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Can Semantic Compression Be Formalized for AGI-Scale Interpretability? (Initial experiments via an open-source reasoning kernel)

by onestardao
18th Jul 2025
1 min read
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This post was rejected for the following reason(s):

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    If you want to try again, I recommend writing something short and to the point, focusing on your strongest argument, rather than a long, comprehensive essay. (This is fairly different from common academic norms.) We get lots of AI essays/papers every day and sadly most of them don't make very clear arguments, and we don't have time to review them all thoroughly. 

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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.

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