Adapting Linear-Time Architectures for Tabular In-Context Learning

arXiv:2609.36337 · cs.LG, stat.ML · Submitted 2026-09-28 · Read on arXiv

cs.LG, stat.ML

Submitted: 2026-09-28

Updated: 2026-09-28

Code: https://github.com/schnurrd/ICL-Architectures

Terminology

• AUC — 32× in this paper · explained in 2 episodes
• Benchmark — 19× in this paper · explained in 2 episodes
• Foundation Model — 19× in this paper · explained in 3 episodes
• Transformer — 15× in this paper · explained in 2 episodes
• Foundation Models — 13× in this paper · explained in 7 episodes
• In-Context Learning — 7× in this paper · explained in 4 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Cosine Similarity — 6× in this paper · explained in 3 episodes
• Gated Linear Attention — 6× in this paper
• Core — 4× in this paper · explained in 2 episodes
• KV cache — 4× in this paper · explained in 3 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Edge Devices — 2× in this paper · explained in 2 episodes
• Frozen model — 2× in this paper · explained in 3 episodes
• Pairwise Comparisons — 2× in this paper · explained in 2 episodes
• World Model — 2× in this paper · explained in 7 episodes
• Associative Memory — 1× in this paper · explained in 2 episodes
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Bayesian Inference — 1× in this paper · explained in 5 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Elias — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• In-Context Learning (ICL) — 1× in this paper · explained in 5 episodes
• Positional Bias — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Time Series Forecasting — 1× in this paper · explained in 3 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models
• TempoPFN: Synthetic Pre-training of Linear RNNs for Zero-shot Time Series Forecasting
• FEAT: A Linear-Complexity Foundation Model for Extremely Large Structured Data
• End-to-End Test-Time Training for Long Context

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