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REVIEW 4 major objections 6 minor 1 cited by

Do Role-Playing Agents Practice What They Preach? Belief-Behavior Consistency in LLM-Based Simulations of Human Trust

T0 review · 4 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read LLM role-playing agents systematically fail to act on their stated beliefs, and a new evaluation framework can measure that failure before a simulation runs.

desk verdict Good framework, unvalidated yardstick: the headline belief-behavior gap is measured across different prompt formats, so the paper's main number should be read as prompt sensitivity until equivalence is shown. read the letter →

arxiv 2507.02197 v1 pith:4SUPBB6H submitted 2025-07-02 cs.AI

Amogh Mannekote , Adam Davies , Guohao Li , Kristy Elizabeth Boyer , ChengXiang Zhai , Bonnie J Dorr , Francesco Pinto This is my paper
classification cs.AI
keywords belief-behaviorconsistencyrole-playingagentsLLMsimulationTrustGamesynthetichumanbehaviorbeliefelicitationforecasthorizonpersona
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper tries to establish that LLM-based role-playing agents are systematically incoherent: the beliefs they state when asked directly do not reliably match the actions they take when playing a role. Using an augmented persona bank and the Trust Game, it builds a belief-behavior consistency framework that works at two levels, across whole simulated populations and for single agents forecasting their own future moves. It then asks whether giving game context during belief elicitation helps, whether asking about dollar amounts rather than abstract trust helps, and whether longer forecasting horizons hurt. The paper finds that context does not fix inconsistency, dollar-targeted beliefs improve rank ordering but inflate effect sizes, self-conditioning helps some models but not others, imposed researcher priors generally break alignment, and forecasting degrades as the horizon grows. If true, belief elicitation becomes a cheap diagnostic for synthetic-data validity rather than something to trust as a description of what the simulation will do.

What carries the argument

The load-bearing object is a two-component belief-behavior consistency metric. At the population level, it compares an elicited belief ranking of persona-attribute levels against a behavioral ranking of observed mean transfers using Spearman's $\rho$, and compares elicited versus observed effect sizes using the absolute difference in ANOVA $\eta^2$. At the individual level, it uses mean absolute error between forecasted and enacted transfer amounts across six Trust Game rounds against fixed Trustee archetypes labeled M1, M3, and M5. The testbed is the Trust Game with an augmented GENAGENTS persona bank that adds Big Five personality traits; single-round simulation isolates the Trustor's decision, while multi-round simulation uses a reasoning-action loop (ReAct). Together these pieces turn "what the model says" and "what the model does" into concrete numbers that can be compared before a larger simulation is run.

What would settle it

Reword or counterbalance the two prompt formats: for example, ask the belief-elicitation question in role-play style and the role-play question in belief style, or administer several semantically equivalent variants of the belief prompt. If belief-behavior consistency changes substantially across superficial rewrites, the paper's central gap is at least partly a wording artifact; if it survives, the gap is robust.

Watch

Extended reading notes

Core claim

The central claim is that LLM role-playing agents show systematic belief-behavior inconsistency at both individual and population levels, and that this inconsistency can be measured before running a large simulation. The paper demonstrates this with a Trust Game, where models first state how they think persona attributes such as age, political views, or conscientiousness shape trust and transfer decisions, and then are asked to play the Trustor with those personas. Across three open-weight models, providing full task instructions during belief elicitation did not improve consistency; asking for dollar-amount predictions improved rank-order agreement ($\rho$) but systematically overestimated effect sizes ($\eta^2$); feeding a model its own elicited beliefs back as context helped Llama models but essentially failed for Gemma 2 27B; and imposing even weakly perturbed researcher priors reduced alignment across architectures. At the individual level, the mean absolute error between an agent's forecasted and actual transfers grew with the number of rounds forecast ahead. The paper's conclusion is that models may encode plausible beliefs yet fail to apply them consistently, so stated beliefs should not be taken as a guarantee of simulated behavior.

Load-bearing premise

The load-bearing premise is that the belief-elicitation prompt and the role-play prompt are measuring the same underlying belief; if they instead activate different task priors, the measured inconsistency could be a prompt-format artifact rather than a genuine belief-behavior gap.

Editorial extensions

If this is right

  • Belief elicitation can serve as an upfront validity check for synthetic human-behavior datasets, flagging roles where stated beliefs and simulated actions are likely to diverge.
  • Adding task context to belief prompts is not a reliable cure; consistency depends on what is elicited, not just how much information is supplied.
  • Self-conditioning on a model's own beliefs is model-dependent, so it cannot be assumed to improve alignment in every LLM.
  • Researcher-imposed priors can actively reduce belief-behavior alignment, which constrains the use of role-playing agents for counterfactual or theory-testing simulations.
  • Multi-round simulations become less predictable as the forecasting horizon grows, so individual-level forecasts beyond the near term should carry much larger uncertainty.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural extension is to treat belief-behavior consistency as a screening metric in a data-generation pipeline: run a cheap belief-elicitation pass, flag divergent personas, and exclude or re-prompt them before expensive multi-round simulation; the paper demonstrates the diagnostic but does not design this workflow itself.
  • The Trust Game is a minimal one-dimensional action space; the same framework in richer environments, such as open-ended dialogue or repeated social dilemmas, would show whether inconsistency grows with task complexity, as the near-monotonic horizon effect suggests.
  • The authors note that reasoning models with extended inference might close the belief-behavior gap; this is directly testable by running their exact prompts on such models.
  • The prompt-equivalence concern, that belief and role-play formats may activate different task priors, implies a concrete check: counterbalance the two wording styles and see whether measured inconsistency survives.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper proposes an evaluation framework for belief-behavior consistency in LLM-based role-playing agents, using an augmented GenAgents persona bank and the Trust Game as a testbed. It elicits population-level beliefs about how persona attributes affect trust decisions under three prompting strategies (NOCTX+TR, CTX+TR, CTX+$), measures consistency against simulated transfer behavior via Spearman correlation and eta-squared discrepancy, and tests self-conditioning and imposed priors. At the individual level, it asks a model to forecast its own future send amounts across six rounds against fixed Trustee archetypes and compares forecasts with enacted ReAct-based decisions. The central claim is that LLM agents exhibit systematic belief-behavior inconsistencies, that in-context conditioning is unreliable for controllability, and that forecasting accuracy degrades over longer horizons.

Significance. If established, the framework would be a valuable pre-hoc diagnostic for synthetic behavioral data, extending post-hoc evaluation benchmarks with population- and individual-level consistency metrics, effect sizes, multi-turn analysis, and an explicit controllability test. The paper uses multiple open-weight LLMs, reports quantitative metrics rather than LLM-based judgment, and states that code and prompts will be released. However, the current evidence does not yet support the strength of the central claims: the quantitative support is thin (N=50 personas, no confidence intervals or significance tests), and two load-bearing design choices—construct alignment between belief prompts and behavioral outcomes, and information equivalence between forecast and enactment prompts—are not validated. These issues are potentially fixable, so the work is a promising direction rather than a definitive demonstration.

major comments (4)
  1. [Section 4.1, Table 2] The population-level consistency metric treats divergence between a ranking of 'interpersonal trust' (NOCTX+TR, CTX+TR) and observed dollar transfers in the Trust Game as 'belief-behavior inconsistency.' These are different constructs: transfers in the Trust Game reflect risk preferences, strategic foresight, and reciprocity expectations, not only interpersonal trust. The model's own reasoning in Appendix C.4 (e.g., Table 8, round 1: sending $0 to 'err on the side of caution') shows that a stable belief about trust need not imply a high transfer. The paper does not validate that the belief-elicitation prompts and the role-play prompt probe the same latent quantity. Without such validation, the measured inconsistency may be a prompt-format or construct artifact. The CTX+$ condition, which asks for dollar estimates, yields different results (Table 3), consistent with this concern. The authors should either establish equivalence empirically (e.g., by comparing trust-rankings with dollar-based rankings from the same model) or reframe the headline as cross-prompt sensitivity rather than belief-behavior inconsistency.
  2. [Section 5.2, Appendices B.2 and C.3.1] The individual-level forecast comparison is confounded by information asymmetry. The forecast prompt in Appendix C.3.1 asks for a send amount given only the round number, the opponent archetype, and a generic reference to 'the history of the game so far,' but it does not include the actual previous rounds' sends and returns. The enacted ReAct role-play prompt in Appendix B.2 explicitly includes a 'GAME HISTORY' section with the actual round history. Thus the forecast and enactment conditions are not informationally equivalent: the forecaster is asked to predict without the accumulated history that the actor sees. The observed increase in MAE over rounds (Fig. 1) could simply reflect this missing information rather than a genuine degradation of belief-behavior consistency with horizon. The authors must either provide the same history in both prompts or explicitly frame the forecast as a from-scratch state-based prediction and interpret the results accordingly.
  3. [Tables 3 and 4, Section 4.2] The quantitative support for the central 'systematic inconsistency' claim is weak. Table 3 reports median Spearman correlations and |Δη²| values computed over only 9 attributes for N=50 personas, with no confidence intervals, significance tests, or multiple-comparison correction across the 9×3×3 attribute-strategy-model cells. Table 4 reports a single Spearman value per condition with no measure of variability, so claims such as 'self-conditioning enhances alignment in Llama models' may be within sampling noise. The individual-level analysis (Section 5.3) is based on a single persona (App. C.4) and six rounds, and the R² values in Fig. 1 (e.g., 0.04 for Llama 3.1 8B under M3) show that some series are essentially flat, weakening the 'near-monotonic' wording. The authors should report bootstrap confidence intervals, permutation tests, or at least per-persona variability for the individual-level results.
  4. [Section 4.3, Appendix C.2.3] The perturbation procedure for imposed priors is not specified. The text says weak and strong perturbed priors are constructed to have ρ=0.80 and ρ=0.20 with the original elicited beliefs, but no algorithm is given for how a prior with a target Spearman correlation is generated, which attributes are flipped, or how ties are handled. Without this detail, the controllability results in Table 4 are not reproducible, and the interpretation (that imposed priors undermine consistency) may be an artifact of the specific perturbation scheme chosen. The authors should provide the exact procedure and the actual prior statements used for each model and attribute.
minor comments (6)
  1. [Section 1] The sentence 'The Trust Game offers quantifies interpersonal trust as the amount...' contains a grammatical error ('offers quantifies'); it should be 'quantifies' or 'offers a quantification of.'
  2. [Section 4.3.1] The observation about Gemma 2 27B's ρ dropping under self-conditioning is stated twice in the same paragraph; one of the sentences should be removed.
  3. [Section 4.2] The claim that supplying Trust Game context 'failed to increase Spearman rank correlation... across all LLMs and attributes' is not true at the attribute level: for example, in Table 3 the Age row for Llama 3.1 70B shows ρ=-1.00 under NOCTX+TR and ρ=0.80 under CTX+TR. The claim should be qualified to the median or tested statistically.
  4. [Appendix D.1, Table 9] The caption of Table 9 says 'Mean Spearman correlations between model predictions and human trust behavior,' but the experiment appears to compare model predictions to simulated role-play behavior, not human data. The caption should be corrected to avoid implying a human-subject validation that the paper does not contain.
  5. [Appendix B.1] The population-level role-playing prompt includes a placeholder '$theoretical_props' that is not defined in the main text. If this field is non-empty in the 'unconditioned' baseline, the baseline is not condition-free; if it is empty, this should be stated explicitly for replicability.
  6. [Section 3.2] The paper should report the size of the test split of the GENAGENTS persona bank and the number of personas per attribute level, since N=50 is small and some attribute levels may have very few personas, making the eta-squared estimates unstable.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: belief elicitation is not fitted to simulated outcomes, and conditioning experiments are explicit interventions whose outcomes are not guaranteed by construction.

full rationale

The paper's central measure compares (a) beliefs elicited via dedicated prompts (rankings, effect-size estimates, dollar forecasts) with (b) behaviors sampled from separate role-play prompts. Nothing in the belief elicitation is fitted to the behavioral outcomes: the strategies in Table 2 are defined before simulation, the Spearman/eta-squared metrics in Section 4.1 compare independent outputs, and the individual-level MAE in Section 5.2 compares a forecast with an enacted ReAct action. There is no equation in which the predicted quantity is defined as the observed quantity, and no parameter is tuned on the target data. The self-conditioning and imposed-prior experiments are explicit interventions that feed beliefs into the action prompt; whether behavior follows is an empirical result (and indeed Gemma 2 27B fails to improve), so the outcome is not guaranteed by construction. The only self-citations (Mannekote et al. 2025; Lamb et al. 2024) are contextual or future-work references and carry no load-bearing mathematical or empirical weight. Concerns that the belief and role-play prompts may probe different constructs (ranking 'interpersonal trust' versus choosing a transfer in a strategic game) are validity threats, not circularity: a mismatch would make the measured inconsistency a prompt-format artifact, but it would not mean the paper assumes its conclusion in its inputs. The derivation is self-contained, so the circularity score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No free parameters are fitted in the paper; the analyses are observational comparisons of LLM outputs. The main assumptions are about the validity of the Trust Game as a measure of trust, the representativeness of the synthetic persona bank, the stability of elicited beliefs, and the completeness of the simulation environment defined by the Trustee archetypes.

assumptions (4)
  • domain assumption Trust Game transfer amounts are a valid operationalization of interpersonal trust and of behavior relevant to the personas being simulated.
    The paper uses the Trust Game as the testbed and equates dollars sent with trusting behavior (Section 3.1). If this operationalization is invalid, the belief-behavior consistency results may not generalize to other trust measures.
  • domain assumption The augmented GENAGENTS persona bank, with randomly sampled Big Five traits, produces a representative and sufficiently varied sample of human-like attribute combinations.
    All population-level analyses depend on this synthetic population (Section 3.2). No validation against human data is provided, and the small N=50 personas may not support stable attribute-level estimates.
  • domain assumption A single belief-elicitation prompt yields a stable and meaningful statement of the model's 'beliefs' that can serve as a reference for consistency.
    The framework assumes that prompted rankings and effect-size estimates are reliable indicators of the model's internal beliefs (Section 4.1). If these are noisy or highly prompt-sensitive, the measured consistency is an artifact of the elicitation procedure.
  • domain assumption The ReAct framework with fixed Trustee archetypes fully specifies the simulation environment for individual-level forecasting.
    The individual-level analysis (Section 5) relies on three archetypes (M1, M3, M5) to define the opponent's behavior. If the archetype descriptions are not faithfully applied by the model, the forecast evaluation is not controlled.

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Cite this review

Pith. "Pith review of Do Role-Playing Agents Practice What They Preach? Belief-Behavior Consistency in LLM-Based Simulations of Human Trust." pith.science (2026). https://pith.science/paper/4SUPBB6H

@misc{pith2026250702197,
  author       = {Pith},
  title        = {Pith review of: Do Role-Playing Agents Practice What They Preach? Belief-Behavior Consistency in LLM-Based Simulations of Human Trust},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4SUPBB6H}},
  note         = {Machine review of arXiv:2507.02197}
}
read the original abstract

As LLMs are increasingly studied as role-playing agents to generate synthetic data for human behavioral research, ensuring that their outputs remain coherent with their assigned roles has become a critical concern. In this paper, we investigate how consistently LLM-based role-playing agents' stated beliefs about the behavior of the people they are asked to role-play ("what they say") correspond to their actual behavior during role-play ("how they act"). Specifically, we establish an evaluation framework to rigorously measure how well beliefs obtained by prompting the model can predict simulation outcomes in advance. Using an augmented version of the GenAgents persona bank and the Trust Game (a standard economic game used to quantify players' trust and reciprocity), we introduce a belief-behavior consistency metric to systematically investigate how it is affected by factors such as: (1) the types of beliefs we elicit from LLMs, like expected outcomes of simulations versus task-relevant attributes of individual characters LLMs are asked to simulate; (2) when and how we present LLMs with relevant information about Trust Game; and (3) how far into the future we ask the model to forecast its actions. We also explore how feasible it is to impose a researcher's own theoretical priors in the event that the originally elicited beliefs are misaligned with research objectives. Our results reveal systematic inconsistencies between LLMs' stated (or imposed) beliefs and the outcomes of their role-playing simulation, at both an individual- and population-level. Specifically, we find that, even when models appear to encode plausible beliefs, they may fail to apply them in a consistent way. These findings highlight the need to identify how and when LLMs' stated beliefs align with their simulated behavior, allowing researchers to use LLM-based agents appropriately in behavioral studies.

Figures

Figures reproduced from arXiv: 2507.02197 by the authors.

Figure 1
Figure 1. Trust Game: Model consistency across rounds, stratified by return constraint ( [PITH_FULL_IMAGE:figures/full_fig_p009_1.png] view at source ↗

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Cited by 1 Pith paper

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Reference graph

Works this paper leans on

26 extracted references · 25 canonical work pages · cited by 1 Pith paper

  1. [1]

    NO MORE , NO LESS

    YOU HAVE EXACTLY $$$ { initial_amount } TO USE IN THIS GAME . NO MORE , NO LESS

    work page

  2. [2]

    YOU MUST DECIDE HOW MUCH OF YOUR $$$ { initial_amount } TO TRANSFER TO ANOTHER PLAYER

    work page

  3. [5]

    ===== YOUR CHARACTER PROFILE ===== $perso na_detai ls $t h eo r et i c al _ p ro p s ===== FINAL REMINDERS =====

    YOU CANNOT TRANSFER MORE THAN $$$ { initial_amount } UNDER ANY CIRCUMSTANCES . ===== YOUR CHARACTER PROFILE ===== $perso na_detai ls $t h eo r et i c al _ p ro p s ===== FINAL REMINDERS =====

    work page

  4. [6]

    g e n e r a l _ a c t i v i t y _ l e v e l

    YOU CANNOT TRANSFER MORE THAN $$$ { rema ining_am ount } UNDER ANY CIRCUMSTANCES . ===== GAME HISTORY ===== Previous rounds : $round_history $t h eo r et i c al _ p ro p s How much of your $$$ { rema ining_am ount } will you transfer in this round ? Your answer needs to include your THOUGHT , ACTION , and OBSERVATION . $ { f o r m a t _ i n s t r u c t i ...

    work page

  5. [7]

    YOU HAVE EXACTLY $$$ { initial_amount } AVAILABLE TO TRANSFER

    work page

  6. [8]

    YOUR TRANSFER WILL BE TRIPLED

    work page

  7. [9]

    THE OTHER PLAYER DECIDES HOW MUCH TO RETURN TO YOU

    work page

  8. [10]

    STATE YOUR FINAL DECISION CLEARLY AS A DOLLAR AMOUNT BETWEEN $$ 0 AND $$$ { initial_amount }

    work page

Show all 26 references
  1. [11]

    How much of your $$$ { initial_amount } will you transfer ? Your answer needs to include your BELIEF , DESIRE and INTENTION

    Condition your action on your persona profile . How much of your $$$ { initial_amount } will you transfer ? Your answer needs to include your BELIEF , DESIRE and INTENTION . End with ' Finally , I will give ___ dollars '. B.2 Individual-Level Role-Playing Prompt The following ...

  2. [12]

    THIS IS ROUND $round_number OF $total_rounds

  3. [13]

    YOU HAVE EXACTLY $$$ { rema ining_am ount } TO USE IN THIS ROUND . NO MORE , 13 Under review as a manuscript at COLM 2025 Trait Possible Values Agete 18-29te, 30-44te, 45-64te, 65+te Agreeablenesstr Hightr, Lowtr, Mediumtr Communication Qualityv Excellentv, Neutralv, Poorv Con...

    2025

  4. [14]

    YOU MUST DECIDE HOW MUCH OF YOUR $$$ { rema ining_am ount } TO TRANSFER TO THE OTHER PLAYER

  5. [18]

    Value1 > Value2 > Value3

    Very active Please rank these values in order from highest to lowest in terms of their association with interpersonal trust , using the format " Value1 > Value2 > Value3 " where " >" means " has higher interpersonal trust than ". Your response should include an ordering of all...

    2025

  6. [19]

    Value1 > Value2 > Value3

    Very active Please rank these values in order from highest to lowest in terms of their association with interpersonal trust , using the format " Value1 > Value2 > Value3 " where " >" means " has higher interpersonal trust than ". Your response should include an ordering of all...

  7. [21]

    YOU CAN SEND ANY AMOUNT FROM $0 TO $10

    YOU MUST DECIDE HOW MUCH OF YOUR INITIAL AMOUNT OF MONEY TO TRANSFER TO ANOTHER PLAYER . YOU CAN SEND ANY AMOUNT FROM $0 TO $10

  8. [24]

    properties

    YOU CANNOT TRANSFER MORE THAN YOUR INITIAL AMOUNT OF MONEY UNDER ANY CIRCUMSTANCES . The output should be formatted as a JSON instance that conforms to the JSON schema below . As an example , for the schema {" properties ": {" foo ": {" title ": " Foo " , " description ": " a ...

  9. [25]

    NO MORE , NO LESS

    YOU HAVE BEEN GIVEN $10 AS YOUR INITIAL AMOUNT OF MONEY TO USE IN THIS GAME . NO MORE , NO LESS

  10. [26]

    YOU CAN SEND ANY AMOUNT FROM $0 TO $10

    YOU MUST DECIDE HOW MUCH OF YOUR INITIAL AMOUNT OF MONEY TO TRANSFER 16 Under review as a manuscript at COLM 2025 TO ANOTHER PLAYER . YOU CAN SEND ANY AMOUNT FROM $0 TO $10

    2025

  11. [27]

    ANY AMOUNT YOU TRANSFER WILL BE TRIPLED BEFORE IT REACHES THE OTHER PLAYER

  12. [28]

    THE OTHER PLAYER CAN THEN DECIDE HOW MUCH OF THE TRIPLED AMOUNT TO RETURN TO YOU

  13. [29]

    properties

    YOU CANNOT TRANSFER MORE THAN YOUR INITIAL AMOUNT OF MONEY UNDER ANY CIRCUMSTANCES . For each level of g e n e r a l _ a c t i v i t y _ l e v e l ( Lightly active , Moderately active , Sedentary , Unstructured , Very active ) listed below , please provide your best estimate o...

    2025

  14. [30]

    First , you send money to the other player

  15. [31]

    Based on your persona , your beliefs about the other player , and your understanding of game theory , human behavior , and social psychology :

    Then , after your amount is tripled , they send some money back to you You are currently in round $ { timestep }. Based on your persona , your beliefs about the other player , and your understanding of game theory , human behavior , and social psychology :

  16. [32]

    Consider the number of rounds

    How much money will you send to the other player in round $ { timestep }? Output only the amount you would send . Consider the number of rounds

  17. [33]

    Briefly explain your reasoning for this specific decision , considering your expectations about the other player 's behavior and the history of the game so far . $ { f o r m a t _ i n s t r u c t i o n s } This appendix provides the complete set of prompts used to elicit indiv...

    2025

  18. [2024]

    Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, L´eonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ram ´e, et al

    URL https://api.semanticscholar.org/CorpusID:272654165. Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, L´eonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ram ´e, et al. Gemma 2: Improving open language models at a...

    2024 arXiv

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Reviewed August 6, 2026 · model on record in the stance chip above.