CordisBench: Can Language Models Reason About Component Lifecycles in Dynamic Agent Harnesses?
CordisBench benchmarks LLMs' reasoning about component lifecycles in dynamic systems, with 1200 questions revealing performance drops as interactions increase.
Key Findings
Methodology
CordisBench integrates formal semantics with executable Cordis programs to evaluate language models on tasks like component localization, schedule prediction, condition verification, and reconfiguration. The benchmark spans six interaction scales (2-32) to test scalability. Models such as GPT-5.6 Luna and DeepSeek V4 are assessed under low reasoning effort, with deterministic scoring based on structured outputs. The framework compares model predictions against precise reference semantics derived from finite state models and runtime execution, enabling rigorous validation. The study emphasizes the impact of increasing interactions on model reliability, especially in predicting final states and reasoning across teardown orders.
Key Results
- Models perform well on small systems (2-4 interactions), with localization accuracy exceeding 90%. However, as interactions grow to 16 or more, performance deteriorates; GPT-5.6 Luna's final state prediction accuracy drops from 91.7% to 14.1%. Increasing reasoning effort improves results significantly, e.g., Luna's accuracy from 31.2% to 85.4% at 16 interactions, but at high token costs (~3000 tokens per question).
- Localization remains relatively stable across sizes, but complex reasoning tasks like guaranteed and reachable conditions decline sharply, indicating limitations in model understanding of interference effects and dependency interactions.
- Empirical validation shows that finite reference semantics perfectly match Cordis execution outcomes, confirming the correctness of the evaluation framework despite high inference costs. The results highlight the gap between localization and final state prediction, especially under complex interference scenarios.
Significance
This work exposes fundamental challenges faced by large language models in reasoning about dynamic, dependency-rich systems. By formalizing the problem and providing a scalable benchmark, it guides future research toward more robust models capable of understanding intricate component interactions. The findings have broad implications for deploying AI in real-world systems such as software maintenance, autonomous agents, and adaptive systems, where reliable lifecycle reasoning is critical. The benchmark also sets a standard for evaluating model scalability and reasoning under complexity, fostering progress in formal reasoning and AI reliability.
Technical Contribution
The paper introduces a novel multi-task evaluation framework combining formal semantics and executable system simulation to assess models' lifecycle reasoning. It systematically scales interaction complexity, providing insights into how model performance degrades with size. The approach includes rigorous validation against runtime execution, establishing a high-confidence standard for correctness. The framework enables detailed analysis of model capabilities in dependency tracking, state prediction, and reconfiguration, offering a foundation for future improvements in AI reasoning about complex systems.
Novelty
This is the first comprehensive benchmark explicitly targeting dynamic component lifecycle reasoning in large language models, integrating formal semantics with real execution validation. Unlike prior static dependency analyses, it evaluates models’ ability to handle interference, non-independence, and complex teardown sequences across multiple scales. The scale-up mechanism and multi-task design provide a unique challenge, pushing models beyond static reasoning into dynamic, interference-aware inference, representing a significant step forward in AI system reliability research.
Limitations
- The benchmark simplifies real-world scenarios by focusing on interference outside of guaranteed recovery conditions, excluding failures, irreversible actions, and hot-swapping complexities. This limits direct applicability to production environments.
- Inference costs are high; at larger scales, token consumption approaches 3000 tokens per question, hindering practical deployment in resource-constrained settings.
- The evaluation primarily considers models without real-time feedback or tool integration, which could significantly alter performance in operational systems.
Future Work
Future directions include extending the benchmark to incorporate failure modes, irreversible actions, and asynchronous events. Developing more efficient inference techniques or integrating formal verification tools could reduce costs. Combining models with formal methods and reinforcement learning may enhance reasoning robustness. Additionally, applying this framework to real-world software systems and autonomous agents will be crucial for translating research into practical AI solutions.
AI Executive Summary
This study introduces CordisBench, a comprehensive benchmark designed to evaluate large language models' reasoning capabilities regarding component lifecycles in dynamic systems. As modern AI agents increasingly manipulate software components at runtime, understanding the consequences of plugin modifications, dependency effects, and cleanup sequences becomes vital. Traditional static analysis tools fall short in these scenarios, prompting the need for models that can reason about complex interactions without symbolic assistance.
CordisBench combines formal semantics with executable Cordis programs to create a rigorous, scalable testing environment. It features six interaction scales, from 2 to 32, challenging models to identify affected components, predict post-teardown states, verify conditions across different teardown orders, and determine minimal reconfiguration sets. The benchmark evaluates models like GPT-5.6 Luna and DeepSeek V4 under low reasoning effort, revealing that performance on small systems remains high but degrades significantly as complexity increases. Notably, Luna's final state prediction accuracy drops from over 90% at small sizes to below 15% at 16 interactions.
Experimental results demonstrate that increasing inference effort can recover some performance, but at a high token cost (~3000 tokens per question). The framework's validation against exact reference semantics confirms the correctness of model predictions, highlighting the persistent challenge of reasoning under interference and non-independence effects. These findings underscore the gap between localization accuracy and reliable state prediction in complex, interference-rich environments.
Overall, CordisBench provides a vital tool for advancing AI reasoning in dynamic, dependency-driven systems. It emphasizes the importance of integrating formal verification with model inference, guiding future research toward more scalable, reliable models capable of managing real-world system complexities. Despite current limitations, such as high inference costs and simplified scenarios, this work lays a foundation for future breakthroughs in autonomous system management and formal AI reasoning.
Deep Dive
Abstract
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.