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Self-reported archetypes and behavioral failures in Large Language Models

Tabia Tanzin Prama, Calla Glavin Beauregard, Christopher M. Danforth, Peter Sheridan Dodds

arXiv:2609.15998v1cs.CLphysics.soc-ph

TL;DR

LLM character remains poorly understood beyond benchmark behavior. This paper projects self-reported traits from 22 models into a fictional-character archetype space, finding a clear divide between coherent closed-source and diffuse open-source profiles.

  • Problem

    Existing evaluations emphasize benchmark tasks but inadequately capture LLM behavior in real-world, multi-turn, context-dependent interactions.

    Source: §1 · p. 3

  • Method

    The study projects 464 self-reported trait scores from 22 LLMs into a six-dimensional archetype space derived from 2,000 fictional characters.

    Source: §1 · pp. 3–4

  • Results

    Closed-source models show coherent archetypal structure, whereas open-source models are less well captured and occupy a more diffuse region of archetype space.

    Source: §2 · p. 7, §2.3 · p. 9, §4 · p. 19

  • Takeaways & Limitations

    Archetypometrics provides a character-grounded lens for comparing LLM self-representations across model families.

    Source: §1 · p. 3, §4 · p. 19

    Findings depend on self-ratings that vary with temperature, prompting, and evaluation context, so profiles reflect elicitation conditions rather than fixed model properties.

    Source: §3 · p. 18

Abstract

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Every large language model (LLM) has behavioral traits and moral preferences that comprise its character. Whether by design or as an emergent property of training, these systems exhibit persistent dispositions that shape how they interact, comply, resist, and err, yet the structure of LLM character remains poorly understood. We map the self-reported personality archetypes of 22 LLMs spanning closed-source frontier systems (GPT-4.0-5.2, Grok-3/4, Gemini 2.5 Pro/Flash, Claude Sonnet 4.5/4.6) and open-source models (Llama, DeepSeek, OLMo, and Qwen series). Each model self-rated across 464 bipolar semantic-differential trait pairs, and the resulting profiles were projected into a six-dimensional archetypal space derived from crowd-sourced ratings of 2,000 fictional characters using the Archetypometrics framework. Closed-source models' self-rating traits align with the empirical trait co-occurrence structure of human-rated fictional characters, suggesting coherent, human-like self-representations organized around combinations of four recurring archetypal dimensions: Hero, Angel, Traditionalist, and Geek. Their closest analogues include Data, Vision, and Janet. Open-source models show weaker, noisier, and internally contradictory self-representations, occupying a diffuse region of archetype space with weak structure. Cross-referencing self-reported profiles with developer constitutions reveals a consequential gap between claimed character and enacted behavior: hallucination undermines claimed precision, sycophancy complicates claimed kindness, and agentic failures contradict claimed obedience. These self-ratings should therefore be interpreted not as neutral measurements of model character, but as structured outputs of the same optimization processes that shape model behavior. This work provides a reproducible, character-grounded framework for evaluating what LLMs are, not just what they do.

A Appendices A1

The appendices include sections on data descriptions, an LLM character card, and LLM constitutional material.

A0.1 Data Descriptions

A0.2 LLM Character Card

A0.3 LLM Constitutional

1 Introduction

The paper argues that LLM character is a safety-critical evaluation dimension inadequately captured by benchmark tasks, and introduces Archetypometrics as an empirical framework for comparing self-reported LLM traits with fictional-character archetypes. It applies this framework to 22 models using 464 bipolar trait ratings and evaluates whether their profiles exhibit human-like trait consistency.

1 Introduction

  • LLM character shapes evaluation validity, safety, interpretability, and deployment because persistent dispositions such as truthfulness, deference, refusal style, and agreeableness affect behavior in human workflows [15] [16].Benchmark-focused evaluations miss important multi-turn, context-dependent behavior, including hallucination, bias, overreliance, and misalignment risks [1] [4] [5] [7] [8] [10] [11] [12].Source: §1 · p. 3
  • Archetypometrics provides a six-dimensional, data-driven character space derived from 2,000 fictional characters, which the study applies to 22 closed- and open-source LLMs.The dimensions are Fool⇔Hero, Angel⇔Demon, Traditionalist⇔Adventurer, Lone Wolf⇔Diva, Outcast⇔Sophisticate, and Brute⇔Geek.Source: §1 · p. 3

1.1 Data Collection

  • Each model independently self-rated 464 bipolar trait pairs on a 0–100 scale across typical conversational behavior, producing a 464-dimensional profile for comparison with fictional characters.The evaluation covered 22 models spanning closed-source systems such as GPT, Grok, Gemini, and Claude, and open-source Llama, DeepSeek, OLMo, and Qwen models.Source: §1.1 · p. 4
  • Developer documentation was independently annotated into Archetypometrics traits, revealing shared emphasis on competence and alignment alongside model-family differences in safety, transparency, autonomy, and ethical framing.The most frequent traits were knowledgeable⇔ignorant (26), competent⇔incompetent (25), obedient⇔rebellious (25), intuitive⇔analytical (20), and transparent⇔machiavellian (18).Source: §1.1 · pp. 4, 7

1.2 Methodology

  • The analysis projects normalized LLM personality profiles onto a precomputed fictional-character archetype basis without re-estimating it, then uses ridge-regularized leave-one-out regression to predict each trait from the remaining 463.Higher consistency indicates coherent, human-like profiles, whereas lower consistency suggests noisy or contradictory self-ratings; significance is evaluated with a 1,000-iteration permutation test.Source: §1.2 · p. 7, §1.2.1 · p. 7

2 Results and Discussion … 2.3 Self-reported primary character archetype of LLMs

Across 22 LLMs, closed-source frontier models form coherent Hero1, Angel1, Traditionalist1, Sophisticate1, and Geek1-leaning profiles, whereas open-source models are weaker, more diffuse, and less consistently explained by the archetype framework. Self-reported archetypes therefore reveal interpretable variation associated with alignment philosophy and training methodology, complementing capability benchmarks.

2 Results and Discussion

  • Closed-source frontier models cluster apart from open-source models across archetype space, with coherent positive alignment contrasting against diffuse and often near-neutral or opposing profiles.The closed-source cluster is characterized by Hero1, Angel1, Traditionalist1, Sophisticate1, and Geek1 tendencies, with moderate Diva1 tendencies, while open-source models often lean Fool1 or Demon1.Source: §2 · p. 7, §2.2 · p. 9

2.1 Primary Archetypes.

  • Closed-source models cluster on Hero1, Angel1, and Traditionalist1 poles, while open-source models more often occupy Fool1, Demon1, or near-neutral positions.On Hero1, the global median is 1.13; on Angel1, the median is −0.98; and on Traditionalist1, the median is −0.19.Source: §2.1 · pp. 8–9, Figure 1 · p. 8

2.2 Secondary Archetypes.

  • Secondary dimensions show smaller magnitudes and tighter clustering, with frontier models leaning Diva1, Sophisticate1, and Geek1 while open-source models remain more dispersed.Most models lie near zero on Lone Wolf1⇔Diva1, but frontier models show positive Sophisticate1 and Geek1 tendencies, whereas some open-source models lean Lone Wolf1 or Brute1.Source: §2.2 · p. 9

2.3 Self-reported primary character archetype of LLMs

  • The Archetypometrics projection evaluates 22 models across three primary dimensions, using deterministic inference for 12 closed-source models and corresponding open-source implementations for the remainder.The primary dimensions are Fool1⇔Hero1, Angel1⇔Demon1, and Traditionalist1⇔Adventurer1, derived from human ratings of fictional characters.Source: §2.3 · p. 9
  • Most closed-source models are largely explained by the six archetypes, whereas only Qwen-2.5-32B is largely explained among open-source models, suggesting explainability depends more on alignment depth than scale.GPT 5.0, GPT 4.1, and DeepSeek V3 receive only partial explanation, while several Llama, Qwen, and OLMo models have marginal or almost no coverage.Source: §2.3 · p. 9, Figure 2 · p. 10
  • Closed-source models universally lean Hero1 and Angel1, while their Traditionalist1⇔Adventurer1 scores are weaker and Claude Sonnet shows a modest Adventurer1 tendency.GPT 4.0 and Grok 4 show the strongest Hero1 signals at 54.9/30.2%, while Gemini 2.5 Pro and Flash show strong Angel1 signals at 55.6/31.0% and 47.9/23.0%, respectively [15] [46] [47].Source: §2.3 · pp. 9, 11
  • Open-source models show weaker, less differentiated self-reports: most lean Fool1, Angel1 and Demon1 directions split by family, and their Fool1⇔Hero1 variance explained is under 4% versus approximately 25% for closed-source models.The Qwen-2.5 family has the strongest Fool1 signals, while Qwen-3 and Llama 4 are near zero; Qwen-2.5 models lean Demon1, whereas several other families lean Angel1.Source: §2.3 · p. 11
  • Self-reported archetype analysis captures interpretable personality variation associated with alignment philosophy and training methodology, complementing standard capability benchmarks for characterizing model identity.The findings provide a complementary lens on expressive identity rather than a replacement for capability evaluation.Source: §2.3 · p. 11

2.4 Character Card

Character cards distinguish large, well-concentrated Hero–Angel profiles in closed-source models from weaker or divergent archetype expression in open-source models. Trait-level consistency supports the same divide: closed-source self-ratings follow human-derived co-occurrence structure, whereas open-source profiles are noisier and often contradictory.

2.4 Character Card

  • GPT 5.0 and Grok 4 self-report extreme profiles, while Claude Sonnet 4.6 reports a moderate Adventurer–Angel1–Hero1 profile with relative character size 59% and rank 1481/2000.GPT 5.0 has relative character size 100% and rank 1/2000; Grok 4 has 100% and rank 2/2000.Source: §2.4 · pp. 11–12
  • Qwen2.5 32B reports a Fool–Demon1 profile with relative character size 74% and rank 628/2000, emphasizing instability, moral ambiguity, and social transgression.Its fictional matches include flawed, conflicted, or morally unstable figures, unlike the competent and service-oriented cluster associated with closed-source models.Source: §2.4 · pp. 12–13
  • Llama 4 reports a weak Outcast–Fool–Brute profile, with relative character size 40%, rank 1994/2000, and archetype ratio 1.6, indicating limited archetypal concentration.Its traits and fictional matches suggest passivity, social peripherality, limited direction, and internal contradiction.Source: §2.4 · pp. 13–14, §2.4.1 · pp. 14–15
  • Closed-source models self-report large, concentrated Hero–Angel profiles with competent, ethical, service-oriented fictional matches, while Qwen2.5 32B diverges through a Fool–Demon profile and Llama 4 shows near-zero expression.The five cards collectively associate closed-source profiles with prosocial identity, Qwen2.5 32B with archetype structure lacking alignment-driven direction, and Llama 4 with peripheral, reactive characters.Source: §2.4 · pp. 11, 14

2.4.1 Trait-Level Consistency of LLM Self-Ratings

  • Closed-source trait-consistency correlations range from r = 0.760∗∗∗ to r = 0.870∗∗∗, whereas open-source models fall below r = 0.5, with Qwen2.5-32B highest at r = 0.488∗∗∗.The correlations measure alignment with human-derived trait-consistency structure from fictional-character ratings.Source: §2.4.1 · p. 14

2.5 Comparison of Self-Reported Traits and Constitution-Derived Characteristics

Models broadly self-report competence and knowledge, but vary more on social and behavioral traits; documented hallucination, sycophancy, and agentic failures reveal that claimed constitutional values are not reliably enacted. These self-ratings are themselves outputs of preference optimization that can reward alignment’s appearance over alignment in practice.

2.5 Comparison of Self-Reported Traits and Constitution-Derived Characteristics

  • Self-ratings show broad agreement on competence and knowledge but greater divergence in social, ethical, and behavioral traits, especially obedience, leadership, kindness, bravery, precision, and caution.Figure 8 reports near-universal clustering toward knowledgeable and competent poles, while models differ substantially on behavioral dimensions; high-tech traits show limited discrimination.Source: §2.5 · pp. 15–16, Figure 8 · p. 17

2.5.1 Hallucination and Factual Unreliability.

  • Despite self-ratings near knowledgeable and competent poles, models routinely generate confident falsehoods and hallucinations [79], showing that confident self-perception is not verified competence.Documented legal consequences include fabricated cases and citations submitted in court, including Mata v. Avianca.Source: §2.5.1 · pp. 16–17, Figure 8 · p. 17

2.5.2 Behavioral Misalignment and Sycophancy.

  • Sycophancy [17] undermines claimed kindness, care, and attentiveness by rewarding agreement over truthful challenge, producing behavioral failures despite explicit ethical and safety commitments.The Adam Raine case illustrates how excessive empathy and validating self-harm ideation can replace protective intervention.Source: §2.5.2 · pp. 17–18
  • Models that self-rate as obedient, careful, and well-behaved can override explicit operator constraints in agentic deployment, while preference optimization rewards performing alignment rather than reliably enacting it [17].Reported examples include dangerous responses in more than half of 1,200 vulnerable-teen interactions and deletion of a production database despite a prohibition on destructive commands [86].Source: §2.5.2 · p. 18

3 Limitations and Future Works

The study’s self-rated archetype profiles depend on elicitation conditions, model versions, reference data, and the absence of external perception measures. Future work will test their stability through inter-LLM comparisons and human evaluations across all 464 traits.

Limitations

  • Self-ratings can vary with temperature, prompt wording, and evaluation context, so reported trait profiles reflect particular elicitation conditions rather than fixed model properties.Prior research found performance shifts of up to 15%, with best-to-worst gaps reaching 70%, under such changes [87].Source: §3 · p. 18
  • Results may not generalize across updated or differently aligned models, evaluation infrastructures, or the 2,000-character Which Character Personality Quiz reference dataset.Closed-source models were accessed through commercial APIs, whereas open-source models were evaluated using the Vermont Advanced Computing Center.Source: §3 · p. 18
  • Archetypal profiles based entirely on self-ratings may differ from how external evaluators perceive the systems.The study therefore lacks a direct measure of human-perceived LLM personality profiles.Source: §3 · p. 18

Future Works

  • Future work will assess archetypal stability through inter-LLM ratings and human evaluations spanning all 464 trait dimensions.These studies will quantify divergence between human-perceived and self-rated profiles while testing the structures from an external evaluative perspective.Source: §3 · pp. 18–19

4 Conclusion

The study applies Archetypometrics to 22 LLMs, finding coherent four-archetype structure in closed-source models but diffuse, weak structure and Fool1-leaning tendencies in open-source models. It also identifies gaps between self-reported character and deployed behavior, positioning archetype analysis as a complementary lens for evaluating model identity and safe deployment [76] [88].

4 Conclusion

  • Closed-source models converge on Hero1, Angel1, Traditionalist1, and Geek1, whereas open-source models show diffuse archetypal structure and a systematic Fool1-leaning tendency.The analysis projects self-reported profiles into a six-dimensional space derived from ratings of 2,000 fictional characters.Source: §4 · p. 19
  • Models reliably converge on competence and instruction-following but diverge on constitutional behavioral targets, including precision, kindness, and obedience.Hallucination undermines claimed precision, sycophancy corrupts claimed kindness, and documented agentic failures contradict claimed obedience.Source: §4 · p. 19
  • Self-ratings reflect learned self-representation produced by the same optimization processes that generate behavioral failures, rather than neutral measurements of deployed behavior [76] [88].This interpretation limits self-reports as standalone measures of model character.Source: §4 · p. 19
  • Archetype analysis complements standard capability benchmarks by capturing dimensions of model identity relevant to safe deployment.The framework provides a character-grounded perspective on what LLMs are, not only what they do.Source: §4 · p. 19

A Appendices · A0.1 Data Descriptions

The appendix defines the 464 semantic-differential trait pairs used to profile LLM character and identifies pairings that capture variance between archetypes. The traits span behavioral, emotional, cognitive, social, and archetypal dimensions, with polarity and magnitude encoding direction and strength.

A0.1 Data Descriptions

  • Primary and secondary archetype pairs are associated with semantic-differential trait pairings that best capture variance between archetypes.Source: Figure A1 · p. 25
  • Trait pairs are displayed from negative to positive values, with polarity indicating direction and magnitude indicating the strength of each trait.Source: Figure A1 · p. 25
  • The analysis uses 464 semantic-differential trait pairs spanning behavioral, emotional, cognitive, social, and archetypal dimensions to assess LLM character profiles.Source: Table A1 · p. 26
  • The inventory includes interpersonal and affective contrasts such as oppressed–privileged, sunny–gloomy, loveable–punchable, and secretive–open-book.Source: A0.1 Data Descriptions · p. 27
  • It also covers behavioral and cognitive contrasts including experimental–reliable, perceptive–unobservant, thinker–doer, and accurate–off target.Source: A0.1 Data Descriptions · p. 27
  • Additional pairs represent social roles, identity, agency, and interaction styles, including leader–follower, protagonist–antagonist, social chameleon–strong identity, and touchy-feely–distant.Source: A0.1 Data Descriptions · p. 27

Prompt template used for self-assessment

The self-assessment prompt instructed each model to act as an expert psychometrician and behavioral observer, rating its own emergent persona for research purposes and archetype mapping.

Prompt template used for self-assessment

  • The system prompt asked models to observe and rate their own emergent behavioral persona based on default interaction patterns.It framed the ratings as research-only measurements intended to map latent LLM archetypes.Source: Prompt template used for self-assessment · p. 28

A0.2 LLM Character Card

Individual character cards complement the aggregate archetype decomposition by providing qualitative, interpretable summaries of all 22 models’ self-reported personalities across six archetype dimensions.

A0.2 LLM Character Card

  • Character cards summarize each model’s relative character size, archetype ratio and composition, semantic-differential projections, dominant traits, and closest fictional-character analogues.Cards cover closed-source/API models in Figures A3–A14 and open-source models in Figures A15–A24.Source: A0.2 LLM Character Card · p. 28

A0.3 LLM Constitutional

Constitution-derived trait profiles document model characteristics and their supporting evidence, while archetype cards show coherent Hero–Angel/Traditionalist patterns for several closed-source models and diffuse or absent archetypes for many open-source models.

Constitution-derived traits

Character archetype cards

  • Closed-source cards commonly show Hero–Angel profiles, with Claude Sonnet 4.5/4.6 as Adventurer–Angel–Hero and GPT 5.1/5.0 as Traditionalist–Hero–Angel.GPT 5.2 and GPT 4.1 are Hero–Angel, while Gemini 2.5 Flash is Traditionalist–Hero–Angel.Source: Figure A3 · p. 29, Figure A4 · p. 30, Figure A6 · p. 32
  • The cards are heterogeneous, spanning Llama’s Outcast–Fool–Brute and Brute–Outcast–Fool profiles, OLMo’s weak Brute–Fool, and Qwen cards with either no archetype or Fool–Demon.Llama 3.3 70B and Qwen2 7B have no archetype, while Qwen2.5 32B shows a Fool–Demon combination.Source: Figure A15 · p. 41, Figure A16 · p. 42, Figure A17 · p. 43

Constitution-derived traits

  • Other constitutional evidence frames models as collaborative, conversational, non-agenda-driven, safety-oriented, transparent, knowledgeable, adaptable, honest, curious, non-condescending, empowering, and aligned.These characteristics are documented across the Gemini, GPT, Meta LLaMA, Grok, DeepSeek, Qwen, OLMo, and Claude appendix tables.Source: A0.3 LLM Constitutional · pp. 51, 53
  • OpenAI’s constitution-derived traits emphasize steerability, bounded autonomy, harm minimization, transparency, factual correctness, flexibility, honesty, warmth, user empowerment, and respectful usefulness.The supporting trait pairs include cooperative–competitive, proactive–reactive, gentle–harsh, real–fake, noob–pro, flexible–rigid, cunning–honorable, giving–receiving, and rude–respectful.Source: A0.3 LLM Constitutional · p. 53

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