Toward a Theory of Generalizability in LLM Mechanistic Interpretability Research

• URL: http://arxiv.org/abs/2509.22831v1
• Date: Fri, 26 Sep 2025 18:38:16 GMT
• Title: Toward a Theory of Generalizability in LLM Mechanistic Interpretability Research
• Abstract summary: I propose five axes of correspondence along which mechanistic claims might generalize.<n>I analyze "1-back attention heads" across pretraining in random seeds of the Pythia models.
• Score: 3.1304584228602685
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Research on Large Language Models (LLMs) increasingly focuses on identifying mechanistic explanations for their behaviors, yet the field lacks clear principles for determining when (and how) findings from one model instance generalize to another. This paper addresses a fundamental epistemological challenge: given a mechanistic claim about a particular model, what justifies extrapolating this finding to other LLMs -- and along which dimensions might such generalizations hold? I propose five potential axes of correspondence along which mechanistic claims might generalize, including: functional (whether they satisfy the same functional criteria), developmental (whether they develop at similar points during pretraining), positional (whether they occupy similar absolute or relative positions), relational (whether they interact with other model components in similar ways), and configurational (whether they correspond to particular regions or structures in weight-space). To empirically validate this framework, I analyze "1-back attention heads" (components attending to previous tokens) across pretraining in random seeds of the Pythia models (14M, 70M, 160M, 410M). The results reveal striking consistency in the developmental trajectories of 1-back attention across models, while positional consistency is more limited. Moreover, seeds of larger models systematically show earlier onsets, steeper slopes, and higher peaks of 1-back attention. I also address possible objections to the arguments and proposals outlined here. Finally, I conclude by arguing that progress on the generalizability of mechanistic interpretability research will consist in mapping constitutive design properties of LLMs to their emergent behaviors and mechanisms.

Related papers

• Mechanistic Interpretability of Structure-Aware Numerical Reasoning in LLaMA 3.1 8B [51.10897657510313]
  Large language models (LLMs) exhibit strong numerical sequence modeling capabilities and show promise in time-series prediction.<n>We investigate Llama 3.1-8B from a mechanistic interpretability point of view.<n>We find that Llama displays strong performance on our tasks suggesting that it can pick up on the underlying structure.
  arXiv  Detail & Related papers  (2026-08-19T01:09:51Z)
• CDFM: Towards a General-Purpose Causal Discovery Foundation Model [62.32138962508216]
  Causal discovery is the process of recovering underlying causal structures from observational data.<n>We formulate the Causal Discovery Foundation Model (CDFM) as a unified, general-purpose framework for zero-shot structural inference.<n>CDFM consistently outperforms traditional algorithms, driving a paradigm shift toward a general-purpose causal discovery foundation model.
  arXiv  Detail & Related papers  (2026-07-13T12:58:28Z)
• Position: Prioritize Identifying Structure, Not Complex Models, for Scientific Discovery [1.3875545441867139]
  Modern Machine Learning (ML) and Artificial Intelligence (AI) models are increasingly used to generate scientific hypotheses and mechanistic explanations from observational data.<n>This paper argues that in the high-dimensional proxy regimes where modern ML excels, mechanistic learning is generically underdetermined.<n>This underdetermination becomes uniquely hazardous with large language models (LLMs), which tend to collapse large equivalence classes of explanations into a single fluent narrative.
  arXiv  Detail & Related papers  (2026-05-30T15:21:58Z)
• How and Why LLMs Generalize: A Fine-Grained Analysis of LLM Reasoning from Cognitive Behaviors to Low-Level Patterns [51.02752099869218]
  Large Language Models (LLMs) display strikingly different generalization behaviors.<n>We introduce a novel benchmark that decomposes reasoning into atomic core skills.<n>We show that RL-tuned models maintain more stable behavioral profiles and resist collapse in reasoning skills, whereas SFT models exhibit sharper drift and overfit to surface patterns.
  arXiv  Detail & Related papers  (2025-12-30T08:16:20Z)
• From Black-box to Causal-box: Towards Building More Interpretable Models [57.23201263629627]
  We introduce the notion of causal interpretability, which formalizes when counterfactual queries can be evaluated from a specific class of models.<n>We derive a complete graphical criterion that determines whether a given model architecture supports a given counterfactual query.
  arXiv  Detail & Related papers  (2025-10-24T20:03:18Z)
• Can Interpretation Predict Behavior on Unseen Data? [11.280404893713213]
  Interpretability research often aims to predict how a model will respond to targeted interventions on specific mechanisms.<n>This paper explores the promises and challenges of interpretability as a tool for predicting out-of-distribution model behavior.
  arXiv  Detail & Related papers  (2025-07-08T23:07:33Z)
• From Thinking to Output: Chain-of-Thought and Text Generation Characteristics in Reasoning Language Models [10.38327947136263]
  This paper proposes a novel framework for analyzing the reasoning characteristics of four cutting-edge large reasoning models.<n>A diverse dataset consists of real-world scenario-based questions covering logical deduction, causal inference, and multi-step problem-solving.<n>The research results uncover various patterns of how these models balance exploration and exploitation, deal with problems, and reach conclusions.
  arXiv  Detail & Related papers  (2025-06-20T14:02:16Z)
• ProtoReasoning: Prototypes as the Foundation for Generalizable Reasoning in LLMs [54.154593699263074]
  ProtoReasoning is a framework that enhances the reasoning ability of Large Reasoning Models.<n>ProtoReasoning transforms problems into corresponding prototype representations.<n>ProtoReasoning achieves 4.7% improvement over baseline models on logical reasoning.
  arXiv  Detail & Related papers  (2025-06-18T07:44:09Z)
• Internal Causal Mechanisms Robustly Predict Language Model Out-of-Distribution Behaviors [61.92704516732144]
  We show that the most robust features for correctness prediction are those that play a distinctive causal role in the model's behavior.<n>We propose two methods that leverage causal mechanisms to predict the correctness of model outputs.
  arXiv  Detail & Related papers  (2025-05-17T00:31:39Z)
• Computation Mechanism Behind LLM Position Generalization [59.013857707250814]
  Large language models (LLMs) exhibit flexibility in handling textual positions.<n>They can understand texts with position perturbations and generalize to longer texts.<n>This work connects the linguistic phenomenon with LLMs' computational mechanisms.
  arXiv  Detail & Related papers  (2025-03-17T15:47:37Z)
• On the Reasoning Capacity of AI Models and How to Quantify It [0.0]
  Large Language Models (LLMs) have intensified the debate surrounding the fundamental nature of their reasoning capabilities.<n>While achieving high performance on benchmarks such as GPQA and MMLU, these models exhibit limitations in more complex reasoning tasks.<n>We propose a novel phenomenological approach that goes beyond traditional accuracy metrics to probe the underlying mechanisms of model behavior.
  arXiv  Detail & Related papers  (2025-01-23T16:58:18Z)
• Failure Modes of LLMs for Causal Reasoning on Narratives [51.19592551510628]
  We investigate the interaction between world knowledge and logical reasoning.<n>We find that state-of-the-art large language models (LLMs) often rely on superficial generalizations.<n>We show that simple reformulations of the task can elicit more robust reasoning behavior.
  arXiv  Detail & Related papers  (2024-10-31T12:48:58Z)
• Predictive Churn with the Set of Good Models [61.00058053669447]
  This paper explores connections between two seemingly unrelated concepts of predictive inconsistency.<n>The first, known as predictive multiplicity, occurs when models that perform similarly produce conflicting predictions for individual samples.<n>The second concept, predictive churn, examines the differences in individual predictions before and after model updates.
  arXiv  Detail & Related papers  (2024-02-12T16:15:25Z)
• Building Object-based Causal Programs for Human-like Generalization [0.0]
  We present a novel task that measures how people generalize objects' causal powers. We propose a computational modeling framework that can synthesize human-like generalization patterns.
  arXiv  Detail & Related papers  (2021-11-20T15:21:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.