ParityFuzz finds 64 new inconsistencies across six Solidity compilers by combining fine-grained mutation rules with reinforcement learning for differential testing.
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SSPT turns space-syntax integration metrics into post-training feedback signals that improve public-space dominance and functional hierarchy in AI-generated residential floor plans.
PF-CD3Q uses online particle filtering to estimate fatigue parameters and constrains a deep Q-learning agent to solve fatigue-aware human-robot task planning as a CMDP.
citing papers explorer
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ParityFuzz: Finding Inconsistencies across Solidity Compilers via Fine-Grained Mutation and Differential Analysis
ParityFuzz finds 64 new inconsistencies across six Solidity compilers by combining fine-grained mutation rules with reinforcement learning for differential testing.
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Space Syntax-guided Post-training for Residential Floor Plan Generation
SSPT turns space-syntax integration metrics into post-training feedback signals that improve public-space dominance and functional hierarchy in AI-generated residential floor plans.
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Safe reinforcement learning with online filtering for fatigue-predictive human-robot task planning and allocation in production
PF-CD3Q uses online particle filtering to estimate fatigue parameters and constrains a deep Q-learning agent to solve fatigue-aware human-robot task planning as a CMDP.