LLM blackboard architecture beats master-slave multi-agent setups on data discovery benchmarks
mrdrozdov · x · 2026-09-22
Salemi et al. (arXiv:2510.01285) propose an LLM multi-agent paradigm inspired by the classic blackboard architecture: a central agent posts requests to a shared blackboard and autonomous subordinate agents volunteer based on their own capabilities, removing the need for a rigid central controller. It substantially outperforms strong baselines on KramaBench and modified DSBench/DA-Code data discovery benchmarks.
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