Bank-World:一个评测 AI 智能体完成银行工作流的基准
Bank-World: A Benchmark for Evaluating Agents on Banking Workflows
AI 导读
Bank-World 是一个包含 50 项银行任务、覆盖两家模拟机构的智能体评测基准,通过单轮任务和与模拟员工的多轮交互,考察记录核对、审批、交易执行及指令一致性等能力。在四个模型共 1,600 次尝试中,正确决策常未能落实为完成的任务,例如重复收取手续费、金额分配出错、审批未完成就执行付款。Sol 单轮完成率最高,Opus 多轮完成率最高,Grok 覆盖更多多轮任务但稳定性较差。
正文 · 原文
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.
| Comments: | 43 pages, 3 figures, including appendices. Ancillary files contain the task ID mapping, reward specifications, and single-turn check-authority mapping |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.39733 [cs.AI] |
| (or arXiv:2610.39733v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.39733 arXiv-issued DOI via DataCite |
Submission history
From: Daniel Rupawalla [view email]
[v1]
Thu, 29 Oct 2026 09:59:39 UTC (88 KB)
来源:arXiv cs.AI · arxiv.org