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@omarsar0

RT @dair_ai: How can graphs improve coding agents? Multi-agent systems can boost code generation, but fixed interaction topologies don't adapt to task difficulty. This research introduces AgentConductor, a system where an orchestrator agent uses RL to dynamically generate task-adapted interaction topologies based on inferred agent roles and difficulty levels. A topological density function that captures communication-aware characterizations of multi-agent interactions, plus difficulty interval partitioning that prevents excessive pruning and provides precise topology control. Across five code datasets, AgentConductor achieves up to 14.6% improvement in pass@1 accuracy while reducing density by 13% and token costs by 68%. The great benefit of this approach is better performance with lower costs. Dynamic agent coordination is more efficient than static workflows for complex code generation. Paper: https://t.co/BypJZfU49q Learn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c

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