Mechanistic Interpretability of Structure-Aware Numerical Reasoning in LLaMA 3.1 8B
Recent work has shown that large language models (LLMs) exhibit strong numerical sequence modeling capabilities and show promise in time-series prediction. While LLMs display in-context learning capabilities, the mechanisms with which they accomplish time-series prediction remain unclear. Specifically, whether they truly understand the underlying structure, which at a minimum requires reasoning over first differences in the sequence of numbers. To study this, we investigate Llama 3.1-8B from a mechanistic interpretability point of view. Mechanistic interpretability is an emerging field concerned with the reverse engineering of the algorithms learned by neural networks such as LLMs. To ass...
| Authors | Rahul Chowdhury, Timothy A Rupprecht, Senhao Cao et al. |
|---|---|
| Primary category | cs.LG |
| Published | 19 August 2026 |
| Last updated at source | 19 August 2026 |
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