BuySideFlow揭示投資研究結果中的時點錯誤

2026年11月13日 00:00
站內 AI 整理稿

Computer Science > Artificial Intelligence arXiv:2611.

12966 (cs) [Submitted on 7 Nov 2026] Title:BuySideFlow: A Point-in-Time Benchmark for LLM Agents in Investment Research Authors:Dong Bo, Fanjin Zhang, Ke Cheng, Xinxin Zhao, Xiaowei Su, Yang Wang, Jinghang Xu, Cuiping Li, Hong Chen, Jing Zhang View a PDF of the paper titled BuySideFlow: A Point-in-Time Benchmark for LLM Agents in Investment Research, by Dong Bo and 9 other authors View PDF HTML (experimental) 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 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Dong Bo [view email] [v1] Sat, 7 Nov 2026 12:55:19 UTC (502 KB) Full-text links: Access Paper: View a PDF of the paper titled BuySideFlow: A Point-in-Time Benchmark for LLM Agents in Investment Research, by Dong Bo and 9 other authorsView PDFHTML (experimental)TeX Source view license Additional Features Audio Summary Current browse context: cs.

AI < prev | next > new | recent | 2026-11 Change to browse by: cs References & Citations SciXGoogle Scholar Semantic Scholar export BibTeX citation Loading...BibTeX formatted citation × loading...

Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.

ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?

) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.

AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?

) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy.arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.Which authors of this paper are endorsers?| Disable MathJax (What is MathJax?)

Related

相關文章

"Happy系列"再添一員,阿里以端到端整麴生成切入AI音樂賽道

該產品由阿里ATH(TokenHub)事業群研發,2026年8月17日以PC網頁端Beta形式首發,是阿里"Happy系列"AI模型的第三位成員,前兩位分別為視頻生成模型HappyHorse和三維交互視頻模型HappyOyster。產品主打端到端整曲一體化生成,用戶以自然語言描述情緒、故事或風格,即可完成歌詞、作曲、編曲與人聲演唱,也可自備歌詞。

17 分鐘前4600

Edge0 重磅開源 145B 端側模型,內存狂省 60% 還能滿血寫代碼

開源項目 Edge0近日正式發佈並開源了 Edge0-145B 與 Edge0-120B 模型,同時帶來了一套統一的推理框架與部署工具。作為全球首個宣稱可在個人電腦本地流暢運行的1450億參數開源模型,Edge0-145B 成功將足以比肩雲端前沿大模型的編程、推理與智能體能力,直接帶到了用戶的本地筆記本上。

17 分鐘前5300

關停五年,"蝦米"借 AI 還魂:阿里 30 元推出快樂蝦米

它並非簡單地把老產品搬回來,而是阿里"Happy 系列"的第三款模型產品——前兩位是視頻生成模型 HappyHorse 與三維交互視頻模型 HappyOyster。用戶用大白話講出情緒、故事或曲風,AI 便一口氣完成作詞、作曲、編曲與人聲演唱,主打端到端整麴生成而非步驟拼接,並提供"智能歌詞""純音樂"兩路輸出,外加"音樂配方""做同款"等複用玩法。

3 小時前6400
雷峰網生成式AI

獨家丨騰訊 AI 李牧青離職,出任 AI 公司聯席 CEO

本文作者: 高允毅 2026-11-13 11:43 專題:AI+教育:AI教學,未來已來 導語:騰訊雲 AI 全球業務負責人,出任啟鳴達人聯席 CEO。獨家獲悉,近日,騰訊雲 AI 全球業務負責人李牧青,正式出任教育 AI 科技公司啟鳴達人聯席 CEO。

3 小時前

月之暗面徐天天亮相財新峰會:AI 能力遠未觸及天花板,三維曲線正驅動智能持續加速

三條疊加的增長曲線:驅動大模型持續進化的底層邏輯在主題發言中,徐天天對人工智能的進化動力進行了深入剖析。她指出,當前 AI 能力水平的快速躍升並非單一因素造成,而是三條關鍵增長曲線共同疊加、同頻共振的結果:第一條:傳統的預訓練曲線。這涵蓋了算力基礎設施、能源供給以及模型參數規模等傳統維度的持續增長,目前這條基礎曲線依然保持著上升勢頭。

3 小時前5600