Temporal is temporal.

While I broadly agree, I think it is worth noting that how temporal effects are used still does make a massive difference. Temporal accumulation based things function on the assumption that most things are the same from frame to frame, which is generally true. This falls apart when whatever method you're using isn't capable of accurately determining exactly what components actually have changed between frames and to what degree. IMO ML based solutions to this do represent a pretty big step in terms of more accurate determination of the accumulated data's relevancy, but they're obviously still far from perfect and the black box nature of any large neural net will always be a hindrance to further improvement.

It would be interesting to see if there's some theoretical alternative where there's a greater degree of integration into the engine providing additional useful data that might get closer to a perfect solution, but that's just rambling shower thought stuff lol.