Interpretability on Omanshu Thapliyal

A Privileged Basis for the Recurrent State of SSMs

Tue, 03 Nov 2026 14:00:00 -0800

This post describes ongoing research. Preliminary code is available in ssm-interp and ssm-interp-runs, and the per-layer statistics are on Hugging Face.

Most of the tools we use to look inside language models were built for transformers. When we want to know where a model computes something, we patch or ablate an attention head or a direction in the residual stream at some position, and watch what changes in the output. These tools have worked remarkably well, and the indirect object identification (IOI) circuit in GPT-2 is perhaps the best known example of what they can find.¹

A Probe Knows More Than the Model Says

Fri, 28 Aug 2026 10:00:00 -0700

Written for Apart Research’s Digital Minds Research Sprint. The report and code are linked at the end, and Do Language Models Mean What They Choose? covers my other project from the same sprint.

When a language model tells you something about itself, such as whether a fact you mentioned earlier shaped its answer, it is tempting to take it at its word. But a chain of thought can be unfaithful to the reasoning that actually produced an answer,¹ and the more a welfare or safety argument leans on what models say about themselves, the more it matters whether those reports track anything real.