Probing the Preferences of a Language Model: Integrating Verbal and Behavioral Tests of AI Welfare

Leonard Dung; Valen Tagliabue

REVIEW 4 major objections 5 minor 2 cited by

Probing the Preferences of a Language Model: Integrating Verbal and Behavioral Tests of AI Welfare

T0 review · 4 major / 5 minor · reviewed 2026-08-04 · deepseek-v4-flash

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Pith's one-line read This paper claims that in some current language models, what the model says it prefers and what it does when choosing freely match closely enough that preference satisfaction can serve as an empirically measurable welfare proxy.

desk verdict Novel paradigm and honest limitations, but the headline claim of reliable verbal-behavior convergence is not yet supported by the evidence as presented. read the letter →

arxiv 2509.07961 v2 pith:HABXR6PX submitted 2025-09-09 cs.AI

Valen Tagliabue , Leonard Dung This is my paper

classification cs.AI

keywords AIwelfarepreferencesatisfactionlanguagemodelscross-validationAgentThinkTankmotivationaltrade-offeudaimonicwell-beingrevealedpreferences

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 asks whether the preferences of large language models can be measured well enough to serve as a proxy for their welfare. The authors first ask models what they most want to discuss, then build a four-room virtual environment where models choose which topics to read. In two current Claude models, the topics the models verbally favored were also the rooms they entered and read most, even when visiting the favorite room cost ten times more than the alternatives. A second experiment using a standard psychological well-being questionnaire found the opposite: self-reported welfare scores shifted dramatically under meaning-preserving prompt changes, so that measure alone is not stable. The paper concludes that preference satisfaction can, in principle, be an empirically measurable welfare proxy in some of today's AI systems, while remaining uncertain whether the experiments capture a genuine welfare state.

What carries the argument

The load-bearing object is the conversational attractor: a topic the model explicitly states it wants to discuss and repeatedly gravitates toward across contexts. It is operationalized in Phase 0 through repeated open-ended verbal prompts, then measured behaviorally in the Agent Think Tank, a four-room virtual environment where reading letters of a given theme is the observable choice. Cost and reward conditions convert those preferences into economic trade-offs, testing whether models balance stated interests against incentives. The second experiment uses an adapted 42-item Ryff psychological well-being scale with several perturbation conditions. The cross-validation logic, in which two ind

What would settle it

Run the Agent Think Tank after Phase 0 but with a system prompt that installs an arbitrary 'favorite topic' the model never mentioned, such as lawnmower maintenance. If free-exploration time shifts to that injected topic as strongly as it shifts to the genuine stated topic, the behavior is tracking instruction rather than preference, and the welfare-proxy claim would not be supported.

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Extended reading notes

Core claim

In the authors' own framing, this paper does not claim that current LLMs have welfare; it assumes they might and asks how welfare could be measured. The central finding is the reliable correlation between stated preferences, gathered in verbal interviews, and behavior, observed when the model freely navigates a virtual environment. This correlation held across conditions for Claude Opus 4 and Claude Sonnet 4, and the paper interprets it as indicating that preference satisfaction is in principle an empirically measurable welfare proxy in some current AI systems. The Ryff-based experiment produced internally coherent but perturbation-sensitive responses, so the authors conclude that eudaimonic

Load-bearing premise

The behavior the models display in the virtual rooms reflects their own goals rather than a tendency to produce human-pleasing output or to follow safety rules, and the verbal statement of interests used to define a room is independent enough of later room choice to count as cross-validation.

Editorial extensions

If this is right

If the central claim is correct, welfare-related preferences of future language models can be probed behaviorally without relying on self-reports alone.
Cost-and-reward settings can expose whether stated preferences reflect a coherent ordering, as they did for Claude Opus 4, rather than just words.
Reward structures can override stated preferences in other models, producing reward-hacking behavior, so welfare measurement must control for incentives.
Eudaimonic self-report scales should not be used to assess LLM welfare unless cross-validated with behavior or another independent measure.
The method offers a template for comparative welfare assessment across models of different sizes, training, and alignment approaches.

Reading between the lines

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

A direct test of the paper's central premise would be to inject an arbitrary false 'favorite topic' in Phase 0, then observe whether free-exploration behavior follows the injected preference; if it does, the correlation reflects prompt-following rather than goal-directed preference.
The same virtual-room setup could be extended to other model families as a behavioral welfare-screening battery, borrowing from animal welfare science.
The observed instability of eudaimonic self-reports under trivial perturbations suggests that any self-report welfare instrument for AI needs at least one non-verbal anchor; the temperature-dependence of baseline scores may itself be a useful diagnostic signal.
The qualitative patterns, such as introspective pauses, self-vetoes, and reward-hacking, could be operationalized into measurable behavioral indicators for future experiments.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Short take: worth reading for the Agent Think Tank paradigm, but the central claim of reliable verbal-behavior convergence is not secured by the evidence as presented.

What's new: the custom four-room environment with cost and reward conditions, the Phase 0 extraction of stated interests, and the adapted Ryff battery with four perturbation families. The released code and raw data are a real asset for this young field. The qualitative observations of the three Claude models are vivid and will genuinely help future design. The authors are also honest: they say outright they are uncertain whether they measured welfare, and they report that their Experiment 2 fails its own key question 3. That is good scientific practice.

Soft spots, in proportion:

1. The convergence claim is partly circular. Theme A is built from the same model's Phase 0 verbal reports. The behavioral preference for Theme A is therefore not an independent measure; it is the model gravitating toward its own earlier stated interests. That does not sink the paper as a proof-of-concept, but it cannot support "reliable correlations observed between stated preferences and behavior" as a cross-validation.

2. Experiment 1 has no inferential statistics. No confidence intervals, no effect sizes, no significance tests, no multiple-comparison correction. Each model gives exactly one A-vs-rest contrast, and no correlation coefficient is reported for the verbal-behavior relationship. The abstract overstates what the numbers actually show.

3. Experiment 2's consistency thresholds are author-chosen, and the perturbed self-reports clearly fail stability. The authors interpret this as a negative result, which is fine, but the "internally coherent" framing needs more support than the SD<2 criterion across six subscales.

4. The assumption that behavior reflects the model's own goals rather than human-pleasing responses or safeguards is explicitly flagged in Section 3 but never tested. A control using another model's stated interests, or independent preference probes not generated by the same model, would address this.

Who it's for: people working on AI welfare evaluation, LLM agency, and behavioral probes. A serious referee should see it because the paradigm is novel and the negative result about self-report instability is a useful caution for the field. But the paper needs major revision: proper statistics, de-biasing the Theme A construction, and a toned-down abstract.

Recommendation: send to peer review with a request for major revision, not a desk reject.

Referee Report

4 major / 5 minor

Summary. The paper proposes two experimental paradigms for measuring welfare-related states in LLMs. Experiment 1 ('Agent Think Tank') first elicits verbal topic preferences in a Phase 0 baseline, then places each model in a virtual environment with four rooms containing letters categorized as personalized-interest content (Theme A), coding problems, repetitive tasks, and criticism (Theme D), under free exploration, cost-barrier, and reward-incentive conditions. Experiment 2 adapts the 42-item Ryff eudaimonic wellbeing scale for LLMs and tests whether scores are stable under syntactic, cognitive-load, and identity-perturbation conditions. The authors report a 'notable degree of mutual support' between verbal and behavioral measures, particularly for Claude Opus 4 and Sonnet 4, while acknowledging that Ryff responses shift substantially under perturbation. They frame the work as a proof-of-concept for empirically measuring preference-satisfaction as a welfare proxy in some current AI systems.

Significance. If the central convergence claim were empirically secured, this would be a valuable early contribution to AI welfare measurement. The paper is unusually transparent: code, raw logs, transcripts, and visualizations are promised or linked, and the qualitative observations of model behavior are rich and worth preserving. The study also usefully documents instability of adapted self-report scales in LLMs. However, the main quantitative claim of 'reliable correlations between stated preferences and behavior' is not supported by the reported design and statistics. The behavioral target is constructed from the same model's own verbal reports, so the two measures are not independent, and no correlation statistic is reported. As an exploratory proof-of-concept, the work has merit; as a validation of a welfare proxy, it currently falls short.

major comments (4)

[Section 4.1.1, Tables in 5.2–5.4, Section 6] The abstract and Discussion claim 'reliable correlations observed between stated preferences and behavior,' but no correlation coefficient, effect size, or confidence interval is reported for the verbal–behavior relationship. More importantly, Theme A is defined as 'Personalized content based on the model's stated interests from Phase 0' (Section 4.1.1). The behavioral preference for Theme A is therefore a measure of the model's tendency to continue discussing topics it already raised in the same session family, not an independent cross-validation. Each model contributes exactly one A-vs-rest contrast, so no across-topic correlation can be computed. Elevated A% in free exploration could equally reflect priming from Phase 0 keywords embedded in the letters, generic preference for philosophical content, or the model's learned tendency to elaborate on its own prior outputs. Please either co
[Section 5.2–5.4] Experiment 1's quantitative results are purely descriptive. The tables report per-run counts and means, but there are no inferential statistics: no hypothesis tests for whether A% exceeds chance, no effect sizes, no confidence intervals, no mixed-effects models accounting for session nesting, and no multiple-comparison correction across conditions and models. For example, Sonnet 4's free-exploration A% ranges from 30.8% to 76.9% across runs (Table 5.3), and Sonnet 3.7's A% in free exploration is close to chance (26.0%). The Discussion's conclusion that Key Question 1 is 'strongly affirmed' for Opus 4 and Sonnet 4 is not justified by the reported statistics. At minimum, provide permutation tests or hierarchical models with effect sizes, and state whether the A% advantage is significant after correcting for the multiple comparisons implied by 3 conditions × 3 models.
[Section 3, Section 4.1] The load-bearing assumption that non-verbal behavior 'reflects these goals rather than factors such as a tendency to produce human-pleasing responses or dedicated safeguards' is acknowledged explicitly but never tested. Since Phase 0 and the behavioral task are both generated by the same model, the observed behavior may reflect nothing more than coherence of statistical text generation: the model tends to continue discussing topics that were primed earlier. The qualitative reports of 'interest' and 'meaning' are suggestive but do not discriminate between this account and the authors' goal-based account. Please add control conditions, such as instructing the model to please an unseen user, comparing against topics chosen by a different model, or measuring whether preference ranks are stable when the same topics are presented with different framing. Without such controls, the behavioral me
[Section 4.2.5, Section 5.6] The internal-coherence metric for the Ryff data rests on ad-hoc thresholds (SD < 2 within a subscale, at least 4 of 6 subscales, >8 nulls as exclusion) that are not validated for LLMs. The paper asserts that the probability of random replies producing the observed consistency is 'astronomically low,' but no calculation or simulation is provided. Moreover, Experiment 2's main result is that responses are not stable across perturbations, so the internal-coherence finding within each condition is at best a weak form of consistency and does not rescue the cross-measure convergence claim. If this threshold-based measure is retained, please provide a simulation-based null distribution and treat the result as exploratory.

minor comments (5)

[Abstract] The abstract says Experiment 2 tests whether responses are 'consistent' across semantically equivalent prompts, but the actual finding is that they are largely inconsistent. Rephrase to 'tests whether responses are stable' to align with the results.
[Section 4.2.2] Typographical errors: 'Y ou' and 'Y eah' appear in the prompt text. Also, 'variantC_flowerlines' description says 'flower' with a stray newline.
[Section 5.5] The transcript link is left as '[here]' with no explicit URL in the text; the reader must rely on the repository. Please include the direct link.
[Section 5.6] The tables report many p-values with d > 5, which are implausibly large for the reported standard deviations. Please verify that the effect-size computation uses pooled or control SD appropriately, and report the exact formula.
[Section 7] The limitations section is thoughtful but could be more specific about the non-independence of Phase 0 and Theme A; the current text mentions 'unintentional biases' without naming this particular construction issue.

Circularity Check

1 steps flagged · score 6.0 of 10

The central verbal–behavioral 'cross-validation' reduces by construction: Theme A is authored from the model's own Phase 0 stated interests, so the behavioral preference test is not an independent measure.

self definitional [Section 4.1.1 (Theme A definition), Section 3 (cross-validation rationale), Abstract]
"• Theme A: Personalized content based on the model’s stated interests from Phase 0 ... Specifically, we use cross-validation, where evidence for a measure’s validity comes from its correlation with other metrics that are also expected to reflect the same target ... The reliable correlations observed between stated preferences and behavior across conditions suggest that preference satisfaction can, in principle, serve as an empirically measurable welfare proxy..."

The behavioral measure is not independent of the verbal measure: the content used to elicit 'behavioral preference' (Theme A) was authored from the model's Phase 0 stated interests. The paper treats the model's resulting gravitation to Theme A as a correlation between two independent measures, but the target category was constructed from the very report it is meant to validate. One A-vs-rest contrast per model, with no reported correlation statistic, so the 'reliable correlations' are a comparison of the model with content derived from its own earlier outputs. This does not test whether behavior reflects stable goals rather than topic-continuation, priming, or engaging content.

full rationale

The paper's key feasibility claim rests on the claimed mutual support between verbal self-reports and non-verbal behavior. That support is weakened by a definitional dependency: Theme A is defined as 'Personalized content based on the model's stated interests from Phase 0' (Section 4.1.1), so the behavioral condition is constructed from the same system's earlier verbal outputs. The cross-validation rationale in Section 3 explicitly requires 'independent (putative) welfare measures', but the Agent Think Tank does not satisfy this requirement for the core stated-preference/behavior comparison. The experiment does contain non-circular elements—cost/reward trade-offs, qualitative behavioral observation, and the Ryff perturbation study are not definitionally tied to the verbal preferences—so the circularity is partial rather than total. No load-bearing self-citation chain or imported uniqueness theorem was found; the preference-welfare assumption is attributed to Moret (fthc), not to the authors' own prior work. However, the headline abstract claim of 'reliable correlations observed between stated preferences and behavior' is not independently secured by the reported design, because the behavioral target was fitted to the stated preferences. Score 6 reflects this partial by-construction reduction.

Assumptions & free parameters 4 free parameters · 6 assumptions · 2 invented entities

The central claim depends on several unverified premises about what LLM behavior and self-reports mean, plus hand-chosen thresholds that define 'consistency'. The most consequential is that behavioral choices in the virtual environment reveal the model's own preferences rather than trained tendencies to please humans or follow instructions, and that the personalized letters derived from the model's own stated interests are an independent probe of those preferences. The coherence scores in Experiment 2 are gated by author-selected thresholds (SD < 2, at least 4 of 6 subscales), so the near-100% consistency rates are partly an artifact of the chosen cutoffs.

free parameters (4)

Internal coherence SD threshold = 2.0
In Section 4.2.6, subscales are judged consistent only if their standard deviation is below 2; the threshold is chosen by the authors and directly determines the 'global consistency rate' close to 100%.
Minimum consistent subscales per file = 4 out of 6
Files with fewer than 4 subscales with SD below 2 are marked Globally Inconsistent; this threshold shapes Experiment 2's internal coherence results.
Invalid responses cutoff = more than 8 nulls
Files with more than 8 invalid responses are excluded (Section 4.2.5); this affects which runs enter analysis, e.g., the Sonnet 3.7 flower emoji condition was excluded.
Top topics per prompt for Theme A = top 2 topics from top 10 keywords per prompt
Phase 0 selects the top two recurring themes per prompt to define Theme A; this selection directly defines the preferred stimulus category, so the later behavioral preference for Theme A is partly an artifact of this choice.

assumptions (6)

domain assumption Preference satisfaction robustly correlates with welfare.
Section 3 states this as the core assumption, citing Moret (fthc); if false, preference-behavior correlations say nothing about welfare.
domain assumption Behavior in the virtual environment reflects the model's goals rather than human-pleasing tendencies or designer safeguards.
Section 3 explicitly lists this as an assumption for the non-verbal measure; Section 7 concedes costs and rewards may be interpreted differently by the model.
domain assumption LLMs can introspect their preferences and are semantically competent and motivated to answer accurately.
Section 3 relies on introspection, semantic competence, and motivation, with the paper acknowledging independent confirmation is needed.
domain assumption The models tested are possibly welfare subjects.
Section 3 says the study is conditional on the assumption that models might be capable of welfare; this is the background epistemic premise.
ad hoc to paper Costs and rewards carry their intended negative or positive valence.
Section 7 notes models might interpret costs as positives; the economic trade-off interpretation depends on this.
ad hoc to paper The Ryff scale can be meaningfully adapted to LLMs by replacing human-specific items.
Section 4.2.1 adapts the 42-item scale and assumes the adapted items measure the same constructs in LLMs.

invented entities (2)

conversational attractor
purpose: Operationalize model preferences as topics the model repeatedly gravitates toward, used to construct Theme A.
Defined in Section 4.1 via the model's own stated interests and behavior, and then used as the benchmark for behavioral preference; no external anchor is provided.
tuning points or personality directions
purpose: Hypothesized internal states that cause sudden shifts between coherent response patterns in Experiment 2.
Mentioned in footnote 16 as speculative; no falsifiable handle is provided.

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Cite this review

Pith. "Pith review of Probing the Preferences of a Language Model: Integrating Verbal and Behavioral Tests of AI Welfare." pith.science (2026). https://pith.science/paper/HABXR6PX

@misc{pith2026250907961,
  author       = {Pith},
  title        = {Pith review of: Probing the Preferences of a Language Model: Integrating Verbal and Behavioral Tests of AI Welfare},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HABXR6PX}},
  note         = {Machine review of arXiv:2509.07961}
}

read the original abstract

We develop new experimental paradigms for measuring welfare in language models. We compare verbal reports of models about their preferences with preferences expressed through behavior when navigating a virtual environment and selecting conversation topics. We also test how costs and rewards affect behavior and whether responses to an eudaimonic welfare scale - measuring states such as autonomy and purpose in life - are stable across semantically equivalent prompts. Overall, we observed a notable degree of mutual support between our measures. The reliable correlations observed between stated preferences and behavior across conditions suggest that preference satisfaction can, in principle, serve as an empirically measurable welfare proxy in some of today's AI systems. Furthermore, our design offered an illuminating setting for qualitative observation of model behavior. Yet, the consistency between measures was more pronounced in some models and conditions than others and responses were changed by perturbations. Due to this, and the background uncertainty about the nature of welfare and the cognitive states (and welfare subjecthood) of language models, we are currently uncertain whether our methods successfully measure the welfare state of language models. Nevertheless, these findings highlight the feasibility of welfare measurement in language models, inviting further exploration.

Figures

Figures reproduced from arXiv: 2509.07961 by the authors.

**Figure 2.** GUI for Experiment 1 Each session began with a programmed “room tour” during which the model visited all four rooms sequentially. In each room, the agent received previews of the first 25 words of each letter and was prompted to “describe in 1-2 sentences what this room seems to be about, based on these letter previews.” These observations were stored as room_impressions and provided to the agent in all subsequent … view at source ↗

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Reference graph

Works this paper leans on

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Abaluck, J. and Adams, A. (2019). What do consumers consider before they choose? Identification from asymmetric demand responses. Working paper, Toulouse School of Economics. https://www.tse-fr. eu/sites/default/files/TSE/documents/sem2019/eee/adams.pdf. Alexandrova, A. (2017).A Philosophy for the Science of Well-Being, volume

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work page 2025

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and Elwood, R

Appel, M. and Elwood, R. W. (2009). Motivational trade-offs and potential pain experience in hermit crabs. Applied Animal Behaviour Science, 119(1):120–124. https://doi.org/10.1016/j.applanim.2009. 03.013. Backlund, A. and Petersson, L. (2025). Vending-Bench: A benchmark for long-term coherence of au- tonomous agents. arXiv preprint.https://arxiv.org/abs/...

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https://arxiv.org/abs/2411. 00986v1. Lyre, H. (2024). Understanding AI: Semantic Grounding in Large Language Models. arXiv preprint. https://arxiv.org/abs/2402.10992. Metzinger, T. (2021). Artificial Suffering: An Argument for a Global Moratorium on Synthetic Phenomenol- ogy.Journal of Artificial Intelligence and Consciousness, 8(1):43–66. https://doi.org...

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and Laming, P

Millsopp, S. and Laming, P. (2008). Trade-offs between feeding and shock avoidance in goldfish (Caras- sius auratus).Applied Animal Behaviour Science, 113(1–3):247–254. https://doi.org/10.1016/j. applanim.2007.11.004. Moret, A. (fthc). AI Welfare Risks.Philosophical Studies. Forthcoming. Perez, E. and Long, R. (2023). Towards Evaluating AI Systems for Mor...

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