Determine how prediction targets interact with LSC topology and feature-quality curves

Determine how the interaction between the diffusion backbone’s prediction target, the skew of its vanilla quality-layer curve, and the topology of long skip connections affects representation quality and generation performance in DiT models.

Background

The paper compares DiT models trained with ε-, v-, and x-prediction objectives and examines how long skip connections alter their quality-layer curves, where each curve records the discriminative quality of features extracted at different depths. The authors observe that the prediction target can shift the peak position of the quality-layer curve and that skewing the LSC topology to match the vanilla peak position can improve results in some settings.

Although these empirical relationships are reported, the mechanism governing the interaction among prediction type, quality-layer-curve skew, and LSC topology is not established. Understanding this interaction would clarify how LSC architectures should be designed for different diffusion objectives.

References

Nevertheless, how this interaction actually works remains an open question.

— Why DiT Models Underperform as Representation Learners without Long Skip Connections  (2610.13344 - Meng et al., 9 Oct 2026) in Appendix, Section 'Effect of Backbone Prediction Type' (label: sec:app-evx)

Nevertheless, since the training process is highly complex, we cannot say for sure from the results that diffusion noises are more important than high-frequency semantics or vice versa.

— Why DiT Models Underperform as Representation Learners without Long Skip Connections  (2610.13344 - Meng et al., 9 Oct 2026) in Section 'Diffusion Noise Bypassing Hypothesis', subsection 'Hypothesis Validation'

Unfortunately, we were not able to effectively augment existing pre-trained DiT models with LSCs, which has been found to be very difficult (\Cref{sec:add-lsc}).

— Why DiT Models Underperform as Representation Learners without Long Skip Connections  (2610.13344 - Meng et al., 9 Oct 2026) in Conclusion and Limitation, Section 'Attempts to Add LSCs to Pre-Trained DiTs' (label: sec:add-lsc)