Tootfinder

CAI Fluency: A Framework for Cybersecurity AI Fluency
V\'ictor Mayoral-Vilches, Jasmin Wachter, Crist\'obal R. J. Veas Chavez, Cathrin Schachner, Luis Javier Navarrete-Lozano, Mar\'ia Sanz-G\'omez
https://arxiv.org/abs/2508.13588

CAI Fluency: A Framework for Cybersecurity AI Fluency
This work introduces CAI Fluency, an an educational platform of the Cybersecurity AI (CAI) framework dedicated to democratizing the knowledge and application of cybersecurity AI tools in the global security community. The main objective of the CAI framework is to accelerate the widespread adoption and effective use of artificial intelligence-based cybersecurity solutions, pathing the way to vibe-hacking, the cybersecurity analogon to vibe-coding. CAI Fluency builds upon the Framework for AI F…

Beyond Copenhagen: Following the Trail of Decoherence in Feynman's Light Microscope
Brian C. Odom
https://arxiv.org/abs/2508.13385 https://arxiv.org/pd…

Beyond Copenhagen: Following the Trail of Decoherence in Feynman's Light Microscope
Feynman's light microscope invites us to reconsider what we thought we knew about quantum reality. Rather than invoking wavefunction collapse to predict the loss of fringes in a monitored interferometer, Feynman analyzes the problem in terms of a disturbance. This approach raises the question of whether the classical world, including its localized particles and definite measurement outcomes, might emerge as the universe evolves smoothly according to ß. Treating the particle and its environment…

CAST: Counterfactual Labels Improve Instruction Following in Vision-Language-Action Models
Catherine Glossop, William Chen, Arjun Bhorkar, Dhruv Shah, Sergey Levine
https://arxiv.org/abs/2508.13446

CAST: Counterfactual Labels Improve Instruction Following in Vision-Language-Action Models
Generalist robots should be able to understand and follow user instructions, but current vision-language-action (VLA) models struggle with following fine-grained commands despite providing a powerful architecture for mapping open-vocabulary natural language instructions to robot actions. One cause for this is a lack of semantic diversity and language grounding in existing robot datasets and, specifically, a lack of fine-grained task diversity for similar observations. To address this, we presen…

Holy Flying Circus
British Broadcasting Corporation : Free Download, Borrow, and Streaming : Internet Archive
https://archive.org/details/holy.flying.circus.2011.dvdrip.xvid-unveil
Funny. Jesus, 1995...

Filling in the semantics for intuitionistic conditional logic
Brendan Dufty, Jim de Groot
https://arxiv.org/abs/2508.11972 https://arxiv.org/pdf/2508.11972…

Filling in the semantics for intuitionistic conditional logic
We prove completeness results for a wide variety of intuitionistic conditional logics. We do so by first using a duality to transfer algebraic completeness to completeness with respect to conditional Esakia spaces, a topologised version of a conditional Kripke frame. We then use so-called fill-ins to obtain completeness results with respect to classes conditional of Kripke frames. The fill-ins close the gap between conditional Esakia spaces, which do not have a canonical underlying frame, and c…

Automated Energy-Aware Time-Series Model Deployment on Embedded FPGAs for Resilient Combined Sewer Overflow Management
Tianheng Ling, Vipin Singh, Chao Qian, Felix Biessmann, Gregor Schiele
https://arxiv.org/abs/2508.13905

Automated Energy-Aware Time-Series Model Deployment on Embedded FPGAs for Resilient Combined Sewer Overflow Management
Extreme weather events, intensified by climate change, increasingly challenge aging combined sewer systems, raising the risk of untreated wastewater overflow. Accurate forecasting of sewer overflow basin filling levels can provide actionable insights for early intervention, helping mitigating uncontrolled discharge. In recent years, AI-based forecasting methods have offered scalable alternatives to traditional physics-based models, but their reliance on cloud computing limits their reliability …

Beyond Random Masking: A Dual-Stream Approach for Rotation-Invariant Point Cloud Masked Autoencoders
Xuanhua Yin, Dingxin Zhang, Yu Feng, Shunqi Mao, Jianhui Yu, Weidong Cai
https://arxiv.org/abs/2509.14975

Beyond Random Masking: A Dual-Stream Approach for Rotation-Invariant Point Cloud Masked Autoencoders
Existing rotation-invariant point cloud masked autoencoders (MAE) rely on random masking strategies that overlook geometric structure and semantic coherence. Random masking treats patches independently, failing to capture spatial relationships consistent across orientations and overlooking semantic object parts that maintain identity regardless of rotation. We propose a dual-stream masking approach combining 3D Spatial Grid Masking and Progressive Semantic Masking to address these fundamental l…

Expertise-aware Multi-LLM Recruitment and Collaboration for Medical Decision-Making
Liuxin Bao, Zhihao Peng, Xiaofei Zhou, Runmin Cong, Jiyong Zhang, Yixuan Yuan
https://arxiv.org/abs/2508.13754

Expertise-aware Multi-LLM Recruitment and Collaboration for Medical Decision-Making
Medical Decision-Making (MDM) is a complex process requiring substantial domain-specific expertise to effectively synthesize heterogeneous and complicated clinical information. While recent advancements in Large Language Models (LLMs) show promise in supporting MDM, single-LLM approaches are limited by their parametric knowledge constraints and static training corpora, failing to robustly integrate the clinical information. To address this challenge, we propose the Expertise-aware Multi-LLM Rec…

ChronoLLM: Customizing Language Models for Physics-Based Simulation Code Generation
Jingquan Wang, Andrew Negrut, Harry Zhang, Khailanii Slaton, Shu Wang, Radu Serban, Jinlong Wu, Dan Negrut
https://arxiv.org/abs/2508.13975

ChronoLLM: Customizing Language Models for Physics-Based Simulation Code Generation
This contribution is concerned with the following issue: can pretrained large language models (LLMs) be refined and customized to the point where they become virtual assistants helping experts with the effective use of a simulation tool? In this case study, the ``simulation tool'' considered is PyChrono, an open source multi-physics dynamics engine for multibody systems. We present a framework for refining and customizing both open- and closed-source LLMs to harness the power of AI in generatin…

Beyond the high score: Prosocial ability profiles of multi-agent populations
Marko Tesic, Yue Zhao, Joel Z. Leibo, Rakshit S. Trivedi, Jose Hernandez-Orallo
https://arxiv.org/abs/2509.14485

Beyond the high score: Prosocial ability profiles of multi-agent populations
The development and evaluation of social capabilities in AI agents require complex environments where competitive and cooperative behaviours naturally emerge. While game-theoretic properties can explain why certain teams or agent populations outperform others, more abstract behaviours, such as convention following, are harder to control in training and evaluation settings. The Melting Pot contest is a social AI evaluation suite designed to assess the cooperation capabilities of AI systems. In t…