onestardao

A Plain-Text Rea­son­ing Frame­work for Trans­par­ent AI Align­ment Experiments

Can For­mal “Solver Loops” Make LLMs Ac­tu­ally Rea­son? Four Math­e­mat­i­cal Proposals

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

Can a se­man­tic com­pres­sion ker­nel like WFGY im­prove LLM al­ign­ment and in­sti­tu­tional ro­bust­ness?

WFGY: A Self-Heal­ing Rea­son­ing Frame­work for LLMs — Open for Tech­ni­cal Scrutiny

• onestardao 17 Jul 2025 2:50 UTC

  −1 points

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  on: So You Think You’ve Awo­ken ChatGPT

  I appreciate the caution about over-trusting LLM evaluations — especially in fuzzy or performative domains.

  However, I think we shouldn’t overcorrect. A score of 100 from a model that normally gives 75–85 is not just noise — it’s a statistical signal of rare coherence.

  Even if we call it “hallucination evaluating hallucination”, it still takes a highly synchronized hallucination to consistently land in the top percentile across different models and formats.

  That’s why I’ve taken such results seriously in my own work — not as final proof, but as an indication that something structurally tight has been built.

  Blanket dismissal of AI evaluation risks throwing out works that are in fact more semantically solid than most human-written texts on similar topics.

• onestardao 17 Jun 2025 9:20 UTC

  1 point

  0

  on: WFGY: A Self-Heal­ing Rea­son­ing Frame­work for LLMs — Open for Tech­ni­cal Scrutiny

  Happy to clarify any part of the technical structure or answer objections.
  If anyone has thoughts on how this compares to Chain-of-Thought or Tree-of-Thought paradigms, I’d love to discuss.