Editing Openai/693c0f4f-255c-8008-92e9-0cd44c6d6226 (section)

==== So, to answer your question directly: ====
* Yes, there is a conceptual connection: both papers say “don’t just directly train new things for each case; instead, learn a structured generator (over data or weights) and then adapt via inversion or generation.”
* The compositional paper is more theoretical, giving a clean story about why structure on the generator side leads to compositional OOD generalization and why encoder-only approaches are under-constrained. 2512.08854v1
* DnD is more engineering / meta-learning, showing empirically that a generator over LoRA parameters conditioned on prompts can replace gradient-based adapter training in many scenarios. 2506.16406v1

But DnD doesn’t yet have the kind of identifiability / “right inverse” guarantees the first paper has. If you’re thinking about bridging the two, I’d focus on:
* designing structured parameter generators with explicit factorization,
* and asking whether you can prove “good OOD behavior over tasks” in the same spirit as their compositional generalization over slot combinations.

If you want, we can try to sketch a concrete toy setting where:
* tasks are defined by combinations of a few discrete “factors”, and
* a DnD-style hypernetwork with an FintF_{\text{int}}Fint-like constraint over LoRA space could provably generalize to unseen task combinations.