Researchers propose RSD metric to measure LLM agent consistency
A new paper on arXiv introduces Recursive Semantic Divergence (RSD), an unsupervised metric designed to quantify how LLM-based agents behave inconsistently across repeated runs. Current evaluation methods focus on task correctness but fail to capture how agents diverge over time—calling different tools, producing contradictory outputs, or pursuing conflicting strategies. RSD adapts techniques…
Key points
- RSD measures LLM agent consistency recursively across future states using natural language inference
- Method requires no ground-truth labels and matches supervised fine-tuning performance with lower variance
The authors claim RSD, when used as a fine-tuning reward signal, reduces task-progress variability by 132% to 91% on the tau2-bench and by 90% to 44% on toolsandbox. This approach reportedly matches supervised fine-tuning performance while requiring no ground-truth labels and cutting variance. The paper does not include external validation beyond these benchmark claims.
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