TL;DR: We train LLMs to accept LLM neural activations as inputs and answer arbitrary questions about them in natural language. These Activation Oracles generalize far beyond their training distribution, for example uncovering misalignment or secret knowledge introduced via fine-tuning. Activation Oracles can be improved simply by scaling training data quantity and diversity. The below is a reproduction of our X thread on this paper and the Anthropic Alignment blog post. Thread New paper:
We train Activation Oracles: LLMs that decode their own neural activations and answer questions about them in natural language.
We find surprising generalization. For instance, our AOs uncover misaligned goals in fine-tuned models, without training to do so. We aim to make a general-purpose LLM for explaining activations by:
1. Training on a diverse set of tasks
2. Evaluating on tasks very different from training
This extends prior work (LatentQA) that studied activation verbalization in narrow settings. Our main evaluations are downstream auditing tasks. The goal is to uncover information about a model's knowledge or tendencies.

Applying Activation Oracles is easy. Choose the activation (or set of activations) you want to interpret and ask any question you like! We [...] ---Outline:(00:46) Thread(04:49) Blog post(05:27) Introduction(07:29) Method(10:15) Activation Oracles generalize to downstream auditing tasks(13:47) How does Activation Oracle training scale?(15:01) How do Activation Oracles relate to mechanistic approaches to interpretability?(19:31) Conclusion The original text contained 3 footnotes which were omitted from this narration. ---
First published:
December 18th, 2025

Source:
https://www.lesswrong.com/posts/rwoEz3bA9ekxkabc7/activation-oracles-training-and-evaluating-llms-as-general
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