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Bank-World:评估智能体在银行工作流上的基准

Bank-World: A Benchmark for Evaluating Agents on Banking Workflows

Daniel Rupawalla

中文摘要

Bank-World 是一个包含 50 项银行任务的基准,覆盖两家模拟机构。完成银行工作不仅需要正确计算,智能体还必须核对记录、获取授权、执行交易,并在情况变化时保持指令与状态报告一致。该基准通过单轮任务以及与模拟员工的多轮交互来检验这些要求,模拟员工会提供信息、批准操作并修改指令。评分结合对已记录操作和状态的可执行检查,以及对任务要求的模型判断,包括有效的替代方案是否满足要求。在四个模型共 1,600 次尝试中,正确的决策往往未能转化为已完成的工作:智能体重复收费、把正确的总额以错误金额分配给收款人、在审批待定时执行付款。各模型优势也不同:Sol 在单轮完成上领先,Opus 的多轮完成率最高,Grok 成功的多轮任务更多但稳定性较差。Bank-World 通过任务级结果和连接源证据、智能体决策与模拟后果的示例,使这些差异可被检查。任务环境、可执行评分器和完整轨迹被私下保留,以减少污染并为未来评估保留该基准。

关键要点

  1. 01银行工作流要求核对记录、获取授权、执行交易并保持状态一致,而非仅正确计算。
  2. 02基准含 50 项任务、两家模拟机构,支持单轮任务与多轮员工交互。
  3. 03四个模型 1,600 次尝试中,正确决策常未转化为完成工作,出现重复收费、金额分配错误、审批未完成即付款。
  4. 04模型表现分化:Sol 单轮领先,Opus 多轮完成率最高,Grok 多轮成功更多但不稳定。
  5. 05任务环境、评分器与轨迹私下保留,以降低污染并供未来评估。

解读

尚无解读。

原始英文摘要

arXiv:2610.39733v1 Announce Type: new Abstract: Bank-World is a benchmark of 50 banking tasks across two simulated institutions. Completing banking work requires more than producing the right calculation: agents must reconcile records, obtain permission, execute transactions, and keep instructions and status reports consistent as circumstances change. Bank-World tests these requirements through single-turn assignments and multi-turn interactions with simulated employees who supply information, approve actions, and revise instructions. Grading combines executable checks of recorded actions and state with model judgments of task requirements, including whether valid alternative solutions satisfy them. Across 1,600 attempts by four models, we find that correct decisions often fail to become completed work. Agents charge fees twice, distribute the correct total in incorrect amounts among the recipients, and execute payments while approval is pending. Their strengths also differ: Sol leads single-turn completion, Opus has the highest observed multi-turn completion rate, and Grok succeeds across more multi-turn tasks but less consistently. Bank-World makes these differences inspectable through task-level results and worked examples connecting source evidence, agent decisions, and simulated consequences. We retain the task environments, executable graders, and full trajectories privately to reduce contamination and preserve the benchmark for future evaluations.

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