Determine generalizability of mechanistic interpretability findings across model families

Determine the extent to which mechanistic interpretability findings derived from CNN-based image models, BERT-based text models, and GPT-based language models generalize to other architectures and application contexts.

Background

Most mechanistic interpretability research has focused on CNNs and transformer families (BERT, GPT). Whether conclusions from these families transfer to alternative architectures and modalities is unclear.

As future frontier models diversify (e.g., multimodal or non-transformer architectures), understanding generalizability is important to ensure that methods and insights remain relevant and effective.

References

The degree of generalizability of these findings to other models and contexts is currently a somewhat open question.

— 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)

Whether it generalizes to other frontier mixture-of-experts models, and what carries the refusal that resists, remain unmeasured.

— How Fragile Is Safety Alignment at Frontier Scale? A Single-Direction Attack on a 320B MoE  (2609.09793 - Shi et al., 9 Sep 2026) in Conclusion; reiterated in Section 'Limitations', paragraph 'A single model'

The generalizability of the UED to non-mechanical domains, which typically require different model types for investigation, remains to be validated.

— Unified Embodiment Description for functional evaluation of used components in circular manufacturing systems  (2608.16206 - Hemmerich et al., 17 Aug 2026) in Section 5.2, subsection “Positioning and limitations”

Several questions remain open. The middle layers may be linear, as the last layer of Pythia-410m is, but an instrument with finer resolution is needed to tell. The cost gap at the last layer of Pythia-160m, absent in Pythia-410m, may close with scale or may just be an artifact of Pythia's models. Finally, nothing in our results says whether the agreement with optimal transport is a property of LLMs or of trained transformers in general.

— Measuring Optimal Transport in Transformer Depth  (2609.00748 - Quemy, 1 Sep 2026) in Section 7, “Conclusion”

Consequently, it remains unexplored whether this distribution-aware neuron identification generalizes effectively to models employing standard MLP architectures that lack this bifurcated signal structure.

— Distribution-aware Language Neuron Identification in Multilingual Large Language Models  (2609.10993 - Kim et al., 10 Sep 2026) in Limitations, paragraph “Architectural restriction to GLU”