Embedding and Tokenization Audit

Audit nearest-neighbor behavior and tokenizer coverage across domains and languages. Identify harmful geometry and cost outliers.

Evidence and review

Deliver probes, examples, and limitations. Claims must trace to versioned code and artifacts. Screenshots, unexplained generated prose, and a single favorable run do not pass.

Oral defense

Explain the mechanism, identify the most dangerous assumption, interpret one failure from raw evidence, and name the condition that would reverse your decision.

Source backbone

Use Dive into Deep Learning, PyTorch documentation, primary papers, and Designing Data-Intensive Applications selectively.