"The hidden tests are lossless" is empty by construction
A criterion R440P set in advance was refuted nine days later: in LCB, 91/91 hidden test sets are supersets of the visible tests, so "would pass hidden but fails visible" cannot happen by construction. This criterion had zero statistical power.
So the criterion "visible filtering is lossless" has zero statistical power on these two task banks. 0/2394 does not mean "we verified that it is lossless"; it means "the definition of this task bank guarantees it holds."
If a requirement cannot be compiled into executable tests, this mechanism has no free referee. It degrades into "ask a model" — and that is exactly the thing that measured worst.
This is the concrete form, in this system, of the test oracle problem reviewed by Barr et al. (2015):
Generating test inputs and running the program is not the same as knowing whether the output is correct.
Large language models make generating candidate programs extremely cheap, but they do not remove the oracle problem. Worse, checking a model with a model does not automatically supply a basis for judgment.
We deleted the AI review committee and replaced it with "run the customer's acceptance tests, deliver the first candidate that passes, refuse delivery if none passes." On three task banks it delivered 4.58 to 19.17 percentage points more, at only 1.5 calls per task.
"The model is too strong" and "the tasks are too easy" were two explanations proposed by humans. Two experiments excluded both, and only then was the reviewer mechanism forced into a redesign — and the direction was to change the referee, not the reviewer model.
Criteria, statistical methods and the four state rules are all frozen and timestamped before the run starts. This post explains how we preregister, and why "adding a criterion after the fact" doesn't count in this research.
An experiment with 836 tasks, 43 blocks and 0 voided. The thesis's "halved" was wrong — because the two rates used different denominators. This correction changes the gate's effect from "in half" to "roughly 40%."
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Department of Information and Communications, Shih Hsin University · 2026 capstone project · on-site exhibition