Weksa/docs/roadmap.md

Roadmap

Now

• Keep the persistent state spine small and readable.
• Stabilize the interlingua packet standard before runtime code.
• Prove interlingua lowering to human target-language realizations before attempting alien-language lowering, starting with English, pt-BR, and Japanese.
• Decide the first language-project state format after the interlingua boundary is clear.
• Build a hand-authored toy language-project slice before automating generation.
• Use Zyphos / Eusocial Interbeing as the current higher-priority proving ground while keeping Weksa setting-neutral. Rust to Dust remains an earlier pressure surface, not the active default.

First Prototype

• Validate one minimal interlingua packet.
• Lower one packet through Nibu's projected agent state into English, Brazilian Portuguese, and Japanese realizations, each through an explicit target-language cultural ontology profile.
• Add one English pronunciation plan that strings the lowered line into broad IPA.
• Add one toy utterance embedding handoff fixture, then replace hand-authored values with the AquaSynth-owned learned encoder.
• Follow the cut sequence in the lowering map: packet store, lowering, the golf loop, then a service if one is earned.
• Preserve the first English-only lowering proof under examples/english-lowering/, and the shared multilingual proof under examples/multilingual-lowering/.
• Preserve the Airawa first-contact slice as a hand-authored language-project pressure note before attempting full alien-language translation.
• Parse or load one language project definition.
• Accept a Weksa interlingua packet.
• Render glossed output through grammar, morphology, phonology, and diachrony.
• Emit a trace explaining which stage made each decision.
• Keep runtime LLM-free; model use belongs in authoring and critique.
• Keep CLI tools and fixtures on the same commit and derivation paths so Weksa does not grow split-brained intent truth.

Later

• Add model-assisted semantic decomposition from English.
• Add embeddings for concept clustering.
• Add model critique against the explicit language state.
• Add corpus generation and consistency checks.