Universal syntactic priors emerge from network growth without linguistic data

The paper claims syntactic structure probabilities can be derived from network growth principles alone, without empirical language data. The critical qualifier: this generates priors that match observed patterns, but doesn't explain why humans or models actually learn those priors during training.

This connects directly to the Mandarin reduplication work from earlier today, which showed that distributional semantics recover latent grammatical structure. Both papers argue that linguistic patterns aren't arbitrary but reflect deeper organizational principles. However, where the reduplication study validates embeddings as tools for reverse-engineering existing structure, this paper proposes structure emerges from first principles. The gap matters: one shows embeddings capture what's there; the other claims what's there follows from math, not data. If true, it reshapes what we mean by 'learning' in both cognitive and artificial systems.

If researchers can show that LLMs initialized with these network-growth priors require measurably less linguistic data to reach performance parity with standard initialization, that would validate the framework's practical relevance. Without that experiment, the work remains theoretically elegant but disconnected from how actual language systems train.