AgentsMedia Generation 🇨🇳 24.07.2026 10:02

New Agent 'Wenxiaobai 5 Pro' Solves AI's 'Memory Loss' in Long Content: Minimal Entry Barrier – Just Need to Type

Yuanshi TechnologyYuanshi Technology
The Wenxiaobai 5 Pro Agent, developed by Yuanshi Technology, addresses AI's memory issues in long-form content creation. It requires only a single instruction to generate long, stable, and verifiable outputs, making it suitable for writing reports, novels, and scripts. Its key features include a cross-modal file sandbox, task decomposition, and a traceable workspace.
Yuanshi Technology's Wenxiaobai 5 Pro is a new Agent designed to solve the 'memory loss' problem that AI models face when generating long content. Unlike other AI agents that require detailed back-and-forth, users only need to type a single command, even if it is vague, and the Agent starts working. It first creates a .brief to decompose the task into a to-do list, then generates content sequentially, ensuring consistency. The system supports input from multiple files (Word, PDF, Excel) and can theoretically output unlimited length content. For quality control, users can edit, annotate, or request rewrites directly in the generated content, and the Agent learns from these adjustments to align with user preferences. The product emphasizes three core strengths: length (handling long inputs and outputs), stability (maintaining logical consistency through a cross-modal file sandbox), and verifiability (keeping all drafts, sources, and processes traceable in the workspace). Yuanshi Technology has relevant research published in ACL 2025, and the DeepResearch Bench from the team was included in NVIDIA's AI-Q evaluation. Founder Li Yan describes the Agent as 'result-oriented delivery' – it handles all complex workflows behind the scenes so users only need to specify the desired output. Wenxiaobai 5 Pro is particularly suited for knowledge workers, exam candidates, and writers needing reliable, long-form content, but less for casual AI use.
Source: QbitAI 量子位 — original
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