The advent of complex, interconnected long-horizon LLM systems has made it incredibly tricky to identify where and when these systems break down. Evaluation capabilities that currently exist today are limited in that they often focus on simple metrics, end-to-end outcomes, and are dependent on the perspectives of humans. In order to match the increasing complexity of these many component systems, evaluation frameworks must also be able to reason, probe, iterate, and understand the nuanced logic passing through these systems. In this paper, we present RAFFLES, an offline evaluation architecture that incorporates iterative reasoning. Specifically, RAFFLES operates as an iterative, multi-component pipeline, using a central Judge to systematically identify faults and a set of specialized Evaluators to assess the quality of the candidate faults as well as rationales of the Judge. We evaluated RAFFLES with several benchmarks - the Who&When dataset to identify step-level faults in multi-agent systems and the ReasonEval datasets to diagnose step-level mathematical reasoning errors. RAFFLES outperforms strong baselines, achieving an accuracy of over 20% and 50% on the Who&When Hand-Crafted and Algorithmically-Generated datasets, and over 80% on the ReasonEval datasets. These results demonstrate a key step towards introducing automated fault detection for autonomous systems over labor-intensive manual review.

翻译：随着复杂、互联的长周期大型语言模型系统的出现，识别这些系统在何处及何时发生故障变得极其困难。现有的评估能力存在局限，往往侧重于简单指标和端到端结果，并依赖于人类的主观判断。为了适应这些多组件系统日益增长的复杂性，评估框架也必须具备推理、探查、迭代和理解系统间传递的细微逻辑的能力。本文提出RAFFLES，一种融合迭代推理的离线评估架构。具体而言，RAFFLES作为一个迭代的多组件流水线运行，通过一个核心的Judge模块系统性地识别故障，并利用一组专门的Evaluator模块评估候选故障的质量以及Judge模块的推理依据。我们在多个基准测试中评估了RAFFLES——使用Who&When数据集识别多智能体系统中的步骤级故障，以及使用ReasonEval数据集诊断步骤级数学推理错误。RAFFLES在多个强基线方法上表现优异，在Who&When人工构建数据集和算法生成数据集上分别取得了超过20%和50%的准确率，在ReasonEval数据集上准确率超过80%。这些结果表明，我们在为自主系统引入自动化故障检测以替代劳动密集型人工审查方面迈出了关键一步。