A researcher has posted an unpeer-reviewed proof of an anytime computable Bayesian mixture of all computable measures on the Alignment Forum.
The author notes the work carries a high chance of typos and a small chance of errors, and has not undergone peer review.
What happened
A researcher posted an unpeer-reviewed proof on the Alignment Forum demonstrating the existence of an anytime computable Bayesian mixture of all computable measures. The post notes it was written entirely by the author and checked by someone named Fable.
Why it matters
This appears to be a theoretical computer science / AI alignment contribution addressing how to combine multiple computational probability models, though the post body provided does not explain the practical implications or why this result advances the field.
What to watch
The author flags the work as not peer-reviewed and acknowledges a high chance of typos and a small chance of errors, so independent verification is pending.
Ask the AI about this article →
The post is a theoretical contribution to the Alignment Forum, a community platform focused on AI alignment and safety research. The author has been transparent about the work's status: it is unpeer-reviewed and subject to revision. The mention of checking by Fable suggests some level of informal review, but the author emphasizes uncertainty by flagging both typos and the possibility of deeper errors. This transparency is typical of pre-publication technical posts in the AI safety community, where researchers often share results openly before formal peer review to gather feedback.
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