LifeBench: Evaluating Large Language Models as Lifelong Decision-Makers in a Simulated Human Life

Large language models have improved rapidly on reasoning, coding, and mathematics, yet comparatively little is known about how these gains transfer to tasks that demand long-horizon planning, awareness of compounding consequences, and the management of risk and uncertainty. We introduce LifeBench, an evaluation suite that probes these capabilities by casting an LLM as the decision-maker in a simulated human life. LifeBench is a life-course harness: beginning at age 18, an agent commits a single action each year until death, spanning education, career, finance, health, relationships, and well-being. Each action modifies the underlying stocks that govern how the life trajectory evolves. The simulator is stochastic, and the evaluation is designed to reflect the uncertainty inherent to real lives. Life trajectories are scored on three fronts: wealth, health, and well-being. We aggregate these three fronts into a single index called the Life Trajectory Index (LTI). On this index, the evaluated models cluster in a narrow band, and all fall well short of a ceiling established by search over the action space. To resolve this clustering and to assess trajectories on more subjective grounds, we also score using an LLM-as-judge with research-grounded rubrics that compare lives pairwise, switching presentation order to reduce position bias and aggregating verdicts into a Bradley-Terry score per model. We further characterize the steering behavior these models exhibit over long horizons by ablating across prompt variants.