Large language models write Hindi that carries the shape of English, and measuring how often is the open problem, because the phraselevel instruments available are lexical and brittle. We build a 26-pair Hindi contrast set over eight idiom families, verified by three native Hindi speakers. Prompt framing moves supplied-candidate scores toward the calque: an English-literal cue shifts them in 8 of 8 families, and a separate translate-from-English instruction shifts them again, with the direction holding on a family split frozen before the outcome was seen. Free generation appears to show far less calquing, so we audit the lexical indicator behind that appearance against native-speaker judgement. Its precision on its own positives is 1.00, while 24 of 60 sampled negatives are calques by majority verdict, and the misses concentrate in the families where its exact-match trigger was never observed to fire. The apparent scarcity is substantially a property of the instrument. We recommend that detectors of this form report coverage alongside hits.

Translating a story renders its words into a new language; adapting it transposes its world into a new culture. Large Language Models (LLMs) translate and adapt individual passages effectively, but long-form novels spanning ∼100 chapters suffer model failures. We formalize long-form cross-cultural story adaptation as a generation task parameterized by source and target language-culture pairs, with three structural axes: A1 identity coherence across narrative arc, A2 cultural-system alignment within scenes, and A3 dependency-chain integrity among related entities. This is addressed through a five-stage agentic adaptation framework comprising entity registry, target-culture plan, dependency-aware batched localization, validation and rewrite. Evaluation is conducted through ablations across three language-culture pairs and two model families; the proposed system outperforms across all three axes, reducing identity errors by 65%, dependency errors by 64%, and cultural misalignment errors by 27% against competitive baselines. Further validation through a production A/B test on a commercial storytelling platform demonstrates listener retention comparable to that for human-adapted content, reflecting viability of the framework.