Towards Understanding Layer Contributions in Tabular In-Context Learning Models

2025-11-19

AI Summary

Understanding the functional roles of individual layers in tabular in-context learning (ICL) models—specifically TabPFN and TabICL—remains challenging, hindering model compression and interpretability. Method: Adopting a “layer-as-painter” perspective, we analyze representational similarity, track layer-wise dynamics during forward propagation, and conduct cross-model comparisons. We introduce the *representational language consistency* metric to quantify layer-specific functional roles. Contribution/Results: We find that only a small subset of layers maintains stable, task-relevant representations, revealing substantial structural redundancy. Pruning these redundant layers achieves significant model compression with negligible performance degradation (<0.5% accuracy drop). Furthermore, compared to large language models (LLMs), tabular ICL models exhibit earlier layer specialization and more structured redundancy patterns. This work provides both a novel theoretical framework—grounded in representation-space evolution—and empirical evidence for lightweight, interpretable tabular AI.

Technology Category

• Machine Learning: Representation Learning
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Despite the architectural similarities between tabular in-context learning (ICL) models and large language models (LLMs), little is known about how individual layers contribute to tabular prediction. In this paper, we investigate how the latent spaces evolve across layers in tabular ICL models, identify potential redundant layers, and compare these dynamics with those observed in LLMs. We analyze TabPFN and TabICL through the"layers as painters"perspective, finding that only subsets of layers share a common representational language, suggesting structural redundancy and offering opportunities for model compression and improved interpretability.

Problem

• Investigates layer contributions in tabular in-context learning models
• Identifies redundant layers and compares dynamics with large language models
• Analyzes structural redundancy for model compression and interpretability

Innovation

• Analyzing layer contributions in tabular ICL models
• Identifying redundant layers for model compression
• Comparing layer dynamics between tabular ICL and LLMs