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HyWe framework unifies web search and interaction for AI agents

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced HyWe, a novel framework designed to unify information gathering and page interaction capabilities within a single AI model for complex web tasks. This framework utilizes dedicated prompts and tool sets for sub-goal execution, with explicit switching mechanisms and shared history allowing modes to build upon each other's findings. A new benchmark, HyWeBench, comprising 100 human-written tasks, was developed to evaluate hybrid task completion, where HyWe-35B achieved a 47.0% success rate, significantly outperforming existing specialized agents. AI

IMPACT This research could lead to more capable AI agents that can perform complex, multi-step tasks on the web, improving automation and user assistance.

RANK_REASON The cluster describes a new research paper introducing a novel framework and benchmark for AI agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

HyWe framework unifies web search and interaction for AI agents

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The cluster describes a new research paper introducing a novel framework and benchmark for AI agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Zijian Cai, Haowei Liu, Kailai Chen, Lin Peng, Cong Wan, Ziqi Zhang, Chunfeng Yuan, Bing Li, Weiming Hu, Haiyang Xu ·

    HyWe: Unifying Search and Operation for Real-World Hybrid Web Tasks

    arXiv:2610.16319v1 Announce Type: new Abstract: Real-world hybrid web tasks need both information gathering and page interaction, yet search-based and operation-based agents are developed and evaluated separately. To bridge this gap, we introduce HyWe, a framework that unifies bo…

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