Concept Flow Models: Anchoring Concept-Based Reasoning with Hierarchical Bottlenecks

2026-06-17

AI Summary

This work addresses the issue of information leakage in existing Concept Bottleneck Models (CBMs), which occurs when the number of concepts approaches the embedding dimension, leading models to rely on spurious correlations and compromising interpretability. To mitigate this, the authors propose the Concept Flow Model (CFM), a hierarchical, concept-driven differentiable probabilistic decision tree that focuses on locally discriminative concepts at each internal node to progressively narrow down predictions. CFM leverages vision-language models to generate concept embeddings and constructs a decision hierarchy grounded in visual embeddings, optimizing hierarchical concept weights in an end-to-end manner. Experiments demonstrate that CFM achieves prediction performance comparable to flat CBMs while significantly reducing the number of effective concepts used, thereby alleviating information leakage and enabling transparent, auditable reasoning aligned with hierarchical class structures.

Abstract

Concept Bottleneck Models (CBMs) enhance interpretability by projecting learned features into a human-understandable concept space. Recent approaches leverage vision-language models to generate concept embeddings, reducing the need for manual concept annotations. However, these models suffer from a critical limitation: as the number of concepts approaches the embedding dimension, information leakage increases, enabling the model to exploit spurious or semantically irrelevant correlations and undermining interpretability. In this work, we propose Concept Flow Models (CFMs), which replace the flat bottleneck with a hierarchical, concept-driven decision tree. Each internal node in the hierarchy focuses on a localized subset of discriminative concepts, progressively narrowing the prediction scope. Our framework constructs decision hierarchies from visual embeddings, distributes semantic concepts at each hierarchy level, and trains differentiable concept weights through probabilistic tree traversal. Extensive experiments on diverse benchmarks demonstrate that CFMs match the predictive performance of flat CBMs, while substantially mitigating information leakage by reducing effective concept usage. Furthermore, CFMs yield stepwise decision flows that enable transparent and auditable model reasoning with hierarchical class structures.

Problem

• Concept Bottleneck Models
• information leakage
• interpretability
• concept embeddings
• spurious correlations

Innovation

• Concept Bottleneck Models
• Hierarchical Decision Tree
• Information Leakage
• Interpretability
• Differentiable Reasoning