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WebChallenger achieves performance close to proprietary systems on open-source models through PageMem structured page representation and three cognitive mechanisms, with significantly reduced costs
Section 01
WebChallenger achieves performance close to proprietary systems on open-source models through PageMem structured page representation and three cognitive mechanisms, with significantly reduced costs. The framework has been open-sourced, providing a reusable technical foundation for the development of universal Web agents.
Section 02
Autonomous web navigation is a core challenge for LLM agents. Current systems rely on proprietary models with excessively high costs; existing architectures lack three key cognitive advantages of humans:
Section 03
Structured pages built from DOM, features:
Section 04
Performance of open-source models on authoritative benchmarks:
| Benchmark | Score | Description |
|---|---|---|
| WebArena | 56.3% | Real website tasks |
| VisualWebArena | 48.7% | Visual enhancement tasks |
| Online-Mind2Web | 51.0% | Multi-step tasks |
| WorkArena | 70.9% | Office scenario tasks |
The performance is close to proprietary systems, with lower costs and cross-site generalization without adapters
Section 05
Key principles:
Section 06
Application scenarios:
Section 07
Already open-sourced (GitHub), promoting:
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