AgenticSZZ reframes bug-inducing commit identification as temporal knowledge graph search navigated by an LLM agent, reporting F1 scores of 0.47-0.79 and up to 34% improvement over prior SZZ methods on three datasets.
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A hierarchical GNN-RL framework for joint beamforming and trajectory optimization in multi-UAV systems outperforms baselines in sum rate, convergence, and generalization.
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AgenticSZZ: Temporal Knowledge Graph-Guided Agentic Bug-Inducing Commit Identification
AgenticSZZ reframes bug-inducing commit identification as temporal knowledge graph search navigated by an LLM agent, reporting F1 scores of 0.47-0.79 and up to 34% improvement over prior SZZ methods on three datasets.
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Two-Layer Reinforcement Learning-Assisted Joint Beamforming and Trajectory Optimization for Multi-UAV Downlink Communications
A hierarchical GNN-RL framework for joint beamforming and trajectory optimization in multi-UAV systems outperforms baselines in sum rate, convergence, and generalization.