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/workflow/src/main/kotlin/com/correx/infrastructure/workflow/ExecutionPlanCompiler.kt

@@ -59,12 +59,6 @@ private const val DEFAULT_STAGE_TOKEN_BUDGET = 16384
 // default, or the model is truncated (finishReason=length) mid-artifact — and a degenerating local
 // model burns the whole 2048 on garbage (e.g. a `<|channel>thought` repetition loop) before it can
 // stop. Mirror the static TomlWorkflowLoader path: pin the completion cap to the stage token budget.
-private val DEFAULT_STAGE_GENERATION = GenerationConfig(
-    temperature = 0.7,
-    topP = 1.0,
-    maxTokens = DEFAULT_STAGE_TOKEN_BUDGET,
-)
-
 class ExecutionPlanCompiler(
     private val registry: ArtifactKindRegistry,
     // Names of every registered tool. A stage that references a tool the runtime can't resolve
@@ -79,7 +73,11 @@ class ExecutionPlanCompiler(
     // retries (Vikunja #41). Off by default so the compiler's own unit tests see only plan stages;
     // the server's freestyle path (Main.kt) turns it on.
     private val injectRecovery: Boolean = false,
+    // Operator sampling defaults for freestyle-compiled stages; maxTokens pinned to the stage budget.
+    // Default reproduces the former hardcoded temperature=0.7/topP=1.0.
+    private val samplingDefaults: GenerationConfig = GenerationConfig(temperature = 0.7, topP = 1.0, maxTokens = 0),
 ) {
+    private val defaultStageGeneration = samplingDefaults.copy(maxTokens = DEFAULT_STAGE_TOKEN_BUDGET)
     private val mapper = JsonMapper.builder()
         .addModule(kotlinModule())
         .disable(DeserializationFeature.FAIL_ON_UNKNOWN_PROPERTIES)
@@ -150,7 +148,7 @@ class ExecutionPlanCompiler(
                 autoBuildGate = s.id == autoGateStageId,
                 semanticReview = s.semanticReview,
                 tokenBudget = DEFAULT_STAGE_TOKEN_BUDGET,
-                generationConfig = DEFAULT_STAGE_GENERATION,
+                generationConfig = defaultStageGeneration,
                 metadata = mapOf("promptInline" to s.prompt),
             )
         }
@@ -168,7 +166,7 @@ class ExecutionPlanCompiler(
                 StageId(RECOVERY_STAGE) to StageConfig(
                     allowedTools = knownTools.ifEmpty { setOf("file_write", "file_edit", "shell") },
                     tokenBudget = DEFAULT_STAGE_TOKEN_BUDGET,
-                    generationConfig = DEFAULT_STAGE_GENERATION,
+                    generationConfig = defaultStageGeneration,
                     metadata = mapOf("role" to "recovery", "promptInline" to RECOVERY_PROMPT),
                 )
             }

infrastructure/workflow/src/main/kotlin/com/correx/infrastructure/workflow/TomlWorkflowLoader.kt

@@ -76,6 +76,9 @@ private val mapper = TomlMapper.builder()
 
 class TomlWorkflowLoader(
     private val registry: ArtifactKindRegistry = DefaultArtifactKindRegistry(),
+    // Operator sampling defaults applied to every stage's inference request (maxTokens is still pinned
+    // per-stage to the token budget). Defaults reproduce the former hardcoded temperature=0.7/topP=1.0.
+    private val samplingDefaults: GenerationConfig = GenerationConfig(temperature = 0.7, topP = 1.0, maxTokens = 0),
 ) : WorkflowLoader {
     override fun load(path: Path): WorkflowGraph {
         val raw = path.readText()
@@ -116,11 +119,7 @@ class TomlWorkflowLoader(
                 // Propagate the declared token budget to the inference completion cap.
                 // Without this the StageConfig default (maxTokens=2048) is used, truncating
                 // larger artifacts (finishReason=length) → invalid JSON → validation failure.
-                generationConfig = GenerationConfig(
-                    temperature = 0.7,
-                    topP = 1.0,
-                    maxTokens = s.tokenBudget,
-                ),
+                generationConfig = samplingDefaults.copy(maxTokens = s.tokenBudget),
                 maxRetries = s.maxRetries,
                 metadata = s.toMetadata(workflowDir),
             )