# DataSpace 基准：最强模型数据智能体准确率仅 66.34%

- 来源：Rohan Paul (@rohanpaul_ai)
- 发布时间：2026-08-24 22:27
- AIHOT 分数：37
- AIHOT 链接：https://aihot.virxact.com/items/cmt7cpldk2270ro738f5lbmrx
- 原文链接：https://x.com/rohanpaul_ai/status/2091895051500580882

## AI 摘要

DataSpace 新基准测试数据智能体在异构工作区中的可验证分析能力，涵盖 410 个需结合数据库、CSV/JSON、长文档与视频并返回精确表格的任务。最强模型准确率仅 66.34%；固定模型后仅更换智能体框架即可将准确率从 30.98% 提升至 46.34%。跨所有测试模型，跨源连接与多数据类型混合是持续短板，且许多失败发生在智能体已找到正确信息后误解请求或提交错误列。

## 正文

A strong LLM does not automatically make a reliable data agent: DataSpace shows that the harness, cross-source joins, and final-table handling all materially affect whether the job actually gets done.

It tests 410 tasks where agents must combine databases, CSV/JSON files, long documents, and video, then return the exact requested table.

The best model gets only 66.34% right.

And with the model fixed, simply changing the agent harness moves accuracy from 30.98% to 46.34%.

Across every tested model, joins and mixing multiple data types are consistent weak spots.

But many failures happen even later: the agent has already found or computed the right information, then misunderstands the requested result or submits the wrong columns.

– arxiv. org/abs/2608.03451

Title: "DataSpace: Benchmarking Data Agents for Verifiable Analytics over Heterogeneous Workspaces"