HumanStudy-Bench: Towards AI Agent Design for Participant Simulation

arXiv:2602.00685 · cs.AI · Submitted 2026-01-31 · Read on arXiv

cs.AI

Submitted: 2026-01-31

Updated: 2026-08-29

Code: https://github.com/XuanL17/HumanStudy-Bench

Terminology

• Alignment — 45× in this paper · explained in 1 episode
• Benchmark — 29× in this paper · explained in 2 episodes
• Fidelity — 19× in this paper · explained in 2 episodes
• Aggregation — 16× in this paper · explained in 1 episode
• Consistency — 13× in this paper · explained in 3 episodes
• Probability Alignment Score — 8× in this paper
• Core — 7× in this paper · explained in 2 episodes
• Benchmarking — 6× in this paper · explained in 3 episodes
• Effect Consistency Score — 5× in this paper
• AI Agents — 4× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Calibration — 4× in this paper · explained in 7 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Large Language Models (LLMs) — 3× in this paper · explained in 6 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Alignment Scores — 2× in this paper · explained in 1 episode
• Generalization — 2× in this paper · explained in 13 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Ontology — 2× in this paper · explained in 2 episodes
• Scaling Laws — 2× in this paper · explained in 4 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Agent-based modeling — 1× in this paper · explained in 1 episode
• Calculate the Bayesian Alignment Score — 1× in this paper
• Clustering — 1× in this paper · explained in 2 episodes
• Cooperation — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Homogeneity — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Maximum Likelihood Estimator — 1× in this paper
• Multi-agent Collaboration — 1× in this paper · explained in 2 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Persona Prompting — 1× in this paper · explained in 2 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes
• Wasserstein Distance — 1× in this paper · explained in 4 episodes

Sources

• CoBRA: Programming Cognitive Bias in Social Agents Using Classic Social Science Experiments
• Generative Agents: Interactive Simulacra of Human Behavior
• Graph-Based Alternatives to LLMs for Human Simulation
• LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals
• On Benchmarking Human-Like Intelligence in Machines

Related papers

• MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
• Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
• The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
• MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
• Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems
• VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection