MatrAIx: Simulating the World with 8.3 Billion Persona Agents

• URL: http://arxiv.org/abs/2608.04205v1
• Date: Tue, 04 Aug 2026 20:04:49 GMT
• Title: MatrAIx: Simulating the World with 8.3 Billion Persona Agents
• Abstract summary: MatrAIx is a population-scale simulated-user evaluation infrastructure for testing AI systems and digital products with heterogeneous users.<n>MatrAIx has three core components: First, Persona 8B contains 8.3 billion persona records represented by 1,290 categorical dimensions.<n>Second, the MatrAIx Playground provides four environments in which users evaluate and interact with digital products.<n>Third, MatrAIx provides 1,010 application tasks spanning more than 25 domains, including Commerce, Software, Finance, and Healthcare.
• Score: 175.06699039914625
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human evaluation of AI systems and digital products is costly, slow, and difficult to scale. Offline evaluations are more scalable but often abstract away human diversity and interactive behavior. We therefore introduce MatrAIx, a population-scale simulated-user evaluation infrastructure for testing AI systems and digital products with heterogeneous users. MatrAIx has three core components: First, Persona 8B contains 8.3 billion persona records represented by 1,290 categorical dimensions. Records are either sampled from a dependency graph that preserves correlated attributes or derived from human-authored profiles. We release a quality-filtered coreset of approximately 1 million personas, comprising 599,847 human-grounded and 400,000 synthetic records. Second, the MatrAIx Playground provides four environments in which diverse users evaluate and interact with digital products: Survey, AI Chatbot, Web, and App. Third, MatrAIx provides 1,010 application tasks spanning more than 25 domains, including Commerce, Software, Finance, and Healthcare. We conducted 18,189 evaluation trials across eight representative tasks. Persona agents were powered by three LLMs: Claude Opus 4.8, GPT 5.5, and Claude Haiku 4.5. The resulting feedback captures how decisions and preferences vary across persona backgrounds, including hesitation after a price increase, willingness to continue after an AI assistant fails, and latency tolerance. We conducted two main validation studies: First, a 400-trial controlled study evaluated persona adherence across ten behavioral attributes and all four environments. The declared behavior was expressed or correctly suppressed in 366 trials (91.5%). Second, human and LLM judges evaluated the extraction quality of human-grounded personas. Overall, MatrAIx provides an end-to-end infrastructure for evaluating AI systems and digital products with diverse simulated human users.

Related papers

• Efficient Test-Time Adaptation through Human-AI Interaction [112.5131603022122]
  We propose test-time adaptation through human-agent interaction (TAHI)<n>We adapt agents to 30 individuals in two high-utility domains, writing and visual creation, on a total of 600 tasks.<n>Our agents improve solo task success by 4.5-20.9% within only tens of tasks.
  arXiv  Detail & Related papers  (2026-09-03T17:33:18Z)
• PersonaForge: Realistic Multi-Turn User Simulation for Agentic Systems [53.65117408512214]
  We introduce textbfPersonaForge, a user simulation framework for synthesizing realistic multi-turn user--agent interactions.<n>Using PersonaForge, we construct a 6.3K-record training dataset and textbfPersonaForge-Bench, a manually annotated 138-task benchmark.<n>Experiments show that PersonaForge training improves the composite score by +4.1%, with gains across all four dimensions.
  arXiv  Detail & Related papers  (2026-08-28T14:33:19Z)
• On the limits and opportunities of AI reviewers: Reviewing the reviews of Nature-family papers with 45 expert scientists [113.03797263688519]
  Many scientists view AI reviewers as probabilistic systems without the expertise to evaluate research.<n>Existing evaluations of AI reviewers have focused on whether their verdicts match human verdicts.
  arXiv  Detail & Related papers  (2026-05-20T03:33:55Z)
• DeepPersona: A Generative Engine for Scaling Deep Synthetic Personas [13.83414782465312]
  We introduce DEEPPERSONA, a scalable generative engine for synthesizing narrative-complete synthetic personas.<n>First, we algorithmically construct the largest-ever human-attribute taxonomy, comprising over hundreds of hierarchically organized attributes.<n>We conditionally generate coherent and realistic personas that average hundreds of structured attributes and roughly 1 MB of narrative text.
  arXiv  Detail & Related papers  (2025-11-10T17:37:56Z)
• Everyone prefers human writers, including AI [0.0]
  We conducted experiments using Raymond Queneaus Exercises Style (1947) to measure attribution bias.<n>Humans showed +13.7 percentage point (pp) bias (Cohen's h = 0.28, 95% CI: 0.21-0.34), while AI models showed +34.3 percentage point bias (h = 0.70, 95% CI: 0.65-0.76), a 2.5-fold stronger effect.
  arXiv  Detail & Related papers  (2025-10-09T21:33:30Z)
• SimulatorArena: Are User Simulators Reliable Proxies for Multi-Turn Evaluation of AI Assistants? [61.07963107032645]
  Large language models (LLMs) are increasingly used in interactive applications.<n>Human evaluation remains the gold standard for assessing their performance in multi-turn conversations.<n>We introduce SimulatorArena, a benchmark of 909 annotated human-LLM conversations on two interactive tasks.
  arXiv  Detail & Related papers  (2025-10-06T23:17:44Z)
• Human-MME: A Holistic Evaluation Benchmark for Human-Centric Multimodal Large Language Models [118.44328586173556]
  Multimodal Large Language Models (MLLMs) have demonstrated significant advances in visual understanding tasks.<n>Human-MME is a curated benchmark designed to provide a more holistic evaluation of MLLMs in human-centric scene understanding.<n>Our benchmark extends the single-target understanding to the multi-person and multi-image mutual understanding.
  arXiv  Detail & Related papers  (2025-09-30T12:20:57Z)
• Adaptive Monitoring and Real-World Evaluation of Agentic AI Systems [3.215065407261898]
  Multi-agent systems that combine large language models with external tools are rapidly transitioning from research laboratories into high-stakes domains.<n>This "Advanced" sequel fills that gap by providing an algorithmic instantiation or empirical evidence.<n>AMDM cuts anomaly-detection latency from 12.3 s to 5.6 s on simulated goal drift and reduces false-positive rates from 4.5% to 0.9%.
  arXiv  Detail & Related papers  (2025-08-28T15:52:49Z)
• Twin-2K-500: A dataset for building digital twins of over 2,000 people based on their answers to over 500 questions [11.751234495886674]
  LLM-based digital twin simulation holds great promise for research in AI, social science, and digital experimentation.<n>We survey a representative sample of $N = 2,058$ participants (average 2.42 hours per person) in the US across four waves with 500 questions in total.<n>Initial analyses suggest the data are of high quality and show promise for constructing digital twins that predict human behavior well at the individual and aggregate levels.
  arXiv  Detail & Related papers  (2025-05-23T05:05:11Z)
• Can Machines Imitate Humans? Integrative Turing-like tests for Language and Vision Demonstrate a Narrowing Gap [56.611702960809644]
  We benchmark AI's ability to imitate humans in three language tasks and three vision tasks.<n>Next, we conducted 72,191 Turing-like tests with 1,916 human judges and 10 AI judges.<n>Imitation ability showed minimal correlation with conventional AI performance metrics.
  arXiv  Detail & Related papers  (2022-11-23T16:16:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.