Does ONNX aim to serve as a human-readable and human-maintainable network description for experimentation and research, or does ONNX aim to serve only as a model export interchange format (primarily to be machine-interpreted)?

This could serve very well in the latter case, but would be quite problematic in the former -- where conditionals, for loops, etc. are essential to a compact and readable representation of many network architectures.

Reading a static neural network architecture descriptor of a very deep neural network is akin to reading assembly code from a compiler that does loop unrolling. Static network descriptions work great when pushing a model to production, but are much less suitable for the research and development stages (IMO).