用积分制模拟真实编程竞赛，让代码代理学会权衡效率与成本。
Credit-Budgeted ICPC-Style Coding: When Agents Must Pay for Every Decision
- 引入积分经济机制，每行代码、测试和时间都消耗固定预算。
- 实测显示当前顶尖代理在资源约束下无法平衡准确率与耗能。
- 适合研发高效、可持续的智能编程系统的研究者使用。
当前自主编程代理的评估假设了无限资源环境，这与现实软件工程中的资源限制严重不符。随着代理集群规模扩大，忽略计算和时间成本可能导致预算耗尽的灾难性后果。为将关注点从单一准确率转向成本敏感的问题解决，我们提出了USACOArena——一个基于严格‘积分’经济的互动式ACM-ICPC风格竞技场。每个生成的词元、本地测试和流逝的秒数都会消耗固定预算，迫使代理做出战略性权衡。全面分析表明，当前领先的单个代理及代理集群在资源约束下无法最优平衡准确率与成本，表现出差异化的、路径依赖的行为。最终，USACOArena为开发高度高效、资源感知的代理架构提供了关键的动态训练平台。
原文摘要 · Abstract (English)
Current evaluations of autonomous coding agents assume an unrealistic, infinite-resource environment. However, real-world software engineering is a resource-bound competition. As we scale toward large agent swarms, ignoring compute and time costs risks catastrophic budget exhaustion. To shift the focus from isolated accuracy to cost-aware problem-solving, we introduce USACOArena, an interactive ACM-ICPC-style arena driven by a strict "credit" economy. Every generated token, local test, and elapsed second depletes a fixed budget, forcing agents to make strategic trade-offs. Our comprehensive profiling reveals that frontier single agents and swarms currently fail to optimally balance accuracy with these constraints, exhibiting divergent, path-dependent behaviors. Ultimately, USACOArena provides an essential dynamic training ground for developing highly efficient, resource-aware agent architectures.
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