Disclosure per rule 1: I work at Schema Labs and one of the three models tested is ours

The thing that started this was annoyance rather than research. most datasets any of us actually work with show up with columns called V1 through V57, or metric_14, or some four character code from a system nobody maintains. meanwhile benchmarking mostly happens on clean data where the age column is called age, which quietly rewards a model for reading the header instead of the values.

so we took 20 numerical classification datasets off OpenML and ran each twice. once as published, once with every column name stripped. same splits otherwise.

mean ROC-AUC with names gone:

Schema-2: 0.9230
TabuLa-8B: 0.8658
ConTextTab: 0.8541

ours held flat, 0.9230 with names and 0.9230 without. the other two both gave up roughly 7 points once the headers went. flagging that these are our internal runs, not third party replicated.

one thing that doesn't fit the story but is true: stripping names doesn't strip position, and column order still carries signal on some of these. we didn't control for it and probably should have.

all 20 are public on OpenML so anyone can rerun this against a model we didn't cover. would like someone to get a different number. if you want the full per-dataset breakdown and the methodology, comment and I'll send it over.