KOBONGOLA-Lite: Design and Prospective Evaluation Protocol for a Hybrid Offline French–Lingala Neural Translator on Resource-Constrained Smartphones

French and Lingala coexist in institutional, educational, and everyday communication in the Democratic Republic of the Congo, where connectivity is uneven and entry-level Android devices remain widespread. This paper specifies KOBONGOLA-Lite, a proposed bidirectional offline French–Lingala neural translation architecture, and defines a prospective protocol for its implementation and confirmatory evaluation. The contribution is deliberately narrower than claims of first bidirectionality or first compression, because recent French–Lingala systems and AfriNLLB already cover these dimensions. KOBONGOLA-Lite instead coordinates four testable components: a governed and stratified Congolese corpus; explicit treatment of natural French–Lingala code-switching; a context-gated lexical mechanism with neural fallback; and physical-device validation of linguistic quality, memory, latency, energy, thermal behavior, reliability, and network silence. Interface prototypes specify the intended user workflows, while the compact encoder–decoder model, tokenizer, domain lexicon, span policies, hybrid reranker, and self-contained mobile bundle remain objects of prospective implementation and validation. The planned study separates external baselines from controlled internal ablations that isolate sequence-level distillation, lexical support, hybrid reranking, ONNX export, and dynamic INT8 quantization. The primary automatic endpoint is chrF++; complementary evidence comprises SacreBLEU, locally validated AfriCOMET, terminology and entity preservation, omission analysis, blinded human assessment, paired document-clustered bootstrap intervals, effect sizes, and an explicitly defined ten-test Holm family. No implementation or confirmatory performance result is claimed at this stage. The protocol establishes a falsifiable and reproducible basis for assessing offline neural machine translation in a low-resource African and edge-computing context.