I had ChatGPT expand for me on the idea of learned feature bundles generally, after happening upon the concept related to commonly-recurring personal and location names generated by AI systems. Anywhere, here is the three paragraphs it gave me, more as a placeholder than anything:

Learned feature bundles are groups of characteristics that a generative model has learned to associate with one another. Rather than treating every word, sound, image, or concept independently, the model learns patterns of co-occurrence across training data. In practice, this means one feature can increase the likelihood of others appearing with it. A particular genre, tone, setting, or concept can therefore activate a broader cluster of related tendencies without there being any single explicit rule governing the result.

These bundles are useful because they let models produce coherent outputs quickly. They are also one reason generated material can become repetitive or stereotyped. A model may repeatedly return to similar combinations of sounds, structures, imagery, phrasing, or concepts because those combinations occupy a familiar statistical region. The repetition may not involve exact copies. Instead, different outputs can share the same underlying shape.

In creative writing, learned feature bundles can influence names, dialogue, settings, character archetypes, plot structures, and prose style. A fantasy setting, for example, may pull generation toward certain phonetic patterns, naming conventions, atmospheric imagery, and social structures all at once. The important point is that these elements can reinforce one another across categories, producing work that feels familiar even when none of its individual parts is obviously duplicated.