Scalability of the algorithmic core framework to contemporary large language models

Ascertain whether the Algorithmic Core Extraction framework for identifying low-dimensional causal subspaces scales to the complexity of contemporary large-scale language models.

Background

The work presents evidence that transformers converge to invariant, low-dimensional cores across several tasks and model sizes, including GPT-2 Small, Medium, and Large. These results suggest a general interpretability approach focused on invariant algorithmic structures rather than implementation-specific circuits.

However, the authors explicitly acknowledge that it remains unresolved whether this framework scales to the complexity of frontier LLMs, framing a clear open question about applicability and robustness at modern scales.