CordisBench tests how language models reason about software lifecycles inside agents
The 1,200-question benchmark reveals that frontier models struggle to anticipate how plugin changes and component teardowns propagate through complex software environments.
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- Researchers introduced CordisBench, a 1,200-question benchmark designed to test how language models reason about component lifecycles in dynamic agent harnesses, per a September 1 arXiv paper.
- The evaluation reveals that while models handle simple environments well, their reliability collapses when tracking complex interactions, requiring up to 3,000 reasoning tokens per question.
- The authors suggest that developers can bypass this high computational cost by integrating independent finite reference semantics that align perfectly with runtime executions.
A new research paper published on arXiv on September 1 has introduced CordisBench, a benchmark designed to evaluate how effectively language models (LMs) reason about the software components they interact with. Authored by Damien Sileo and Dimitri Kachler, the paper, titled 'CordisBench: Can Language Models Reason About Component Lifecycles in Dynamic Agent Harnesses?', highlights a critical reasoning bottleneck for modern autonomous agents.
Dynamic agent harnesses let LMs modify the very software environment that shapes their own execution, such as adding or disabling plugins. This capability introduces a new cognitive burden: a local component change can propagate through software dependencies, requiring meticulous teardowns and cleanups. CordisBench evaluates this ability across 1,200 questions using Cordis, a runtime environment that manages component dependencies and system reconfigurations.
The authors found that while frontier models excel at managing simple setups, their performance degrades significantly when tracking systems with up to 32 cascading interactions. Reasoning about final states and sequential teardowns proved particularly difficult. The benchmark revealed a high computational cost: for a 16-interaction task, OpenAI's GPT-5.6 Luna required nearly 3,000 reasoning tokens per question at a medium reasoning setting. To address this, the authors developed independent finite reference semantics that match Cordis executions perfectly, suggesting developers can use deterministic structures to bypass expensive LM reasoning.
As agents are given more autonomy to reconfigure their own software harnesses, they must be able to predict how their environment will react to a change before they execute it. CordisBench exposes that frontier models are highly inefficient at tracking these state cascades, warning developers that relying purely on LLM reasoning for system configuration is a high-latency, high-cost anti-pattern that should be replaced with deterministic state tracking.