arXiv AI
Jun 30
On the Faithfulness of Post-Hoc Concept Bottleneck Models
arXiv:2606. 30498v1 Announce Type: cross Abstract: Human decision-making interprets the world through high-level concepts, such as recognizing a bird by its belly color.
Assessing Reliability of Symbol Detection in Concept Bottleneck Models
arXiv:2606. 16535v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) are a relevant tool for explainable Artificial Intelligence because they make their predictions through human-interpretable symbols.
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arXiv AI
Sep 24
Parameter-Efficient Construction of the Rashomon Slice for Concept Bottleneck Models
The paper introduces a method for efficiently exploring the Rashomon set of Concept Bottleneck Models (CBMs) by using a parallel parameter‑efficient adaptation module, checkpointing, and a concept diversity objective. This approach generates multiple equally accurate CBMs from a single training process, achieving greater diversity than baseline methods while consuming less memory. The resulting diverse models enable trustworthy selection, reduce inter‑class confusion, and support reliable abstention in decision‑making.
arXiv AI
Jun 4
Measuring What Matters: Synthetic Benchmarks for Concept Bottleneck Models
arXiv:2606. 04326v1 Announce Type: cross Abstract: Concept bottleneck models predict outcomes from high-level concepts detected in inputs.
arXiv Machine Learning
Aug 12
Concept Labels Are Not Enough: Rethinking Concept Bottleneck Models through Representation Integrity
arXiv:2510. 15770v4 Announce Type: replace-cross Abstract: Although deep neural networks achieve strong predictive performance, their internal reasoning often remains difficult to inspect and control.
arXiv AI
Jul 7
Exploring the Rashomon Set for Concept-Based Models
arXiv:2511. 19636v2 Announce Type: replace-cross Abstract: In many machine learning problems, there may exist multiple models that achieve nearly identical predictive performance while relying on fundamentally different internal logic.
arXiv Machine Learning
Aug 12
Clarity: The Flexibility-Interpretability Trade-Off in Sparsity-aware Concept Bottleneck Models
arXiv:2601. 21944v3 Announce Type: replace Abstract: The widespread adoption of deep learning models in computer vision has intensified concerns about interpretability.
Hugging Face Trending Papers
Jun 3
Measuring What Matters: Synthetic Benchmarks for Concept Bottleneck Models
Concept bottleneck models predict outcomes from high-level concepts detected in inputs. Although concepts provide a simple way to reap benefits from interpretability, very few datasets include concept labels.
arXiv AI
3d ago
Interpretable but Fragile? Robustness of Concept Bottlenecks under Geometric-Semantic Perturbations
arXiv:2609.38625v1 Announce Type: cross Abstract: Concept Bottleneck Models (CBMs) are designed to provide interpretable intermediate representations, yet how such bottlenecks affect robustness remai...
arXiv AI
Jul 7
Explainable Novel Category Discovery in Semantic Concept Space
arXiv:2607. 04548v1 Announce Type: cross Abstract: Novel category discovery aims to identify unseen classes from unlabeled data by transferring knowledge from labeled categories, but most existing methods perform discovery in opaque latent feature spaces.
arXiv AI
Jun 19
Concept Flow Models: Anchoring Concept-Based Reasoning with Hierarchical Bottlenecks
arXiv:2606. 19489v1 Announce Type: cross Abstract: Concept Bottleneck Models (CBMs) enhance interpretability by projecting learned features into a human-understandable concept space.
arXiv AI
Jun 10
In Defense of Information Leakage in Concept-based Models
arXiv:2606. 10669v1 Announce Type: cross Abstract: Concept-based models (CMs), deep neural networks that ground their predictions on representations aligned with human-understandable concepts (e.
arXiv AI
Sep 10
Limits of Reliability and Scaling in Language Models
The paper argues that large language models cannot achieve perfect reliability for any task, even with unlimited scale. It establishes that each generative task has an inherent reliability ceiling set by how much output uncertainty can be resolved from observable context, with a resolvable part that can be improved by more context and a subjective part tied to task ambiguity. The authors derive a scaling law showing that performance is limited by the scarcer resource—either training data or model capacity—and explain how this law explains phenomena such as retrieval augmentation and catastrophic forgetting.