Overhaul model system with multi-stage training, variants, benchmarks, and eval

Replace the single-stage training + flat capability score with a realistic AI
development pipeline: pre-training with Chinchilla scaling laws, SFT with
specializations, alignment with safety/capability tradeoffs (RLHF/DPO/Constitutional),
model families with distillation/fine-tuning/quantization variants, named benchmark
suite with compute-costing eval jobs, and segment-specific market quality.

Phases 1-6 of the model rework plan: new types, engine rewrite, save migration,
training events/risk system, concurrent training, variant creation, benchmark
evaluation with leaderboard, and market integration.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

packages/game-engine/src/tick.ts

@@ -40,13 +40,14 @@ export function processTick(state: GameState): Partial<GameState> {
   const stateWithInfra = { ...state, infrastructure };
   const modelResult = processModels(stateWithInfra);
 
-  if (modelResult.modelCompleted) {
+  for (const completed of modelResult.completedModels) {
     notifications.push({
       title: 'Training Complete',
-      message: `${modelResult.modelCompleted.name} is ready! Benchmark: ${modelResult.modelCompleted.benchmarkScore.toFixed(1)}/100`,
+      message: `${completed.name} is ready! Capability: ${completed.rawCapability.toFixed(1)}/100`,
       type: 'success',
     });
   }
+  notifications.push(...modelResult.notifications);
 
   const stateWithModels = { ...stateWithInfra, models: modelResult.modelsState };