CordisBench基准测试了模型在动态代理环境中的组件生命周期推理能力，发现模型随复杂度增加可靠性下降，GPT-5.6 Luna在中等推理下每题需近3000个token。
研究人员发布CordisBench基准，包含1200个问题，测试语言模型在动态代理框架中推理组件生命周期变化的能力。该基准使用Cordis运行时管理组件依赖和清理，要求模型识别受影响组件、预测指定拆卸顺序后的状态、判断不同条件下的适用性，并选择成功的重新配置方案。评测显示模型在小系统中表现良好，但随着交互数量增加可靠性下降，特别是在预测最终状态和跨拆卸顺序推理时。
CordisBench: Can Language Models Reason About Component Lifecycles in Dynamic Agent Harnesses?
Dynamic agent harnesses let language models change the software that shapes their own execution. This flexibility brings a new reasoning burden: a local plugin change can propagate through dependencies and cleanup. We introduce CordisBench, a 1,200-question benchmark of this lifecycle reasoning. It combines a controlled formal setting with programs executed against Cordis, a runtime that manages component dependencies and cleanup, and asks models to identify affected components, predict state after a specified teardown order, determine which conditions hold under all or some orders, and choose reconfigurations that succeed when executed. Across these tasks, we evaluate three efficiency-oriented models at low reasoning effort with 2, 4, 8, 16, 24, or 32 relevant interactions, using deterministic task-specific scoring. Models usually handle small systems well but grow less reliable as more interactions become relevant, especially when predicting final state and when reasoning across teardown orders. Additional inference effort recovers marked gains for some models. The cost is nontrivial: on our 16-interaction subset, GPT-5.6 Luna uses nearly 3,000 reasoning tokens per question at medium effort. For these controlled instances, that cost is avoidable: an independent finite reference semantics agrees with Cordis execution on every observation and action outcome used for scoring across all 528 executable questions.