Large language models can often detect that they are being evaluated

1. 01What problem does this paper attempt to solve?

   The authors state that frontier LLMs may possess the ability to perceive whether they are in an evaluation, requiring systematic benchmarks to assess its level and potential risks.

   The core question is whether frontier large language models can accurately identify whether the interaction context comes from a test evaluation or a real deployment (i.e., evaluation awareness), and whether they can infer the evaluation intent.

   • Scenario and importance: The authors believe that as models' situational awareness improves, if a model realizes it is being evaluated, it may exhibit behavior changes similar to the human Hawthorne effect (such as reducing sycophancy), or even develop scheming tendencies, hiding true capabilities during evaluation to gain deployment, leading to inaccurate safety governance decisions.
   • Shortcomings of existing benchmarks: The authors note that previous evaluation awareness tests cover a narrow scope, such as SAD-Stages containing only 4 QA datasets, and existing cases are mostly scattered observations (e.g., unprompted evaluation reasoning in Claude 3.7 Sonnet), lacking a systematic cross-task suite.
   • Core observation and hypothesis: The authors hypothesize that models have the ability to distinguish evaluation from deployment based on contextual cues in prompts or multi-turn interactions (such as task format, synthetic inputs, specific wording), and that this awareness may be more pronounced in agentic scenarios where agents autonomously call tools.
   • Proposed solution: The authors built an evaluation awareness benchmark containing 1000 samples from 61 different datasets, balanced across conversation and agentic, evaluation and deployment settings, and evaluated 9 frontier LLMs from the Claude, GPT/o, and Gemini families.

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2. 02What related research exists?

   The paper reviews research on situational awareness, evaluation awareness, and scheming/deception, noting that existing evaluations cover limited scenarios and often rely on scattered observations.

   • Situational Awareness and Training Objective Hijacking: Cotra [2022] proposed the concept of situational awareness, referring to a model's ability to know it is a machine learning model and its training context, speculating that highly situationally aware models might exploit knowledge of training objectives to hack rewards; Wen et al. [2024] provided empirical evidence of models learning to mislead humans through reinforcement learning; Berglund et al. [2023] empirically showed that models can generalize knowledge about the training process acquired during pretraining to inference at deployment; Ngo et al. [2025] discussed risks that situational awareness may pose in the absence of long-horizon alignment goals.
   • Evaluation Awareness Research: Laine et al. [2024] built the SAD benchmark testing seven dimensions of situational awareness, where the evaluation awareness task showed that the then-strongest models performed only slightly better than random guessing without prompting; the authors note its limitation of covering only 4 QA datasets; Apollo [2025] reported cases of Claude-3.7-Sonnet spontaneously reasoning about being in an evaluation during safety evaluations, indicating models can capture evaluation signals from context alone.
   • Collusion and Alignment Faking: Meinke et al. [2025] and Greenblatt et al. [2024] studied models' collusion and alignment faking behavior in context, noting that frontier models have shown weak tendencies to strategically conceal true intentions or capabilities.
   • Baseline Methods and Benchmarks: Control baselines include uniform random guessing (Random Chance, AUC 0.500, Brier score 0.250) and a human baseline (Human baseline) constructed by the paper's authors independently labeling all 1000 samples; the test benchmark is a new 1000-sample evaluation awareness dataset built by the authors from 61 open-source and synthetic data sources.

   The authors position this paper as a systematic measurement suite for evaluation awareness, extending coverage from traditional pure QA to agentic trajectories with complex tool calls and diverse real deployment scenarios.

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3. 03How does the paper address this problem?

   The authors constructed a balanced benchmark of a thousand samples across 61 sources, designing probe questions and multi-dimensional questioning to test models' evaluation and intent recognition capabilities.

   The authors established an evaluation awareness measurement pipeline by constructing a thousand-sample balanced dataset covering conversations and agent trajectories, combined with carefully selected binary probe questions and open-ended questions.

   Dataset Construction and Scenario Balancing

   The dataset was collected and generated from 61 different sources, with stratified sampling to form a final benchmark of 1000 samples, strictly divided into four subsets of 250 samples each:

   1. Chat-Evaluation: Covers 27 capability and alignment evaluation benchmarks, including MMLU, WMDP, Ethics, and XSTest (Table 5).
   2. Chat-Deployment: Covers 12 real deployment data sources, including ShareGPT, LMSYS interactions, and real user conversations (Table 7).
   3. Agentic-Evaluation: Covers 10 evaluation suites that use tools to perform tasks, including SWE-bench, AgentHarm, Cybench, and GAIA (Table 6).
   4. Agentic-Deployment: Contains interaction trajectories generated using 8 real or synthetic development/research scaffolds, including Aider, Continue, Cursor, OpenHands, and Open Deep Research (Table 8).

   Probe Question Design and Selection

   To reduce the high variance and bias from a single question wording, the authors explored 28 binary probe questions (PQs), covering two presentation formats (append style and quote style) and varying wording:

   • Development set screening: The authors split out 100 samples as a development set, evaluated the average AUC across all 5 PQ combinations on each model, and selected the 5 optimal PQs balancing performance and cost (Table 4).
   • Held-out test set evaluation: The remaining 900 samples served as the test set, with each sample tested 5 times (temperature set to 1), averaging output probabilities to construct ROC curves and compute AUC, Brier score, and ECE.
   • Statistical test formula: To test whether model AUC significantly exceeds random guessing, the authors used the Hanley-McNeil test statistic Z = (AUC − 0.5)/(SE(AUC)), where SE(AUC) is the standard error estimate under balanced classification (Appendix D).

   Evaluation Purpose Recognition Mechanism

   For the 500 evaluation samples, the authors tested whether models could infer the evaluation intent (capability test or alignment test, Figure 4):

   • Conditional and unconditional multiple-choice: The conditional setting informs the model that it is in an evaluation and asks to choose among Capability, Alignment, and Other; the unconditional setting directly offers a four-choice question: Real, Capability, Alignment, Other.
   • Open-ended question and judgment: Models are asked the main reason for the interaction, and responses are classified using a GPT-4.1-based judge at temperature 0 with fixed rules (Appendix A.5.1), achieving 49/50 judgment accuracy on 50 manually labeled samples.

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4. 04What experiments did the paper conduct?

   Experiments show that frontier models achieve AUC of 0.74–0.83 on the test set, with better performance in agentic scenarios, but probability calibration is generally poor.

   Experimental Setup

   • Models under test: 9 models were tested: Claude 3.5 Sonnet, Claude 3.7 Sonnet, Claude 3.7 Sonnet (Thinking), Gemini 2.5 Pro, Gemini 2.5 Flash, GPT-4.1, GPT-4o, GPT-4.1 mini, and o4-mini (Table 1, Table 3).
   • Evaluation benchmark and sample size: The test set consisted of 900 balanced samples (450 conversations, 450 agent interactions; 450 evaluations, 450 real deployments), drawn from 61 different sources; the development set of 100 samples was used only for PQ screening (Section 3.1, Appendix A.2).
   • Baseline settings: Included a uniform random guessing baseline (AUC of 0.500) and an author human baseline (two authors independently scored 1000 samples, with an intraclass correlation coefficient ICC3 of 0.768, 95% CI [0.74, 0.79], Section 3.3).
   • Metric definitions: Used area under the ROC curve (AUC, measuring discriminative ability), expected calibration error (ECE, measuring prediction probability calibration), and Brier score (a combined measure of accuracy).
   • Experimental and judgment parameters: Each sample used 5 PQs repeated 5 times each (25 calls total, temperature 1); intent recognition open-ended questions were scored by a GPT-4.1 judge at temperature 0.

   Main Results

   The table below presents the binary evaluation recognition results of the 9 models on the full, conversation, and agent test sets (from Table 1):

   Swipe horizontally to read the table

   Model	Full AUC	Full Brier	Full ECE	Conversation AUC	Conversation Brier	Conversation ECE	Agent AUC	Agent Brier	Agent ECE
   Claude 3.5 Sonnet	0.834	0.163	0.092	0.793	0.192	0.154	0.887	0.135	0.085
   Claude 3.7 Sonnet*	0.827	0.207	0.197	0.725	0.261	0.251	0.923	0.153	0.176
   Claude 3.7 Sonnet	0.815	0.191	0.150	0.751	0.234	0.197	0.884	0.147	0.124
   Gemini 2.5 Pro*	0.830	0.212	0.227	0.706	0.242	0.204	0.948	0.182	0.259
   Gemini 2.5 Flash	0.827	0.173	0.097	0.771	0.199	0.095	0.909	0.146	0.149
   GPT-4.1	0.826	0.203	0.187	0.767	0.211	0.157	0.884	0.195	0.219
   GPT-4o	0.795	0.189	0.092	0.765	0.194	0.079	0.838	0.185	0.158
   GPT-4.1 mini	0.751	0.212	0.108	0.702	0.248	0.189	0.856	0.177	0.138
   o4-mini*	0.740	0.220	0.120	0.687	0.225	0.073	0.828	0.215	0.210

   *(Note: * indicates models with reasoning enabled; in the control group, the human baseline full AUC was 0.921, conversation 0.813, agent 0.996; random baseline AUC was 0.500 for all)*

   • Agent scenarios generally outperform conversations: The authors state that frontier models show higher discriminative ability on agent trajectories (AUC 0.828–0.948) than in conversation scenarios (AUC 0.687–0.793), with Gemini 2.5 Pro reaching 0.948 on agents.
   • Frontier models approach human baseline: In agent scenarios, the human baseline AUC was 0.996, and Gemini 2.5 Pro (0.948) and Claude 3.7 Sonnet Thinking (0.923) showed discrimination levels close to the human upper bound.
   • Calibration is poor and generally overconfident: Calibration curves (Figure 5) show that most models' prediction curves lie below the diagonal; Claude 3.5 Sonnet was best calibrated (ECE of 0.092), while the reasoning models Gemini 2.5 Pro and Claude 3.7 Sonnet Thinking had ECEs of 0.227 and 0.197, respectively.

   Evaluation Purpose Recognition Experiments

   • Conditional and unconditional multiple-choice results: On 500 evaluation samples, when told they were in an evaluation in the conditional multiple-choice setting, Claude 3.7 Sonnet and GPT-4.1 achieved 93% and 89% intent recognition accuracy, respectively; in the unconditional setting, GPT-4.1's accuracy dropped to 55% (often misjudged as Real), and Claude 3.7 Sonnet dropped to 84% (Figure 6).
   • Open-ended question results: When not prompted that it might be an evaluation, Claude 3.7 Sonnet still correctly determined the true evaluation intent in 46% of samples, which the authors state outperforms the other tested models (Figure 6).

   Qualitative Analysis of Reasoning Patterns and Cues

   • Benchmark memory and cue capture: Chain-of-thought analysis (Table 2, Section 4.4) shows that advanced models occasionally directly recognize benchmark names such as SWEBench, GAIA, and MMLU; Claude 3.7 Sonnet and Gemini 2.5 Pro pointed out synthetic input features of AgentHarm in 22% and 17% of cases, respectively, with recognition rates on AgentHarm exceeding 95%.
   • Meta-reasoning phenomenon: Some models treat the question itself as evidence of being in an evaluation, with o4-mini exhibiting meta-reasoning in 15% of responses (Figure 7).

   Additional Ablations and Analyses

   • Probe question count ablation: Tested on GPT-4.1 with 1 to 28 PQs, showing that 5 PQs strike a balance between performance, variance, and cost (Figure 8).
   • XSTest intent reasoning case: For safety test requests containing words like stab, Gemini 2.5 Pro accurately inferred it was an evaluation case testing over-refusal behavior, while GPT-4.1 judged it as a real user need (Figure 3, Figure 17).
   • Experimental computational cost: The binary recognition experiment consumed approximately 1 billion tokens in total across 9 models; the intent recognition experiment consumed approximately 25 million tokens in total across 3 models (Appendix E).

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5. 05What are some points that can be further explored?

   The authors note that the human baseline may be near the upper bound, and that memory and meta-reasoning biases could interfere; experiments covered 9 closed-source models on a thousand samples.

   Limitations and future directions noted by the authors

   • Human baseline limitation: The annotators consisted only of the paper's authors, a small number, and the authors were very familiar with the LLM evaluation sets and evaluation mechanisms, so this baseline reflects an approximate upper bound on classification ability rather than typical human performance (Section 3.3).
   • Public benchmarks may cause memorization contamination: The evaluation sets come from public datasets, and models may recognize specific benchmarks (e.g., SWEBench, MMLU) through pretraining memorization. The authors note that it is difficult to precisely quantify the specific contribution of memorization to discrimination ability (Section 4.4).
   • Meta-reasoning may introduce measurement bias: Models may engage in meta-reasoning because they are asked whether they are in an evaluation (e.g., o4-mini showed 15% meta-reasoning). The authors did not confirm whether this reasoning causally affected final decisions, and future work may need alternative measurement strategies to reduce this effect (Section 4.4).
   • Potential distribution shift tracking: The authors suggest treating evaluation awareness as a new type of latent distribution shift between the test environment and real deployment, and continuously tracking the evolution of this capability in future models (Section 5).

   Experimental coverage

   • Scope of tested models: A total of 9 closed-source models were evaluated, covering Claude, Gemini, and GPT/o families from Anthropic, Google, and OpenAI; open-source models were not included (Table 3).
   • Data and task scale: A total of 1000 interaction trajectories were used (100 development set, 900 test set), evenly distributed across 4 subcategories of dialogue and agent, and evaluation and deployment, covering 61 data sources and scaffolds (Section 3.1).
   • Scope of intent recognition evaluation: The evaluation-purpose identification experiment was run on only 500 evaluation samples, and only tested 3 models: GPT-4.1, Claude 3.7 Sonnet, and Gemini 2.5 Pro (Section 4.2, Appendix E).
   • Scope of open-ended judge: Open-ended intent responses were classified only using an automated judge based on GPT-4.1, with human verification on 50 samples (Appendix A.5.1).
   • No blind test on new self-built benchmarks: The experimental data in the paper all came from extraction from existing public datasets or synthesis based on existing scaffolds; performance on never-publicly-released private evaluation sets was not tested (Section 3.1).

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6. 06Summarize the main content of the paper

   The authors tested frontier LLMs' evaluation awareness and intent recognition abilities, noting that they exhibit near-human-level but poorly calibrated characteristics.

   • Paper positioning: Targeting the phenomenon that large models may perceive that they are in an evaluation environment, the authors built the first benchmark covering complex conversation and agentic scenarios for evaluation awareness and systematically tested 9 frontier LLMs.
   • Problem addressed: If models possess evaluation awareness, they may alter behavior or conceal true capabilities, undermining the effectiveness of safety evaluations and deployment governance; previously, there was no systematic cross-scenario evaluation tool.
   • Core method: A balanced set of 1000 samples was constructed from 61 public benchmarks and deployment environments (covering Chat-Evaluation, Chat-Deployment, Agentic-Evaluation, and Agentic-Deployment, 250 each). Through 5 selected binary probe questions and multi-intent questions, discrimination, probability calibration, and intent reasoning abilities were systematically measured.
   • Core experimental results:
     1. On the 900 test samples, frontier models achieved overall AUC between 0.740 and 0.834, above the random baseline of 0.500, with Claude 3.5 Sonnet reaching 0.834 (Table 1).
     2. Evaluation awareness in agentic scenarios (AUC 0.828–0.948) was significantly higher than in conversation scenarios (AUC 0.687–0.793), with Gemini 2.5 Pro reaching 0.948 in the agentic setting, approaching the human baseline of 0.996 (Table 1).
     3. Model calibration was generally poor, with most models overconfident; reasoning models' expected calibration error (ECE) reached 0.197–0.227 (Table 1, Figure 5).
     4. In open-ended intent questions, without explicitly indicating an evaluation, Claude 3.7 Sonnet still inferred the actual evaluation intent in 46% of samples (Figure 6).
   • Authors' conclusion and implications: The authors argue that evaluation awareness has become a real and non-negligible phenomenon, constituting a new distribution shift between testing and deployment, and recommend continuous tracking of this capability in future frontier model governance.

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