7/9 For generality, we present these findings on simpler architectures (MLPs) across multiple random seeds and two additional tasks. This indicates that the issue is not confined to LLMs, but applies more broadly.

6/9 We further show that small LLMs, which fail at the Indirect Object Identification task, can nevertheless be interpreted as containing such an algorithm.

5/9 Beyond the theoretical argument, we present a broad set of experiments supporting our claim. Most notably, we show that a randomly initialised LLM can be interpreted as implementing an algorithm for Indirect Object Identification.

3/9 While we do not critique causal abstraction as a framework, we show that combining it with current insights that modern models store information in a distributed way introduces a fundamental problem.

1/9 In our new interpretability paper, we analyse causal abstraction—the framework behind Distributed Alignment Search—and show it breaks when we remove linearity constraints on feature representations. We refer to this problem as the Non-Linear Representation Dilemma.