personas

This paper proposes a method to automatically generate diverse user personas using large language models for testing interview dialogue systems, reducing manual effort and increasing variation in simulated user behaviors.

An analysis piece discussing why persona depth in LLM systems fails to improve output diversity, and how isolating readers with broad personas yields better convergence evidence.

Praxis is an open-source CLI that creates AI development workflows with personas, skills, and orchestrators. It generates configuration files for tools like Claude Code and Cursor, aiming to start development from product goals rather than directly coding.

A deep dive into red-teaming voice agents, highlighting audio as an attack surface, the need for multi-turn testing, and practical baseline methodologies (1,200 calls) for pre-launch safety.

A small DIY review team using personas (bug-hunter, keeper, sweeper) achieved higher performance on a 50-PR benchmark than Cursor Bugbot and CodeRabbit, demonstrating the effectiveness of role-based review strategies.

This paper examines how persona prompts influence strategic behavior of large language model agents in an iterated Split or Steal game, finding that mutual Split outcomes dominate and that model choice and persona type significantly affect cooperation and exploitation.

A Codex Skill called meeting-room that provides a multi-expert meeting function with 260 independent personas, capable of transforming questions into multi-angle professional analysis and decisions.

An AI researcher ran 13 controlled experiments on a multi-agent coding system, finding that dependency-ordered coordination significantly improved success rates while persona backstories had no measurable benefit.

Precomputed embedding vectors for the Nemotron-Personas dataset using Qwen 0.6B, enabling semantic search and clustering of synthetic personas via a web demo.

This scoping review analyzes 81 articles (2022-2025) examining the use of generative AI for creating and evaluating user personas, identifying strengths in reproducibility but critical issues including lack of evaluation in 45% of studies, over-reliance on GPT models (86%), and risks of circularity where the same model generates and evaluates personas.