1 citations · 1 across the 10 of their papers we have counts for
12 papers
ROOT: Discovering Rewards for User-Specified Embodied Behaviors
Eren Sadikoglu, Aditya Taparia, Xinyuan Liu +1
Reinforcement learning for embodied control remains constrained by the difficulty of reward specification. Although recent large language model (LLM)-based methods can synthesize r…
PhysicsLENS: Diagnosing Physical Property Blindness in Video Generation Models
Isaiah Milkey, Som Sagar, Aditya Taparia +3
Reliable video world models could provide scalable predictive environments for robot learning, planning, and evaluation. However, generated robot videos can violate physical princi…
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…
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…
Consistency-based Abductive Reasoning over Perceptual Errors of Multiple Pre-trained Models in Novel Environments
Mario Leiva, Noel Ngu, Joshua Shay Kricheli +6
The deployment of pre-trained perception models in novel environments often leads to performance degradation due to distributional shifts. Although recent artificial intelligence a…
VLC Fusion: Vision-Language Conditioned Sensor Fusion for Robust Object Detection
Aditya Taparia, Noel Ngu, Mario Leiva +6
Although fusing multiple sensor modalities can enhance object detection performance, existing fusion approaches often overlook subtle variations in environmental conditions and sen…
Multiple Distribution Shift -- Aerial (MDS-A): A Dataset for Test-Time Error Detection and Model Adaptation
Noel Ngu, Aditya Taparia, Gerardo I. Simari +5
Machine learning models assume that training and test samples are drawn from the same distribution. As such, significant differences between training and test distributions often l…
ExpressivityBench: Can LLMs Communicate Implicitly?
Joshua Tint, Som Sagar, Aditya Taparia +4
Human communication is often implicit, conveying tone, identity, and intent beyond literal meanings. While large language models have achieved strong performance on explicit tasks…
LLM-Assisted Red Teaming of Diffusion Models through "Failures Are Fated, But Can Be Faded"
Som Sagar, Aditya Taparia, Ransalu Senanayake
In large deep neural networks that seem to perform surprisingly well on many tasks, we also observe a few failures related to accuracy, social biases, and alignment with human valu…
BaTCAVe: Trustworthy Explanations for Robot Behaviors
Som Sagar, Aditya Taparia, Harsh Mankodiya +3
Black box neural networks are an indispensable part of modern robots. Nevertheless, deploying such high-stakes systems in real-world scenarios poses significant challenges when the…
Explainable Concept Generation through Vision-Language Preference Learning for Understanding Neural Networks' Internal Representations
Aditya Taparia, Som Sagar, Ransalu Senanayake
Understanding the inner representation of a neural network helps users improve models. Concept-based methods have become a popular choice for explaining deep neural networks post-h…
Failures Are Fated, But Can Be Faded: Characterizing and Mitigating Unwanted Behaviors in Large-Scale Vision and Language Models
Som Sagar, Aditya Taparia, Ransalu Senanayake
In large deep neural networks that seem to perform surprisingly well on many tasks, we also observe a few failures related to accuracy, social biases, and alignment with human valu…