mlsanityData to 14 Oct 2026

Circuit Stability Characterizes Language Model Generalization

arXiv 2505.24731 ↗First posted 30 May 2025cs.CLcited by 6

On the map: Mechanistic interpretability → Circuits and mechanistic analysis

Abstract (arXiv)

Extensively evaluating the capabilities of (large) language models is difficult. Rapid development of state-of-the-art models induce benchmark saturation, while creating more challenging datasets is labor-intensive. Inspired by the recent developments in mechanistic interpretability, we introduce circuit stability as a new way to assess model performance. Circuit stability refers to a model's ability to apply a consistent reasoning process-its circuit-across various inputs. We mathematically formalize circuit stability and circuit equivalence. Then, through three case studies, we empirically show that circuit stability and the lack thereof can characterize and predict different aspects of generalization. Our proposed methods offer a step towards rigorously relating the generality of models to their interpretability.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Placed in Circuits and mechanistic analysis, one of 13 directions in Mechanistic interpretability. Builds on 40 of its 63 references that are in the map. First posted on arXiv 30 May 2025.

Builds on

40 of 63 references.

Closest ancestors · 8

Same direction, newest first

  1. 2024Hypothesis Testing the Circuit Hypothesis in LLMs16 Oct
  2. 2024The Computational Complexity of Circuit Discovery for Inner Interpretability10 Oct
  3. 2024InterpBench: Semi-Synthetic Transformers for Evaluating Mechanistic Interpretability Techniques19 Jul
  4. 2023Attribution Patching Outperforms Automated Circuit Discovery16 Oct
  5. 2023Towards Automated Circuit Discovery for Mechanistic Interpretability28 Apr
  6. 2023A Toy Model of Universality: Reverse Engineering How Networks Learn Group Operations6 Feb
  7. 2023Progress measures for grokking via mechanistic interpretability12 Jan
  8. 2022Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small1 Nov

Other references · 32

  1. 2017Attention Is All You Needcited by 60,361
  2. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
  3. 2022Locating and Editing Factual Associations in GPTcited by 2,691
  4. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,998
  5. 2024Gemma 2: Improving Open Language Models at a Practical Sizecited by 1,998
  6. 2022Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Themcited by 1,807
  7. 2024Refusal in Language Models Is Mediated by a Single Directioncited by 1,032
  8. 2022In-context Learning and Induction Headscited by 964
  9. 2021Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layerscited by 608
  10. 2023Textbooks Are All You Need II: phi-1.5 technical reportcited by 565
  11. 2021Causal Abstractions of Neural Networkscited by 422
  12. 2024FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AIcited by 243
20 more
  1. 2024How to use and interpret activation patchingcited by 214
  2. 2024A Mechanistic Understanding of Alignment Algorithms: A Case Study on DPO and Toxicitycited by 185
  3. 2024An Introduction to Vision-Language Modelingcited by 178
  4. 2023The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networkscited by 156
  5. 2023Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchillacited by 149
  6. 2024Have Faith in Faithfulness: Going Beyond Circuit Overlap When Finding Model Mechanismscited by 149
  7. 2023A Theory for Emergence of Complex Skills in Language Modelscited by 113
  8. 2024AtP*: An efficient and scalable method for localizing LLM behaviour to componentscited by 111
  9. 2024Arithmetic Without Algorithms: Language Models Solve Math With a Bag of Heuristicscited by 97
  10. 2021Towards a Theoretical Framework of Out-of-Distribution Generalizationcited by 94
  11. 2023Compositional Generalization from First Principlescited by 64
  12. 2023Skill-Mix: a Flexible and Expandable Family of Evaluations for AI modelscited by 64
  13. 2024LLM Circuit Analyses Are Consistent Across Training and Scalecited by 63
  14. 2024Finding Transformer Circuits with Edge Pruningcited by 60
  15. 2024Learning to grok: Emergence of in-context learning and skill composition in modular arithmetic taskscited by 40
  16. 2022A Measure-Theoretic Characterization of Tight Language Modelscited by 33
  17. 2024Arithmetic Transformers Can Length-Generalize in Both Operand Length and Countcited by 18
  18. 2023Do Deep Neural Networks Capture Compositionality in Arithmetic Reasoning?cited by 11
  19. 2024Minimum Description Length and Generalization Guarantees for Representation Learningcited by 11
  20. 2024Achieving Domain-Independent Certified Robustness via Knowledge Continuitycited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 5.

6 papers.

  1. 2026AutoSOTA: An End-to-End Automated Research System for State-of-the-Art AI Model Discoverycited by 23
  2. 2025CorrSteer: Generation-Time LLM Steering via Correlated Sparse Autoencoder Featurescited by 12
  3. 2026Navigating by Old Maps: The Pitfalls of Static Mechanistic Localization in LLM Post-Trainingcited by 4
  4. 2026Reforming the Mechanism: Editing Reasoning Patterns in LLMs with Circuit Reshapingcited by 1
  5. 2026CircuitKIT : Circuit Discovery, Evaluation, and Application Toolkit for Mechanistic Interpretabilitycited by 0
  6. 2026Shared circuits predict whether LLMs generalize across formats in arithmetic reasoningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Are Your LLMs Capable of Stable Reasoning?
  2. 2025Towards Understanding Fine-Tuning Mechanisms of LLMs via Circuit Analysis
  3. 2026Quantifying the Stability of Multi-Step Reasoning via Error Amplification
  4. 2025Query Circuits: Explaining How Language Models Answer User Prompts
  5. 2026A Mechanistic Analysis of Looped Reasoning Language Models
  6. 2026Mechanistic Interpretability for Large Language Model Alignment: Progress, Challenges, and Future Directions
  7. 2024Adaptive Circuit Behavior and Generalization in Mechanistic Interpretability
  8. 2026Can Language Model Agents be Helpful Circuit Explainers in Mechanistic Interpretability?