arXiv:2611.14068v1 Announce Type: new Abstract: CAGE Challenge 4 is the benchmark on which learned multi-agent cyber defenders are compared. We reproduce its two reference defenders, KEEP and H-MARL Expert, in a pinned container and show that three things read from it measure less than they are taken to measure. First, the simulator silently discards any action asserted for an agent whose previous action is still running, and charges one point for a discarded Restore as for one that runs. The documented observation does not expose the busy flag. Both reference defenders read it in their own wrappers, and switching that check off at evaluation costs KEEP's released policy 99.58 points per episode and H-MARL's 145.98, in both cases the charge for Restores sent to busy agents, with H-MARL's executed actions unchanged step for step. On a supervisory harness, holding interventions until the agent is idle is worth 25.3 and 27.6 points per episode at the published setting. In training, the check decides how a busy step enters the trajectory. Trained without it, KEEP leads at a hundred updates in all four paired seeds and trails from two hundred on in all four. Continued to two thousand updates on three seeds, the repaired recipe ends inside the released interval on one seed and short on two, by 27 and 125 points, a spread across training seeds far wider than the released reproduction interval. Second, the evaluation seeds the environment but not the policy, so a reported mean is one draw, and 88.9% of per-episode outcome variance is the policy's own sampling draw. Third, recovery precision depends on the truth it is scored against. Across five published comparison tables, none states the artifact, sampling, criterion and adversary that would make its numbers comparable. We release the instrument, our patches to both defenders, the traces and checkpoints behind every number, and a five-item disclosure checklist.