AI RESEARCH
Rethinking Positional Encoding for Neural Vehicle Routing
arXiv CS.AI
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ArXi:2605.11910v1 Announce Type: new Transformer-based models have become the dominant paradigm for neural combinatorial optimization (NCO) of vehicle routing problems (VRPs), yet the role of positional encoding (PE) in these architectures remains largely unexplored. Unlike natural language, where tokens are uniformly spaced on a line, routing solutions exhibit several properties that render standard NLP positional encodings inadequate.