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AI Research
arXiv CS.AI·May 28·8 min read
TCP-MCP: Landscape-Guided Co-Evolution of Prompts and Communication Topologies for Multi-Agent Systems
TL;DR
Multi-agent systems struggle to coordinate effectively because agent prompts and communication network structures are typically designed separately and remain static. TCP-MCP introduces a co-evolution method that simultaneously optimizes both agent prompts and the topology (network structure) of how agents communicate, guided by landscape analysis.
✦ Why It Matters
Engineers can improve multi-agent system performance by co-optimizing communication structure and agent instructions rather than tuning them separately.
Key Takeaways
- Explore TCP-MCP for co-evolving prompts and communication structures.
- Utilize Pareto-front diagnostics to balance performance and cost.
- Aim for reduced token usage in multi-agent communications.
- Test TCP-MCP on your specific benchmarks for performance evaluation.
How It Works
TCP-MCP operates by treating prompts and communication topologies as a unified genome, allowing them to evolve together. The framework starts with a landscape probe to assess the initial search space, guiding the optimization process.
It then employs Pareto-front diagnostics to adaptively explore solutions that optimize for task performance, minimize token costs, and manage structural complexity.