ARC: Leveraging Compositional Representations for Cross-Problem Learning on VRPs

• URL: http://arxiv.org/abs/2512.18633v1
• Date: Sun, 21 Dec 2025 08:06:27 GMT
• Title: ARC: Leveraging Compositional Representations for Cross-Problem Learning on VRPs
• Authors: Han-Seul Jeong, Youngjoon Park, Hyungseok Song, Woohyung Lim,
• Abstract summary: ARC (Attribute Representation via Compositional Learning) is a cross-problem learning framework that learns disentangled attribute representations.<n> ARC achieves state-of-the-art performance across in-distribution, zero-shot generalization, few-shot adaptation, and real-world benchmarks.
• Score: 4.428052252120204
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vehicle Routing Problems (VRPs) with diverse real-world attributes have driven recent interest in cross-problem learning approaches that efficiently generalize across problem variants. We propose ARC (Attribute Representation via Compositional Learning), a cross-problem learning framework that learns disentangled attribute representations by decomposing them into two complementary components: an Intrinsic Attribute Embedding (IAE) for invariant attribute semantics and a Contextual Interaction Embedding (CIE) for attribute-combination effects. This disentanglement is achieved by enforcing analogical consistency in the embedding space to ensure the semantic transformation of adding an attribute (e.g., a length constraint) remains invariant across different problem contexts. This enables our model to reuse invariant semantics across trained variants and construct representations for unseen combinations. ARC achieves state-of-the-art performance across in-distribution, zero-shot generalization, few-shot adaptation, and real-world benchmarks.

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