Concept Flow Models use hierarchical concept-driven decision trees to mitigate information leakage in concept bottleneck models while matching their predictive performance.
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Do concept bottleneck models learn as intended?
11 Pith papers cite this work. Polarity classification is still indexing.
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Concept-based models can use controlled 'benign' information leakage to remain accurate and intervenable under real-world concept incompleteness by reframing their training objective.
OceanCBM is the first concept bottleneck model for spatiotemporal ocean prediction that uses mixed supervision on physical concepts and a free concept to deliver consistent mechanistic representations for mixed layer heat content forecasts.
Caption Bottleneck Models use LMM-generated image captions as the sole input to a text classifier, creating leakage-free interpretable models that discover dataset-specific concepts without predefined lists or manual labels.
GRAPE augments prototype medical image classifiers with graph attention for co-occurrence, a mismatch safety check, and open-vocabulary anchoring to support incremental addition of findings from single examples.
SynCB adds a dynamic routing module and joint training to a hybrid concept-plus-neural architecture, reporting up to 3.9 pp higher accuracy than a full neural baseline and up to 6.43 pp better intervention responsiveness than prior hybrids across five datasets.
VH-CBM uses a Gaussian process in VLM embedding space to propagate sparse human annotations and improve concept accuracy and calibration over pure VLM-guided concept bottleneck models.
Sparse Concept Anchoring biases neural latent spaces toward targeted concepts using under 0.1% labels per concept, enabling reversible steering via projection and permanent removal via weight ablation with minimal side effects on other features.
ViSAE supplies a 64K-image probing suite with 16K concepts, top-down/bottom-up circuit algorithms, and editing methods that raise WaterBirds worst-group accuracy by 48.2% over baselines.
CHiQPM is a hierarchical interpretable image classifier that maintains 99% of non-interpretable model accuracy while supplying contrastive global explanations, human-like hierarchical paths, and calibrated interpretable set predictions via conformal prediction.
Empirical tests of VLM-CBMs show VLM supervision differs from expert annotations depending on task and that concept accuracy correlates weakly with quality metrics.
citing papers explorer
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Concept Flow Models: Anchoring Concept-Based Reasoning with Hierarchical Bottlenecks
Concept Flow Models use hierarchical concept-driven decision trees to mitigate information leakage in concept bottleneck models while matching their predictive performance.
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In Defense of Information Leakage in Concept-based Models
Concept-based models can use controlled 'benign' information leakage to remain accurate and intervenable under real-world concept incompleteness by reframing their training objective.
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OceanCBM: A Concept Bottleneck Model for Mechanistic Interpretability in Ocean Forecasting
OceanCBM is the first concept bottleneck model for spatiotemporal ocean prediction that uses mixed supervision on physical concepts and a free concept to deliver consistent mechanistic representations for mixed layer heat content forecasts.
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Caption Bottleneck Models
Caption Bottleneck Models use LMM-generated image captions as the sole input to a text classifier, creating leakage-free interpretable models that discover dataset-specific concepts without predefined lists or manual labels.
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GRAPE: Graph-Augmented Prototype Explanations for Interactive Medical Image Diagnosis
GRAPE augments prototype medical image classifiers with graph attention for co-occurrence, a mismatch safety check, and open-vocabulary anchoring to support incremental addition of findings from single examples.
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SynCB: A Synergy Concept-Based Model with Dynamic Routing Between Concepts and Complementary Neural Branches
SynCB adds a dynamic routing module and joint training to a hybrid concept-plus-neural architecture, reporting up to 3.9 pp higher accuracy than a full neural baseline and up to 6.43 pp better intervention responsiveness than prior hybrids across five datasets.
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Concepts Worth Having: Refining VLM-Guided Concept Bottleneck Models with Minimal Annotations
VH-CBM uses a Gaussian process in VLM embedding space to propagate sparse human annotations and improve concept accuracy and calibration over pure VLM-guided concept bottleneck models.
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Sparse Concept Anchoring for Interpretable and Controllable Neural Representations
Sparse Concept Anchoring biases neural latent spaces toward targeted concepts using under 0.1% labels per concept, enabling reversible steering via projection and permanent removal via weight ablation with minimal side effects on other features.
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Inside the Visual Mind: Neuroscience-Motivated Concept Circuits for Interpreting and Steering Vision Transformers
ViSAE supplies a 64K-image probing suite with 16K concepts, top-down/bottom-up circuit algorithms, and editing methods that raise WaterBirds worst-group accuracy by 48.2% over baselines.
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CHiQPM: Calibrated Hierarchical Interpretable Image Classification
CHiQPM is a hierarchical interpretable image classifier that maintains 99% of non-interpretable model accuracy while supplying contrastive global explanations, human-like hierarchical paths, and calibrated interpretable set predictions via conformal prediction.
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If Concept Bottlenecks are the Question, are Foundation Models the Answer?
Empirical tests of VLM-CBMs show VLM supervision differs from expert annotations depending on task and that concept accuracy correlates weakly with quality metrics.