>"Eldan and Li observed hints that networks with fewer layers but more neurons per layer were better at answering questions that required factual knowledge; con

>"Eldan and Li observed hints that networks with fewer layers but more neurons per layer were better at answering questions that required factual knowledge; conversely, networks with more layers and fewer neurons per layer were better at keeping track of characters and plot points from earlier in the story." That is a very interesting result! This may point to the idea that the notion of time itself (as history/historical knowledge of the state of the system at an earlier point in its evaluation/evolution/computations) being more "present" (or pronounced, "relative to results", "apparent in results") in neural networks which are deep (many layers) rather than wide (more neurons per layer). Which, if true and widely confirmed to be so by other researchers -- would be an interesting discovery indeed! Also, if true (and it's a big 'if'!), this may help researchers in other areas better understand the relationship between time and information better (i.e., Leonard Susskind, ER=EPR, etc.: https://en.wikipedia.org/wiki/Leonard_Susskind https://en.wikipedia.org/wiki/Leonard_Susskind , https://en.wikipedia.org/wiki/ER_%3D_EPR https://en.wikipedia.org/wiki/ER_%3D_EPR , https://www.youtube.com/results?search_query=leonard+susskind https://www.youtube.com/results?search_query=leonard+susskin... )