[kumarhn]

TurboQuant is starting to look like a case study in how to turn a fragile paper into a breakthrough story.

The attribution is thin, the “6x compression” headline is not clearly separated from prior KV-cache quantization baselines like KIVI, and the RaBitQ comparison is hard to take seriously: single-core CPU for the baseline, A100 GPU for TurboQuant. It is comparing apples-to-datacenter. Worse, there are also public OpenReview comments saying that even the reported accuracy results are not reproducible.

Hard to believe this is the standard for something being promoted as a breakthrough. If this came from a random startup blog, people would be much harsher about it.

[oofbey]

But how can these poor googlers be expected to sift through the thousands of research papers published on these topics to find relevant citations? They don’t have time for such trivialities. They have far more important work to be doing not being evil. /s

[fnordpiglet]

Gemini helped them build it but didn’t / couldn’t attribute it from its corpus. I think we will see a surge of “rediscovery” that’s unattributed training surfacing of prior work that wasn’t widely recognized at the time.

[oofbey]

Gemini is perfectly capable of searching the web. Pretty good at it really. As are most agents. If such a surge happens, it’s purely because of laziness.

[fnordpiglet]

Laziness, aka, the human condition?