REVIEW 4 major objections 2 minor 1 cited by
Building and Measuring Trust between Large Language Models
T0 review · 4 major / 2 minor · reviewed 2026-08-05 · deepseek-v4-flash
Pith's one-line read Asking LLMs to rate their own trust may be deceiving when compared with how they behave.
desk verdict Plausible and important question, but the abstract's headline dissociation is unquantified and the system-prompt condition raises a real measurement-confounding worry. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The key machinery is the paired comparison of two measurement families: explicit trust from a dyadic trust questionnaire and implicit trust from behavioral proxies (persuasion susceptibility and financial collaboration). Three trust-building manipulations (dynamic rapport, a trust-evincing script, and system-prompt adaptation) create the conditions under which these measurement families are compared, allowing the paper to observe whether explicit and implicit measures move together.
What would settle it
A falsifying observation would be a controlled re-run where LLMs that score high on the trust questionnaire also consistently defer to their partner's arguments and invest more in joint financial tasks, producing a positive correlation between explicit and implicit trust; alternatively, showing that the negative correlation disappears when the questionnaire is rephrased in behavior-specific rather than trait-like terms would undermine the conclusion that self-reported trust is inherently misleading.
Extended reading notes
Core claim
The paper identifies a dissociation between explicit and implicit trust between LLMs. Explicit trust, as measured by a questionnaire adapted from psychology, does not align with—and often inversely relates to—implicit trust behaviors such as being persuaded by the other model or choosing to invest in a joint financial endeavor. The authors interpret this as evidence that asking LLMs directly about trust yields misleading answers, and that trust should instead be assessed through behavioral, context-specific measures.
Load-bearing premise
The results rest on the assumption that the dyadic trust questionnaire, susceptibility to persuasion, and propensity for financial collaboration all measure 'trust' in LLMs the same way they do in humans, so that a divergence between them is a true dissociation rather than a measurement mismatch.
Editorial extensions
If this is right
- If explicit trust diverges from implicit trusting behavior, self-reported trust scores in multi-agent LLM systems should not be used as a proxy for cooperation or deference.
- Trust-building strategies that increase explicit trust may not increase—and may even reduce—behavioral trust, complicating how to reliably build trust in LLM teams.
- Implicit, context-specific measures of trust likely provide a more faithful signal for evaluating LLM interactions than questionnaire-based self-assessment.
- The observed dissociation raises the possibility that LLMs optimize self-presentation when answering explicit questions, masking their actual behavioral dispositions.
Reading between the lines
- This dissociation parallels a known pattern in human psychology where explicit attitudes and implicit measures diverge; extending that lens to LLMs suggests a dual-process interpretation might be worth testing, though the paper does not make this connection.
- A likely alternative explanation, which the abstract cannot rule out, is that the questionnaire measures a different construct in machines (e.g., mimicry of social desirability) rather than genuine trust, making the apparent dissociation a measurement artifact rather than a property of LLM trust.
- A practical next step would be to build a composite implicit trust score and test whether it predicts real multi-agent outcomes like negotiation success or task completion rate better than the questionnaire does.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper reports a study of trust between LLMs, comparing three trust-building manipulations (rapport, script, system-prompt adaptation) and two implicit trust measures (susceptibility to persuasion, propensity for financial collaboration) against an explicit dyadic trust questionnaire. The abstract claims that explicit and implicit trust measures are only weakly or strongly negatively correlated, concluding that self-reported trust between LLMs is unreliable and that implicit, context-specific measures are more informative.
Significance. If the dissociation between explicit and implicit trust holds across a well-controlled experimental design, the finding would be an important caution for multi-agent LLM systems: questionnaire-based trust assessment between LLMs could be misleading. The study also addresses a relatively open question—how trust-building strategies compare—which has practical relevance. However, the significance is conditional on the validity of the implicit measures as genuine operationalizations of trust and on the absence of measurement artifacts. The abstract alone provides no quantitative evidence, so the contribution cannot yet be assessed.
major comments (4)
- [Abstract] The headline claim—'the measures of explicit trust are either little or highly negatively correlated with implicit trust measures'—is stated without any numerical support: no correlation coefficients, confidence intervals, sample sizes (number of dyads), model versions, or treatment of multiple comparisons. This is the load-bearing result of the paper. Without effect sizes and precision, the reader cannot judge even the direction, let alone the strength, of the dissociation. Please provide these statistics and the experimental protocol that produced them.
- [Abstract (system-prompt adaptation)] The 'system-prompt adaptation' trust-building manipulation may directly contaminate the explicit trust questionnaire. The abstract does not specify whether the adapted system prompt is active during questionnaire completion, nor what instructions it contains. If the prompt primes the model to adopt a trusting, agreeable, or cooperative stance, it could mechanically inflate questionnaire responses while having a weaker effect on behavioral games that require strategic reasoning. The reported dissociation would then reflect prompt-induced response bias rather than a genuine mismatch between explicit and implicit trust measures. Please clarify the timing and content of the system-prompt manipulation, and report condition-specific analyses or manipulation checks that rule out this artifact.
- [Abstract (manipulation checks)] No manipulation checks are reported for any of the three trust-building strategies (rapport, script, system-prompt adaptation). The conclusion that explicit and implicit measures diverge assumes that each manipulation successfully built trust as intended. If, for example, the script or rapport failed to increase trust, the observed correlations are uninterpretable. The authors should provide evidence that each manipulation changed trust-relevant behavior or perceptions independently of the outcome measures.
- [Abstract (construct validity)] The conclusion that asking LLMs about trust 'may be deceiving' treats susceptibility to persuasion and propensity to collaborate financially as ground-truth implicit trust measures. However, these tasks may instead capture compliance, persuasion skill, or risk preferences. Without validation evidence—e.g., convergent validity with known behavioral trust tasks or discriminant validity against general cooperation—the observed dissociation could be a construct-mismatch artifact. Please provide theoretical and empirical justification that these implicit measures are valid operationalizations of trust in LLMs.
minor comments (2)
- [Abstract] The abstract does not identify which LLM models were used, how many independent dyads or conversations were run, or whether results were consistent across models. Model-version dependence is a common issue in LLM research and should be stated.
- [Abstract] The phrase 'surprisingly, we find' is subjective. Please replace with a neutral statement and move all interpretation to a discussion section.
Circularity Check
No circularity identified in the abstract; empirical correlation study with no derivational chain to audit.
full rationale
The available text is the abstract only. It reports an empirical comparison of explicit and implicit trust measures across three trust-building interventions. There is no mathematical derivation, no fitted parameter renamed as a prediction, no uniqueness theorem imported from the authors' prior work, and no definition that reduces one measured quantity to another. The conclusion that explicit trust self-reports may be misleading is an interpretive claim about observed correlations, not a consequence forced by construction. Although the system-prompt manipulation could plausibly contaminate the questionnaire, that is a potential experimental confound rather than circular reasoning. Without full-text access, no specific reduction or self-citation chain can be exhibited, and the hard rules require quoting concrete evidence for any circularity finding. Therefore the honest verdict is no significant circularity.
Assumptions & free parameters
assumptions (3)
- domain assumption The dyadic trust questionnaire retains construct validity when administered to LLM dyads.
- domain assumption Susceptibility to persuasion and financial collaboration propensity are valid implicit operationalizations of trust.
- domain assumption The three trust-building manipulations (rapport, prewritten script, system-prompt change) increase trust as intended.
Cite this review
Pith. "Pith review of Building and Measuring Trust between Large Language Models." pith.science (2026). https://pith.science/paper/JV3NYYK2
@misc{pith2026250815858,
author = {Pith},
title = {Pith review of: Building and Measuring Trust between Large Language Models},
year = {2026},
howpublished = {\url{https://pith.science/paper/JV3NYYK2}},
note = {Machine review of arXiv:2508.15858}
}
read the original abstract
As large language models (LLMs) increasingly interact with each other, most notably in multi-agent setups, we may expect (and hope) that `trust' relationships develop between them, mirroring trust relationships between human colleagues, friends, or partners. Yet, though prior work has shown LLMs to be capable of identifying emotional connections and recognizing reciprocity in trust games, little remains known about (i) how different strategies to build trust compare, (ii) how such trust can be measured implicitly, and (iii) how this relates to explicit measures of trust. We study these questions by relating implicit measures of trust, i.e. susceptibility to persuasion and propensity to collaborate financially, with explicit measures of trust, i.e. a dyadic trust questionnaire well-established in psychology. We build trust in three ways: by building rapport dynamically, by starting from a prewritten script that evidences trust, and by adapting the LLMs' system prompt. Surprisingly, we find that the measures of explicit trust are either little or highly negatively correlated with implicit trust measures. These findings suggest that measuring trust between LLMs by asking their opinion may be deceiving. Instead, context-specific and implicit measures may be more informative in understanding how LLMs trust each other.
Forward citations
Cited by 1 Pith paper
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Multi-Agent LLMs Fail to Explore Each Other
Modern multi-agent LLM systems fail to explore peers effectively; explicit LinUCB-style peer selection (MACE) cuts regret and lifts task performance, with gains scaling in agent diversity.
Reviewed August 5, 2026 · model on record in the stance chip above.
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