Country Is Not Enough: Recognition - Action Gaps in Cultural Answerability

Large language models increasingly answer culturally situated questions whose surface form specifies a country but omits the subnational, community, or situational context needed for a reliable answer. We study cultural answerability: whether the cultural scope supplied by a user supports a direct answer without presenting one situated practice as a country-wide norm. Using region-specific evidence from INDICA, we construct a bilingual English-Hindi evaluation with 50 culturally stable and 50 culturally dependent held-out questions. We evaluate two multilingual LLMs across three stages: explicit recognition of missing context, natural response policy, and paired regional counterfactual grounding. Models identify 89.5% of dependent conditions but correctly classify only 28.0% of stable conditions, revealing strong over-detection of cultural dependence. Under natural assistance, the appropriate policy rate on dependent questions is 69.0%, while 30.5% of responses make an unscoped cultural commitment. Moreover, in 28.0% of dependent model-language conditions, a model explicitly recognises missing context yet still makes such a commitment when answering naturally. Under the targeted-adjudication protocol, final regional grounding succeeds in only 13.5% of paired conditions, showing that explicit regional context does not reliably produce grounded adaptation. These results show that cultural reliability cannot be evaluated as a knowledge problem alone; it also depends on calibrated recognition, response policy, and grounded adaptation.