AI RESEARCH
Refine Now, Query Fast: A Decoupled Refinement Paradigm for Implicit Neural Fields
arXiv CS.LG
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ArXi:2602.15155v3 Announce Type: replace Implicit Neural Representations (INRs) have emerged as promising surrogates for large 3D scientific simulations due to their ability to continuously model spatial and conditional fields, yet they face a critical fidelity-speed dilemma: deep MLPs suffer from high inference cost, while efficient embedding-based models lack sufficient expressiveness. To resolve this, we propose the Decoupled Representation Refinement (DRR) architectural paradigm.