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

• URL: http://arxiv.org/abs/2512.17060v1
• Date: Thu, 18 Dec 2025 20:53:31 GMT
• Title: On the Role of Contextual Information and Ego States in LLM Agent Behavior for Transactional Analysis Dialogues
• Authors: Monika Zamojska, Jarosław A. Chudziak,
• Abstract summary: This paper proposes a Multi-Agent System inspired by Transactional Analysis (TA) theory.<n>In the proposed system, each agent is divided into three ego states - Parent, Adult, and Child.<n>The results are promising and open up new directions for exploring how psychologically grounded structures can enrich agent behavior.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Simulating Misinformation Vulnerabilities With Agent Personas [1.0120858915885353]
  We develop an agent-based simulation using Large Language Models to model responses to misinformation.<n>We construct agent personas spanning five professions and three mental schemas, and evaluate their reactions to news headlines.<n>Our findings show that LLM-generated agents align closely with ground-truth labels and human predictions, supporting their use as proxies for studying information responses.
  arXiv  Detail & Related papers  (2025-10-31T18:44:00Z)
• A Survey on Agentic Multimodal Large Language Models [84.18778056010629]
  We present a comprehensive survey on Agentic Multimodal Large Language Models (Agentic MLLMs)<n>We explore the emerging paradigm of agentic MLLMs, delineating their conceptual foundations and distinguishing characteristics from conventional MLLM-based agents.<n>To further accelerate research in this area for the community, we compile open-source training frameworks, training and evaluation datasets for developing agentic MLLMs.
  arXiv  Detail & Related papers  (2025-10-13T04:07:01Z)
• Fundamentals of Building Autonomous LLM Agents [64.39018305018904]
  This paper reviews the architecture and implementation methods of agents powered by large language models (LLMs)<n>The research aims to explore patterns to develop "agentic" LLMs that can automate complex tasks and bridge the performance gap with human capabilities.
  arXiv  Detail & Related papers  (2025-10-10T10:32:39Z)
• Games Agents Play: Towards Transactional Analysis in LLM-based Multi-Agent Systems [0.0]
  We introduce Trans-ACT, an approach embedding Transactional Analysis (TA) principles into multi-agent systems.<n>Trans-ACT integrates the Parent, Adult, and Child ego states into an agent's cognitive architecture.<n>Our experimental simulation, which reproduces the Stupid game scenario, demonstrates that agents grounded in cognitive and TA principles produce deeper and context-aware interactions.
  arXiv  Detail & Related papers  (2025-07-28T21:46:21Z)
• AutoGen Driven Multi Agent Framework for Iterative Crime Data Analysis and Prediction [0.3749861135832073]
  This paper introduces LUCID-MA, an innovative AI powered framework where multiple AI agents collaboratively analyze and understand crime data.<n>Our system that consists of three core components: an analysis assistant highlights crime patterns; a feedback component that reviews and refines analytical results; and a prediction component that forecasts future crime trends.<n>A scoring function is incorporated to evaluate agent performance, providing visual plots to track learning progress.
  arXiv  Detail & Related papers  (2025-06-13T05:39:28Z)
• Large Language Model Agent: A Survey on Methodology, Applications and Challenges [88.3032929492409]
  Large Language Model (LLM) agents, with goal-driven behaviors and dynamic adaptation capabilities, potentially represent a critical pathway toward artificial general intelligence.<n>This survey systematically deconstructs LLM agent systems through a methodology-centered taxonomy.<n>Our work provides a unified architectural perspective, examining how agents are constructed, how they collaborate, and how they evolve over time.
  arXiv  Detail & Related papers  (2025-03-27T12:50:17Z)
• Spontaneous Emergence of Agent Individuality through Social Interactions in LLM-Based Communities [0.0]
  We study the emergence of agency from scratch by using Large Language Model (LLM)-based agents. By analyzing this multi-agent simulation, we report valuable new insights into how social norms, cooperation, and personality traits can emerge spontaneously.
  arXiv  Detail & Related papers  (2024-11-05T16:49:33Z)
• AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios [38.878966229688054]
  We introduce AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios. Drawing on Dramaturgical Theory, AgentSense employs a bottom-up approach to create 1,225 diverse social scenarios constructed from extensive scripts. We analyze goals using ERG theory and conduct comprehensive experiments. Our findings highlight that LLMs struggle with goals in complex social scenarios, especially high-level growth needs, and even GPT-4o requires improvement in private information reasoning.
  arXiv  Detail & Related papers  (2024-10-25T07:04:16Z)
• ALYMPICS: LLM Agents Meet Game Theory -- Exploring Strategic Decision-Making with AI Agents [77.34720446306419]
  Alympics is a systematic simulation framework utilizing Large Language Model (LLM) agents for game theory research. Alympics creates a versatile platform for studying complex game theory problems.
  arXiv  Detail & Related papers  (2023-11-06T16:03:46Z)
• LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplay [55.12945794835791]
  Using Avalon as a testbed, we employ system prompts to guide LLM agents in gameplay. We propose a novel framework, tailored for Avalon, features a multi-agent system facilitating efficient communication and interaction. Results affirm the framework's effectiveness in creating adaptive agents and suggest LLM-based agents' potential in navigating dynamic social interactions.
  arXiv  Detail & Related papers  (2023-10-23T14:35:26Z)
• The Rise and Potential of Large Language Model Based Agents: A Survey [91.71061158000953]
  Large language models (LLMs) are regarded as potential sparks for Artificial General Intelligence (AGI) We start by tracing the concept of agents from its philosophical origins to its development in AI, and explain why LLMs are suitable foundations for agents. We explore the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation.
  arXiv  Detail & Related papers  (2023-09-14T17:12:03Z)
• Towards Socially Intelligent Agents with Mental State Transition and Human Utility [97.01430011496576]
  We propose to incorporate a mental state and utility model into dialogue agents. The hybrid mental state extracts information from both the dialogue and event observations. The utility model is a ranking model that learns human preferences from a crowd-sourced social commonsense dataset.
  arXiv  Detail & Related papers  (2021-03-12T00:06:51Z)

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.