arXiv:2606.00926v1 Announce Type: cross Abstract: Mechanistic studies of sequence models often treat layerwise state encodings as architectural traits: recurrent models concentrate readable state, attention-based models distribute it. We find that the same architecture reverses this profile when the task changes. Across Transformers, Mamba, Mamba-2, LSTMs, and GRUs, Parity is concentrated late in Mamba and the recurrent baselines and built gradually by Transformer; on bounded-depth Dyck-k the pattern flips. The same flip appears in fine-tuned Mamba-130M and Pythia-160M, and the Pythia Dyck bot
The proliferation of various sequence model architectures, including Mamba and Transformers, has led to increased mechanistic research into their internal workings and state encoding at this time.
Understanding how different sequence models encode information based on task structure is critical for developing more efficient, reliable, and interpretable AI systems, influencing future architectural choices and training methodologies.
The previous assumption that layerwise state encoding is purely an architectural trait is now questioned, revealing a deeper dependency on the task itself across diverse model types.
- · AI researchers
- · ML framework developers
- · Model interpretability tools
- · AI development relying on simplistic architectural assumptions
- · Black-box model approaches
More sophisticated model design principles will emerge, taking into account task-dependent state encoding.
This could lead to domain-specific or task-adaptive model architectures that are considerably more efficient.
Improved understanding of internal representations might accelerate progress in AI safety and alignment by enabling better control over model behavior.
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Read at arXiv cs.CL