From Entropy to Epiplexity: Rethinking Information for Computationally Bounded Intelligence · HackerPapers discussions

From Entropy to Epiplexity: Rethinking Information for Computationally Bounded Intelligence — mentioned by Noumenon72

Mon, 23 Mar 2026 11:21:14 GMT

This presumes that the value was created by the authors and not the people who found a way to use the structure in the training set to create intelligence. Like crediting wi-fi router owners with creating Wi-Fi Positioning System instead of the people who realized they could wardrive around to create maps and extract a new kind of value that would never have existed without them. Or Egyptians for deciphering hieroglyphics instead of the Frenchman who realized the Rosetta Stone they were using to hold up a wall could actually be used to do much more. My thinking here is coming from the paper…

Paper and discussion history. A mention is not an endorsement.

From Entropy to Epiplexity: Rethinking Information for Computationally Bounded Intelligence — HN submission

Wed, 18 Mar 2026 15:22:13 GMT

Epiplexity: Rethinking Information for Computationally Bounded Intelligence

2 points; 2 discussion replies on HN. Paper and source history. A submission is not an endorsement.

From Entropy to Epiplexity: Rethinking Information for Computationally Bounded Intelligence — mentioned by codeflo

Mon, 16 Mar 2026 07:46:43 GMT

>> the architect → developer → reviewer pipeline actually produces better results than just... talking to one strong model in one session? > There's a 63 page paper with mathematical proof if you really into this. > https://arxiv.org/html/2601.03220v1 I'm confused. The linked paper is not primarily a mathematics paper, and to the extent that it is, proves nothing remotely like the question that was asked.

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From Entropy to Epiplexity: Rethinking Information for Computationally Bounded Intelligence — mentioned by est

Mon, 16 Mar 2026 07:19:34 GMT

> the architect → developer → reviewer pipeline actually produces better results than just... talking to one strong model in one session? There's a 63 pages paper with mathematical proof if you really into this. https://arxiv.org/html/2601.03220v1 My takeaway: AI learns from real-world texts, and real-world corpus are used to have a role split of architect/developer/reviewer

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From Entropy to Epiplexity: Rethinking Information for Computationally Bounded Intelligence — mentioned by kingstnap

Thu, 29 Jan 2026 21:58:03 GMT

Related: https://arxiv.org/abs/2601.03220 This is a paper that recently got popular ish and discusses the counter to your viewpoint. > Paradox 1: Information cannot be increased by deterministic processes. For both Shannon entropy and Kolmogorov complexity, deterministic transformations cannot meaningfully increase the information content of an object. And yet, we use pseudorandom number generators to produce randomness, synthetic data improves model capabilities, mathematicians can derive new knowledge by reasoning from axioms without external information, dynamical systems produce emergent…

Paper and discussion history. A mention is not an endorsement.

From Entropy to Epiplexity: Rethinking Information for Computationally Bounded Intelligence — HN submission

Fri, 09 Jan 2026 07:36:24 GMT

Rethinking Information for Computationally Bounded Intelligence

1 point; 1 discussion reply on HN. Paper and source history. A submission is not an endorsement.

From Entropy to Epiplexity: Rethinking Information for Computationally Bounded Intelligence — HN submission

Thu, 08 Jan 2026 15:34:18 GMT

Epiplexity: Rethinking Information for Computationally Bounded Intelligence

5 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.