Not Birds of a Feather: Personality-Based Partner Selection in LLM Agents
Authors: Tao Wang, Hsiang-Ling Chiu, Chihang Wei, Zhonghao Hou, Yang Xiu
Organizations: University of Toronto
Abstract
LLM-based agents increasingly operate in multi-agent ecosystems where a coordinating agent chooses which other agents to work with, and agents are increasingly given personalities through persona prompts. However, whether personality itself influences this endogenous partner choice has not been sufficiently examined: prior work on personality in multi-agent teams has typically fixed team composition exogenously. We present a controlled selection paradigm in which a host agent chooses among six candidate agents that differ only in their Big Five personality descriptions, with capability explicitly equalized (375 trials across five task categories). We find that selection is strongly and systematically personality-dependent. Neutral hosts matched personalities to task types, choosing the open candidate for creative work and the conscientious candidate for most other categories, while the extraverted, agreeable, and balanced candidates were almost never chosen, despite human evidence that agreeableness is among the most performance-relevant traits for teams. Hosts that were themselves assigned personalities selected self-similar partners below chance and chose partners farther from themselves in trait space than random choice would produce. These results suggest that hosts read personality descriptions as signals of task fit rather than as grounds for similarity-based attraction: selection follows task stereotypes and favors complements, the opposite of human homophily. Our findings have direct implications for bias auditing in agent marketplaces and orchestration frameworks.
Personality prompting shapes how large language models communicate, yet whether these behavioral shifts affect objective task outcomes remains under-explored. Prior work shows that agents prompted with low agreeableness produce adversarial language, while those prompted with high agreeableness become cooperative, but the relationship between communication style and task performance has not been systematically examined across multiple domains. In this work, we investigate whether personality composition matters for multi-agent team performance by manipulating personality traits across frontier LLMs on three task domains: structured coding, open-ended research collaboration, and competitive bargaining. We find that personality effects depend critically on task structure. In coding tasks, low agreeableness leads to large communication shifts that have little effect on milestone completion. In open-ended collaboration and bargaining, the same manipulation substantially degrades performance. We discuss implications for multi-agent system design and the limits of personality manipulation.
Personality-conditioned LLM agents (PC-Agents) are increasingly used in emotional support, social simulation, and role-playing, motivating the development of lifelong agents that remain coherent over extended interactions. A key component of such coherence is personality evolution: agents should undergo plausible, psychology-grounded changes as they experience life events in different contexts. Although prior work shows that LLM personalities can shift under contextual perturbations, how these shifts vary across traits, events, personas, and models remains poorly understood. We study event-induced personality change after 11 major life events, using the Big Five traits as a psychometric anchor and interpreting the resulting trajectories against longitudinal evidence from human personality psychology. Across four diagnostic axes, PC-Agents exhibit measurable trait shifts at similar rates for event-trait pairs with and without documented human change directions. Even when shifts follow the expected direction, their magnitudes usually fall below human effect-size ranges. Gender and cultural-region prompts show little moderating effect, while persona-level dispersion is compressed three- to four-fold relative to human samples. To enable systematic comparison, we introduce BFI-Adapt, a reusable benchmark for scoring the directional fidelity of event-induced personality change, and use it to rank 14 models. A validation suite shows that the measured shifts exceed no-event retest noise, remain stable under independently paraphrased prompts, exhibit limited and model-dependent convergence with scenario-based behavioral choices, and persist across intervening unrelated dialogue. Together, these checks establish the measured trajectories as robust event-conditioned response patterns. Our results suggest that current PC-Agents simulate the mean of human personality dynamics, but not its shape.
LLM-based multi-agent systems (MAS) show promise on complex tasks but remain prone to coordination failures such as goal drift, error cascades, and misaligned behaviors. We propose Explicit Trait Inference (ETI), a psychologically grounded method for improving coordination. ETI enables agents to infer and track partner characteristics along two established psychological dimensions--warmth (e.g., trust) and competence (e.g., skill)--from interaction histories to guide decisions. We evaluate ETI in controlled settings (economic games), where it reduces payoff loss by 45-77%, and in more realistic, complex multi-agent settings (MultiAgentBench), where it improves performance by 3-29% depending on the scenario and model, relative to a CoT baseline. Additional analysis shows that gains are closely linked to trait inference: ETI profiles predict agents' actions, and informative profiles drive improvements. These results highlight ETI as a lightweight and robust mechanism for improving coordination in diverse multi-agent settings, and provide the first systematic evidence that LLM agents can (i) reliably infer others' traits from interaction histories and (ii) leverage structured awareness of others' traits for coordination.