Architecture choice should start with the problem shape, not with the newest model name.
The practical workflow is boring in the useful way: identify the input structure, define the output, list the constraints, then choose the simplest family that matches the bottleneck.
Start With Input Structure
Ask what structure already exists.
If the input is a grid or image, spatial models have a natural advantage. If it is a graph, message passing may be a strong fit. If it is a stream, temporal models deserve attention. If it is a token sequence with rich long-range relationships, attention becomes attractive. If it is a latent representation for generation, generative architecture choices dominate.
Do not erase structure too early. A model that ignores useful structure must relearn it from data.
Define The Output Shape
A classifier, an embedding model, a dense segmentation map, a generated image, and an autoregressive assistant have different needs.
If the output is a class or score, the architecture can focus on representation and decision boundaries. If the output is dense, spatial detail matters. If the output is a sequence, ordering matters. If the output is a sample, distribution modeling and sampling behavior matter.
The output tells you what the model must preserve.
List The Constraints
Constraints turn a good architecture on paper into a bad production choice.
Latency, memory, data volume, context length, sampling cost, interpretability, deployment hardware, and maintenance all matter. A huge attention model may be unnecessary for local structure. A cheap temporal model may be wrong if the task needs arbitrary comparisons. A diffusion model may be high quality but too slow without distillation or caching.
Add Mechanisms Last
Mechanisms should answer a bottleneck.
Add cross-attention when one representation must condition another. Add MoE when dense capacity is the cost problem. Add sparse attention when full attention is the limiting factor. Add gating when state control matters. Add normalization and residual paths because deep models need stable signal flow.
Mechanisms are not badges. They are tools.
A Useful Selection Rule
Choose the simplest family whose structural bias matches the job, then add mechanisms only where they pay rent.
That rule will not make architecture selection effortless, but it keeps the discussion grounded. It moves the team away from model-name shopping and toward the engineering question that matters: what computation pattern should do the main work?