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.

core/inference/src/main/kotlin/com/correx/core/inference/GenerationConfig.kt

@@ -13,4 +13,9 @@ data class GenerationConfig(
     val maxTokens: Int,
     val stopSequences: List<String> = emptyList(),
     val seed: Long? = null,          // null = non-deterministic; set for replay
+    // Sampling knobs. null = omit from the request so the provider/model keeps its own default,
+    // preserving prior behavior. Serialized only when set (top_k / min_p / repeat_penalty).
+    val topK: Int? = null,
+    val minP: Double? = null,
+    val repeatPenalty: Double? = null,
 )