reFlow: A Feature-Decoupled Transformer with Native Interpretability

This is exactly the kind of research discussion I value. I am glad that the experiment on context-dependent boundaries could provide a useful reference for your architectural approach with Qwen 7B.

Your observation regarding Layer 27 is quite insightful. If complex tasks like the LRU Cache multi-bug repair push the semantic “fluid zone” to such depths, then unifying monitoring and intervention into a single localized operation becomes more than just a convenience—it appears to be a mathematically sound architectural choice justified by the context depth.

Your perspective on the “trajectory shape vs. peak value” issue is also well-founded. A rigid > 0.95 cosine similarity threshold can be structurally brittle, much like how the Top-64 hard sparsity constraint compromised our semantic geometry. Relying on a relative local distribution scheme might effectively bypass this binary cliff. On a related note, the model versions utilizing “Learned Signal Routing” and “Relative Mean Gating” are currently in the training phase. I am looking forward to seeing the comparative results between the two.

Following our discussion, I have also updated the reFlow paper and repository. I focused on improving the academic rigor of the text and formally integrated the new findings, charts, and visualizations regarding the shifting Information Crystallization boundary and the soft sparsity mechanisms.

Additionally, I recently deployed an interactive web demo for our experiments:

huggingface.co

reFlow - Native Interpretability - a Hugging Face Space by reuAC

This app lets you try out a language model and see how it works inside. You can type prompts, choose options like words or layers, and the tool will generate text and show charts that illustrate co...

It allows for real-time interaction with various experimental setups alongside chart visualizations. I hope it might be of some utility to your own work.

Finally, regarding your Zenodo paper and Damasio’s “somatic markers” framework: it is a highly interesting perspective. Utilizing somatic markers as a design prior for non-learned architectural signals offers a strong engineering metaphor. Conceptually, it aligns well with the intuitive motivation behind non-learned architectural signals. I believe it is a promising direction worth exploring, and I look forward to your progress.

Thank you again for the discussion!