# Adaptive Circuit Behavior and Generalization in Mechanistic Interpretability

arXiv: https://arxiv.org/abs/2411.16105 · HTML: https://mlsanity.com/paper/2411.16105/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Circuits and mechanistic analysis, one of 13 directions in Mechanistic interpretability.
- Builds on 5 of its 8 references that are in the map.
- First posted on arXiv 25 Nov 2024.

## Placed in

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

## Builds on (most cited first, 5 of 5)

- [Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small](https://mlsanity.com/paper/2211.00593/) (2022)
- [How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model](https://mlsanity.com/paper/2305.00586/) (2023)
- [Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking](https://mlsanity.com/paper/2402.14811/) (2024)
- [Have Faith in Faithfulness: Going Beyond Circuit Overlap When Finding Model Mechanisms](https://mlsanity.com/paper/2403.17806/) (2024)
- [A Mechanistic Interpretation of Arithmetic Reasoning in Language Models using Causal Mediation Analysis](https://mlsanity.com/paper/2305.15054/) (2023)

## Built on by (most cited first, 7 of 7)

- [Unboxing the Black Box: Mechanistic Interpretability for Algorithmic Understanding of Neural Networks](https://mlsanity.com/paper/2511.19265/) (2025)
- [Many Circuits, One Mechanism: Input Variation and Evaluation Granularity in Circuit Discovery](https://mlsanity.com/paper/2606.06267/) (2026)
- [Interpreting the Latent Structure of Operator Precedence in Language Models](https://mlsanity.com/paper/2510.13908/) (2025)
- [From Indirect Object Identification to Syllogisms: Exploring Binary Mechanisms in Transformer Circuits](https://mlsanity.com/paper/2508.16109/) (2025)
- [Distilled Circuits: A Mechanistic Study of Internal Restructuring in Knowledge Distillation](https://mlsanity.com/paper/2505.10822/) (2025)
- [EXP-CAM: Explanation Generation and Circuit Discovery Using Classifier Activation Matching](https://mlsanity.com/paper/2509.25686/) (2025)
- [Inside-Out: Measuring Generalization in Vision Transformers Through Inner Workings](https://mlsanity.com/paper/2604.08192/) (2026)

## Abstract (arXiv)

Mechanistic interpretability aims to understand the inner workings of large neural networks by identifying circuits, or minimal subgraphs within the model that implement algorithms responsible for performing specific tasks. These circuits are typically discovered and analyzed using a narrowly defined prompt format. However, given the abilities of large language models (LLMs) to generalize across various prompt formats for the same task, it remains unclear how well these circuits generalize. For instance, it is unclear whether the models generalization results from reusing the same circuit components, the components behaving differently, or the use of entirely different components. In this paper, we investigate the generality of the indirect object identification (IOI) circuit in GPT-2 small, which is well-studied and believed to implement a simple, interpretable algorithm. We evaluate its performance on prompt variants that challenge the assumptions of this algorithm. Our findings reveal that the circuit generalizes surprisingly well, reusing all of its components and mechanisms while only adding additional input edges. Notably, the circuit generalizes even to prompt variants where the original algorithm should fail; we discover a mechanism that explains this which we term S2 Hacking. Our findings indicate that circuits within LLMs may be more flexible and general than previously recognized, underscoring the importance of studying circuit generalization to better understand the broader capabilities of these models.