Large language models (LLMs) have shown promise for healthcare text coding through in-context learning, yet their generated codes may still suffer from hallucination, inconsistency, and overconfidence. Although LLM-as-a-judge methods provide a flexible way to evaluate generated outputs, they are often computationally e...
In recent years, time-series question answering (QA) systems have made significant progress. However, generating a correct answer does not show whether retaining the supplied numerical series improves task performance, nor whether the prediction is sensitive to changes in that input. While some systems provide rational...
Zhuo-Min Chen, Jing-Chao Ni, Xu Zheng et al.· 0 citations
A unified objective function for explainable time series learning that bridges attribution and counterfactual reasoning within a single framework is proposed and a novel explanation framework is introduced that learns a parametric transformation network to construct explanation-embedded instances.
Xu Zheng, Zichuan Liu, Zhuo-Min Chen et al.· 0 citations
IB-Forecast is proposed, an inherently interpretable multivariate time-series forecasting framework that decomposes forecasting into a learned periodic component and a residual component computed with explainable masks over input tokens that guarantees high explanation fidelity.
This work proposes Trajectory Graph Copilot, a novel framework that acts as a ``copilot'' for LLM agents by diagnosing potential action errors before they are executed, significantly enhancing the agent's ability to complete long-horizon tasks successfully.
Xu Zheng, Zhuomin Chen, Chaohao Lin et al.· arXiv.org· 0 citations
A novel framework, \textit{GAMER}~(Graph-based Action-centric Memory with Episodic Reasoning), that bridges the gap between inference scaling and episodic memory by decoupling the memory mechanism from LLMs.
