---
format: "aidr-story-markdown/v1"
id: "96a0b349c8be6b2e2df3c271d69ee82ee47af93171f91b07537a1e6a8a8e92f6"
canonical_url: "https://aidr.today/96a0b349?lang=en"
title: "Deep Search in Real-World Web Environments"
lang: "en"
requested_lang: "en"
available_langs: ["en","vi"]
translation_fallback: null
fallback_fields: []
published_at: "2026-10-23T00:00:00.000Z"
category: "Data"
topics: ["agent","data-engineering"]
source_urls: ["https://huggingface.co/papers/2610.30181"]
summary: "The OpenDS task and OpenDS-World benchmark have been introduced to evaluate deep search agents in real-world web environments. The benchmark consists of 200 challenging tasks requiring agents to autonomously explore the live web, integrate evidence from multiple sources, and determine unique factual answers, with human verification ensuring evidence validity and answer uniqueness."
---

# Deep Search in Real\-World Web Environments

> [Open the canonical story](<https://aidr.today/96a0b349?lang=en>)

**Published:** 2026-10-23T00:00:00.000Z
**Category:** Data
**Topics:** agent, data\-engineering

## Summary

The OpenDS task and OpenDS\-World benchmark have been introduced to evaluate deep search agents in real\-world web environments\. The benchmark consists of 200 challenging tasks requiring agents to autonomously explore the live web, integrate evidence from multiple sources, and determine unique factual answers, with human verification ensuring evidence validity and answer uniqueness\.

## Sources

- [Story source](<https://huggingface.co/papers/2610.30181>)