docs: resolve 4 open questions, add research, spec codebook package structure

Research-driven resolution of OQ-01, OQ-02, OQ-05, OQ-06:

- OQ-01: Remove ONNX Runtime from scope entirely — doesn't support
  activation extraction natively (optimum #972 closed as not planned),
  bloated model exports; burn/cublas via safetensors is a better future path

- OQ-02: Codebook compresses ~65% (1,245 → 500-600 lines); add Package
  Structure and Extraction from PoC sections to codebook.md based on PoC
  analysis of metaspline firewall_codebook.py

- OQ-05: Standalone API + thin adapter pattern (ADR-011); Phase 1 ships
  Firewall.screen() only, Phase 2 adds <100-line adapter packages for
  LlamaFirewall, OpenAI Agents SDK, NeMo Guardrails

- OQ-06: TOML for file-based config — standard modern Python, two-way door

Also: research OQ-03 rolling windows from taskgraph-semantic reference code,
remove onnxruntime/optimum from dependencies, move streaming screening to
Phase 2, add burn/cublas as Phase 3 alternative backend.

docs/architecture/model.md

@@ -72,8 +72,7 @@ class DetectorModel(Protocol):
 ```
 
 The `infer` method returns hidden states at key layers, abstracting away
-whether the backend is PyTorch, ONNX Runtime, or a future Rust inference
-engine.
+whether the backend is PyTorch or a future alternative inference engine.
 
 ### Lazy Loading
 
@@ -158,4 +157,4 @@ class HFDetectorModel:
 Open questions are tracked in [open-questions.md](open-questions.md). Key
 questions affecting this document:
 
-- **OQ-01**: Should ONNX Runtime be a supported inference backend in Phase 1? (open)
+- **OQ-01**: ~~Should ONNX Runtime be a supported inference backend in Phase 1?~~ (resolved — removed from scope; burn/cublas is a better future path)