arXiv cs.AI· Dong Bo, Fanjin Zhang, Ke Cheng, Xinxin Zhao, Xiaowei Su, Yang Wang, Jinghang Xu, Cuiping Li, Hong Chen, Jing Zhang·· 10 小时前AI 评分34
BuySideFlow：面向投资研究 LLM 智能体的时点有效性基准
BuySideFlow: A Point-in-Time Benchmark for LLM Agents in Investment Research
AI 导读
研究者推出 BuySideFlow，一个包含 408 个从业者编写的中文投资研究任务的基准，覆盖股票、基金、固定收益与宏观经济，用于检验 LLM 智能体在数据检索与分析中是否遵守披露截止时点（PIT）。
正文 · 原文
Abstract:Large language models (LLMs) can generate SQL and SQL--Python workflows for investment research, but ensuring point-in-time (PIT) validity remains challenging. Agents must enforce disclosure cutoffs and select the appropriate historical states and record versions throughout data retrieval and financial analysis. We introduce \textsc{BuySideFlow}, a benchmark of 408 practitioner-derived Chinese-language tasks spanning equities, funds, fixed income, and macroeconomics over a fixed snapshot from a licensed production financial database. We provide executable references and a taxonomy of temporal errors, pairing result checks with expert audits of executed workflows. We evaluate nine agent configurations on all tasks, with result accuracy ranging from 16.2\% to 36.8\%. An audit of 900 runs on 100 shared tasks confirms PIT violations in 193 of the 380 runs that pass result checks (50.8\%), showing that output agreement alone does not establish temporal validity.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.12966 [cs.AI] |
| (or arXiv:2611.12966v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.12966 arXiv-issued DOI via DataCite |
Submission history
From: Dong Bo [view email]
[v1]
Sat, 7 Nov 2026 12:55:19 UTC (502 KB)
来源：arXiv cs.AI · arxiv.org