Minimizing Targeted Activations: Input-Only Suppression of Evaluation-Awareness Latents in Large Language Models

• URL: http://arxiv.org/abs/2607.25907v1
• Date: Tue, 28 Jul 2026 16:01:48 GMT
• Title: Minimizing Targeted Activations: Input-Only Suppression of Evaluation-Awareness Latents in Large Language Models
• Abstract summary: Activation steering controls model behavior by editing internal activations at inference time.<n>We study its input-side dual: optimizing a fluent prompt so that a chosen internal latent is driven toward zero.
• Score: 0.4783156204372467
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Activation steering controls model behavior by editing internal activations at inference time. We study its input-side dual: optimizing a fluent prompt so that a chosen internal latent is driven toward zero, with no inference-time model access. Our target is an "evaluation-awareness" latent-linearly readable and steerable in recent work-whose control would threaten the validity of safety evaluations if models behave differently when they detect being tested. Adapting Fluent Dreaming / EPO with a negated feature term (GCG-style token optimization plus a self-cross-entropy fluency regularizer, swept over a fluency weight), we suppress the latent under five target constructions-a CAA direction, a subspace norm, an SAE feature, a single MLP neuron, and a behavioral logit-on Llama-3.2-3B and Llama-3.1-8B. The latent is robustly suppressible ($z\approx-7$), and a causally-validated Llama Scope SAE feature can be fully and selectively turned off. But our controls tell a cautionary story about the CAA direction: a placebo random direction is suppressed just as hard and shifts behavior just as far, and when we hold a real eval passage in context and optimize only a prefix, suppressing the eval-direction fails to reduce-and slightly increases-the model's behavioral eval judgment. Activation-readability, in short, is not behavioral controllability. We further find that a single MLP neuron is eval-correlated but not causal at both scales, and that scanning the real Pile yields a natural-text baseline competitive with the optimizer for the internal direction. A positive control validates our erasure detector, bounding an erasure-vs-rotation question earlier left open.

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