Many interpretability approaches focus on weights, circuits, or activation clusters. But what if we instead considered semantic misalignment as a runtime phenomenon, and tried to repair it purely at the prompt level?
Over the last year, I’ve been prototyping a lightweight semantic reasoning kernel — one that decomposes prompts not by syntax, but by identifying contradictions, redundancies, and cross-modal inference leaks.
It doesn’t retrain the model. It reshapes how the model “sees” the query.
Early tests show:
Reasoning success ↑ 42.1%
Semantic precision ↑ 22.4%
Output stability ↑ 3.6×
These were obtained using models ranging from GPT-2 to GPT-4.
I’m not claiming this solves alignment — but perhaps it opens a new axis: “Prompt-level interpretability” as a semantic protocol.
Full paper and implementation are open-source (Zenodo + GitHub). Happy to hear if anyone’s seen related work or philosophical precursors.
Can Semantic Compression Be Formalized for AGI-Scale Interpretability? (Initial experiments via an open-source reasoning kernel)
Many interpretability approaches focus on weights, circuits, or activation clusters.
But what if we instead considered semantic misalignment as a runtime phenomenon, and tried to repair it purely at the prompt level?
These were obtained using models ranging from GPT-2 to GPT-4.
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