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arXiv cs.AI· Sinuo Wang, Wentao Zhao, Jiacheng Lu, Rui Sun, Beidi Luan, Qiming Shi, Jing Li, Daxin Jiang, Zuo Bai·· 13 小时前AI 评分38

LiveSearch:把问题写成可执行验证代码的金融搜索智能体实时基准

LiveSearch: Questions as Executable Verifier Code for a Live Benchmark of Professional-Source Financial Search Agents

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LiveSearch 是一个面向金融搜索智能体的实时基准,每个问题以可执行验证代码形式存储——对专业金融数据服务的类型化调用程序加声明式提取器,评测时执行程序得出金标准答案,并每日自动重跑。在通用网页搜索下,六个当代工具调用模型取得 70.9% 至 76.7% 的答案准确率;连续三天内 70% 的题目金标准值发生变化,即使采用容错匹配,直接复用旧快照的答案在一天内也会在多数题目上失效。

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Abstract:In finance, the answer that matters is usually the current one: prices, rates, and disclosures hold only as of a given day, so retrieving up-to-date facts is a core capability of a financial agent. Yet most financial benchmarks overlook timeliness. They fix each answer at annotation time, so the gold goes stale as markets move and, once leaked into training data, rewards recall rather than search and analysis. We introduce LiveSearch, a live benchmark that stores each question as executable verifier code: a typed program of calls to a professional financial data service, followed by a declarative extractor. The question is frozen, while its gold answer is determined by executing the program against professional financial data at evaluation time, and a daily job re-executes it with no human or language model in the loop. A memorized answer is thus likely outdated, and a model must reach the current one through tools and analysis. Construction is equally automatic: a language model only verbalizes programs enumerated from real tool responses and never chooses an answer. Under general web search, six contemporary tool-calling models reach 70.9% to 76.7% answer accuracy. Over three consecutive days, 70% of served questions change their gold value, and even under tolerance-aware matching, a literally reused earlier snapshot fails on a majority of questions within a single day. Per-question tracking uncovers dynamic shifts in correctness masked by aggregate accuracy.
Subjects: Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.37166 [cs.AI]
  (or arXiv:2610.37166v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2610.37166

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Sinuo Wang [view email]
[v1] Tue, 27 Oct 2026 12:55:52 UTC (135 KB)

来源:arXiv cs.AI · arxiv.org