiskyrmion Lattice: A Net-Zero Topological Phase in Low-Symmetry Frustrated Chiral Magnets
Sayan Banik, Ashis K. Nandy
https://arxiv.org/abs/2509.14207 https://

Skyrmion-Antiskyrmion Lattice: A Net-Zero Topological Phase in Low-Symmetry Frustrated Chiral Magnets
We report the discovery of a thermodynamically stable skyrmion-antiskyrmion lattice in two-dimensional heterostructures, a novel state exhibiting a net-zero global topological charge owing to an equal population of skyrmions and antiskyrmions. This surprising coexistence of oppositely charged solitons remarkably circumvents their anticipated annihilation. We demonstrate the formation and evolution of this phase in Fe films on C1v -symmetric (110) surfaces of GaAs and CdTe semiconductors. Specif…

Comparative Evaluation of Neural Network Architectures for Generalizable Human Spatial Preference Prediction in Unseen Built Environments
Maral Doctorarastoo, Katherine A. Flanigan, Mario Berg\'es, Christopher McComb
https://arxiv.org/abs/2510.10954

Comparative Evaluation of Neural Network Architectures for Generalizable Human Spatial Preference Prediction in Unseen Built Environments
The capacity to predict human spatial preferences within built environments is instrumental for developing Cyber-Physical-Social Infrastructure Systems (CPSIS). A significant challenge in this domain is the generalizability of preference models, particularly their efficacy in predicting preferences within environmental configurations not encountered during training. While deep learning models have shown promise in learning complex spatial and contextual dependencies, it remains unclear which ne…

AEGIS: Authenticity Evaluation Benchmark for AI-Generated Video Sequences
Jieyu Li, Xin Zhang, Joey Tianyi Zhou
https://arxiv.org/abs/2508.10771 https://ar…

AEGIS: Authenticity Evaluation Benchmark for AI-Generated Video Sequences
Recent advances in AI-generated content have fueled the rise of highly realistic synthetic videos, posing severe risks to societal trust and digital integrity. Existing benchmarks for video authenticity detection typically suffer from limited realism, insufficient scale, and inadequate complexity, failing to effectively evaluate modern vision-language models against sophisticated forgeries. To address this critical gap, we introduce AEGIS, a novel large-scale benchmark explicitly targeting the …

LoRA-fine-tuned Large Vision Models for Automated Assessment of Post-SBRT Lung Injury
M. Bolhassani, B. Veasey, E. Daugherty, S. Keltner, N. Kumar, N. Dunlap, A. Amini
https://arxiv.org/abs/2509.12155 …

LoRA-fine-tuned Large Vision Models for Automated Assessment of Post-SBRT Lung Injury
This study investigates the efficacy of Low-Rank Adaptation (LoRA) for fine-tuning large Vision Models, DinoV2 and SwinV2, to diagnose Radiation-Induced Lung Injury (RILI) from X-ray CT scans following Stereotactic Body Radiation Therapy (SBRT). To evaluate the robustness and efficiency of this approach, we compare LoRA with traditional full fine-tuning and inference-only (no fine-tuning) methods. Cropped images of two sizes (50 mm3 and 75 mm3), centered at the treatment isocenter, in addition …

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…

SciVideoBench: Benchmarking Scientific Video Reasoning in Large Multimodal Models
Andong Deng, Taojiannan Yang, Shoubin Yu, Lincoln Spencer, Mohit Bansal, Chen Chen, Serena Yeung-Levy, Xiaohan Wang
https://arxiv.org/abs/2510.08559

SciVideoBench: Benchmarking Scientific Video Reasoning in Large Multimodal Models
Large Multimodal Models (LMMs) have achieved remarkable progress across various capabilities; however, complex video reasoning in the scientific domain remains a significant and challenging frontier. Current video benchmarks predominantly target general scenarios where perception/recognition is heavily relied on, while with relatively simple reasoning tasks, leading to saturation and thus failing to effectively evaluate advanced multimodal cognitive skills. To address this critical gap, we intr…