# Open Questions

Centralized tracker for unresolved questions across all architecture documents.

## Theme: Inference Backend

### ~~OQ-01: Should ONNX Runtime be a supported inference backend in Phase 1?~~

- **Origin**: [model.md](model.md), [overview.md](overview.md)
- **Status**: **resolved**
- **Priority**: medium
- **Resolution**: Removed from scope entirely. ONNX Runtime does not support
  `output_hidden_states=True` natively (HuggingFace optimum issue #972 was
  closed as "not planned"), making activation extraction — the core operation —
  impractical without a custom ONNX graph modification pipeline. The ONNX
  model format also produces bloated exports. A future alternative inference
  path using burn/cublas with safetensors is more promising since it supports
  all platforms and uses the same model format we already require.
- **Cross-references**: ADR-006

---

## Theme: Codebook Design

### ~~OQ-02: What is the minimum viable codebook — can the 1,245-line PoC codebook be compressed?~~

- **Origin**: [codebook.md](codebook.md)
- **Status**: **resolved**
- **Priority**: high
- **Resolution**: Yes — ~65% compression to 500–600 lines total (400–500 runtime
  + 150–200 training). The PoC contains ~480 lines of essential runtime code
  plus ~178 lines needed from metaspline core. The 5x-repeated decomposition
  pipeline collapses into a single `decompose()` function (~50 lines saved).
  The histogram classifier (~130 lines) is exploratory and not MVP. The
  `build()` method (429 lines) is decomposed: training logic moves to
  `training/compiler.py`, runtime state becomes immutable serialized data.
  See [poc-architecture.md](../research/codebook-analysis/poc-architecture.md)
  and the Package Structure section in [codebook.md](codebook.md).
- **Cross-references**: ADR-004

---

## Theme: API Design

### OQ-03: Should the firewall support streaming/chunked input screening?

- **Origin**: [firewall.md](firewall.md)
- **Status**: open
- **Priority**: medium
- **Cross-references**: ADR-003, OQ-05

Some inputs arrive in chunks (streaming API responses, large documents). Should
the firewall support incremental screening as chunks arrive, or require the
full input before screening? Incremental screening could detect attacks earlier
but requires buffering and state management.

**Rolling window approach**: One promising direction is rolling windows of
tokens — chunking large text into overlapping windows and screening each
window independently. This enables:

1. **Granular detection**: For the instruction firewall use case (screening
   academic papers converted from PDF to markdown), rolling windows can
   red-flag specific *sections* of a document rather than the whole thing.
   This is directly useful for catching hidden prompt injections in academic
   research papers (~20 real examples found of researchers slipping injections
   past peer review).
2. **Parallel processing**: Windows can be screened in parallel, enabling
   throughput scaling.
3. **Large input handling**: No need to truncate long documents; each window
   is independently screened within the model's context length.

The PoC has directional (but buggy) Rust code for creating rolling windows
that can be referenced when designing this feature. This connects to OQ-05
because streaming/chunking affects how the firewall composes with other
guardrail systems in a pipeline.

Leave open for Phase 1 design, but the rolling window approach is the leading
candidate for Phase 2.

---

### ~~OQ-04: Should detection thresholds be per-model or globally configurable?~~

- **Origin**: [configuration.md](configuration.md), [codebook.md](codebook.md)
- **Status**: **resolved**
- **Priority**: medium
- **Resolution**: Both — thresholds are **model-specific by default** (shipped
  with the codebook) but **globally overridable by the user**. Once calibrated,
  models produce remarkably similar behavioral patterns across models (inspired
  by the "platonic representation hypothesis" — different models converge on
  similar internal representations of the same data). The individual activation
  spaces differ, but the behavioral patterns they encode are consistent enough
  that thresholds transfer reasonably well. The codebook ships recommended
  thresholds calibrated for its model; users can adjust.
- **Cross-references**: ADR-003, ADR-004

---

## Theme: Integration

### ~~OQ-05: How should the firewall integrate with existing guardrail systems?~~

- **Origin**: [firewall.md](firewall.md), [overview.md](overview.md)
- **Status**: **resolved**
- **Priority**: medium
- **Resolution**: Standalone API + thin adapter pattern (ADR-011). Phase 1:
  ship the standalone `Firewall.screen(text) → Alarm` API only. Phase 2:
  build thin adapter packages (<100 lines each) for LlamaFirewall,
  OpenAI Agents SDK, and NeMo Guardrails as optional dependencies. Do NOT
  build a common `ScreeningProvider` interface — behavioral detection is
  fundamentally different from text-surface defenses and premature abstraction
  would be constraining.
- **Cross-references**: ADR-002, ADR-011

---

## Theme: Project Setup

### ~~OQ-06: Should file-based configuration use TOML or YAML?~~

- **Origin**: [configuration.md](configuration.md)
- **Status**: **resolved**
- **Priority**: low
- **Resolution**: TOML. Consistent with modern Python packaging conventions
  (`pyproject.toml`) and increasingly the standard for Python configuration.
  This is a two-way door decision — reverting to YAML later is straightforward.
- **Cross-references**: None