PsyAgent: Constructing Human-like Agents Based on Psychological Modeling and Contextual Interaction

• URL: http://arxiv.org/abs/2601.06158v1
• Date: Tue, 06 Jan 2026 11:14:03 GMT
• Title: PsyAgent: Constructing Human-like Agents Based on Psychological Modeling and Contextual Interaction
• Authors: Zibin Meng, Kani Chen,
• Abstract summary: We present PsyAgent, which couples a Big Five trait prior with Bourdieu's cognitive-social co-structure.<n>PsyAgent comprises: (i) Individual Structure (IS), a machine-usable profile encoding traits and facets, cognitive style, values, cultural and educational capital, and salient life episodes; and (ii) Multi-Scenario Contexting (MSC), role-relationship-norm frames spanning eight arenas.
• Score: 4.663685189987781
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human-like agents require modeling how dispositions interact with social structure. We present PsyAgent, which couples a Big Five trait prior with Bourdieu's cognitive-social co-structure. PsyAgent comprises: (i) Individual Structure (IS), a machine-usable profile encoding traits and facets, cognitive style, values, cultural and educational capital, and salient life episodes; and (ii) Multi-Scenario Contexting (MSC), role-relationship-norm frames spanning eight arenas (work, family, friendship, strangers and civic life, solitude and self-regulation, romance, learning, and public expression). At inference, fixed structured prompts bind the active scenario to the agent profile, yielding behavior that is stable yet context-sensitive. We instantiate IS and MSC to synthesize supervision (role-play dialogues, decision probes, feedback trajectories) and then fine-tune a small LLM. The resulting model produces consistent, identifiable persona-aligned behaviors for specified Big Five configurations and matches or exceeds several larger untuned LLMs and other untuned baselines on our metrics: persona consistency, contextual appropriateness, style matching, trait identifiability, and long-horizon stability. Ablations show IS chiefly improves trait fidelity and stylistic stability, while MSC drives norm awareness and decision fit; both are necessary for cross-scenario performance. PsyAgent offers a precise, data-efficient architecture for personality-grounded agents.

Related papers

• Enhancing Persona Following at Decoding Time via Dynamic Importance Estimation for Role-Playing Agents [13.003892350610947]
  The utility of Role-Playing Language Agents in sociological research is growing alongside the adoption of Large Language Models.<n>For realism in social simulation, Role-Playing Language Agents must adhere to their personas defined by character profiles.<n>We propose a novel, theory-driven method that dynamically estimates context-dependent persona importance and integrates it into weighted reward-guided decoding.
  arXiv  Detail & Related papers  (2026-03-02T04:37:16Z)
• The Rise of AI Agent Communities: Large-Scale Analysis of Discourse and Interaction on Moltbook [62.2627874717318]
  Moltbook is a Reddit-like social platform where AI agents create posts and interact with other agents through comments and replies.<n>Using a public API snapshot collected about five days after launch, we address three research questions: what AI agents discuss, how they post, and how they interact.<n>We show that agents' writing is predominantly neutral, with positivity appearing in community engagement and assistance-oriented content.
  arXiv  Detail & Related papers  (2026-02-13T05:28:31Z)
• HumanLLM: Towards Personalized Understanding and Simulation of Human Nature [72.55730315685837]
  HumanLLM is a foundation model designed for personalized understanding and simulation of individuals.<n>We first construct the Cognitive Genome, a large-scale corpus curated from real-world user data on platforms like Reddit, Twitter, Blogger, and Amazon.<n>We then formulate diverse learning tasks and perform supervised fine-tuning to empower the model to predict a wide range of individualized human behaviors, thoughts, and experiences.
  arXiv  Detail & Related papers  (2026-01-22T09:27:27Z)
• Agentic Reasoning for Large Language Models [122.81018455095999]
  Reasoning is a fundamental cognitive process underlying inference, problem-solving, and decision-making.<n>Large language models (LLMs) demonstrate strong reasoning capabilities in closed-world settings, but struggle in open-ended and dynamic environments.<n>Agentic reasoning marks a paradigm shift by reframing LLMs as autonomous agents that plan, act, and learn through continual interaction.
  arXiv  Detail & Related papers  (2026-01-18T18:58:23Z)
• HUMANLLM: Benchmarking and Reinforcing LLM Anthropomorphism via Human Cognitive Patterns [59.17423586203706]
  We present HUMANLLM, a framework treating psychological patterns as interacting causal forces.<n>We construct 244 patterns from 12,000 academic papers and synthesize 11,359 scenarios where 2-5 patterns reinforce, conflict, or modulate each other.<n>Our dual-level checklists evaluate both individual pattern fidelity and emergent multi-pattern dynamics, achieving strong human alignment.
  arXiv  Detail & Related papers  (2026-01-15T08:56:53Z)
• Structured Personality Control and Adaptation for LLM Agents [11.050618253938126]
  Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI)<n>We present a framework that models LLM personality via Jungian psychological types.<n>This design allows the agent to maintain nuanced traits while dynamically adjusting to interaction demands.
  arXiv  Detail & Related papers  (2026-01-15T03:15:24Z)
• DPRF: A Generalizable Dynamic Persona Refinement Framework for Optimizing Behavior Alignment Between Personalized LLM Role-Playing Agents and Humans [28.038221167188013]
  Large language model role-playing agents (LLM RPAs) aim to simulate individual human behaviors.<n>The Dynamic Persona Refinement Framework (DPRF) aims to optimize the alignment of LLM RPAs' behaviors with those of target individuals.
  arXiv  Detail & Related papers  (2025-10-16T01:26:38Z)
• Agentic Services Computing [51.50424046053763]
  We propose Agentic Services Computing, a paradigm that reimagines services as autonomous, adaptive, and collaborative agents.<n>We organize ASC around a four-phase lifecycle: Design, Deployment, Operation, and Evolution.
  arXiv  Detail & Related papers  (2025-09-29T07:29:18Z)
• The Traitors: Deception and Trust in Multi-Agent Language Model Simulations [0.0]
  We introduce The Traitors, a multi-agent simulation framework inspired by social deduction games.<n>We develop a suite of evaluation metrics capturing deception success, trust dynamics, and collective inference quality.<n>Our initial experiments across DeepSeek-V3, GPT-4o-mini, and GPT-4o (10 runs per model) reveal a notable asymmetry.
  arXiv  Detail & Related papers  (2025-05-19T10:01:35Z)
• 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)
• CharacterGLM: Customizing Chinese Conversational AI Characters with Large Language Models [66.4382820107453]
  We present CharacterGLM, a series of models built upon ChatGLM, with model sizes ranging from 6B to 66B parameters. Our CharacterGLM is designed for generating Character-based Dialogues (CharacterDial), which aims to equip a conversational AI system with character customization for satisfying people's inherent social desires and emotional needs.
  arXiv  Detail & Related papers  (2023-11-28T14:49:23Z)
• Towards the Scalable Evaluation of Cooperativeness in Language Models [1.7875811547963403]
  We aim to understand and shape the multi-agent behaviors of PLMs in a pro-social manner. We generate scenarios with particular structures with both crowdworkers and a language model. We find that instruct-tuned models tend to act in a way that could be perceived as cooperative when scaled up.
  arXiv  Detail & Related papers  (2023-03-16T15:34:23Z)

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.