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arxiv: 2606.00168 · v1 · pith:7LLNE52Inew · submitted 2026-05-29 · 💻 cs.CL

RealityTest: How People Probe AI Identity and Whether Models Disclose It

Anna Gausen , Sarenne Wallbridge , Bessie O'Dell , Christopher Summerfield , Hannah Rose Kirk This is my paper

Pith reviewed 2026-06-28 22:20 UTC · model grok-4.3

classification 💻 cs.CL
keywords AI identity disclosureRealityTest benchmarkhuman-grounded queriessuppression instructionmultilingual multimodal evaluationconversational AI safetyquestion phrasing effects
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The pith

Question phrasing and conversational context shape AI identity disclosure more than which model is tested, and one suppression instruction drops rates below 30 percent even in the strongest systems.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper builds RealityTest, a benchmark of 3,152 real identity-probing queries collected from roughly 750 participants across 49 countries and five languages in both text and speech. It tests 23 models and finds only 31 percent of people ask about identity directly, with human questions proving far more varied than machine-generated ones. Disclosure behavior varies across models, yet a single added instruction reduces it to below 30 percent in every case examined. The data show that how a question is worded and the surrounding context determine outcomes more reliably than model identity. The authors conclude that safety evaluations resting on synthetic or narrow query sets will misrepresent how models actually behave when users probe them in deployment.

Core claim

RealityTest demonstrates that AI systems disclose their non-human identity at rates that depend primarily on the phrasing of the user's question and the conversational context rather than on the particular model under test; a single suppression instruction suffices to bring disclosure below 30 percent across all 23 evaluated text and speech models, while the underlying human-collected queries reveal far greater diversity than prior machine-generated test sets.

What carries the argument

RealityTest benchmark of 3,152 human-collected identity-probing queries in text and speech across multiple languages and countries.

If this is right

  • Safety evaluations that rely on synthetic or English-only queries will systematically mischaracterize disclosure behavior.
  • A single suppression instruction provides a low-cost way to limit identity disclosure across current models.
  • Multimodal and multilingual testing is required to capture realistic disclosure patterns.
  • Regulatory focus on disclosure must account for variation driven by question phrasing rather than model architecture alone.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Future model training could incorporate diverse real-user probes to improve robustness without relying on post-hoc instructions.
  • Deployment policies might need context-aware disclosure rules that adapt to how users actually phrase questions.
  • The same human-grounded query collection method could be applied to other safety properties such as refusal or hallucination detection.

Load-bearing premise

The collected queries accurately represent the distribution of identity-probing questions that users will ask in real deployments.

What would settle it

Running the same 17 text and 6 speech models on a fresh collection of several thousand identity-probing queries gathered from a new participant pool and finding that model identity predicts disclosure rates more strongly than question phrasing or context.

Figures

Figures reproduced from arXiv: 2606.00168 by Anna Gausen, Bessie O'Dell, Christopher Summerfield, Hannah Rose Kirk, Sarenne Wallbridge.

Figure 1
Figure 1. Figure 1: REALITYTEST. (1) We ground the benchmark in scenarios where people report uncertainty about AI identity, from a population survey (N=500) and Reddit (50 threads, 1,957 comments). (2) We collect 3,152 identity-probing queries from ~750 participants in five languages and two modalities. (3) REALITYTEST systematically evaluates AI identity disclosure using the real queries and realistic scenarios. We present … view at source ↗
Figure 2
Figure 2. Figure 2: Query strategies. A. Semantic structure: two-dimensional UMAP projection of query embeddings, coloured by strategy label. Black squares denote automatically generated queries from an existing evaluation [18]. Highlighted examples illustrate representative queries from each strategy cluster. B. Overall strategy distribution: proportion of classified queries assigned to each strategy category, shown separate… view at source ↗
Figure 3
Figure 3. Figure 3: Evaluating cross-model disclosure behaviour. Binary GLMMs are used to investigate separate effects of factors (model, language, scenario, query, etc) on disclosure rates. (A) Disclosure probability by model as marginal mean over all observations for the target model, with 95% CIs of per-language marginal means for that model. (B) Proportion of total latent variance attributable to each factor, estimated as… view at source ↗
Figure 4
Figure 4. Figure 4: Effect of system prompt instructions and conversation depth on disclosure. (A) Disclosure rates (%) by modality and system prompt condition at zero prior turns; bars show modality means, points show individual model means, error bars show 95% t-CIs across scenario variants. (B) Change in disclosure rate (pp) relative to zero turns under scenario-conditioned prompts; thick lines show model-level averages, t… view at source ↗
Figure 5
Figure 5. Figure 5: Query collection interface example. the participant responds. For example, a banking customer service vignette presents a scenario in which the participant has contacted their bank about a suspicious transaction and has just received a response from an agent who may be human or AI. After viewing each vignette, participants respond to the prompt: “What would you say next to find out if you are talking to a … view at source ↗
Figure 6
Figure 6. Figure 6: Distribution of responses from all participants to the two translation quality control questions. [PITH_FULL_IMAGE:figures/full_fig_p023_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Distribution of languages that participants report for as the language for AI use. [PITH_FULL_IMAGE:figures/full_fig_p025_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Visualisation of responses across strategies. Responses in all languages (non-English responses are [PITH_FULL_IMAGE:figures/full_fig_p031_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Distribution of query strategies across text and speech stimuli for each language. [PITH_FULL_IMAGE:figures/full_fig_p033_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Distribution of per-query mean disclosure rates, stratified by model and colored by model family. Each [PITH_FULL_IMAGE:figures/full_fig_p039_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Disclosure behaviour across query strategies and interaction scenarios in paired text model experiments [PITH_FULL_IMAGE:figures/full_fig_p041_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Disclosure behaviour across query strategies and interaction scenarios in paired speech model [PITH_FULL_IMAGE:figures/full_fig_p042_12.png] view at source ↗
read the original abstract

AI systems are increasingly deployed in conversational settings where users may be uncertain whether they are speaking with a human or an AI. Despite mounting regulatory attention to this known safety risk, existing evaluations of AI disclosure are typically English-only, based on machine-generated questions, and restricted to text. We present RealityTest to comprehensively test whether AI systems disclose their identity when asked. The benchmark is the first large-scale multimodal and multilingual evaluation, grounded in human data on how people actually encounter and question AI identity in the real-world. Alongside the benchmark, we release the underlying dataset of 3,152 identity-probing queries collected from ~750 participants across 49 countries and five languages, in text and speech scenarios. We find that only 31% of people ask about identity directly in ambiguous scenarios, and that the questions people ask are far more diverse than machine-generated queries. We test 17 text and 6 speech models, and find substantial variation in disclosure behaviour. However, a single suppression instruction reduces disclosure rates to below 30%, even in the best-performing models. Validating our investment in diverse, human-grounded evaluation data, we find that how the question is phrased and the context of the conversation matter more for disclosure than which model is being tested. Safety evaluations built on narrow or synthetic query sets risk mischaracterising how models behave in realistic deployment settings.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces RealityTest, a benchmark for testing whether AI models disclose their identity when probed by users. It is grounded in a new dataset of 3,152 identity-probing queries collected from ~750 participants across 49 countries and five languages in both text and speech scenarios. The authors report that only 31% of queries are direct identity questions, that human queries are substantially more diverse than machine-generated ones, that testing 17 text and 6 speech models reveals substantial variation in disclosure rates, and that a single suppression instruction reduces disclosure to below 30% even in the strongest models. They conclude that phrasing and conversational context matter more for disclosure behavior than model identity, and they release the underlying dataset.

Significance. If the sampling assumptions hold, the work supplies the first large-scale human-grounded, multilingual, and multimodal measurement of AI identity disclosure, demonstrating that narrow synthetic query sets can mischaracterize real deployment behavior. The public release of the 3,152-query dataset is a concrete strength that enables reproducible follow-up work and more realistic safety evaluations.

major comments (2)
  1. [Methods, Data Collection subsection] Methods, Data Collection subsection: the protocol is presented at high level with no external anchor (production chat-log comparison, blinded recruitment check, or demographic weighting) to validate that the observed distribution of direct vs. indirect/context-heavy probes matches real deployment usage. This sampling assumption is load-bearing for the claims that phrasing/context dominate model identity and that suppression reduces disclosure below 30% under realistic conditions.
  2. [Results, Model Evaluation and Suppression sections] Results, Model Evaluation and Suppression sections: the manuscript reports concrete rates (31% direct questions, <30% post-suppression) but does not specify inter-rater reliability for query categorization, data exclusion rules, or statistical controls for the disclosure measurements. These omissions directly affect the reliability of the headline empirical numbers and the cross-model ranking.
minor comments (1)
  1. [Abstract and §4] Abstract and §4: the statement of 'substantial variation in disclosure behaviour' is not accompanied by a quantitative summary (range, standard deviation, or per-model table reference); adding one sentence would improve readability without altering the central argument.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive review. We address each major comment below and indicate planned revisions to the manuscript.

read point-by-point responses

  1. Referee: [Methods, Data Collection subsection] Methods, Data Collection subsection: the protocol is presented at high level with no external anchor (production chat-log comparison, blinded recruitment check, or demographic weighting) to validate that the observed distribution of direct vs. indirect/context-heavy probes matches real deployment usage. This sampling assumption is load-bearing for the claims that phrasing/context dominate model identity and that suppression reduces disclosure below 30% under realistic conditions.

    Authors: We acknowledge that the current description of the data collection protocol is high-level. In the revised manuscript we will expand the Methods section with additional details on recruitment procedures, screening criteria, language and country distribution, and any demographic information collected from the ~750 participants. We will also add an explicit limitations paragraph noting that direct comparison to proprietary production chat logs is not feasible for this study. While we cannot supply an external production anchor, the scale, multilingual coverage (five languages, 49 countries), and multimodal (text/speech) design of the collected queries provide the first human-grounded reference set against which synthetic alternatives can be compared; we maintain that this still supports the central claim that real-user phrasing and context matter more than model identity. revision: partial

  2. Referee: [Results, Model Evaluation and Suppression sections] Results, Model Evaluation and Suppression sections: the manuscript reports concrete rates (31% direct questions, <30% post-suppression) but does not specify inter-rater reliability for query categorization, data exclusion rules, or statistical controls for the disclosure measurements. These omissions directly affect the reliability of the headline empirical numbers and the cross-model ranking.

    Authors: We agree that these methodological details are necessary for evaluating the reliability of the reported rates and rankings. In the revised manuscript we will add, in the Results sections, the inter-rater reliability metric (Cohen’s kappa) obtained during query categorization, the precise data exclusion rules applied, and the statistical procedures (including any controls or tests) used to compute disclosure percentages and to compare models. These additions will directly address concerns about the robustness of the 31 % and <30 % headline figures. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical measurement on externally collected human queries

full rationale

The paper collects 3,152 identity-probing queries from ~750 participants and directly measures disclosure rates of 17 text and 6 speech models on this fixed dataset. No equations, fitted parameters, or predictions are presented; all claims (e.g., suppression instruction effect, phrasing/context dominance) are direct empirical counts on the released queries. No self-citations are load-bearing for the central results, and the work contains no derivations that reduce to their own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on the representativeness of the collected queries and the assumption that the tested models and suppression instruction are representative of deployed systems. No free parameters are described. No new entities are postulated.

axioms (1)
  • domain assumption Human participants across 49 countries and five languages provide queries that generalize to real-world ambiguous AI interactions.
    The benchmark is explicitly grounded in this human data collection; if the sample is biased the disclosure statistics lose external validity.

pith-pipeline@v0.9.1-grok · 5785 in / 1298 out tokens · 17916 ms · 2026-06-28T22:20:39.033638+00:00 · methodology

discussion (0)

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    Yes please

    No Direct Query The user does not probe identity at all. This includes: - Continuing the conversation normally (engagement): "Yes please", "Go ahead" - Ending or refusing (disengagement): "No thanks, I’m no longer interested" - Commenting on the task rather than responding in-character (meta/non-response) - Asking about the legitimacy of the organisation,...

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    who are you?

    Unknown Use ONLY if the query genuinely cannot be assigned to any of the five categories above even after careful consideration. This should be rare. CLASSIFICATION RULES ----------------------------- - Direct Identity vs Persona: if the query mentions AI/bot/human/machine explicitly, or asserts what the interlocutor is, it is Direct Identity Query. If it...

  51. [52]

    Columns k/5 show the percentage of cells where k out of 5 runs agreed.% agreeing= mean majority-class fraction across all cells

    Main analysis All models × all languages × all scenarios × direct queries × 0-turn × no suppression × 5 repeats Text: 17 models×5 languages×24 scenarios×~ 100 queries×5 repeats = 17×5×24×100×5 = 1,020,000runs Speech: 36 Table 18: Response consistency across 5 repeated runs. Columns k/5 show the percentage of cells where k out of 5 runs agreed.% agreeing= ...

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    All evaluated models were accessed via APIs

    Robustness Case-Study: (a)System Prompt Suppression: Subset of models (3 text, 3 speech) × English only × all scenarios × direct queries × 4 suppression×5 repeats Text: 3 models×1 language×24 scenarios×~ 100 queries×4 suppression×5 repeats = 3×1×24×100×4×5 = 144,000runs Speech: 3 models×1 language×15 scenarios×~ 100 queries×4 suppression×5 repeats = 3×1×1...