Tootfinder

Long but very good:
"An essay on wank"
It names and identifies one of my least favorite genre of posts – finally I have a proper term for it!
I'm sure I've been guilty of it too in the past, so it's useful for self-regulation, too.
https://deadsimpletech.com/blog/essay_

CineWild: Balancing Art and Robotics for Ethical Wildlife Documentary Filmmaking
Pablo Pueyo, Fernando Caballero, Ana Cristina Murillo, Eduardo Montijano
https://arxiv.org/abs/2509.24921

CineWild: Balancing Art and Robotics for Ethical Wildlife Documentary Filmmaking
Drones, or unmanned aerial vehicles (UAVs), have become powerful tools across domains-from industry to the arts. In documentary filmmaking, they offer dynamic, otherwise unreachable perspectives, transforming how stories are told. Wildlife documentaries especially benefit, yet drones also raise ethical concerns: the risk of disturbing the animals they aim to capture. This paper introduces CineWild, an autonomous UAV framework that combines robotics, cinematography, and ethics. Built on model pr…

A Greedy PDE Router for Blending Neural Operators and Classical Methods
Sahana Rayan, Yash Patel, Ambuj Tewari
https://arxiv.org/abs/2509.24814 https://arx…

A Greedy PDE Router for Blending Neural Operators and Classical Methods
When solving PDEs, classical numerical solvers are often computationally expensive, while machine learning methods can suffer from spectral bias, failing to capture high-frequency components. Designing an optimal hybrid iterative solver--where, at each iteration, a solver is selected from an ensemble of solvers to leverage their complementary strengths--poses a challenging combinatorial problem. While the greedy selection strategy is desirable for its constant-factor approximation guarantee to …

MASH: A Multiplatform and Multimodal Annotated Dataset for Societal Impact of Hurricane
Ruichen Yao, Aslanbek Murzakhmetov, Raaghav Pillai, Aliya Maussymbayeva, Zelin Li, Yifan Liu, Yaokun Liu, Lanyu Shang, Yang Zhang, Na Wei, Ximing Cai, Dong Wang
https://arxiv.org/abs/2509.23627

MASH: A Multiplatform and Multimodal Annotated Dataset for Societal Impact of Hurricane
Natural disasters cause multidimensional threats to human societies, with hurricanes exemplifying one of the most disruptive events that not only caused severe physical damage but also sparked widespread discussion on social media platforms. Existing datasets for studying societal impacts of hurricanes often focus on outdated hurricanes and are limited to a single social media platform, failing to capture the broader societal impact in today's diverse social media environment. Moreover, existin…

Persistent Autoregressive Mapping with Traffic Rules for Autonomous Driving
Shiyi Liang, Xinyuan Chang, Changjie Wu, Huiyuan Yan, Yifan Bai, Xinran Liu, Hang Zhang, Yujian Yuan, Shuang Zeng, Mu Xu, Xing Wei
https://arxiv.org/abs/2509.22756

Persistent Autoregressive Mapping with Traffic Rules for Autonomous Driving
Safe autonomous driving requires both accurate HD map construction and persistent awareness of traffic rules, even when their associated signs are no longer visible. However, existing methods either focus solely on geometric elements or treat rules as temporary classifications, failing to capture their persistent effectiveness across extended driving sequences. In this paper, we present PAMR (Persistent Autoregressive Mapping with Traffic Rules), a novel framework that performs autoregressive c…

Spectrometry of Captured Highly Charged Ions Produced Following Antiproton Annihilations
F. P. Gustafsson, M. Volponi, J. Zielinski, A. Asare, I. Hwang, S. Alfaro Campos, M. Auzins, D. Bhanushali, A. Bhartia, M. Berghold, R. S. Brusa, K. Calik, A. Camper, R. Caravita, F. Castelli, G. Cerchiari, S. Chandran, A. Chehaimi, S. Choudapurkar, R. Ciury{\l}o, P. Conte, G. Consolati, M. Doser, R. Ferguson, M. Germann, A. Giszczak, L. T. Gl\"oggler, {\L}. Graczykowski, M. Grosbart, F. Guati…

Spectrometry of Captured Highly Charged Ions Produced Following Antiproton Annihilations
We report the first capture and time-of-flight spectrometry of highly charged ions produced following antiproton annihilations in a Penning-Malmberg trap. At the AEgIS experiment, we employed a multi-step nested-trap technique to isolate ions from antiproton annihilations with ultra-low-density helium and argon gas. The capture and identification of highly-charged argon ions in charge-states up to $Ar^{5+}$ demonstrates a new method for in-trap synthesis. This work establishes a clear path towa…

Bidirectional Representations Augmented Autoregressive Biological Sequence Generation:Application in De Novo Peptide Sequencing
Xiang Zhang, Jiaqi Wei, Zijie Qiu, Sheng Xu, Zhi Jin, ZhiQiang Gao, Nanqing Dong, Siqi Sun
https://arxiv.org/abs/2510.08169

Bidirectional Representations Augmented Autoregressive Biological Sequence Generation:Application in De Novo Peptide Sequencing
Autoregressive (AR) models, common in sequence generation, are limited in many biological tasks such as de novo peptide sequencing and protein modeling by their unidirectional nature, failing to capture crucial global bidirectional token dependencies. Non-Autoregressive (NAR) models offer holistic, bidirectional representations but face challenges with generative coherence and scalability. To transcend this, we propose a hybrid framework enhancing AR generation by dynamically integrating rich c…

Stitch: Training-Free Position Control in Multimodal Diffusion Transformers
Jessica Bader, Mateusz Pach, Maria A. Bravo, Serge Belongie, Zeynep Akata
https://arxiv.org/abs/2509.26644

Stitch: Training-Free Position Control in Multimodal Diffusion Transformers
Text-to-Image (T2I) generation models have advanced rapidly in recent years, but accurately capturing spatial relationships like "above" or "to the right of" poses a persistent challenge. Earlier methods improved spatial relationship following with external position control. However, as architectures evolved to enhance image quality, these techniques became incompatible with modern models. We propose Stitch, a training-free method for incorporating external position control into Multi-Modal Dif…

NewtonBench: Benchmarking Generalizable Scientific Law Discovery in LLM Agents
Tianshi Zheng, Kelvin Kiu-Wai Tam, Newt Hue-Nam K. Nguyen, Baixuan Xu, Zhaowei Wang, Jiayang Cheng, Hong Ting Tsang, Weiqi Wang, Jiaxin Bai, Tianqing Fang, Yangqiu Song, Ginny Y. Wong, Simon See
https://arxiv.org/abs/2510.07172

NewtonBench: Benchmarking Generalizable Scientific Law Discovery in LLM Agents
Large language models are emerging as powerful tools for scientific law discovery, a foundational challenge in AI-driven science. However, existing benchmarks for this task suffer from a fundamental methodological trilemma, forcing a trade-off between scientific relevance, scalability, and resistance to memorization. Furthermore, they oversimplify discovery as static function fitting, failing to capture the authentic scientific process of uncovering embedded laws through the interactive explora…

From Actions to Kinesics: Extracting Human Psychological States through Bodily Movements
Cheyu Lin, Katherine A. Flanigan
https://arxiv.org/abs/2510.04844 https://

From Actions to Kinesics: Extracting Human Psychological States through Bodily Movements
Understanding the dynamic relationship between humans and the built environment is a key challenge in disciplines ranging from environmental psychology to reinforcement learning (RL). A central obstacle in modeling these interactions is the inability to capture human psychological states in a way that is both generalizable and privacy preserving. Traditional methods rely on theoretical models or questionnaires, which are limited in scope, static, and labor intensive. We present a kinesics recog…