Evaluate cross-architecture applicability and develop generalizable methods

Determine how well existing interpretability methods (including sparse dictionary learning and circuit analysis) apply to architectures such as diffusion models, vision transformers, RWKV, and state space models, and develop techniques that generalize effectively across architectures.

Background

The paper surveys alternative architectures that are increasingly competitive, noting early signs of transfer for some techniques but no comprehensive evidence of broad applicability.

The authors explicitly call out the need for universal approaches and cross-architecture validation to future‑proof interpretability research as model designs evolve.

References

Assessing how well interpretability methods apply to architectures beyond those for which they were developed, and whether we can develop techniques that generalize effectively across architectures remain open questions.

— Open Problems in Mechanistic Interpretability  (2501.16496 - Sharkey et al., 27 Jan 2025) in Mechanistic interpretability on a broader range of models and model families (Section 3.6)

We do not examine whether similar cancellations arise in other architectures, such as diffusion transformers or state-space models, where residual streams and update branches may interact differently, and note that strong residual cancellation may have broader implications for training dynamics and generalization.

— ResLRP: The Role of Residual Cancellation in Attribution Instability in Vision Transformers  (2609.17152 - Berend et al., 15 Sep 2026) in Conclusion, paragraph “Limitations {content} Outlook.”

Current limitations include the absence of spectral localization, mechanistic evaluation restricted to AMP tasks with ESM2-8M, and unresolved interpretations of non-DC components. Local time-frequency or adaptive spectral methods [51, 52], controlled sequence interventions, and structure-aware validation may clarify these components and test whether the observed mechanisms generalize across modalities.

— FLaG: Frequency-Domain Latent-attention Gated Pooling for Token Aggregation  (2609.00831 - Li et al., 1 Sep 2026) in Section 5, Conclusions