Are LLM Agents Behaviorally Coherent? Latent Profiles for Social Simulation

• URL: http://arxiv.org/abs/2509.03736v1
• Date: Wed, 03 Sep 2025 21:55:29 GMT
• Title: Are LLM Agents Behaviorally Coherent? Latent Profiles for Social Simulation
• Authors: James Mooney, Josef Woldense, Zheng Robert Jia, Shirley Anugrah Hayati, My Ha Nguyen, Vipul Raheja, Dongyeop Kang,
• Abstract summary: Large Language Models (LLMs) have fueled the notion that synthetic agents can serve as substitutes for real participants in human-subject research.<n>Do agents maintain internal consistency, retaining similar behaviors when examined under different experimental settings?<n>This study explores whether an agent's conversation behavior is consistent with what we would expect from their revealed internal state.
• Score: 18.70850695450292
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The impressive capabilities of Large Language Models (LLMs) have fueled the notion that synthetic agents can serve as substitutes for real participants in human-subject research. In an effort to evaluate the merits of this claim, social science researchers have largely focused on whether LLM-generated survey data corresponds to that of a human counterpart whom the LLM is prompted to represent. In contrast, we address a more fundamental question: Do agents maintain internal consistency, retaining similar behaviors when examined under different experimental settings? To this end, we develop a study designed to (a) reveal the agent's internal state and (b) examine agent behavior in a basic dialogue setting. This design enables us to explore a set of behavioral hypotheses to assess whether an agent's conversation behavior is consistent with what we would expect from their revealed internal state. Our findings on these hypotheses show significant internal inconsistencies in LLMs across model families and at differing model sizes. Most importantly, we find that, although agents may generate responses matching those of their human counterparts, they fail to be internally consistent, representing a critical gap in their capabilities to accurately substitute for real participants in human-subject research. Our simulation code and data are publicly accessible.

Related papers

• Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia [100.74015791021044]
  Large Language Model (LLM) agents have demonstrated impressive capabilities for social interaction.<n>Existing evaluation methods fail to measure how well these capabilities generalize to novel social situations.<n>We present empirical results from the NeurIPS 2024 Concordia Contest, where agents were evaluated on their ability to achieve mutual gains.
  arXiv  Detail & Related papers  (2025-12-03T00:11:05Z)
• The Social Laboratory: A Psychometric Framework for Multi-Agent LLM Evaluation [0.16921396880325779]
  We introduce a novel evaluation framework that uses multi-agent debate as a controlled "social laboratory"<n>We show that assigned personas induce stable, measurable psychometric profiles, particularly in cognitive effort.<n>This work provides a blueprint for a new class of dynamic, psychometrically grounded evaluation protocols.
  arXiv  Detail & Related papers  (2025-10-01T07:10:28Z)
• Do Role-Playing Agents Practice What They Preach? Belief-Behavior Consistency in LLM-Based Simulations of Human Trust [32.044592572217475]
  We investigate how consistently role-playing agents' stated beliefs correspond to their actual behavior during role-play.<n>We find systematic inconsistencies between LLMs' stated beliefs and the outcomes of their role-playing simulation.<n>These findings highlight the need to identify how and when LLMs' stated beliefs align with their simulated behavior.
  arXiv  Detail & Related papers  (2025-07-02T23:30:51Z)
• Evaluating the Simulation of Human Personality-Driven Susceptibility to Misinformation with LLMs [0.18416014644193066]
  Large language models (LLMs) make it possible to generate synthetic behavioural data at scale.<n>We evaluate the capacity of LLM agents, conditioned on Big-Five profiles, to reproduce personality-based variation in susceptibility to misinformation.
  arXiv  Detail & Related papers  (2025-06-30T08:16:07Z)
• Large Language Models as Theory of Mind Aware Generative Agents with Counterfactual Reflection [31.38516078163367]
  ToM-agent is designed to empower LLMs-based generative agents to simulate ToM in open-domain conversational interactions.<n>ToM-agent disentangles the confidence from mental states, facilitating the emulation of an agent's perception of its counterpart's mental states.<n>Our findings indicate that the ToM-agent can grasp the underlying reasons for their counterpart's behaviors beyond mere semantic-emotional supporting or decision-making based on common sense.
  arXiv  Detail & Related papers  (2025-01-26T00:32:38Z)
• Generative Agent Simulations of 1,000 People [56.82159813294894]
  We present a novel agent architecture that simulates the attitudes and behaviors of 1,052 real individuals. The generative agents replicate participants' responses on the General Social Survey 85% as accurately as participants replicate their own answers. Our architecture reduces accuracy biases across racial and ideological groups compared to agents given demographic descriptions.
  arXiv  Detail & Related papers  (2024-11-15T11:14:34Z)
• PersLLM: A Personified Training Approach for Large Language Models [66.16513246245401]
  We propose PersLLM, a framework for better data construction and model tuning.<n>For insufficient data usage, we incorporate strategies such as Chain-of-Thought prompting and anti-induction.<n>For rigid behavior patterns, we design the tuning process and introduce automated DPO to enhance the specificity and dynamism of the models' personalities.
  arXiv  Detail & Related papers  (2024-07-17T08:13:22Z)
• Evaluating Large Language Models with Psychometrics [59.821829073478376]
  This paper offers a comprehensive benchmark for quantifying psychological constructs of Large Language Models (LLMs)<n>Our work identifies five key psychological constructs -- personality, values, emotional intelligence, theory of mind, and self-efficacy -- assessed through a suite of 13 datasets.<n>We uncover significant discrepancies between LLMs' self-reported traits and their response patterns in real-world scenarios, revealing complexities in their behaviors.
  arXiv  Detail & Related papers  (2024-06-25T16:09:08Z)
• Can Large Language Model Agents Simulate Human Trust Behavior? [81.45930976132203]
  We investigate whether Large Language Model (LLM) agents can simulate human trust behavior. GPT-4 agents manifest high behavioral alignment with humans in terms of trust behavior. We also probe the biases of agent trust and differences in agent trust towards other LLM agents and humans.
  arXiv  Detail & Related papers  (2024-02-07T03:37:19Z)
• Systematic Biases in LLM Simulations of Debates [12.933509143906141]
  We study the limitations of Large Language Models in simulating human interactions.<n>Our findings indicate a tendency for LLM agents to conform to the model's inherent social biases.<n>These results underscore the need for further research to develop methods that help agents overcome these biases.
  arXiv  Detail & Related papers  (2024-02-06T14:51:55Z)
• AntEval: Evaluation of Social Interaction Competencies in LLM-Driven Agents [65.16893197330589]
  Large Language Models (LLMs) have demonstrated their ability to replicate human behaviors across a wide range of scenarios. However, their capability in handling complex, multi-character social interactions has yet to be fully explored. We introduce the Multi-Agent Interaction Evaluation Framework (AntEval), encompassing a novel interaction framework and evaluation methods.
  arXiv  Detail & Related papers  (2024-01-12T11:18:00Z)
• How Far Are LLMs from Believable AI? A Benchmark for Evaluating the Believability of Human Behavior Simulation [46.42384207122049]
  We design SimulateBench to evaluate the believability of large language models (LLMs) when simulating human behaviors. Based on SimulateBench, we evaluate the performances of 10 widely used LLMs when simulating characters.
  arXiv  Detail & Related papers  (2023-12-28T16:51:11Z)
• Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach [61.04606493712002]
  Susceptibility to misinformation describes the degree of belief in unverifiable claims that is not observable. Existing susceptibility studies heavily rely on self-reported beliefs. We propose a computational approach to model users' latent susceptibility levels.
  arXiv  Detail & Related papers  (2023-11-16T07:22:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.