feat(backend): BACKEND_SLOT_PINNING — per-phase engine slot ownership

Each pipeline phase owns one llama-server slot (steward 0, orchestrator
1, synthesizer 2), carried as id_slot in extra_body through the same
mechanism tool_choice already uses, so the phase's stable prompt prefix
stays in that slot's KV cache and a turn re-prefills only its new
tokens. Off by default; a no-op on the Claude backend and ignored by
Ollama, so the flag is safe on any backend and the cutover itself stays
a pure env swap.

The merge helper preserves existing extra_body keys — mutation-checked
(dropping the merge fails exactly the test written for it).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

src/anthropic/model_selector.py

@@ -259,6 +259,28 @@ def get_tool_choice_settings() -> ModelSettings:
         return ModelSettings(extra_body={"tool_choice": "required"})
 
 
+def with_slot_pinning(settings: ModelSettings | None, slot: int) -> ModelSettings | None:
+    """
+    Merge llama-server slot pinning into model settings when enabled.
+
+    Each pipeline phase owns one engine slot (steward=0, orchestrator=1,
+    synthesizer=2), so the phase's stable prompt prefix stays in that
+    slot's KV cache and a turn re-prefills only its new tokens. Off by
+    default (BACKEND_SLOT_PINNING); a no-op on the Claude backend, and
+    Ollama ignores the field, so enabling it is safe on any backend.
+    """
+    if not config.BACKEND_SLOT_PINNING or resolve_backend() == "claude":
+        return settings
+
+    from pydantic_ai.settings import ModelSettings
+
+    merged = dict(settings or {})
+    extra_body = dict(merged.get("extra_body") or {})
+    extra_body["id_slot"] = slot
+    merged["extra_body"] = extra_body
+    return ModelSettings(**merged)
+
+
 def get_sampling_settings(temperature: float) -> ModelSettings:
     """
     Get model_settings with a sampling temperature where the backend allows it.