Smaller NLAs and probes read eval awareness as well as full size NLAs

TLDR: We distill and train various Natural Language Autoencoders (“NLAs”) to read Qwen3.6-27b’s internal states. A 2B NLA distilled from a 27B NLA matches it on reading agentic misalignment and eval awareness, but is much worse on obscure facts. An NLA trained with extra alignment-focused RL didn’t improve on anything. A linear probe trained on 2B-generated labels matches a 27B NLA + LLM judge on reading eval awareness, at a tiny fraction of the cost. Eval awareness results come from 76 technical tasks with injected cues. We haven’t yet tested how far they generalise. Introduction NLAs train a pair of LLMs: one to construct text from the information in a given LLM’s vector, and the other to reconstruct the vector. The constructed text is an imperfect but useful indicator of what the model is thinking about at any token position. These are expensive to run, as they involve generating whole paragraphs of tokens to read one token of thought. We distill a trained NLA reader for Qwen3.6-27b to a Qwen3.5-2b model, and train the model after distillation to boost its performance. The reason: to read Qwen3.6-27b’s (“Qwen”) activations at a much lower cost, which if performant could enable r