Hierarchical Latent Structures in Data Generation Process Unify Mechanistic Phenomena across Scale

• URL: http://arxiv.org/abs/2603.06592v1
• Date: Wed, 04 Feb 2026 14:49:13 GMT
• Title: Hierarchical Latent Structures in Data Generation Process Unify Mechanistic Phenomena across Scale
• Abstract summary: We use context-free grammars to generate synthetic corpora that are faithful and computationally efficient proxies for web-scale text corpora.<n>We investigate the emergence of three mechanistic phenomena: induction heads, function vectors, and the Hydra effect, under our designed data generation process.<n>Our findings suggest that hierarchical structures in the data generation process serve as the X-factor in explaining the emergence of these phenomena.
• Score: 50.33952894976367
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Contemporary studies have uncovered many puzzling phenomena in the neural information processing of Transformer-based language models. Building a robust, unified understanding of these phenomena requires disassembling a model within the scope of its training. While the intractable scale of pretraining corpora limits a bottom-up investigation in this direction, simplistic assumptions of the data generation process limit the expressivity and fail to explain complex patterns. In this work, we use probabilistic context-free grammars (PCFGs) to generate synthetic corpora that are faithful and computationally efficient proxies for web-scale text corpora. We investigate the emergence of three mechanistic phenomena: induction heads, function vectors, and the Hydra effect, under our designed data generation process, as well as in the checkpoints of real-world language models. Our findings suggest that hierarchical structures in the data generation process serve as the X-factor in explaining the emergence of these phenomena. We provide the theoretical underpinnings of the role played by hierarchy in the training dynamics of language models. In a nutshell, our work is the first of its kind to provide a unified explanation behind the emergence of seemingly unrelated mechanistic phenomena in LLMs, augmented with efficient synthetic tooling for future interpretability research.

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