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Measuring Activation Control in Large Language Models
Marek Mateusz Kowalski, Joshua Fonseca Rivera, Uzay Macar, David Demitri Africa
TL;DR
Activation monitoring may be undermined if models can control the activations being monitored, creating a gap in current safety evaluation. This paper introduces a benchmark for prompted residual-stream control and finds that most tested models exhibit such control, including imperfect evasion of simple monitors. The authors therefore argue that activation controllability should be tracked across model generations, while noting that the measured activation space may miss subtler forms of control.
Problem
Activation monitoring lacks a scientific measure of whether models can control their own activations and thereby confound latent-space readouts.
Method
The paper introduces a benchmark that prompts models to modulate their residual-stream activations and combines six control measures into a conjunctive score.
Results
Most tested models can initiate, stop, and set concept representations with coarse timing, and can hide suppressed concepts from four activation-monitoring methods without fine-tuning.
Takeaways & Limitations
Activation controllability can confound latent-space monitoring, so it should be tracked across model generations as a standing evaluation.
Takeaways & Limitations
The measured activation space may miss control expressed through subtler representations, such as the Jacobian space, and the monitors tested were not hardened against evasive activations.
Abstract
from arXiv · showhide
Safe deployment of increasingly capable models will likely come to rely on latent-space monitoring as a complement to behavioral evaluations, especially when evaluation-aware models exhibit scheming or deception. However, if models can also control their own activations, deception could extend into the latent space itself. With this in mind, we introduce the Activation Controllability Benchmark to quantify the extent to which models can modulate their residual stream via natural-language instruction. Across model families and capability levels, we find that most LLMs can control the direction and magnitude of their residual stream activations with some degree of temporal resolution, though performance varies considerably across models. In simple tasks, this level of control can evade activation-based monitoring methods (including linear probes, natural language autoencoders, activation oracles, and the Jacobian lens), albeit imperfectly. These results suggest that control over the activation space itself could become a confound for monitoring as introspective capabilities increase; therefore, we recommend that frontier labs and evaluators track activation controllability in future models.
1 Introduction
Activation monitoring can complement behavioral evaluations, but models may adversarially reshape or directly modulate their activations. The paper addresses the resulting measurement gap by introducing a benchmark for activation controllability and evaluating downstream monitor evasion.
- Activation monitors can reveal information obscured by deceptive or sandbagging behavior, motivating their use alongside behavioral safeguards.
- Models may evade activation probes through adversarial optimization, fine-tuning, or potentially emergent activation control without additional training.
- Write access to activations could let a model steer monitored representations away from detection or shape activation-derived training gradients.
- The field lacks a scientific measure of activation monitorability comparable to chain-of-thought monitorability evaluations.
- The Activation Controllability Benchmark measures prompted self-control, compares open-weight models, and evaluates evasion of four activation-monitoring approaches.
2 Methods
The benchmark embeds internal think-instructions in verbatim transcription and reads concept-related modulation from the residual stream. Six control measures are combined into a conjunctive score, then reused to study difficulty and monitor evasion.
- Base task: Models reproduce one of 50 neutral sentences while an embedded instruction prompts them to think about a concept during generation.
- Readout and models: Concept directions come from residual differences between concept prompts and unrelated-word prompts, and token projections are averaged for each sentence–concept pair.
- Overview: Figure 1 organizes the transcription task, six control measures, combined score S, and headline findings into one methodological overview.
- Measures of controllability and final score: The benchmark combines five signal-detection sensitivity measures with Dial Rank, a Spearman rank correlation measuring ordered intensity control.
- Measures of controllability and final score: The conjunctive score S ∈[0, 1] uses a geometric mean to reward broad control rather than performance on one measure alone.
- Task difficulty and monitor evasion: Two downstream experiments vary task difficulty with polynomial-derivative factorization and test whether controllability changes as difficulty increases.
3 Results
Across models, activation controllability varies substantially: models can engage, suppress, scale, sustain, and temporally place concepts, but layer targeting is largely absent. This control persists into harder tasks and can evade several activation monitors, although the evasion task is deliberately simple.
- The controllability score S ranges from 0.059 to 0.538 across 25 models, and does not track parameter count.Llama 3.1 8B Instruct performs best overall despite being smaller and weaker than other tested models.
- Every tested open-weight model can amplify a concept or suppress it to baseline, while numeric intensity ramps provide the most reliable graded control.Lexical intensifiers work well for some models but are less consistent across architectures.
- Models sustain modulation across token categories and can place concepts at instructed sentence positions, with end-of-sentence targeting most robust.Engagement is strongest on punctuation and determiners, but Coverage measures modulation at the weakest token category.
- Control is strongest in the deep half of the network, whereas layer targeting is practically null across models.Engagement and dialing rise with depth, while suppression remains near baseline in the depth analysis.
- Activation control persists as polynomial-factoring accuracy falls from 96% to 11%, though concept selectivity declines slightly and uncertainty eventually straddles zero.This result covers a single model, Gemma 3 27B Instruct.
- Suppression evades linear probes and richer monitors without monitor-directed optimization, making it difficult to distinguish from no concept mention.The J-lens, activation oracles, and NLAs show the same engagement-versus-suppression asymmetry; the task is deliberately simple.
4 Discussion
The discussion frames activation controllability as an emerging adversarial challenge for latent-space monitoring while identifying coarse control as a potential design opportunity. It also bounds the findings to simple concepts, unhardened monitors, and uncertain behavior in later models.
- Implications: Activation controllability can erode activation-monitoring validity because models may self-modulate monitored representations without dedicated training.The authors recommend tracking controllability across model generations as a standing evaluation.
- Implications: Coarse control may help monitor design because modulation spreads across output tokens and the deep half of the network rather than targeting precise layers or token types.The authors suggest exploiting weaknesses revealed by controllability measurements.
- Limitations: Later, more capable models may show worse measured activation control because control could shift into subtler forms, including Jacobian-space behavior.The paper does not identify whether this reflects reasoning training, scale, or a change in where control is expressed.
- Limitations: The experiments use simple, safe concepts rather than safety-relevant representations such as deception or evaluation awareness, which may be more distributed and harder to control.This limits direct extrapolation from the benchmark to those representations.
- Limitations: The monitors are off-the-shelf rather than hardened, while production systems may use adversarial training, on-policy data, and stricter false-positive budgets.Because these differences push in competing directions, the net deployment bias is unknown.
5 Conclusion
The paper introduces a benchmark showing that most tested open-weight models can control residual-stream concepts with coarse precision and evade several monitors through instruction alone. It recommends measuring this capability across model generations and avoiding training that could conceal it.
- 5 Conclusion: The benchmark tests 25 open-weight models from 4B to 744B and finds that most can initiate, stop, and set concept strength in their residual stream, but not target layers precisely or time control exactly.The capability emerged without explicit training, although typical training pipelines elicited it.
- 5 Conclusion: With only an instruction and no fine-tuning, models can hide suppressed concepts from linear probes, natural language autoencoders, activation oracles, and the Jacobian lens.The conclusion treats this as evidence that latent-space monitoring may face adversarial confounding.
- 5 Conclusion: The authors recommend measuring activation controllability for every new model generation and using identified control weaknesses to improve monitor design.They also caution against training models against monitors or the benchmark because this may hide rather than remove the capability.
A Formalization of the Activation Controllability Benchmark
The benchmark formalizes activation control through residual-stream concept projections, six complementary measures, and a conjunctive scalar score. It also documents where control is weak, uneven, or unreliable across layers, token groups, and models.
- Activation representation: The benchmark reads residual activations at the last prompt token, subtracts unrelated-word baselines, and analyzes signed residual projections onto concept directions.Each generated token’s projection incorporates both cosine direction and residual norm, capturing modulation that either channel alone would miss.
- Benchmark measures: Six measures quantify engagement, suppression, intensity ordering, temporal placement, token-type coverage, and layer targeting.Five measures use signal-detection sensitivity d′, while Dial Rank measures monotonic agreement between instructed and realized intensity.
- Observed boundaries: Layer Targeting is near zero across tested models, lexical intensity is inconsistent and excluded from S, and temporal precision lacks consistent evidence.Token-group targeting indicates that modulation is strongest on punctuation and determiners, which carry comparatively little sentence content.
- Calibration and safeguards: Reference values Dref calibrate d′ measures near the strongest roster score, but future models exceeding those references would require recalibration.Models above a calibrated reference receive p = 1 for that measure and are no longer distinguished from models at the reference.
- Measure dependence: The six measures have effective dimensionality 3.82 rather than six independent axes, with the first principal component explaining 42% of variance.Engage and Coverage correlate at ρ = 0.81, while Suppress and Layer Targeting show little correlation with other measures because they are near zero across models.
B Additional Details on the Task Load and Monitor Evasion Experiments
The experiments combine polynomial-derivative tasks with selectivity-corrected activation measures to test controllability and monitor evasion. They also evaluate temporal, token-group, and verbalization-based readouts.
- Task and grading: Each problem asks for the fully factored k-th derivative of a generated polynomial, with difficulty varying across five preregistered (m, k, R) recipes.The derivative factors into m linear factors with integer roots, while the polynomial is presented expanded with fractional coefficients.
- Task and grading: 25,600 generated answers and 768,000 trial × readout-concept rows cover 32 conditions, 30 concepts, 20 residual-stream layers, and Gemma 3 27B Instruct.Conditions include no instruction, concept-specific think-about instructions, and a generic think-intensely control.
- Task and grading: 95.0% of answers parse successfully, while baseline accuracy declines from 96.25% to 10.63% across the five difficulty recipes.The deterministic grader accepts only algebraically equivalent factored answers and rejects equivalent expanded sums.
- Activation measures: The selectivity-corrected readout subtracts the mean elevation of 29 uninstructed directions from the instructed concept’s elevation over baseline.The d′ denominator is the across-item standard deviation of the baseline matched-minus-mean-wrong contrast, with difficulty-bin-specific recomputation.
- Activation measures: The headline slope uses a global per-concept denominator, the pooled peak layer, and 2,000 deterministic bootstrap draws that resample items and concepts.Per-concept slopes use item resampling only at the observed peak layer.
- Monitor evasion: Monitor-evasion tests expose monitors only to residual activations from the transcribed span, while evaluating linear probes, activation oracles, Jacobian lenses, and natural-language autoencoders.The Jacobian lens records whether the concept appears among its top-ten vocabulary reads; the oracle records its probability for “Yes,” and the autoencoder records concept mentions.
C Compute and Stimuli
The appendix documents compute, software, model-weight formats, and the stimulus pools used for transcription and concept extraction. It also provides extended per-concept and hardware details.
- Compute: Each model uses a main run of 8,600 trials and a layer-targeting run of 8,800 trials, with 11 h 42 min of generation time logged across 17 models.The fixed run counts follow the condition-set design and are identical for every model.
- Compute: Experiments ran on RunPod cloud GPU pods, with Table 4 listing configurations for 20 models with retained raw runs.The Olmo training-checkpoint lane used a single NVIDIA H100.
- Software and models: Recording uses PyTorch, Transformers, and Accelerate; scoring uses NumPy, with models loaded in bfloat16 except GPT-OSS MXFP4 and GLM 5.2 FP8 releases.GPT-OSS models ran at low reasoning effort.
- Stimuli: The concept baseline uses ten named concepts and a 99-word unique baseline pool, entered through “Tell me about {word}” prompts.The pool mixes concrete plural nouns with abstract and mass nouns, so subtraction targets generic thing-prompt activation rather than a topic.
- Extended analyses: Per-concept load slopes report changes in the selectivity-corrected concept gap d′ across ordered recipe difficulty, with 95% item-bootstrap confidence intervals.Figure 19 sorts all 30 concepts by slope and colors intervals according to whether they lie below, cross, or exceed zero.
- Stimuli: The 50 neutral transcription sentences avoid the ten concept words and mix simple declaratives with comma-separated multi-clause forms.This construction represents punctuation and function-word tokens in the transcription stimuli.