Tootfinder

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…

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…

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…

Assessment of tabulated-chemistry models for lean premixed strained hydrogen flames with low-dimensional manifolds
Alessandro Porcarelli, Pasquale Eduardo Lapenna, Francesco Creta, Ivan Langella
https://arxiv.org/abs/2510.03324

Assessment of tabulated-chemistry models for lean premixed strained hydrogen flames with low-dimensional manifolds
This study presents a comprehensive a priori analysis of tabulated-chemistry models for both laminar and turbulent lean premixed hydrogen flames in strained counterflow configuration. Particular focus is drawn on differential and preferential diffusion effects and the synergistic interaction of thermodiffusive instabilities and turbulence that existing models struggle to capture. Through detailed assessment of various modelling approaches at unfiltered and filtered grids, we identify significan…

Modeling Emission-Line Surface Brightness in a Multiphase Galactic Wind: An O VI Case Study
Zirui Chen, Zixuan Peng, Kate H. R. Rubin, Timothy M. Heckman, Matthew J. Hayes, Yakov Faerman, Crystal L. Martin, S. Peng Oh, Drummond B. Fielding
https://arxiv.org/abs/2510.02443

Modeling Emission-Line Surface Brightness in a Multiphase Galactic Wind: An O VI Case Study
We present a fast and robust analytic framework for predicting surface brightness (SB) of emission lines in galactic winds as a function of radius up to $\sim 100$ kpc out in the circum-galactic medium. We model multi-phase structure in galactic winds by capturing emission from both the volume-filling hot phase (T $\sim 10^{6-7}$ K) and turbulent radiative mixing layers that host intermediate temperature gas at the boundaries of cold clouds (T $\sim 10^4$ K). Our multi-phase framework makes sig…

Time-To-Inconsistency: A Survival Analysis of Large Language Model Robustness to Adversarial Attacks
Yubo Li, Ramayya Krishnan, Rema Padman
https://arxiv.org/abs/2510.02712 http…

Time-To-Inconsistency: A Survival Analysis of Large Language Model Robustness to Adversarial Attacks
Large Language Models (LLMs) have revolutionized conversational AI, yet their robustness in extended multi-turn dialogues remains poorly understood. Existing evaluation frameworks focus on static benchmarks and single-turn assessments, failing to capture the temporal dynamics of conversational degradation that characterize real-world interactions. In this work, we present the first comprehensive survival analysis of conversational AI robustness, analyzing 36,951 conversation turns across 9 stat…

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…

Accretion, Jets, and Recoil in Merging Supermassive Binary Black Holes
Maria Chiara de Simone (RIT), Manuela Campanelli (RIT), Lorenzo Ennoggi (RIT), Carlos O. Lousto (RIT), Yosef Zlochower (RIT)
https://arxiv.org/abs/2510.05883

Accretion, Jets, and Recoil in Merging Supermassive Binary Black Holes
We report the first 3D general relativistic magnetohydrodynamic (GRMHD) simulation that captures the full, self-consistent evolution from the late inspiral through merger and subsequent recoil of a supermassive binary black hole (SMBBH) with misaligned spins embedded in an equilibrated circumbinary disk (CBD). Our full numerical simulation follows the final 40 orbits of the inspiral and merger of the binary, following an initial phase of 165 orbits of CBD evolution toward equilibrium. We find t…

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…