arXiv cs.AI· Sinuo Wang, Wentao Zhao, Jiacheng Lu, Rui Sun, Beidi Luan, Qiming Shi, Jing Li, Daxin Jiang, Zuo Bai·· 3 小时前AI 评分46
LiveSearch:将问题存为可执行验证器代码的专业金融搜索智能体动态基准
LiveSearch: Questions as Executable Verifier Code for a Live Benchmark of Professional-Source Financial Search Agents
AI 导读
LiveSearch 是一个金融搜索智能体动态基准,将每个问题存为对专业金融数据服务的可执行验证器代码,在评测时执行程序得出当期黄金答案,每日自动重跑且无需人工或语言模型参与,从而防止答案过时或因训练数据泄露而奖励记忆。六款工具调用模型在通用网页搜索下答题准确率为 70.9%–76.7%;连续三天内 70% 的题目黄金答案发生变化,重用旧快照多数题一天内即失败。
正文 · 原文
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 A memorized answer is thus likely outdated, and a model must reach the current one through tools and analysis.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