How Far are LLM s from Being Our Digital Twins? A Benchmark for Persona-Based Behavior Chain Simulation (2025)

Abstract: Recently, LLMs have garnered increasing attention across academic disciplines for their potential as human digital twins, virtual proxies designed to replicate individuals and autonomously perform tasks such as decision-making, problem-solving, and reasoning on their behalf.However, current evaluations of LLMs primarily emphasize dialogue simulation while overlooking human behavior simulation, which is crucial for digital twins.To address this gap, we introduce BehaviorChain, the first benchmark for evaluating LLMs’ ability to simulate continuous human behavior.BehaviorChain comprises diverse, high-quality, persona-based behavior chains, totaling 15,846 distinct behaviors across 1,001 unique personas, each with detailed history and profile metadata.For evaluation, we integrate persona metadata into LLMs and employ them to iteratively infer contextually appropriate behaviors within dynamic scenarios provided by BehaviorChain. Comprehensive evaluation results demonstrated that even state-of-the-art models struggle with accurately simulating continuous human behavior.

## introduction the rapid advancement of large language models (llms) has unlocked unprecedented capabilities in artificial intelligence @xcite , enabling systems to interpret natural language @xcite , reason about complex scenarios @xcite , and interact with humans through seamless workflow integration @xcite . these advancements have spurred interest across diverse fields in leveraging llms to create human digital twins-virtual representations of individuals, potentially equipped with personalized data * corresponding author ## benchmark crafting in this section, we introduce the detailed methodology for constructing behaviorchain, focusing on persona-based behavior chain extraction, distractors generation and presenting the multiple-choice and generation task formulations. ## conclusion we introduced behaviorchain, a novel benchmark for evaluating llms' ability to simulate continuous human behavior. comprising 1,001 persona-based behavior chains extracted from literature, it addresses a critical gap in llm assessment, particularly given the scarcity of real-world behavioral data. our evaluation framework, encompassing behavioral recognition and generation tasks, revealed significant challenges for ten stateof-the-art llms. further experiments analyzing the influence of key behaviors, history, and context characteristics provided valuable insights into the factors that contribute to or hinder successful persona-based behavior simulation. behavior-chain provides a valuable resource for developing robust digital twins.