aiqbench analysis
This research introduces K-GAT, a neuro-symbolic framework that explicitly integrates external evidence into the generation of multi-agent collaboration topologies to prevent structure-knowledge misalignment. By treating topology design as a knowledge-conditioned learning problem, the system achieves significant accuracy gains on expert-level benchmarks while reducing computational token usage by more than half compared to standard LLM baselines. The work represents a meaningful advancement in making autonomous agent systems more efficient and reliable for knowledge-intensive tasks.
When Evidence Shapes Collaboration: Knowledge-Conditioned Topology Generation for Multi-Agent Systems
arXiv:2608.27984v1 Announce Type: new Abstract: Multi-Agent Systems (MAS) have recently moved from static workflows toward dynamically generated collaboration topologies. However, existing topology generation methods rely primarily on the parametric knowledge of large language models, with external search or retrieval used only as a reactive tool rather than an explicit determinant of collaboration structure. This leads to structure-knowledge misalignment, where systems exhibit redundant interactions or insufficient verification in knowledge-intensive tasks. We propose K-GAT (Knowledge-Guided Agent Topology Generator), a neuro-symbolic framework that formulates collaboration topology design as a knowledge-conditioned structure learning problem, integrating external evidence directly into autoregressive graph generation. Extensive experiments on knowledge-intensive benchmarks demonstrate K-GAT's efficiency and effectiveness: notably on the expert-level GPQA dataset, K-GAT outperforms the LLM-Debate baseline by a substantial margin of +15.7% in accuracy, while consuming less than half the computational tokens.
Abstract:Multi-Agent Systems (MAS) have recently moved from static workflows toward dynamically generated collaboration topologies. However, existing topology generation methods rely primarily on the parametric knowledge of large language models, with external search or retrieval used only as a reactive tool rather than an explicit determinant of collaboration structure. This leads to structure-knowledge misalignment, where systems exhibit redundant interactions or insufficient verification in knowledge-intensive tasks. We propose K-GAT (Knowledge-Guided Agent Topology Generator), a neuro-symbolic framework that formulates collaboration topology design as a knowledge-conditioned structure learning problem, integrating external evidence directly into autoregressive graph generation. Extensive experiments on knowledge-intensive benchmarks demonstrate K-GAT's efficiency and effectiveness: notably on the expert-level GPQA dataset, K-GAT outperforms the LLM-Debate baseline by a substantial margin of +15.7% in accuracy, while consuming less than half the computational tokens.
Artificial Intelligence (cs.AI)
arXiv:2608.27984 [cs.AI]
(or arXiv:2608.27984v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.27984 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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