基于动态系统预测的低成本黑盒 LLM 幻觉检测

Computer Science > Machine Learning arXiv:2605.05134 (cs) [Submitted on 6 May 2026 (v1), last revised 25 Sep 2026 (this version, v2)] Title:Low-Cost Black-Box Detection of LLM Hallucinations via Dynamical System Prediction Authors:Dan Wilson, Mohamed Akrout View a PDF of the paper titled Low-Cost Black-Box Detection of LLM Hallucinations via Dynamical System Prediction, by Dan Wilson and 1 other authors View PDF Abstract:Large Language Models (LLMs) frequently generate plausible but non-factual content, a phenomenon known as hallucination. While existing detection methods typically rely on computationally expensive sampling-based consistency checks or external knowledge retrieval, we propose a new method that treats the LLM as a black-box dynamical system. By projecting LLM responses into a high-dimensional manifold via an embedding model, we characterize the resulting vector sequences as observable realizations of the model's latent state-space dynamics. Leveraging Koopman operator theory, we fit the transition operators for both factual and hallucinated regimes and define a differential residual score based on their respective prediction errors. This approach enables low-cost hallucination detection in a single-sample pass, avoiding the need for secondary sampling or external grounding. Extensive testing across three data benchmarks demonstrates that our method achieves state-of-the-art performance with reduced resource overhead. Subjects: Machine Learning (cs.LG); Dynamical Systems (math.DS) Cite as: arXiv:2605.05134 [cs.LG] (or arXiv:2605.05134v2 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2605.05134 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Mohamed Akrout [view email] [v1] Wed, 6 May 2026 17:07:29 UTC (1,369 KB) [v2] Fri, 25 Sep 2026 16:31:10 UTC (1,059 KB) Full-text links: Access Paper: View a PDF of the paper titled Low-Cost Black-Box Detection of LLM Hallucinations via Dynamical System Prediction, by Dan Wilson and 1 other authors View PDF TeX Source view license Current browse context: cs.LG new | recent | 2026-05 Change to browse by: cs math math.DS References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)