In this analysis
Teacher recovery is broad comparison evidence; consequential extraction fields still need their own source-grounded acceptance rule.
A teacher-recovery result for LFM2.5 QAD answers a model-comparison question, but it cannot certify a downstream extraction workflow whose errors have different consequences.
Recovery is a relative claim
The QAD release introduces a trained quantized student. Comparing that artifact with a reference can show how it behaves on the stated evaluation. The conclusion is bounded by the tasks and acceptance conditions used there, rather than automatically covering every structured extraction problem an operator might later choose.
Where the comparison comes from
The earlier hybrid release and compact release locate the programme before the trained-quantization announcement. They help identify the lineage without turning every earlier checkpoint into an interchangeable teacher reference. The actual comparison still needs its own artifact identities and stated procedure.
A student can resemble a teacher on aggregate while differing on a small but important class of cases. That possibility does not refute the aggregate result. It explains why a deployment decision needs evidence matched to the errors that matter in its own task.
A technical-extraction fixture
Consider extracting part identifiers from maintenance notes. A fluent summary can preserve the general meaning while altering a suffix that identifies a different component. If the application routes work by that identifier, semantic similarity is insufficient even when the answer reads naturally.
| Task property | Acceptance implication |
|---|---|
| Exact identifier copied from the source | Compare the complete field, not the surrounding explanation. |
| Missing identifier in the source | Preserve absence instead of accepting a plausible completion. |
| Several similar identifiers present | Check the selected association with the requested component. |
Why aggregate recovery can miss it
A broad evaluation may contain few examples with this exact failure cost. Averaging them with ordinary questions can obscure the relevant error category. The issue is task weighting, not a claim that the released student actually made these mistakes in an unreported experiment.
Keep category-level outcomes before calculating an overall acceptance figure. A teacher comparison remains useful context, while the local fixture answers whether the selected artifact preserves the fields the application depends on. Those two evidence sources can agree without being redundant.
A release decision with explicit limits
The retrospective contribution is a trained artifact whose relationship to a reference can be examined. It does not remove the need to define the consuming application’s correctness contract.
Use the published comparison to understand the release, then evaluate the task’s consequential distinctions. That separates evidence of broad recovery from evidence of local suitability without inventing a score or assuming that either comparison is universally sufficient.
Further analysis: Reproducible inference stacks: preserving a GPU pilot’s result; GPU hosting for multimodal models: count more than text.