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Z-Space: A Multi-Agent Tool Orchestration Framework for Enterprise-Grade LLM Automation

Authors: Qingsong He, Jing Nan, Jiayu Jiao, Liangjie Tang, Xiaodong Xu, Mengmeng Sun, Qingyao Wang, Minghui Yan

Published: 2025-11-23

arXiv ID: 2511.19483v1

Added to Library: 2025-11-26 03:00 UTC

📄 Abstract

Large Language Models can break through knowledge and timeliness limitations by invoking external tools within the Model Context Protocol framework to achieve automated execution of complex tasks. However, with the rapid growth of enterprise-scale MCP services, efficiently and accurately matching target functionalities among thousands of heterogeneous tools has become a core challenge restricting system practicality. Existing approaches generally rely on full-prompt injection or static semantic retrieval, facing issues including semantic disconnection between user queries and tool descriptions, context inflation in LLM input, and high inference latency. To address these challenges, this paper proposes Z-Space, a data-generation-oriented multi-agent collaborative tool invocation framework Z-Space. The Z-Space framework establishes a multi-agent collaborative architecture and tool filtering algorithm: (1) A structured semantic understanding of user queries is achieved through an intent parsing model; (2) A tool filtering module (FSWW) based on fused subspace weighted algorithm realizes fine-grained semantic alignment between intents and tools without parameter tuning; (3) An inference execution agent is constructed to support dynamic planning and fault-tolerant execution for multi-step tasks. This framework has been deployed in the Eleme platform's technical division, serving large-scale test data generation scenarios across multiple business units including Taotian, Gaode, and Hema. Production data demonstrates that the system reduces average token consumption in tool inference by 96.26\% while achieving a 92\% tool invocation accuracy rate, significantly enhancing the efficiency and reliability of intelligent test data generation systems.

🔍 Key Points

  • Introduction of a novel automated pipeline for generating psychologically-grounded multi-turn jailbreak datasets which produce 1,500 scenarios based on the Foot-in-the-Door principle.
  • Evaluation of seven models from three major LLM families under multi-turn and single-turn conditions revealing significant differences in contextual robustness, with GPT models showing higher vulnerabilities compared to Gemini 2.5 Flash and Claude 3 Haiku.
  • Establishment of a benchmark to measure Attack Success Rates (ASR) showing up to a 32% increase in vulnerability for GPT models when conversational history is included, suggesting the importance of context in model safety.
  • Detailed discussion of mitigation strategies including architectural changes, adversarial training, and detection mechanisms to bolster the robustness of LLMs against multi-turn conversational attacks.
  • Methodological validation of dataset generation with 98% agreement with human assessments, underpinning the reliability of the automated testing framework.

💡 Why This Paper Matters

This paper advances the understanding of vulnerabilities inherent in Large Language Models (LLMs) when subjected to multi-turn conversational attacks, which exploit psychological principles to bypass model safety mechanisms. By creating an automated pipeline for generating attacks and evaluating multiple LLMs, the authors not only highlight significant differences in model robustness but also propose practical solutions to enhance security. The findings emphasize the necessity of context-aware defenses in AI systems, which is fundamental for developing safer AI applications in sensitive environments.

🎯 Why It's Interesting for AI Security Researchers

AI security researchers would find this paper particularly relevant because it addresses a critical and emergent threat landscape in AI: the exploitation of conversational context to bypass safety measures. The automated generation of adversarial prompts based on psychological techniques provides an innovative method for evaluating model vulnerabilities at scale, which is essential for understanding and mitigating risks in AI deployments. Furthermore, the proposed defense mechanisms could inform future designs of safer LLM architectures, making the findings significant for researchers aiming to enhance the security and reliability of AI systems.

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