This paper presents a conformal interpretability framework for LLM agents to understand temporal concept evolution. It uses step-wise reward modeling and conformal prediction to statistically label internal representations and identify latent directions linked to success, failure, or reasoning drift.
This article proposes a 5-step framework, based on Conformal Prediction, to evaluate the trustworthiness of AI agents. It offers a mathematical guarantee for a provable reliability score, instead of relying on LLMs as judges.
This research addresses the critical challenge of quantifying uncertainty in Large Reasoning Models (LRMs), noting the limitations of traditional and existing Conformal Prediction (CP) methods. It aims to develop a statistically rigorous approach that considers logical connections, interprets uncertainty origins, and disentangles reasoning quality from answer correctness.
This paper introduces a satellite-reanalysis PM2.5 fusion system for air quality monitoring in Africa, employing LightGBM and conformal prediction. The system addresses challenges in geographic generalization and uncertainty quantification crucial for the continent's green industrial transition.