LAST-RAG: Literature-Anchored Stochastic Trajectory Retrieval-Augmented Generation for Knowledge-Conditioned Degradation Model Selection
The paper introduces LAST-RAG, a method for selecting degradation models based on observed health indicator trajectories and domain-specific knowledge. It addresses limitations of existing model selection methods that rely solely on statistical fit. The proposed approach integrates theoretical evidence and uncertainty reasoning to enhance model selection accuracy.
- ▪LAST-RAG stands for Literature-Anchored Stochastic Trajectory Retrieval-Augmented Generation.
- ▪The method improves upon traditional statistical model selection by incorporating domain knowledge.
- ▪Simulation experiments show that LAST-RAG outperforms existing statistical and prognostic methods.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.17902 (cs) [Submitted on 18 May 2026] Title:LAST-RAG: Literature-Anchored Stochastic Trajectory Retrieval-Augmented Generation for Knowledge-Conditioned Degradation Model Selection Authors:Hanbyeol Park, Hyerim Bae View a PDF of the paper titled LAST-RAG: Literature-Anchored Stochastic Trajectory Retrieval-Augmented Generation for Knowledge-Conditioned Degradation Model Selection, by Hanbyeol Park and Hyerim Bae View PDF HTML (experimental) Abstract:Stochastic-process-based degradation modeling is a core approach for estimating the distribution of remaining useful life (RUL); however, the selection of an appropriate stochastic process has not been sufficiently addressed.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
