49 lines
2.2 KiB
Markdown
49 lines
2.2 KiB
Markdown
# Roadmap
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## Now
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- Keep the persistent state spine small and readable.
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- Stabilize the interlingua packet standard before runtime code.
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- Prove interlingua lowering to human target-language realizations before
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attempting alien-language lowering, starting with English, `pt-BR`, and
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Japanese.
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- Decide the first language-project state format after the interlingua boundary
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is clear.
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- Build a hand-authored toy language-project slice before automating
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generation.
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- Use Zyphos / Eusocial Interbeing as the current higher-priority proving
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ground while keeping Weksa setting-neutral. Rust to Dust remains an earlier
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pressure surface, not the active default.
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## First Prototype
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- Validate one minimal interlingua packet.
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- Lower one packet through Nibu's projected agent state into English, Brazilian
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Portuguese, and Japanese realizations, each through an explicit target-language
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cultural ontology profile.
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- Add one English pronunciation plan that strings the lowered line into broad
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IPA.
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- Add one toy utterance embedding handoff fixture, then replace hand-authored
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values with the AquaSynth-owned learned encoder.
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- Follow the cut sequence in
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[the lowering map](./targets/interlingua-lowering-map.md): packet store,
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lowering, the golf loop, then a service if one is earned.
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- Preserve the first English-only lowering proof under
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`examples/english-lowering/`, and the shared multilingual proof under
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`examples/multilingual-lowering/`.
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- Preserve the Airawa first-contact slice as a hand-authored language-project
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pressure note before attempting full alien-language translation.
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- Parse or load one language project definition.
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- Accept a Weksa interlingua packet.
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- Render glossed output through grammar, morphology, phonology, and diachrony.
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- Emit a trace explaining which stage made each decision.
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- Keep runtime LLM-free; model use belongs in authoring and critique.
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- Keep CLI tools and fixtures on the same commit and
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derivation paths so Weksa does not grow split-brained intent truth.
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## Later
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- Add model-assisted semantic decomposition from English.
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- Add embeddings for concept clustering.
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- Add model critique against the explicit language state.
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- Add corpus generation and consistency checks.
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