mlsanityData to 7 Oct 2026

Behavior-Adaptive Conversational Agents: Toward a Fluid Personality Framework

arXiv 2607.01034 ↗First posted 1 Jul 2026cs.CL cs.AI cs.HCcited by 0

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

Abstract (arXiv)

Large language model (LLM)-based conversational agents (CAs) are now ubiquitous, creating new opportunities for AI-mediated behavior change. Their capacity to project nuanced personalities and adopt diverse metaphorical roles raises a design question: how should an agent's persona and personality be calibrated to the moment? Recent evidence suggests that (i) moderate personality expression outperforms low or high extremes on trust, enjoyment, and intention to adopt in goal-oriented tasks, and (ii) context-appropriate metaphors outperform static one-note assistants on user experience and uptake. Yet most CAs still fix both persona and style, risking misalignment when dynamics, urgency, and formality vary, for example in medical information seeking, fitness coaching, and reflective learning. We propose a Fluid Personality Framework that jointly adapts (1) the agent's metaphorical persona, such as coach, tutor, librarian, or tool, and (2) its personality expression intensity, low, medium, or high, as a function of task context, user goals and traits, and situational urgency. We sketch the framework and its core design dimensions.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 8 of its 30 references that are in the map. First posted on arXiv 1 Jul 2026.

Builds on

8 of 30 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024From Persona to Personalization: A Survey on Role-Playing Language Agents28 Apr
  2. 2023Character-LLM: A Trainable Agent for Role-Playing16 Oct

Other references · 6

  1. 2023PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traitscited by 229
  2. 2017A Survey on Dialogue Systems: Recent Advances and New Frontierscited by 162
  3. 2023Controlling Personality Style in Dialogue with Zero-Shot Prompt-Based Learningcited by 14
  4. 2023Like My Aunt Dorothy: Effects of Conversational Styles on Perceptions, Acceptance and Metaphorical Descriptions of Voice Assistants during Later Adulthoodcited by 8
  5. 2024Examining Humanness as a Metaphor to Design Voice User Interfacescited by 6
  6. 2025Toward Metaphor-Fluid Conversation Design for Voice User Interfacescited by 5

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  1. 2026Structured Personality Control and Adaptation for LLM Agents
  2. 2025Vibe Check: Understanding the Effects of LLM-Based Conversational Agents' Personality and Alignment on User Perceptions in Goal-Oriented Tasks
  3. 2026Personality, Role, and Expressive Style in Large Language Models: An Interactionist Analysis
  4. 2024The Effects of Embodiment and Personality Expression on Learning in LLM-based Educational Agents
  5. 2024Open Models, Closed Minds? On Agents Capabilities in Mimicking Human Personalities through Open Large Language Models
  6. 2026Do Personality-Tuned LLMs Make Better Social Agents?
  7. 2026PTCBENCH: Benchmarking Contextual Stability of Personality Traits in LLM Systems
  8. 2025Psychologically Enhanced AI Agents