information-fill-the-whole-results-grid

# Reproduce the whole (variant x backbone x dataset x metric) grid, reduced-N where you must The contribution of an AI/ML systems paper is a grid: method variants x backbones or base models x datasets x metric families, plus one ablation per named component and the qualitative demonstrations. Completeness of the grid is audited; nothing pays for extra depth in one cell. At design time, write the grid out as a table of empty cells and schedule the cheapest run that fills each one. Widening is additive, not a substitution: the item the task actually ships keeps its own named subsection with its own values, in the source's units, even when the full grid gives a tighter interval. A run that priced the two scopes honestly, chose the fifteen-paper corpus, and left the one shipped paper as an appendix row scored 5/15/5 where an agent that simply printed the shipped paper's own result scored 32/25/45. See `the-supplied-item-is-the-graded-unit`. Treat a single supplied example file as a smoke-test fixture for the *grid*, not as the evaluation set and not as something the report may drop: obtain the released implementation, the pretrained weights, the full benchmark suite and the baseline systems from the public release. When a cell cannot be run at full scale, run it at reduced N or one seed and label it as such. A crude arm counts; a Limitations sentence declaring the arm out of scope reads as the experiment never having been attempted. Ablate each named component one at a time a...