MZN's Phase 2 tokenizer work extends beyond a vocabulary file: it connects comparative text tokenization, runtime-control discipline, multilingual boundary handling, concept preservation and a multimodal attachment path. Selected implementation and internal testing exist; independent benchmark, security and IP validation remain Phase 3 work.
The system treats tokenization as a layer that can influence context budget, runtime behavior, multilingual stability, concept boundaries and multimodal grounding. Each concern is separated so technical reviewers can test it independently.
Phase 2 work compared and organized BPE, WordPiece, Unigram and SentencePiece approaches alongside common tokenizer tooling. The purpose is architectural comparison and implementation discipline, not a claim that one family universally wins.
Reserved forms, control-like strings, model-family binding, escapes and count parity were treated as runtime concerns rather than mere encoding details. This is where tokenizer design intersects with operational safety and routing.
Decision-carrying terms, preprocessing boundaries and concept-registry logic are treated explicitly. The review question is whether important concepts remain stable across tokenization, mixed scripts, routing and runtime constraints.
The technical work moved beyond text-only exploration into real media attachment and multimodal refresh testing. That establishes an internal multimodal path; it does not yet establish frontier-scale multimodal performance.
Token count is treated as an architectural variable because fragmentation can affect effective context length, compute budget and downstream system cost. The public page does not claim a verified compression advantage; it identifies the mechanism and the test surface.
Testing is organized into seed, baseline, stress, regression, compatibility, audit-final and multimodal refresh stages. Exact run packs, manifests and integrity records remain part of controlled technical review rather than the public page.
The Phase 2 record includes actual internal runs rather than architectural intent alone. The public technical brief shows the sequence and scope of testing while keeping dataset-level and run-pack detail in controlled review.
Internal testing covered text-side seed material, runtime edge cases, critical-term boundaries, stress families, regression discipline, compatibility/integrity checks and real image/audio/video attachment. The exact datasets, manifests, run outputs and hash records are deliberately not exposed here.
Populated internal seed material spanning text, runtime and multimodal hard cases.
Critical-term and boundary behavior evaluated with runtime and multimodal metrics.
Pressure families included rare terms, mixed script, runtime policy, multimodal grounding and degradation/latency concerns.
Regression locking and failure-to-hook discipline were included in the internal test process.
Manifest continuity, integrity coverage and claim-discipline checks were included in the internal review stack.
Real image, audio and video assets were attached into the multimodal path for internal refresh testing.
The next stress-refresh lane remains explicitly pending rather than being represented as complete.
Tokenizer design is evaluated through operational consequences rather than as an isolated vocabulary exercise. These are engineering surfaces and review hypotheses, not claimed market outcomes.
More or fewer tokens can alter effective context allocation, compute usage and downstream cost discipline. The direction and magnitude require controlled benchmarking.
Reserved forms, special-token collisions, escapes and binding rules can affect runtime behavior and should be tested as system constraints.
Persian/English mixed-script handling and technical-term boundaries are explicit pressure areas. Broader multilingual generalization remains a frontier-scale validation question.
Once image, audio and video enter the path, attachment, anchors and token-space design become part of context management and grounding review.
Tokenizer should be read as a technically implemented Phase 2 asset with an internal test record. Stronger claims belong to asset-specific Phase 3 review and, later, the consolidated MZN Evidence architecture.
This public technical layer can state that the program includes architecture, implementation work and internal testing across text, runtime, multilingual-boundary and multimodal paths.
The page does not convert internal execution into independent validation.
Return to the Phase 2 map for context, or continue to GPU Systems for the next implementation-level infrastructure route.
