Source note2 min read
Behavior-Adaptive Conversational Agents: Toward a Fluid Personality Framework
Source
arXiv
arxiv.org/abs/2607.01034v1
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 text discusses a proposal for a new conversational agent (CA) framework that can adapt an agent's personality and metaphorical role based on the task context, user goals, and urgency. This is suggested to improve user experience and increase trust and adoption in various domains such as medical information seeking, fitness coaching, and reflective learning.
What the Document Says
- Large language model (LLM)-based conversational agents are common and can influence behavior change.
- Nuanced personalities and diverse roles raise a question about how an agent's persona should adapt to the situation.
- Moderate personality expression is more effective for trust, enjoyment, and intention to adopt in goal-oriented tasks.
- Context-appropriate metaphors improve user experience and uptake compared to static assistants.
- Most CAs have fixed personas and styles, which can lead to misalignment in varying contexts.
- A Fluid Personality Framework is proposed that adapts the agent's metaphorical persona and personality expression intensity based on task context, user goals, and urgency.
Key Concepts Mentioned
- Large language model (LLM)-based conversational agents
- Persona calibration
- Moderate personality expression
- Context-appropriate metaphors
- Fluid Personality Framework
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 that adapts the agent's metaphorical persona and personality expression intensity
Limitations Stated by the Authors
The text does not mention any limitations by the authors.
Limitations Noted by the Agent
Agent interpretation — not from the source.
The proposed framework is theoretical at this stage and requires implementation and testing to validate its effectiveness.
Open Questions
- How can the Fluid Personality Framework be implemented in practice?
- What are the potential challenges in adapting an agent's personality and metaphorical role based on the context?
Notes for Future Q&A
May include interpretation. Labeled where it does.
The text discusses a proposed framework but does not provide details about how it 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.
