Research
Peer-reviewable publications, audit results, and benchmarking roadmap. Every claim is testable. Every result is reproducible.
Papers & Technical Reports
The Flat Text Problem in Bantu Languages
Defines the structural failure that occurs when AI systems are trained on ordinary orthographic text for Bantu languages. Demonstrates with Bemba minimal pairs that identical written forms encode different meanings depending on tone. Argues the flat text problem — not data scarcity — is the primary barrier to Bantu language AI.
The Tonal Frontier: Automating Ground Truth for Bantu Language AI
Describes the 5-step deterministic tonal pipeline, the thesis-validation architecture, and results from the Bemba proof-of-concept (23,000+ tonal verb pairs with 100% accuracy on MRS stress-tests).
The 3MegaLabs Framework: Deterministic Data Generation for Low-Resource Languages
Presents the language-agnostic BTS engine architecture, the cartridge system, and results from scaling morphological generation across 9 proof-of-concept languages.
The Tonal Moat: Why BantuNomics Data Is Irreplaceable
Analyzes the compound defensibility of the three-layer system (engine + tonal pipeline + speaker validation) and demonstrates why no single layer is sufficient alone.
Independent Audit Results
Gemini 2.5 Flash independently reviewed the Minimum Representative Sample (MRS) for Bemba tonal data.
Benchmarking Roadmap
Planned evaluations against established Bantu language benchmarks.
Citation
@techreport{cintu2026bantunomics,
title = {BantuNomics: The Tonal Operating System for Bantu Language AI},
author = {Cintu, Conti and others},
year = {2026},
institution = {3MegaLabs},
url = {https://bantunomics.com/research}
}