6 papers
Statistically Indistinguishable, Operationally Distinct: A Formal Barrier for Tabular Foundation Models
Tassilo Klein, Johannes Hoffart
Tabular foundation models cannot reason about data produced by running systems without access to the rules that govern them. We make this statement falsifiable. The \emph{Operation…
Tabular Foundation Model for Generative Modelling
Xiangjian Jiang, Mingxuan Liu, Nikola Simidjievski +2
Generative modelling is a demanding test of foundation models, because it requires robust, holistic representation learning for a given data modality, rather than optimisation for…
The Headless Firm: How AI Reshapes Enterprise Boundaries
Tassilo Klein, Sebastian Wieczorek
The boundary of the firm is determined by coordination cost. We argue that agentic AI induces a structural change in how coordination costs scale: in prior modular systems, integra…
Position: Foundation Models for Tabular Data within Systemic Contexts Need Grounding
Tassilo Klein, Johannes Hoffart
This position paper argues that foundation models for tabular data face inherent limitations when isolated from operational context - the procedural logic, declarative rules, and d…
SALT-KG: A Benchmark for Semantics-Aware Learning on Enterprise Tables
Isaiah Onando Mulang, Felix Sasaki, Tassilo Klein +3
Building upon the SALT benchmark for relational prediction (Klein et al., 2024), we introduce SALT-KG, a benchmark for semantics-aware learning on enterprise tables. SALT-KG extend…
SALT: Sales Autocompletion Linked Business Tables Dataset
Tassilo Klein, Clemens Biehl, Margarida Costa +3
Foundation models, particularly those that incorporate Transformer architectures, have demonstrated exceptional performance in domains such as natural language processing and image…