Andrea Cui hi there!

Cognitive scientist · AI researcher

Andrea Cui

Model Evaluation · Human Preference Alignment · World Models · Embodied AI

I'm a cognitive scientist working on AI evaluation at Luma AI, where I cover evaluation, post-training, and human preference alignment for agentic image and video generation. I also benchmark world models and embodied agents. I hold a PhD in Cognitive Science and master's degrees in Computer Science and Statistics.

Work

Luma AI

Now

Applied Research

Owning evaluation and metrics across generative image and video models, agentic systems, and physical-AI research. The work spans post-training and human preference alignment, benchmarks for world models, and automated research on self-evolving agents.

Rockstar Games

Fall 2025

Research Data Scientist · Internship

A research-oriented data science role developing theory-grounded behavioral models, machine learning methods, and causal frameworks.

University of Illinois Urbana-Champaign

PhD

PhD in Cognitive Science · M.S. in Computer Science · M.S. in Statistics

Researcher at the intersection of human cognition, decision-making, and computational modeling.

Recent Projects

LIBERO-RIG

Luma AI

Lead

Measuring how much physical hardware variation a learned robot policy can tolerate, across 9 vision-language-action models. Paper on arXiv.

Layers

Luma AI

Evaluation lead

Led evaluation for Layers, which generates and decomposes images into independently editable layers.

RLHF

Luma AI

User signals to model alignment

Post-training to improve model alignment with user preferences.

Self-Evolving Agents for Robotics

Luma AI

Evaluation and metrics

Owned evaluation and metrics for the program. More at sear.bot; paper on arXiv.

Selected Publications

Selected research on human cognition, AI evaluation, and human–AI interaction.

LIBERO-RIG: A Hardware-Setup Robustness Benchmark for Vision-Language-Action Models

arXiv, 2026

A benchmark that changes only the robot’s hardware and setup, such as camera mounts, calibration, control rate, and gripper pads, to test how well nine vision-language-action models hold up when the robot itself drifts.

View paper ↗

SEAR: Self-Evolving Agentic Robotics

arXiv, 2026

A framework and benchmark for language-model agents that improve robots on their own, testing whether they truly learn from experience and whether those skills carry over to new tasks, simulators, and a real robot.

View paper ↗

Empowered XR through Generative AI: Balancing Superpowers and Risks

CHI 2026

Analyzes 135 recent works to map the “superpowers” generative AI grants users in immersive spaces, along with the risks they carry, distilling both into a taxonomy and design guidelines.

View paper ↗

LLM Integration in Extended Reality: A Comprehensive Review of Current Trends, Challenges, and Future Perspectives

CHI 2025

A comprehensive review of how large language models are being woven into extended reality, mapping current trends, open challenges, and where immersive AI is headed next.

View paper ↗

RPGBench: Evaluating Large Language Models as Role-Playing Game Engines

arXiv, 2025

A benchmark that tests whether LLMs can run a full role-playing game, keeping state, enforcing rules, and sustaining a coherent interactive story.

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EyeSee: Enhancing Art Appreciation through Anthropomorphic Interpretations from Multiple Perspectives

CHI 2025

An interactive system that lets artworks “speak” from multiple anthropomorphic perspectives, opening new ways for viewers to connect with art.

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Do Language Models Have Bayesian Brains? Distinguishing Stochastic and Deterministic Decision Patterns within Large Language Models

NeurIPS 2024 Workshop on Behavioral Machine Learning

Probes whether LLMs decide like Bayesian reasoners, teasing apart the stochastic and deterministic patterns hiding inside model behavior.

View paper ↗

Complex Background Information Slows Down Parallel Search Efficiency by Reducing the Strength of Interitem Interactions

Journal of Experimental Psychology: Human Perception and Performance, 2023

Shows that cluttered backgrounds slow human visual search not by adding noise, but by weakening the interactions between the items we search through.

View paper ↗

Order Constrained Analyses of Eyewitness Memory Accuracy

Vision Sciences Society (VSS), 2024

Uses order-constrained modeling to test which commonly reported patterns among accuracy, confidence, and response time genuinely hold in eyewitness memory data.

View paper ↗

Blog

Essays on AI, cognition, and creativity.