SOMTab is a Set-Order Mamba architecture designed for efficient tabular in-context learning. It separates representation construction from query-conditioned retrieval, using Mamba-based state‑space mixing to build compact row and column representations while retaining attention for final prediction. The model, along with a synthetic prior called DCH‑TailMix, achieves performance comparable to Transformer‑based tabular foundation models but with faster inference and lower GPU memory usage.
arXiv:2608. 01400v1 Announce Type: new Abstract: Tabular foundation models, driven by in-context learning, have rapidly grown in quality and popularity.
The paper introduces RefineICL, an attention‑gated, feed‑forward‑network‑free framework that refines representations in situ for tabular foundation models. By using support labels to guide episode‑specific updates, the method transfers learned corrections to unlabeled queries without altering model parameters, achieving state‑of‑the‑art performance on AMLB29 and TabArena benchmarks. Experiments and internal interventions demonstrate that intermediate support updates are essential for constructing task‑specific predictors in context.
arXiv:2606. 07345v1 Announce Type: new Abstract: Tabular foundation models, exemplified by TabPFN, perform prediction via in-context learning, inferring test labels directly from labeled training examples.
Tydra is a hybrid Transformer‑State Space Model that interleaves attention and SSM layers for tabular in‑context learning. It achieves a 30% reduction in inference time compared to the Transformer‑only TabPFN while preserving most of its predictive performance. On 30 OpenML datasets, Tydra also outperforms a Hydra model that is roughly ten times larger, demonstrating that hybrid architectures can balance accuracy and efficiency for tabular foundation models.
The paper introduces a reproducible benchmark for evaluating attention mechanisms in tabular foundation models, focusing on the distinct row and column attention patterns that differ from language model attention. It compares several backends—Torch SDPA, FlashAttention variants, vLLM, and SageAttention—across realistic tabular shapes on A100, H100, and B200 GPUs, revealing that optimal backend choice varies by attention type, hardware, and model specifics. The study finds FlashAttention generally performs best, but CuDNN can outperform it for column attention on longer sequences, while SageAttention excels for large row sequences beyond 16k rows.
The paper investigates how test‑time compute techniques—adaptation, aggregation, and context construction—can improve predictions of pretrained tabular foundation models (TFMs). It introduces DiagScale, a lightweight adaptation method that trains a minuscule fraction of parameters yet matches full fine‑tuning gains, and shows that selective aggregation of predictions from diverse preprocessing variants can reduce error, while broader context expansion offers inconsistent benefits. The study concludes that adaptation and selective aggregation consistently yield benchmark‑level improvements, but their combined use demands more computation, guiding strategy selection based on available resources.
The paper introduces Retro, a tabular foundation model that uses retrospective inference to revisit and recombine intermediate representations during inference. Retro employs Attention Residuals to select which past information to reuse and query-conditioned Gated Attention to shape the contextual update for each query. Experiments on TabArena, TALENT, and RelArena show Retro achieves top‑three performance and lies on the Pareto frontier, demonstrating that reusing intermediate representations can better exploit depth in tabular foundation models.
arXiv:2607. 19358v1 Announce Type: new Abstract: Recent advances in long chain-of-thought reasoning models such as DeepSeek-R1 have led to increasingly longer inference context lengths under the test-time scaling paradigm.
The paper investigates how Tabular Foundation Models (TFMs) can achieve strong transfer learning by self‑supervised pre‑training on a single real table. It finds that a table’s usefulness is largely determined by its number of features rather than instances, and that fine‑grained column‑level preprocessing improves downstream performance while dataset‑level filtering does not. The authors propose that tabular in‑context generalization is primarily retrieval‑based, with models learning to identify and aggregate relevant examples from the provided context.
The paper investigates how Tabular Foundation Models (TFMs) can achieve strong transfer learning by self‑supervised pre‑training on a single real table, rather than large synthetic or real datasets. It finds that a table’s usefulness for downstream tasks is mainly determined by the number of features, not instances, and that fine‑grained column‑level preprocessing improves performance while dataset‑level filtering does not. The authors propose a task‑centric, retrieval‑based view of in‑context generalization, suggesting that effective TFMs identify and aggregate relevant examples from the provided context.
arXiv:2609.36337v1 Announce Type: new Abstract: Tabular foundation models achieve strong performance by conditioning on labelled examples in context, but softmax attention limits their use on large d...
arXiv:2609.13141v1 Announce Type: new Abstract: Post-training attention sparsification reduces the quadratic cumulative attention cost of pretrained Transformers by selecting a small set of context u...