Consistency Amplifies: How Behavioral Variance Shapes Agent Accuracy
Analysis of behavioral consistency in large language model agents reveals that while consistent performance correlates with higher accuracy, consistency can amplify both correct and incorrect interpretations, emphasizing that accurate interpretation is more crucial than execution consistency for production deployment. (2 upvotes on HuggingFace)
Published on Mar 26
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Abstract
Analysis of behavioral consistency in large language model agents reveals that while consistent performance correlates with higher accuracy, consistency can amplify both correct and incorrect interpretations, emphasizing that accurate interpretation is more crucial than execution consistency for production deployment.
AI-generated summary
As LLM-based agents are deployed in production systems, understanding their behavioral consistency (whether they produce similar action sequences when given identical tasks) becomes critical for reliability. We study consistency in the context of SWE-bench, a challenging software engineering benchmark requiring complex, multi-step reasoning. Comparing Claude4.5Sonnet, GPT-5, and Llama-3.1-70B across 50 runs each (10 tasks times 5 runs), we find that across models, higher consistency aligns with higher accuracy: Claude achieves the lowest variance (CV: 15.2%) and highest accuracy (58%), GPT-5 is intermediate (CV: 32.2%, accuracy: 32%), and Llama shows the highest variance (CV: 47.0%) with lowest accuracy (4%). However, within a model, consistency can amplify both correct and incorrect interpretations. Our analysis reveals a critical nuance: consistency amplifies outcomes rather than guaranteeing correctness. 71% of Claude's failures stem from "consistent wrong interpretation": making the same incorrect assumption across all runs. Interestingly, GPT-5 achieves similar early strategic agreement as Claude (diverging at step 3.4 vs.\ 3.2) but exhibits 2.1times higher variance, suggesting that divergence timing alone does not determine consistency. These findings suggest that for production deployment, interpretation accuracy matters more than execution consistency, with implications for agent evaluation and training.
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