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The proposed SGA first characterizes the topology of all potential forecast branches using a directed acyclic graph, such that the graph complexity bounds the uncertainty of multi-step TSFM forecasts, and then precisely measures the graph complexity by integrating both topological information and TSFM-inherent stochast...
The Graph-based Reasoning UncErtainty in Trajectories (GRUET) method for the uncertainty quantification of ReAct is presented, comprising turn-level reasoning uncertainty quantification and trajectory-level uncertainty aggregation; the former precisely quantifies reasoning uncertainty via modeling the reasoning space s...
Recent years have witnessed great advances in the reasoning ability of Large Language Models (LLMs). However, the reasoning processes of LLMs often exhibit uncertainty, where LLMs often produce a proliferation of divergent branches at each reasoning step even when fed the same prompting inputs, and certain branches exh...
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