When to Trust, How to Distill: Multi-Foundation Model Guidance for Lightweight, Robust Scientific Time Series Forecasting
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Computer Science > Machine Learning
arXiv:2606.19363 (cs)
[Submitted on 10 Jun 2026]
Title:When to Trust, How to Distill: Multi-Foundation Model Guidance for Lightweight, Robust Scientific Time Series Forecasting
Authors:Rupasree Dey, Abdul Matin, Nathan Orwick, Yao Zhang, Shrideep Pallickara, Sangmi Lee Pallickara
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Abstract:The deployment of Time-Series Foundation Models (TSFMs) in physical sciences is hindered by a critical trade-off: while these models encode rich, universal temporal dynamics, they suffer from severe distributional misalignment when applied zero-shot to specific scientific domains, and their computational cost prohibits deployment in edge-computing sensor networks. We address a fundamental challenge: How can we extract latent structural knowledge from misaligned foundation models (FM) to train lightweight, specialized forecasters? We propose Gated Uncertainty-Aware Routing for Distillation (Guard), a novel framework that reframes multiteacher distillation as an instance-wise decision process with two adaptive mechanisms: (1) a Contextual Router that dynamically selects the most relevant teacher based on local input statistics, exploiting complementarity across diverse foundation models; and (2) an Uncertainty-Gated Temperature mechanism that acts as a "circuit-breaker," automatically attenuating distillation strength when teacher confidence diverges from domain reality. We evaluate our proposed lightweight framework on four climate-critical domains: meteorology, ecosystem carbon flux, soil moisture, and energy grids. Our method significantly reduces RMSE relative to a fixed-weight multi-teacher distillation baseline, successfully distilling knowledge from pretrained FMs (teachers) even when they exhibit suboptimal zero-shot accuracy due to distribution shift between the original and target data domains. We demonstrate that these domain-misaligned teachers can still serve as critical correctives, outperforming the globally superior FMs on 28.5% of the hardest instances. Ultimately, this enables high-precision scientific forecasting suitable for resource-constrained edge deployment. Code is available at this https URL.
| Comments: | KDD 2026, paper decision: Accepted, track: AI for Science. total 12 pages including references and appendix |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.19363 [cs.LG] |
| (or arXiv:2606.19363v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.19363
arXiv-issued DOI via DataCite
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| Related DOI: | https://doi.org/10.1145/3770855.3819018
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View a PDF of the paper titled When to Trust, How to Distill: Multi-Foundation Model Guidance for Lightweight, Robust Scientific Time Series Forecasting, by Rupasree Dey and 5 other authors
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