# LG AI Research — [ICML2026] Foundation Models for Structured Data and the Current State of Time Series Deep Learning

- Company: LG AI Research (lgresearch.ai)
- Announced: 2026-11-03
- Category: research-paper
- Coverage: not counted
- Announcement: yes
- Group: announcements
- Source: https://www.lgresearch.ai/blog/view?seq=734
- Record: https://forck.live/items/30907-icml2026-foundation-models-for-structured-data-and-the-current-state-of-time
- Subject: EXAONE
- Models affected: TabPFN

LG AI Research presented an ICML 2026 paper on label shift in tabular foundation models, showing that TabPFN's minority-class recall collapses under class imbalance. The paper, "Mitigating Label Shift in Tabular In-Context Learning via Test-Time Posterior Adjustment," proposes a simple adjustment at inference time rather than retraining. TabPFN is pre-trained on large-scale synthetic data and predicts in a single forward pass with no parameter updates, an in-context learning approach that has drawn attention for beating XGBoost on small datasets. The authors found that TabPFN folds the class imbalance present in its context directly into its predictions: in extreme cases 98.3% of test samples were assigned to the majority class. An analysis of 253 OpenML tabular datasets found roughly 85%, or 214, showed class imbalance, a pattern the authors say is common in defect detection, anomaly detection and disease diagnosis. The problem appeared even when training and testing used the same dataset, which the authors say underscores its severity. LG AI Research attended ICML 2026 at COEX in Seoul from July 6 to 11, 2026, the conference's 43rd edition and the first held in South Korea, where 23,918 papers were submitted and 6,352 accepted.

## Evidence

Verbatim from https://www.lgresearch.ai/blog/view?seq=734:

> Deep learning, which has evolved primarily around images and language, has achieved remarkable results; however, a significant portion of the data accumulated daily in industrial settings still takes the form of tabular and time series data . Sensor logs from manufacturing equipment, semiconductor yield data, financial transaction records, and hospital electronic health records are all sequences of numbers organized along rows and columns or a time axis. While this structured data is the most common type encountered in industrial settings, paradoxically, it is also the area where deep learning has made its entry the latest. This is evidenced by the fact that tree-based models such as XGBoost and LightGBM have served as the de facto standard for the past decade or so.
> 
> However, this trend has been rapidly shifting over the past two to three years. This is because foundation models , which are pre-trained on large-scale synthetic data and perform predictions with a single inference without requiring additional training, have begun to make a significant impact in the realm of structured data as well.

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Record: https://forck.live/items/30907-icml2026-foundation-models-for-structured-data-and-the-current-state-of-time
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