AI RESEARCH
Bridging the Evaluation Gap: Standardized Benchmarks for Multi-Objective Search
arXiv CS.AI
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ArXi:2603.24084v1 Announce Type: new Empirical evaluation in multi-objective search (MOS) has historically suffered from fragmentation, relying on heterogeneous problem instances with incompatible objective definitions that make cross-study comparisons difficult. This standardization gap is further exacerbated by the realization that DIMACS road networks, a historical default benchmark for the field, exhibit highly correlated objectives that fail to capture diverse Pareto-front structures. To address this, we.