7 papers
Confidence over Time: Confidence Calibration with Temporal Logic for Large Language Model Reasoning
Zhenjiang Mao, Anirudhh Venkat, Artem Bisliouk +4
Large Language Models (LLMs) increasingly rely on long-form, multi-step reasoning to solve complex tasks such as mathematical problem solving and scientific question answering. Des…
Recurrent Confidence Chain: Temporal-Aware Uncertainty Quantification in Large Language Models
Zhenjiang Mao, Anirudhh Venkat
As reasoning modules, such as the chain-of-thought mechanism, are applied to large language models, they achieve strong performance on various tasks such as answering common-sense…
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…
Generalizable Image Repair for Robust Visual Control
Carson Sobolewski, Zhenjiang Mao, Kshitij Maruti Vejre +1
Vision-based control relies on accurate perception to achieve robustness. However, image distribution changes caused by sensor noise, adverse weather, and dynamic lighting can degr…
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…
Language-Enhanced Latent Representations for Out-of-Distribution Detection in Autonomous Driving
Zhenjiang Mao, Dong-You Jhong, Ao Wang +1
Out-of-distribution (OOD) detection is essential in autonomous driving, to determine when learning-based components encounter unexpected inputs. Traditional detectors typically use…