For now, I tried a quick test:
The GGUF side of this release looked especially interesting to me because you already separate quantization-retention measurements from downstream benchmark retention in the announcement.
So I tried a small independent sanity check on the official VeriLoop E2 GGUF release, comparing IQ1_M vs Q4_K_M on an L4.
The result was simple:
| IQ1_M | Q4_K_M | |
|---|---|---|
| Parsed decisions | 8/8 | 8/8 |
| Correct on my tiny panel | 5/8 | 5/8 |
| Decision agreement | - | 8/8 |
So, on this very small forced-choice panel, the two official quants made exactly the same eight decisions, including the same three wrong ones.
I would interpret this narrowly: it is a small behavioral sanity check complementary to the PPL/KLD measurements in the release, not evidence that IQ1_M preserves free-form reasoning, agentic behavior, or downstream benchmark performance in general.
Still, given how aggressive the footprint reduction is, I thought the 8/8 agreement was worth reporting.
Exact setup and what I think this does/does not show Overall, the part I found most encouraging is that the release already exposes several different evidence layers instead of collapsing everything into a single headline score:
checkpoint
training method
Harness / verifier
benchmark evidence
GGUF quantization
distribution-retention measurements
runtime validation
My small IQ1_M/Q4_K_M check adds only one tiny additional point to that map, but so far it points in the same direction as the published GGUF retention measurements.
For this kind of release, I think keeping those layers separate is more useful than trying to turn all of them into one “quality” number.
Thanks for publishing the GGUF ladder and the evidence alongside it — having enough public surface to independently check even a small slice is useful.