RGAO: Adaptive Multi-Agent Code Generation with Budget Conservation
A recent publication on arXiv (2605.05657) presents Retrieval-Guided Adaptive Orchestration (RGAO), a multi-agent LLM system designed for code generation that determines orchestration topologies based on the complexity of the code. Operating within the Code-Agent framework, RGAO employs sub-agents that adhere to formal contracts characterized by six-dimensional budget vectors. The primary innovation lies in merging complexity-conditioned LLM routing with formal resource algebras to ensure verifiable budget conservation during dynamic topology selection. Before selecting a topology, the system derives a structural complexity vector from a hierarchical code index, tackling the routing challenge where the optimal topology is contingent on code structure. This research is significant for AI-enhanced software engineering and multi-agent coordination.
Key facts
- Paper arXiv:2605.05657 introduces RGAO
- RGAO selects orchestration topology based on code complexity
- Operates within Code-Agent multi-agent framework
- Sub-agents governed by formal contracts with six-dimensional budget vectors
- Combines complexity-conditioned LLM routing and formal resource algebras
- Achieves provable budget conservation under dynamic topology selection
- Extracts structural complexity vector from hierarchical code index
- Addresses routing problem in multi-agent code generation
Entities
Institutions
- arXiv