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/inference/llama_cpp/src/main/kotlin/com/correx/infrastructure/inference/llama/cpp/LlamaCppApiModels.kt

@@ -3,6 +3,7 @@ package com.correx.infrastructure.inference.llama.cpp
 import com.correx.core.inference.ChatMessage
 import com.correx.core.inference.ToolCallRequest
 import com.correx.core.inference.ToolDefinition
+import kotlinx.serialization.EncodeDefault
 import kotlinx.serialization.SerialName
 import kotlinx.serialization.Serializable
 
@@ -15,6 +16,12 @@ data class ChatCompletionRequest(
     @SerialName("max_tokens") val maxTokens: Int,
     @SerialName("stop") val stopSequences: List<String> = emptyList(),
     val seed: Long? = null,
+    // EncodeDefault.NEVER overrides the class-level encodeDefaults=true so an unset (null) sampling
+    // knob is omitted from the JSON entirely, letting the model keep its own default (rather than
+    // sending "top_k": null, which llama.cpp may reject or misread).
+    @EncodeDefault(EncodeDefault.Mode.NEVER) @SerialName("top_k") val topK: Int? = null,
+    @EncodeDefault(EncodeDefault.Mode.NEVER) @SerialName("min_p") val minP: Double? = null,
+    @EncodeDefault(EncodeDefault.Mode.NEVER) @SerialName("repeat_penalty") val repeatPenalty: Double? = null,
     val stream: Boolean = false,
     val grammar: String? = null,
     val tools: List<ToolDefinition>? = null,

infrastructure/inference/llama_cpp/src/main/kotlin/com/correx/infrastructure/inference/llama/cpp/LlamaCppInferenceProvider.kt

@@ -170,6 +170,9 @@ class LlamaCppInferenceProvider(
             maxTokens = request.generationConfig.maxTokens,
             stopSequences = request.generationConfig.stopSequences,
             seed = request.generationConfig.seed,
+            topK = request.generationConfig.topK,
+            minP = request.generationConfig.minP,
+            repeatPenalty = request.generationConfig.repeatPenalty,
             stream = false,
             grammar = grammar,
             tools = tools,

infrastructure/inference/llama_cpp/src/test/kotlin/com/correx/infrastructure/inference/llama/cpp/SamplingRequestSerializationTest.kt

@@ -0,0 +1,37 @@
+package com.correx.infrastructure.inference.llama.cpp
+
+import kotlinx.serialization.encodeToString
+import kotlinx.serialization.json.Json
+import kotlin.test.Test
+import kotlin.test.assertFalse
+import kotlin.test.assertTrue
+
+// Guards the EncodeDefault.NEVER behavior on the sampling knobs: with encodeDefaults=true (matching
+// the provider's Json), an unset (null) top_k/min_p/repeat_penalty must be OMITTED from the body so
+// the model keeps its own default; a set value must appear.
+class SamplingRequestSerializationTest {
+    private val json = Json { encodeDefaults = true }
+
+    @Test
+    fun `unset sampling knobs are omitted from the request body`() {
+        val body = ChatCompletionRequest(
+            model = "m", messages = emptyList(), temperature = 0.7, topP = 1.0, maxTokens = 16,
+        )
+        val out = json.encodeToString(body)
+        assertFalse("top_k" in out, out)
+        assertFalse("min_p" in out, out)
+        assertFalse("repeat_penalty" in out, out)
+    }
+
+    @Test
+    fun `set sampling knobs are serialized`() {
+        val body = ChatCompletionRequest(
+            model = "m", messages = emptyList(), temperature = 0.7, topP = 1.0, maxTokens = 16,
+            topK = 40, minP = 0.05, repeatPenalty = 1.1,
+        )
+        val out = json.encodeToString(body)
+        assertTrue("\"top_k\":40" in out, out)
+        assertTrue("\"min_p\":0.05" in out, out)
+        assertTrue("\"repeat_penalty\":1.1" in out, out)
+    }
+}