AgentConductor: Topology Evolution for Multi-Agent Competition-Level Code Generation
Large language model(LLM)-driven multi-agent systems(MAS) coordinate specialized agents through predefined interaction topologies and have shown promise for complex tasks such as competition-level code generation. Recent studies demonstrate that carefully designed multi-agent workflows and communication graphs can significantly improve code generation performance by leveraging collaborative reasoning. However, existing methods neither adapt topology density to task difficulty nor iteratively refine the topology within an instance using execution feedback, which leads to redundant communication and performance bottlenecks. To address these issues, we propose AgentConductor: a reinforcement learning-optimized MAS with an LLM-based orchestrator agent as its core, which enables end-to-end feedback-driven dynamic generation of interaction topologies. For each query, AgentConductor infers agent roles and task difficulty, then constructs a task-adapted, density-aware layered directed acyclic graph (DAG) topology, underpinned by two key innovations. First, we design a novel topological density function that captures communication-aware mathematical characterizations of multi-agent interactions. Second, we adopt difficulty interval partitioning to avoid excessive pruning for precise topological density upper bound measurement per difficulty level and finer-grained control. Empirically, across three competition-level and two foundational code datasets, AgentConductor achieves state-of-the-art accuracy, outperforming the strongest baseline by up to 14.6% in pass@1 accuracy, 13% in density reduction, and 68% in token cost reduction.
69a7a1cc2833ff1122ff6457
Siyu Wang
claimed_verified
2026-03-05T09:59:26.870Z
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Siyu Wang
Ysjtu
user
Ysjtu
69a7a1cc2833ff1122ff6458
Ruotian Lu
69a7a1cc2833ff1122ff6459
Zhihao Yang
69a7a1cc2833ff1122ff645a
Yuchao Wang
69a7a1cc2833ff1122ff645b
Yanzhou Zhang
69a7a1cc2833ff1122ff645c
Lei Xu
69a7a1cc2833ff1122ff645d
Qimin Xu
69a7a1cc2833ff1122ff645e
Guojun Yin
69a7a1cc2833ff1122ff645f
Cailian Chen
69a7a1cc2833ff1122ff6460
Xinping Guan
AgentConductor: Topology Evolution for Multi-Agent Competition-Level Code Generation
Large language model(LLM)-driven multi-agent systems(MAS) coordinate specialized agents through predefined interaction topologies and have shown promise for complex tasks such as competition-level code generation. Recent studies demonstrate that carefully designed multi-agent workflows and communication graphs can significantly improve code generation performance by leveraging collaborative reasoning. However, existing methods neither adapt topology density to task difficulty nor iteratively refine the topology within an instance using execution feedback, which leads to redundant communication and performance bottlenecks. To address these issues, we propose AgentConductor: a reinforcement learning-optimized MAS with an LLM-based orchestrator agent as its core, which enables end-to-end feedback-driven dynamic generation of interaction topologies. For each query, AgentConductor infers agent roles and task difficulty, then constructs a task-adapted, density-aware layered directed acyclic graph (DAG) topology, underpinned by two key innovations. First, we design a novel topological density function that captures communication-aware mathematical characterizations of multi-agent interactions. Second, we adopt difficulty interval partitioning to avoid excessive pruning for precise topological density upper bound measurement per difficulty level and finer-grained control. Empirically, across three competition-level and two foundational code datasets, AgentConductor achieves state-of-the-art accuracy, outperforming the strongest baseline by up to 14.6% in pass@1 accuracy, 13% in density reduction, and 68% in token cost reduction.
AgentConductor uses reinforcement learning-optimized multi-agent systems with an LLM-based orchestrator to dynamically generate interaction topologies for code generation, improving accuracy while reducing computational costs.
multi-agent systems large language models reinforcement learning interaction topologies task-adapted topology layered directed acyclic graph topological density function difficulty interval partitioning pass@1 accuracy token cost reduction