mlsanityData to 14 Oct 2026

Hallucination Detection and Mitigation with Diffusion in Multi-Variate Time-Series Foundation Models

arXiv 2508.00881 ↗First posted 23 Jul 2025cs.LG cs.CLcited by 0

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Abstract (arXiv)

Foundation models for natural language processing have many coherent definitions of hallucination and methods for its detection and mitigation. However, analogous definitions and methods do not exist for multi-variate time-series (MVTS) foundation models. We propose new definitions for MVTS hallucination, along with new detection and mitigation methods using a diffusion model to estimate hallucination levels. We derive relational datasets from popular time-series datasets to benchmark these relational hallucination levels. Using these definitions and models, we find that open-source pre-trained MVTS imputation foundation models relationally hallucinate on average up to 59.5% as much as a weak baseline. The proposed mitigation method reduces this by up to 47.7% for these models. The definition and methods may improve adoption and safe usage of MVTS foundation models.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 52 of its 64 references that are in the map. First posted on arXiv 23 Jul 2025.

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References · 52

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38 more
  1. 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 698
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  15. 2023A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generationcited by 206
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  1. 2026DeMTS: Denoising Trajectories as Multivariate Time Series for Hallucination Detection in Diffusion Language Models
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  3. 2026TDGNet: Hallucination Detection in Diffusion Language Models via Temporal Dynamic Graphs
  4. 2025Theoretical Foundations and Mitigation of Hallucination in Large Language Models
  5. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
  6. 2026Lost in Diffusion: Uncovering Hallucination Patterns and Failure Modes in Diffusion Large Language Models
  7. 2026Domain-Specific Hallucination Detection in Large Language Models
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