Benchmarking Attention for Tabular Foundation Models

arXiv:2609.31306 · cs.LG · Submitted 2026-09-25 · Read on arXiv

cs.LG

Submitted: 2026-09-25

Updated: 2026-09-28

Code: https://github.com/SAP-samples/tabular-attention-benchmark

Terminology

• Benchmark — 51× in this paper · explained in 2 episodes
• Foundation Model — 14× in this paper · explained in 3 episodes
• Benchmarking — 9× in this paper · explained in 4 episodes
• Foundation Models — 9× in this paper · explained in 7 episodes
• Transformer — 8× in this paper · explained in 2 episodes
• Core — 6× in this paper · explained in 2 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Cooperative Launch Control — 2× in this paper
• AI Agents — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Steering — 1× in this paper · explained in 2 episodes

Sources

• cuDNN: Efficient Primitives for Deep Learning
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• TabPFN-3: Technical Report
• TabICLv2: A better, faster, scalable, and open tabular foundation model
• FlashAttention-4: Algorithm and Kernel Pipelining Co-Design for Asymmetric Hardware Scaling

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