In presence of TAA the latter won't work as intended.
It seems they describe using a temporal exponential average to smoothen irradiance in GI probes, which is a similar practice than accumulating multiple samples per pixel in any raytracing method.
They also mention this does not work for them with visibility. I guess they mean the average distance per probe texel, which they use to 'solve' the leakage problem we know from VXGI, for example.
But how is any of this related to TAA? Notice, a error in mapping GI from probes can affect large areas of space. TAA works on a cone with a tiny angle. Errors affect only the neighborhood of a single pixel - this is why it works so well, better than SSR, SSAO, SSGI...
Also, DDGI will work equally well (or bad) no matter what AA method you use. It's a world space method and not related to any image reconstruction method. Even if both methods cause lag, it is not coupled.
I remember NVs early research presentation of DDGI had really bad smearing effects and motion trails, but this is not related to DDGI - it only was because they did not spend time to improve their very basic TAA implementation. (In case you confuse that.)
It seems its (TAA's) real use is to create a haphazard stop band , to lessen symptoms of undersampling.
What's a haphazard? All this sounds like kinds of diseases
TAA solves undersampling, yes. It's not about symptoms - it completely fixes undersampling in practice (AFAIK it's common to use 256 samples before the sequence repeats, but we could use infinite samples as well).
When does it fail? And when does it become wrong for real? I'll answer myself:
* All samples except the current are outdated. If lighting changes rapidly, this error becomes visible, but it is bounded. (No problem in practice.)
* Getting the samples involves reprojection of the previous frame(s). If this fails (it will, e.g. in presence of transparency, or inaccurate motion vectors due to skinning and other non rigid transformation), the unbounded error creeps in and stays for a long time. The root of all problems.
Can this be fixed? Yes. We could just store a history of N frames for N samples. Then reprojection errors could be rejected robustly.
The reason we don't likely see this happening is the same we have no alternative at all: Finite performance. It would cost too much bandwidth. Nobody is to blame here.
However, now with RT there can be additional reason to have at least some small number of frames like 4. Maybe we'll get better AA along as well.
The other hope is ML, which has a better chance to detect and resolve smearing and ghosting things, eventually.