# Granular Concept Circuits: Toward a Fine-Grained Circuit Discovery for Concept Representations

arXiv: https://arxiv.org/abs/2508.01728 · HTML: https://mlsanity.com/paper/2508.01728/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Circuits and mechanistic analysis, one of 13 directions in Mechanistic interpretability.
- Builds on 23 of its 40 references that are in the map.
- First posted on arXiv 3 Aug 2025.

## Placed in

- Mechanistic interpretability / [Circuits and mechanistic analysis](https://mlsanity.com/mechinterp/circuits-and-mechanistic-analysis/)

## Builds on (most cited first, 23 of 23)

- [Locating and Editing Factual Associations in GPT](https://mlsanity.com/paper/2202.05262/) (2022)
- [Transformer Feed-Forward Layers Are Key-Value Memories](https://mlsanity.com/paper/2012.14913/) (2020)
- [Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)](https://mlsanity.com/paper/1711.11279/) (2017)
- [Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small](https://mlsanity.com/paper/2211.00593/) (2022)
- [RISE: Randomized Input Sampling for Explanation of Black-box Models](https://mlsanity.com/paper/1806.07421/) (2018)
- [Towards Automated Circuit Discovery for Mechanistic Interpretability](https://mlsanity.com/paper/2304.14997/) (2023)
- [Network Dissection: Quantifying Interpretability of Deep Visual Representations](https://mlsanity.com/paper/1704.05796/) (2017)
- [Knowledge Neurons in Pretrained Transformers](https://mlsanity.com/paper/2104.08696/) (2021)
- [Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models](https://mlsanity.com/paper/2403.19647/) (2024)
- [Understanding the Role of Individual Units in a Deep Neural Network](https://mlsanity.com/paper/2009.05041/) (2020)
- [CRAFT: Concept Recursive Activation FacTorization for Explainability](https://mlsanity.com/paper/2211.10154/) (2022)
- [CLIP-Dissect: Automatic Description of Neuron Representations in Deep Vision Networks](https://mlsanity.com/paper/2204.10965/) (2022)
- [From Attribution Maps to Human-Understandable Explanations through Concept Relevance Propagation](https://mlsanity.com/paper/2206.03208/) (2022)
- [Natural Language Descriptions of Deep Visual Features](https://mlsanity.com/paper/2201.11114/) (2022)
- [Revisiting the Importance of Individual Units in CNNs via Ablation](https://mlsanity.com/paper/1806.02891/) (2018)
- [Identifying Interpretable Subspaces in Image Representations](https://mlsanity.com/paper/2307.10504/) (2023)
- [From Colors to Classes: Emergence of Concepts in Vision Transformers](https://mlsanity.com/paper/2503.24071/) (2025)
- [Visual Concept Connectome (VCC): Open World Concept Discovery and their Interlayer Connections in Deep Models](https://mlsanity.com/paper/2404.02233/) (2024)
- [HINT: Hierarchical Neuron Concept Explainer](https://mlsanity.com/paper/2203.14196/) (2022)
- [Sheaf Discovery with Joint Computation Graph Pruning and Flexible Granularity](https://mlsanity.com/paper/2407.03779/) (2024)
- [Automatic Discovery of Visual Circuits](https://mlsanity.com/paper/2404.14349/) (2024)
- [Discovering Influential Neuron Path in Vision Transformers](https://mlsanity.com/paper/2503.09046/) (2025)
- [Understanding Distributed Representations of Concepts in Deep Neural Networks without Supervision](https://mlsanity.com/paper/2312.17285/) (2023)

## Built on by (most cited first, 5 of 5)

- [Latent Anomaly Knowledge Excavation: Unveiling Sparse Sensitive Neurons in Vision-Language Models](https://mlsanity.com/paper/2604.07802/) (2026)
- [Query Circuits: Explaining How Language Models Answer User Prompts](https://mlsanity.com/paper/2509.24808/) (2025)
- [V-FIND: Revealing the Intrinsic Forgery Knowledge Encoded in Video Forgery Detectors](https://mlsanity.com/paper/2608.03008/) (2026)
- [From Interpretability Methods to Interpretable Models](https://mlsanity.com/paper/2609.05399/) (2026)
- [SPICE: Simple Polysemantic Feature Interpretation via Clustering-based Explanation](https://mlsanity.com/paper/2609.13198/) (2026)

## Abstract (arXiv)

Deep vision models have achieved remarkable classification performance by leveraging a hierarchical architecture in which human-interpretable concepts emerge through the composition of individual neurons across layers. Given the distributed nature of representations, pinpointing where specific visual concepts are encoded within a model remains a crucial yet challenging task. In this paper, we introduce an effective circuit discovery method, called Granular Concept Circuit (GCC), in which each circuit represents a concept relevant to a given query. To construct each circuit, our method iteratively assesses inter-neuron connectivity, focusing on both functional dependencies and semantic alignment. By automatically discovering multiple circuits, each capturing specific concepts within that query, our approach offers a profound, concept-wise interpretation of models and is the first to identify circuits tied to specific visual concepts at a fine-grained level. We validate the versatility and effectiveness of GCCs across various deep image classification models.