Are LLM Agents Long-Termist? Evaluating Long-Horizon Resource Optimization in Roguelike Environments

In real-world long-horizon tasks, agents must complete immediate objectives while deciding which resources to retain, discard, upgrade and combine, because these choices shape their future options. Existing benchmarks largely assess task progression and final success, leaving ongoing resource management underexamined. We study Long-Horizon Resource Optimization as the ability to sustain progress by constructing, using and revising limited resources as task demands evolve, balancing immediate gains against future performance. The delayed and interacting effects of resource choices make this difficult to assess: poor outcomes may result from either poor resource management or ineffective use of current resources. We introduce Long Game Bench, a unified benchmark spanning Slay the Spire 2, Balatro, and Luck be a Landlord through a common interface for agent interaction. In these roguelike games, early choices constrain later options, and adding a resource can weaken an existing configuration. To examine the two sources of failure, the benchmark measures overall progress, execution with current resources, and the frequency and diversity of active resource interventions. We also compare models under a shared high-quality configuration and with active management disabled to probe the roles of resource management and execution. We evaluate 6 frontier LLMs and 4 coding-agent harnesses across over 1,000 complete game trajectories. Agents often make local progress but fail to reliably convert it into successful runs. Providing models with the same high-quality configuration narrows the performance gap, reducing the standard deviation from 29.4% to 13.8% and raises the average win rate from 17% to 74%, while disabling active management reduces it from 17% to 6%. These findings highlight the importance of resource management for long-horizon performance. These findings suggest that resource management contribute substantially to long-horizon performance.