2 citations · 5 across the 17 of their papers we have counts for
6 papers · 1 filter
Temporalizing Confidence: Evaluation of Chain-of-Thought Reasoning with Signal Temporal Logic
Zhenjiang Mao, Artem Bisliouk, Rohith Reddy Nama +1
Large Language Models (LLMs) have shown impressive performance in mathematical reasoning tasks when guided by Chain-of-Thought (CoT) prompting. However, they tend to produce highly…
Four Principles for Physically Interpretable World Models
Jordan Peper, Zhenjiang Mao, Yuang Geng +2
As autonomous systems are increasingly deployed in open and uncertain settings, there is a growing need for trustworthy world models that can reliably predict future high-dimension…
Physically Interpretable World Models via Weakly Supervised Representation Learning
Zhenjiang Mao, Mrinall Eashaan Umasudhan, Ivan Ruchkin
Learning predictive models from high-dimensional sensory observations is fundamental for cyber-physical systems, yet the latent representations learned by standard world models lac…
Zero-shot Safety Prediction for Autonomous Robots with Foundation World Models
Zhenjiang Mao, Siqi Dai, Yuang Geng +1
A world model creates a surrogate world to train a controller and predict safety violations by learning the internal dynamic model of systems. However, the existing world models re…
Bridging Dimensions: Confident Reachability for High-Dimensional Controllers
Yuang Geng, Jake Brandon Baldauf, Souradeep Dutta +2
Autonomous systems are increasingly implemented using end-to-end learning-based controllers. Such controllers make decisions that are executed on the real system, with images as on…
How Safe Am I Given What I See? Calibrated Prediction of Safety Chances for Image-Controlled Autonomy
Zhenjiang Mao, Carson Sobolewski, Ivan Ruchkin
End-to-end learning has emerged as a major paradigm for developing autonomous systems. Unfortunately, with its performance and convenience comes an even greater challenge of safety…