Jointly synthesize high-dimensional prompt-learning components

Develop a Diffusion Meta-Prompting model that jointly synthesizes the higher-dimensional learned components of prompt-learning methods such as MaPLe and TAC, including projection matrices and task-aware pre-context modules, rather than synthesizing only their soft prompt parameters.

Background

The current Diffusion Meta-Prompting implementation generates only soft prompt parameters for prompt-learning methods that also contain high-dimensional auxiliary components, such as projection matrices or task-aware pre-context modules. These auxiliary parameters are kept fixed during inference because directly modeling them would require substantially greater computational resources and model capacity.

The paper identifies joint synthesis of these components as an unresolved direction and notes that compression or low-rank parameterization may be necessary to make the problem tractable. Solving it would extend Diffusion Meta-Prompting beyond prompt-only adaptation and could provide more complete meta-generation for modern prompt-learning architectures.

References

Extending DMP to jointly synthesize these higher-dimensional components would likely require compression or low-rank parameterization strategies, which we leave for future work.

— Diffusion Meta-Prompting and Steering for Generalizable Foundation Model Adaptation  (2610.11067 - Sridhar et al., 8 Oct 2026) in Appendix, Section “DMP for Classification,” paragraph “Composite classification with other prompt-learning methods”