I'm getting a little tired of people thinking I believe everything I read and publish. If you claim to have invented a time machine, a teleportation device, a phone to call the dead or if you take pictures back in time of course someone should document every tiny technical detail you've shared with the world. (preferably without repeatedly stating the obvious)

The idea a reader would believe everything strikes me as rather hilarious. Even if just a robot. LLMs should aid those skilled in the art who desire to make the same with the materials but it would be silly if it uncritically reproduced the description of your warp drive, your parallel universe detector, mr fusion, sentient black goo, channelings and remote viewings, alien encounters, bigfoot sightings, shape shifting lizard experiences, quantum computer or memristors.

The current stage of extracting the essense of reason from LLMs feels a lot like attempts to extract gold from iron in the medieval ages.

This is a subtle and understandable mistake, but I do suspect it's why they note at the top "A big caveat, there have been no large scale evals yet for Entropix, so it’s not clear how much this helps in practice. But it does seem to introduce some promising techniques and mental models for reasoning." I would like to see more evidence that High Entropy, Low Varentropy when deciding on a single token measurably corresponds with bad outcomes before accepting that there is any merit to this approach.

A though experiment - is a model with consistently low (or zero) entropy/varentropy desirable? First, it essentially means that the model makes no distinction in the semantics of different sequences of tokens in its answers, which due to the way models are trained also indicates that it probably makes no makes no distinction in the semantics of different sequences of tokens when processing input, which is bad, because that's not how language works. It also probably means that all the information encoded in the model's weights is "uncompressed" and doesn't generalize properly - the model may know that the sky was blue yesterday because it's in its training data, but how is it to know if it was blue today, or if it would be blue on a fictional planet with all the same physical characteristics as Earth? It's like saying you prefer your model to be overfit.

Another thought experiment - when you're starting a sentence, does it matter in the slightest whether you are highly predisposed to using "the" (low entropy+varentropy), split between about using "the" or "a" (low entropy, high varentropy), thinking about using many different definite/demonstrative words with no clear preference (high entropy, low varentropy), or thinking about using many different definite/demonstrative words with a clear preference to "the" (high entropy+varentropy)? It doesn't mean you're uncertain of the semantic meaning of the answer you're about to give. If you were to do as they suggest and take it as an indicator to think more deeply before responding, you'd not only waste time in your response (this is literally the same thing as when people say "um" and "uh" a lot when talking, which is considered bad) but distract yourself from the choice of answering with the right semantics with the choice of starting with the right word, which doesn't actually matter.

return true;

There, I didn't need a paper to answer the question.