Tootfinder

Co-Investigator AI: The Rise of Agentic AI for Smarter, Trustworthy AML Compliance Narratives
Prathamesh Vasudeo Naik, Naresh Kumar Dintakurthi, Zhanghao Hu, Yue Wang, Robby Qiu
https://arxiv.org/abs/2509.08380

Co-Investigator AI: The Rise of Agentic AI for Smarter, Trustworthy AML Compliance Narratives
Generating regulatorily compliant Suspicious Activity Report (SAR) remains a high-cost, low-scalability bottleneck in Anti-Money Laundering (AML) workflows. While large language models (LLMs) offer promising fluency, they suffer from factual hallucination, limited crime typology alignment, and poor explainability -- posing unacceptable risks in compliance-critical domains. This paper introduces Co-Investigator AI, an agentic framework optimized to produce Suspicious Activity Reports (SARs) sign…

What you truly need to vibe and thrive is simple, some good food to nourish your body, clean water to keep your spirit flowing, a little movement to keep the energy dancing, and a cozy spot to rest your head. When you’re living in community, sharing the love and looking out for each other, all that falls beautifully into place.
What you don’t need is a giant screen flashing noise at you, the newest shiny gadgets or games, or getting caught up in the endless swirl of political drama. Re…

Towards Data-Driven Metrics for Social Robot Navigation Benchmarking
Pilar Bachiller-Burgos, Ulysses Bernardet, Luis V. Calderita, Pranup Chhetri, Anthony Francis, Noriaki Hirose, No\'e P\'erez, Dhruv Shah, Phani T. Singamaneni, Xuesu Xiao, Luis J. Manso
https://arxiv.org/abs/2509.01251

Towards Data-Driven Metrics for Social Robot Navigation Benchmarking
This paper presents a joint effort towards the development of a data-driven Social Robot Navigation metric to facilitate benchmarking and policy optimization. We provide our motivations for our approach and describe our proposal for storing rated social navigation trajectory datasets. Following these guidelines, we compiled a dataset with 4427 trajectories -- 182 real and 4245 simulated -- and presented it to human raters, yielding a total of 4402 rated trajectories after data quality assurance…

Mineral Detection of Neutrinos and Dark Matter 2025 Proceedings
Shigenobu Hirose, Patrick Stengel, Natsue Abe, Daniel Ang, Lorenzo Apollonio, Gabriela R. Araujo, Yoshihiro Asahara, Laura Baudis, Pranshu Bhaumik, Nathaniel Bowden, Joseph Bramante, Lorenzo Caccianiga, Mason Camp, Qing Chang, Jordan Chapman, Reza Ebadi, Alexey Elykov, Anna Erickson, Valentin Fondement, Katherine Freese, Shota Futamura, Claudio Galelli, Andrew Gilpin, Takeshi Hanyu, Noriko Hasebe, Adam A. Hecht, Samuel C. …

Mineral Detection of Neutrinos and Dark Matter 2025 Proceedings
The third ``Mineral Detection of Neutrinos and Dark Matter'' (MD$ν$DM'25) meeting was held May 20-23, 2025 in Yokohama, Japan, hosted by the Yokohama Institute for Earth Sciences, Japan Agency for Marine-Earth Science and Technology (JAMSTEC). These proceedings compile contributions from the workshop and update the progress of mineral detector research. MD$ν$DM'25 was the third such meeting, following the first in October of 2022 held at the IFPU in Trieste, Italy and the second in January of…

Neutralizing Optical Defects in GeSn
Nirosh M. Eldose, Dinesh Baral, Diandian Zhang, Fernando Maia de Oliveira, Hryhorii Stanchu, Mohammad Zamani-Alavijeh, Yuriy I. Mazur, Wei Du, Shui-Qing Yu, Gregory J. Salamo
https://arxiv.org/abs/2508.13027

Neutralizing Optical Defects in GeSn
Reports of photoluminescence from GeSn grown on Ge substrates by molecular beam epitaxy have been limited. We find that one limiting factor to observing photoluminescence is due to localized defect states marked by photoluminescence at 2400 nm and originating from the Ge substrate and buffer layer. In this study, we report on an optical study utilizing doped Ge(001) substrates to effectively suppress defect-related photoluminescence in GeSn layers by filling localized defect trap states. For th…