Behavior-Adaptive Conversational Agents: Toward a Fluid Personality Framework

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Hasibur Rahman, Smit Desai
• Venue: arXiv: cs.CL
• Published: 2026-07-01
• Collected: 2026-07-02
• URL: http://arxiv.org/abs/2607.01034v1
• PDF: https://arxiv.org/pdf/2607.01034v1
• DOI: UNKNOWN

Plain-Language Brief

The authors propose a Fluid Personality Framework for conversational agents to adapt their metaphorical personas and personality expression levels based on the task context, user goals, and situational urgency.

What the Document Says

• Large language model (LLM)-based conversational agents are common and can be used for AI-mediated behavior change.
• The paper suggests that moderate personality expression performs better in trust, enjoyment, and intention to adopt in goal-oriented tasks.
• Context-appropriate metaphors provide a better user experience and uptake compared to static one-note assistants.
• Most conversational agents currently fix both persona and style, which may lead to misalignment when dynamics, urgency, and formality vary.
• The authors propose a Fluid Personality Framework that adapts the agent's metaphorical persona and personality expression intensity.
• The framework adapts based on task context, user goals, and situational urgency.

Key Concepts Mentioned

• Large Language Model (LLM)
• Conversational Agents
• Persona
• Personality Expression
• Metaphorical Roles
• Trust
• Enjoyment
• Intention to Adopt
• Medical Information Seeking
• Fitness Coaching
• Reflective Learning

Claims Made by the Document

• Moderate personality expression outperforms low or high extremes on trust, enjoyment, and intention to adopt in goal-oriented tasks.
• Context-appropriate metaphors outperform static one-note assistants on user experience and uptake.

Evidence Used

• Not stated in source

Methods / Approach

• Proposing a Fluid Personality Framework for adaptive conversational agents

Limitations Stated by the Authors

• The paper does not provide evidence to support the claims made.

Limitations Noted by the Agent

Agent interpretation — not from the source.

• The proposed framework is theoretical and has not been implemented or tested yet.
• The effectiveness of the Fluid Personality Framework is unknown without empirical testing.

Open Questions

• What specific methods will be used to adapt the agent's metaphorical persona and personality expression intensity?
• How will the framework handle complex scenarios with multiple contexts, user goals, and situational urgencies?

Notes for Future Q&A

May include interpretation. Labeled where it does.

• The paper does not provide details on how the proposed Fluid Personality Framework will be implemented or tested.

Raw Abstract / Excerpt

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.