Neural asset authoring and neural rendering have emerged as largely disjoint threads: one generates digital assets using neural networks for traditional graphics pipelines, while the other develops neural renderers that map conventional assets to images. However, the joint design of the asset representation and renderer remains largely unexplored. We argue that coupling them can unlock an end-to-end learnable graphics stack with benefits in fidelity, consistency, and efficiency. In this paper, we explore this possibility with NeAR: a Coupled Neural Asset–Renderer Stack. On the asset side, we build on Trellis-style Structured 3D Latents and introduce a lighting-homogenized neural asset: from a casually lit input, a rectified-flow backbone predicts a Lighting-Homogenized SLAT that encodes geometry and intrinsic material cues in a compact, view-agnostic latent. On the renderer side, we design a lighting-aware neural renderer that uses this neural asset, along with explicit view embeddings and HDR environment maps, to produce lighting-aware renderings in realtime. We validate NeAR on four tasks: (1) G-buffer–based forward rendering, (2) random-lit single-image reconstruction, (3) unknown-lit single-image relighting, and (4) novel-view relighting, where our coupled stack surpasses state-of-the-art baselines in quantitative metrics and perceptual quality. We hope this coupled asset-renderer perspective inspires new graphics stacks that view neural assets and renderers as co-designed components instead of independent ones.