Tootfinder

Uncertainty-Aware Ankle Exoskeleton Control
Fatima Mumtaza Tourk, Bishoy Galoaa, Sanat Shajan, Aaron J. Young, Michael Everett, Max K. Shepherd
https://arxiv.org/abs/2508.21221 …

Uncertainty-Aware Ankle Exoskeleton Control
Lower limb exoskeletons show promise to assist human movement, but their utility is limited by controllers designed for discrete, predefined actions in controlled environments, restricting their real-world applicability. We present an uncertainty-aware control framework that enables ankle exoskeletons to operate safely across diverse scenarios by automatically disengaging when encountering unfamiliar movements. Our approach uses an uncertainty estimator to classify movements as similar (in-dist…

Applying Psychometrics to Large Language Model Simulated Populations: Recreating the HEXACO Personality Inventory Experiment with Generative Agents
Sarah Mercer, Daniel P. Martin, Phil Swatton
https://arxiv.org/abs/2508.00742

Applying Psychometrics to Large Language Model Simulated Populations: Recreating the HEXACO Personality Inventory Experiment with Generative Agents
Generative agents powered by Large Language Models demonstrate human-like characteristics through sophisticated natural language interactions. Their ability to assume roles and personalities based on predefined character biographies has positioned them as cost-effective substitutes for human participants in social science research. This paper explores the validity of such persona-based agents in representing human populations; we recreate the HEXACO personality inventory experiment by surveying…

Humanizing Automated Programming Feedback: Fine-Tuning Generative Models with Student-Written Feedback
Victor-Alexandru P\u{a}durean, Tung Phung, Nachiket Kotalwar, Michael Liut, Juho Leinonen, Paul Denny, Adish Singla
https://arxiv.org/abs/2509.10647

Humanizing Automated Programming Feedback: Fine-Tuning Generative Models with Student-Written Feedback
The growing need for automated and personalized feedback in programming education has led to recent interest in leveraging generative AI for feedback generation. However, current approaches tend to rely on prompt engineering techniques in which predefined prompts guide the AI to generate feedback. This can result in rigid and constrained responses that fail to accommodate the diverse needs of students and do not reflect the style of human-written feedback from tutors or peers. In this study, we…

SHIELDA: Structured Handling of Exceptions in LLM-Driven Agentic Workflows
Jingwen Zhou, Jieshan Chen, Qinghua Lu, Dehai Zhao, Liming Zhu
https://arxiv.org/abs/2508.07935 https:…

SHIELDA: Structured Handling of Exceptions in LLM-Driven Agentic Workflows
Large Language Model (LLM) agentic systems are software systems powered by LLMs that autonomously reason, plan, and execute multi-step workflows to achieve human goals, rather than merely executing predefined steps. During execution, these workflows frequently encounter exceptions. Existing exception handling solutions often treat exceptions superficially, failing to trace execution-phase exceptions to their reasoning-phase root causes. Furthermore, their recovery logic is brittle, lacking stru…

Interpretable Robot Control via Structured Behavior Trees and Large Language Models
Ingrid Ma\'eva Chekam, Ines Pastor-Martinez, Ali Tourani, Jose Andres Millan-Romera, Laura Ribeiro, Pedro Miguel Bastos Soares, Holger Voos, Jose Luis Sanchez-Lopez
https://arxiv.org/abs/2508.09621

Interpretable Robot Control via Structured Behavior Trees and Large Language Models
As intelligent robots become more integrated into human environments, there is a growing need for intuitive and reliable Human-Robot Interaction (HRI) interfaces that are adaptable and more natural to interact with. Traditional robot control methods often require users to adapt to interfaces or memorize predefined commands, limiting usability in dynamic, unstructured environments. This paper presents a novel framework that bridges natural language understanding and robotic execution by combinin…