Behavior-Adaptive Conversational Agents: Toward a Fluid Personality Framework
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
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- LinkedLinked via unknownYou Can Now Sound the Alarm on AI Behaving Badly →
- LinkedLinked via unknownA field guide to AI agents in 2026 →
- PossiblePossibly related (embedding) · 29%2FastLabs/agent-squad →
“Possibly related via embedding similarity 0.56 (not asserted). Timestamp check: artifact after paper (+1d).”
- PossiblePossibly related (embedding) · 47%cognitive-functors/dsm →
- LinkedLinked via arxiv author · 85%Hasibur Rahman →
“Behavior-Adaptive Conversational Agents: Toward a Fluid Personality Framework”
- LinkedLinked via arxiv author · 85%Smit Desai →
“Behavior-Adaptive Conversational Agents: Toward a Fluid Personality Framework”
- PossiblePossibly related (embedding) · 55%janaador0827-commits/simulacra-forge →