16 papers
The Anatomy of Uncertainty in LLMs
Aditya Taparia, Ransalu Senanayake, Kowshik Thopalli +1
Understanding why a large language model (LLM) is uncertain about the response is important for their reliable deployment. Current approaches, which either provide a single uncerta…
CUPID in the Model Zoo: Online Matchmaking for Selecting Your Dream LLM
Son Nguyen, Xinyuan Liu, Ransalu Senanayake
Users increasingly face the challenge of selecting an appropriate LLM for a given task from a rapidly growing pool of LLMs, each with distinct but often opaque latent properties. C…
Learning to Configure Agentic AI Systems
Aditya Taparia, Som Sagar, Ransalu Senanayake
Configuring LLM-based agent systems involves choosing workflows, tools, token budgets, and prompts from a large combinatorial design space, and is typically handled today by fixed…
RoboMD: Uncovering Robot Vulnerabilities through Semantic Potential Fields
Som Sagar, Jiafei Duan, Sreevishakh Vasudevan +4
Robot manipulation policies, while central to the promise of physical AI, are highly vulnerable in the presence of external variations in the real world. Diagnosing these vulnerabi…
MEMOR-E: In-Context and Fine-Tuned LLM Personalization for Alzheimer's Assistive Robotics
Maissa Abir Smaili, Eren Sadikoglu, Ransalu Senanayake
Alzheimer's disease is a neurodegenerative disorder marked by progressive declines in memory and language that reduce independence in daily life, motivating socially assistive robo…
Humanoid Factors: Design Principles for AI Humanoids in Human Worlds
Xinyuan Liu, Eren Sadikoglu, Ransalu Senanayake +1
Human factors research has long focused on optimizing environments, tools, and systems to account for human performance. Yet, as humanoid robots begin to share our workplaces, home…