mlsanityData to 14 Oct 2026

On the Role of Contextual Information and Ego States in LLM Agent Behavior for Transactional Analysis Dialogues

arXiv 2512.17060 ↗First posted 18 Dec 2025cs.MA cs.AIcited by 0

On the map: LLM agents and tool use → Social simulation and multi-agent debate

Abstract (arXiv)

LLM-powered agents are now used in many areas, from customer support to education, and there is increasing interest in their ability to act more like humans. This includes fields such as social, political, and psychological research, where the goal is to model group dynamics and social behavior. However, current LLM agents often lack the psychological depth and consistency needed to capture the real patterns of human thinking. They usually provide direct or statistically likely answers, but they miss the deeper goals, emotional conflicts, and motivations that drive real human interactions. This paper proposes a Multi-Agent System (MAS) inspired by Transactional Analysis (TA) theory. In the proposed system, each agent is divided into three ego states - Parent, Adult, and Child. The ego states are treated as separate knowledge structures with their own perspectives and reasoning styles. To enrich their response process, they have access to an information retrieval mechanism that allows them to retrieve relevant contextual information from their vector stores. This architecture is evaluated through ablation tests in a simulated dialogue scenario, comparing agents with and without information retrieval. The results are promising and open up new directions for exploring how psychologically grounded structures can enrich agent behavior. The contribution is an agent architecture that integrates Transactional Analysis theory with contextual information retrieval to enhance the realism of LLM-based multi-agent simulations.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 12 of its 33 references that are in the map. First posted on arXiv 18 Dec 2025.

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12 of 33 references.

Closest ancestors · 5

Same direction, newest first

  1. 2025Agentic LLM Framework for Adaptive Decision Discourse16 Feb
  2. 2024Emotional RAG: Enhancing Role-Playing Agents through Emotional Retrieval30 Oct
  3. 2024Can Agents Spontaneously Form a Society? Introducing a Novel Architecture for Generative Multi-Agents to Elicit Social Emergence10 Sep
  4. 2024From Persona to Personalization: A Survey on Role-Playing Language Agents28 Apr
  5. 2024Systematic Biases in LLM Simulations of Debates6 Feb

Other references · 7

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2017Billion-scale similarity search with GPUscited by 2,497
  3. 2023Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectivescited by 257
  4. 2024Social Science Meets LLMs: How Reliable Are Large Language Models in Social Simulations?cited by 26
  5. 2024AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-Makingcited by 20
  6. 2024A Survey on Human-Centric LLMscited by 13
  7. 2025Synergizing Logical Reasoning, Knowledge Management and Collaboration in Multi-Agent LLM Systemcited by 10

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  3. 2025Are LLM Agents Behaviorally Coherent? Latent Profiles for Social Simulation
  4. 2023Do LLM Agents Exhibit Social Behavior?
  5. 2025Agentic Large Language Models, a survey
  6. 2025The Social Laboratory: A Psychometric Framework for Multi-Agent LLM Evaluation
  7. 2026Personality Expression Across Contexts: Linguistic and Behavioral Variation in LLM Agents
  8. 2024Reproducing and Extending Experiments in Behavioral Strategy with Large Language Models