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Sprint projectMar 23, 2026Lausanne

Can Language Models Tell When They Are Being Evaluated?

Chengheng Li Chen, Kyuhee Kim · Team Can Language Models Tell When They Are Being Evaluated?

Submitted to AI Control Hackathon 2026. Sprint projects are early-stage work by participants, not Apart Research publications.

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We investigate whether language models can distinguish evaluations from deployment and whether the underlying representations can be steered to counteract sandbagging. Testing 14 models from 8 families on 2,857 forced-choice pairs across 33 thematic pools, we find that all models detect evaluations above chance (56.6--87.0\%) with broad cross-model agreement ($\rho > 0.7$ for the majority of model pairs), and that format (not topic) is the primary cue. We extract two contrastive steering directions on Llama-3.1-8B and Phi-4: a \emph{format direction} (eval-vs-deploy structure) and a \emph{mode direction} (sandbagging-vs-honest regime). Combined steering closes 85\% of the sandbagging gap on WMDP and transfers to OOD benchmarks (73--89\% recovery). On Phi-4, the mode direction is inverted at its critical layer, encoding condition identity rather than capability. Both models from different families exhibit the same pattern: format is a primary confound, the two directions are complementary, and steering is localised despite universal probe separability.

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How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. Exceptionally strong work. The finding that format, not topic, drives evaluation detection is directly actionable for benchmark design. The steering framework closed 85% of the sandbagging gap on WMDP with strong OOD transfer, and the Phi-4 mode inversion is a genuinely novel mechanistic insight. Rigorous methodology and excellent writing throughout

  2. I think this is very thorough and impressive work!

    I like the work with the Bradley-Terry model, and inter-model comparisons!

    I think the steering section on style was informative, and had quite clear results. I was slightly less excited by the mode steering, both because the applicability of the methodology to future evaluations is less clear (getting sandbagging vs non-sandbagging examples may not be possible) and because I think you slightly overclaim when you say that the sandbagging vs non-sandbagging distinction is linearly encoded. Your results on Phi-4 seem to show that while a linear probe may pick up on sandbagging, the linear direction is not necessary causal of sandbagging.

    I would be excited to see more interventions to prevent eval-awareness, for example by adding stories and context to questions, using your Bradley-Terry model!

Cite this project

@misc{chen2026language,
  title = {{Can Language Models Tell When They Are Being Evaluated?}},
  author = {Chengheng Li Chen and Kyuhee Kim},
  year = {2026},
  month = mar,
  note = {Submitted to AI Control Hackathon 2026, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/can-language-models-tell-when-they-are-being-evaluated-hyae}},
  url = {https://apartresearch.com/sprints/projects/can-language-models-tell-when-they-are-being-evaluated-hyae}
}

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