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cs.LG2026
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…
cs.LG2025
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…
cs.LG2025
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…