Consistency Amplifies: How Behavioral Variance Shapes Agent Accuracy

TL;DR

This paper investigates how behavioral consistency in large language model agents correlates with accuracy, revealing that consistency amplifies outcomes but does not ensure correctness, impacting deployment and evaluation strategies.

Contribution

It provides empirical analysis of consistency and accuracy across multiple models on a complex benchmark, highlighting the nuanced role of consistency in AI reliability.

Findings

Higher consistency correlates with higher accuracy across models.

Consistency can amplify both correct and incorrect outcomes.

Most failures are due to consistent wrong interpretations.

Abstract

As LLM-based AI 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 Claude~4.5~Sonnet, 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: \textbf{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 2.1$\times$ 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.