STAR-Teaming uses a Strategy-Response Multiplex Network inside a multi-agent framework to organize attack strategies into semantic communities, delivering higher attack success rates on LLMs at lower computational cost than prior methods.
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Regulation Zero 2 applies hierarchical MCTS with a local proposal engine and FPFS reward estimation to optimize sequences of flow regulations in ATFM, outperforming flight-centric baselines while limiting network impact.
CluProp reframes varied-density clustering as deterministic label propagation over neighborhood graphs for improved robustness and scalability.
citing papers explorer
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STAR-Teaming: A Strategy-Response Multiplex Network Approach to Automated LLM Red Teaming
STAR-Teaming uses a Strategy-Response Multiplex Network inside a multi-agent framework to organize attack strategies into semantic communities, delivering higher attack success rates on LLMs at lower computational cost than prior methods.
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Regulation Zero 2: A Flow-Centric Sequential Regulation Planning Framework to Counter Regulation Cascading in Pre-tactical Air Traffic Flow Management
Regulation Zero 2 applies hierarchical MCTS with a local proposal engine and FPFS reward estimation to optimize sequences of flow regulations in ATFM, outperforming flight-centric baselines while limiting network impact.
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Towards Robust and Scalable Density-based Clustering via Graph Propagation
CluProp reframes varied-density clustering as deterministic label propagation over neighborhood graphs for improved robustness and scalability.