feat(inference): operator-tunable sampling knobs (top_k/min_p/repeat_penalty) for stage requests

GenerationConfig only carried temperature/top_p/max_tokens/stop/seed. Added nullable
topK/minP/repeatPenalty, serialized to the llama.cpp and OpenAI-compat request bodies
via @EncodeDefault(NEVER) so an unset knob is omitted (the model keeps its own default)
and behavior is unchanged unless the operator opts in.

Surfaced as a new [sampling] config section feeding the default stage GenerationConfig
(the main agentic loop) through TomlWorkflowLoader + ExecutionPlanCompiler; the former
hardcoded temperature=0.7/topP=1.0 stage defaults now come from config. Talkie
chat/narration keep their own generation settings.

Vikunja #46 (task 76) — sampling half.

infrastructure/src/main/kotlin/com/correx/infrastructure/InfrastructureModule.kt

@@ -17,6 +17,7 @@ import com.correx.core.events.EventDispatcher
 import com.correx.core.events.stores.EventStore
 import com.correx.core.inference.CapabilityScore
 import com.correx.core.inference.Embedder
+import com.correx.core.inference.GenerationConfig
 import com.correx.core.inference.InferenceProvider
 import com.correx.core.inference.InferenceRouter
 import com.correx.core.inference.ModelCapability
@@ -210,10 +211,13 @@ object InfrastructureModule {
             ),
         )
 
-    fun createWorkflowLoader(extraKinds: List<ArtifactKind> = emptyList()): WorkflowLoader {
+    fun createWorkflowLoader(
+        extraKinds: List<ArtifactKind> = emptyList(),
+        samplingDefaults: GenerationConfig = GenerationConfig(temperature = 0.7, topP = 1.0, maxTokens = 0),
+    ): WorkflowLoader {
         val registry = DefaultArtifactKindRegistry()
         extraKinds.forEach { registry.register(it) }
-        return TomlWorkflowLoader(registry)
+        return TomlWorkflowLoader(registry, samplingDefaults)
     }
 
     fun createPromptLoader(): PromptLoader = FileSystemPromptLoader()