docs: resolve OQ-03 — adopt rolling token window screening (ADR-012)

Research confirmed rolling token windows as the right approach for long
document screening. ADR-012 formalizes the decision: Phase 2 implements
screen_document() with 25% overlap (512 tokens for SmolLM2-135M), max
pooling aggregation, and character offset tracking. Short inputs fall
through to screen() unchanged.

This resolves the last open question. All 6 original OQs are now resolved:
- OQ-01: ONNX removed (burn/cublas better future path)
- OQ-02: 65% codebook compression achievable
- OQ-03: Rolling token windows for Phase 2 (ADR-012)
- OQ-04: Both model-specific defaults + user-overridable
- OQ-05: Standalone API + thin adapters (ADR-011)
- OQ-06: TOML for file-based config

docs/architecture/decisions/012-rolling-window-screening.md

@@ -0,0 +1,79 @@
+# ADR-012: Rolling Token Window Screening for Long Documents
+
+## Status
+
+Accepted
+
+## Context
+
+The Phase 1 `screen()` API processes the full input as a single forward pass
+through the detector model. This works for inputs within the model's context
+window (2048 tokens for SmolLM2-135M) but fails for longer documents. Two
+distinct windowing concepts exist in the detection pipeline:
+
+1. **Token-level smoothing** (already in the codebook): Within a single
+   forward pass, per-token z-coordinates are smoothed with a rolling average
+   (window=8) before classification. This operates on the `(seq_len, 3)` z
+   coordinate sequence.
+
+2. **Input-level rolling windows** (this ADR): For long documents that exceed
+   the model's context window, chunk the text into overlapping token windows
+   and screen each window independently. Each window produces its own z-vector
+   and alarm. Windows are aggregated into a document-level verdict.
+
+Research ([rolling-window-analysis.md](../../research/streaming-screening-patterns/rolling-window-analysis.md))
+confirmed that:
+- Meta's PromptGuard 2 uses a similar approach (512-token segments)
+- Max pooling is the correct aggregation strategy (consistent with existing
+  weighted-max score composition)
+- 25% overlap (512 tokens for SmolLM2-135M) balances detection quality vs
+  throughput — enough to catch boundary-spanning injections
+- Character offset mapping (from HuggingFace tokenizer `offset_mapping`)
+  enables granular "section X is suspicious" reporting
+- The Rust reference implementation in taskgraph-semantic validates the
+  window creation algorithm
+
+## Decision
+
+Implement rolling token window screening as the Phase 2 `screen_document()`
+API, with the following parameters:
+
+- **Window size**: Model's max sequence length (2048 for SmolLM2-135M)
+- **Overlap**: 25% (512 tokens) — same as PromptGuard's entire context window
+- **Aggregation**: Max pooling across per-window, per-direction P(active)
+  scores
+- **Short input handling**: Inputs shorter than one window fall through to
+  `screen()` with no overhead
+- **Character offset tracking**: Token-to-character mapping for granular
+  reporting of flagged sections
+
+The two windowing concepts (token-level smoothing, input-level rolling windows)
+are composable and solve different problems at different levels.
+
+## Consequences
+
+**Positive**:
+- Long documents (academic papers, reports) can be screened without truncation
+- Granular reporting identifies which sections are suspicious, not just the
+  whole document
+- Windows can be processed in parallel for throughput scaling
+- Natural fallback: short inputs get the fast single-window path
+- Character offsets enable UI integration (highlighting flagged sections)
+- Pattern translates directly to Rust for future embedding system integration
+
+**Negative**:
+- Throughput cost: N windows = N forward passes. A 10K-token document needs
+  ~7 windows at 25% overlap.
+- Overlap regions are processed multiple times, increasing compute
+- API surface expands — users must choose between `screen()` and
+  `screen_document()`
+- Edge cases around window boundaries (partial word tokens, very short
+  windows) need careful handling
+
+## References
+
+- [rolling-window-analysis.md](../../research/streaming-screening-patterns/rolling-window-analysis.md) — Full research with API design and implementation sketch
+- [OQ-03](../open-questions.md) — Original open question
+- [firewall.md](../firewall.md) — Current screening API
+- [codebook.md](../codebook.md) — Token-level smoothing (separate from this)
+- taskgraph-semantic: `/workspace/@alkimiadev/taskgraph-semantic/src/embedding.rs` — Rust reference for `create_rolling_windows()`