Tootfinder

Vision-Based Perception for Autonomous Vehicles in Off-Road Environment Using Deep Learning
Nelson Alves Ferreira Neto
https://arxiv.org/abs/2509.19378 https://

Vision-Based Perception for Autonomous Vehicles in Off-Road Environment Using Deep Learning
Low-latency intelligent systems are required for autonomous driving on non-uniform terrain in open-pit mines and developing countries. This work proposes a perception system for autonomous vehicles on unpaved roads and off-road environments, capable of navigating rough terrain without a predefined trail. The Configurable Modular Segmentation Network (CMSNet) framework is proposed, facilitating different architectural arrangements. CMSNet configurations were trained to segment obstacles and traf…

NL2GenSym: Natural Language to Generative Symbolic Rules for SOAR Cognitive Architecture via Large Language Models
Fang Yuan, Junjie Zeng, Yue Hu, Zhengqiu Zhu, Quanjun Yin, Yuxiang Xie
https://arxiv.org/abs/2510.09355

NL2GenSym: Natural Language to Generative Symbolic Rules for SOAR Cognitive Architecture via Large Language Models
SOAR, a classic symbol-based cognitive architecture, has been fostering the development of general, human-like intelligent agents. Nevertheless, its practical adoption is hindered by the laborious manual rule coding. Emerging Large Language Models (LLMs) present the immense potential for efficient rules generation. However, there is a critical gap that current research predominantly focuses on conceptual frameworks and lacks robust experimental validation. To bridge this gap, we propose \textit…

Sounds like we need to find where the Cluely CEO is illegally living in his office in SoMa and throw him out. He's the guy who started the company with the motto "cheat on anything". https://www.nytimes.com/2025/10/16/technology/san-francisco-rent-ai-boo…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/6]:
- An AI-Based Behavioral Health Safety Filter and Dataset for Identifying Mental Health Crises in T...
Nelson, Wong, Silvestrini, Shin, Robinson, Lee, Yang, Torous, Trister

iDDS: Intelligent Distributed Dispatch and Scheduling for Workflow Orchestration
Wen Guan, Tadashi Maeno, Aleksandr Alekseev, Fernando Harald Barreiro Megino, Kaushik De, Edward Karavakis, Alexei Klimentov, Tatiana Korchuganova, FaHui Lin, Paul Nilsson, Torre Wenaus, Zhaoyu Yang, Xin Zhao
https://arxiv.org/abs/2510.02930

iDDS: Intelligent Distributed Dispatch and Scheduling for Workflow Orchestration
The intelligent Distributed Dispatch and Scheduling (iDDS) service is a versatile workflow orchestration system designed for large-scale, distributed scientific computing. iDDS extends traditional workload and data management by integrating data-aware execution, conditional logic, and programmable workflows, enabling automation of complex and dynamic processing pipelines. Originally developed for the ATLAS experiment at the Large Hadron Collider, iDDS has evolved into an experiment-agnostic pla…

The Enlightenment room at the British Museum. This is is a favorite, both for the cabinet of curiosities pleasure and also for its detailed explication of historiography and European intellectual culture. These photos don't do it justice.

eXplainable Artificial Intelligence for RL-based Networking Solutions
Yeison Stiven Murcia, Oscar Mauricio Caicedo, Daniela Maria Casas, Nelson Luis Saldanha da Fonseca
https://arxiv.org/abs/2509.21649

eXplainable Artificial Intelligence for RL-based Networking Solutions
Reinforcement Learning (RL) agents have been widely used to improve networking tasks. However, understanding the decisions made by these agents is essential for their broader adoption in networking and network management. To address this, we introduce eXplaNet - a pipeline grounded in explainable artificial intelligence - designed to help networking researchers and practitioners gain deeper insights into the decision-making processes of RL-based solutions. We demonstrate how eXplaNet can be app…

XR Blocks: Accelerating Human-centered AI XR Innovation
David Li, Nels Numan, Xun Qian, Yanhe Chen, Zhongyi Zhou, Evgenii Alekseev, Geonsun Lee, Alex Cooper, Min Xia, Scott Chung, Jeremy Nelson, Xiuxiu Yuan, Jolica Dias, Tim Bettridge, Benjamin Hersh, Michelle Huynh, Konrad Piascik, Ricardo Cabello, David Kim, Ruofei Du
https://arxiv.org…

XR Blocks: Accelerating Human-centered AI + XR Innovation
We are on the cusp where Artificial Intelligence (AI) and Extended Reality (XR) are converging to unlock new paradigms of interactive computing. However, a significant gap exists between the ecosystems of these two fields: while AI research and development is accelerated by mature frameworks like JAX and benchmarks like LMArena, prototyping novel AI-driven XR interactions remains a high-friction process, often requiring practitioners to manually integrate disparate, low-level systems for percep…

Replaced article(s) found for cs.OH. https://arxiv.org/list/cs.OH/new
[1/1]:
- A "Good" Regulator May Provide a World Model for Intelligent Systems
Bradly Alicea, Morgan Hough, Amanda Nelson, Jesse Parent

Solar PV Installation Potential Assessment on Building Facades Based on Vision and Language Foundation Models
Ruyu Liu, Dongxu Zhuang, Jianhua Zhang, Arega Getaneh Abate, Per Sieverts Nielsen, Ben Wang, Xiufeng Liu
https://arxiv.org/abs/2510.00797

Solar PV Installation Potential Assessment on Building Facades Based on Vision and Language Foundation Models
Building facades represent a significant untapped resource for solar energy generation in dense urban environments, yet assessing their photovoltaic (PV) potential remains challenging due to complex geometries and semantic com ponents. This study introduces SF-SPA (Semantic Facade Solar-PV Assessment), an automated framework that transforms street-view photographs into quantitative PV deployment assessments. The approach combines com puter vision and artificial intelligence techniques to addres…