LC-MAPF uses multi-round local communication between neighboring agents in a pre-trained model to outperform prior learning-based MAPF solvers on diverse unseen scenarios while preserving scalability.
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Systematic experiments show that text decomposition methods and privacy budget allocation strategies produce significantly different privacy-utility trade-offs even under comparable total epsilon budgets.
citing papers explorer
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Learning to Communicate Locally for Large-Scale Multi-Agent Pathfinding
LC-MAPF uses multi-round local communication between neighboring agents in a pre-trained model to outperform prior learning-based MAPF solvers on diverse unseen scenarios while preserving scalability.
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A Systematic Exploration of Text Decomposition and Budget Distribution in Differentially Private Text Obfuscation
Systematic experiments show that text decomposition methods and privacy budget allocation strategies produce significantly different privacy-utility trade-offs even under comparable total epsilon budgets.
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