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>

apps/web/src/components/dev/StateInspectionTab.tsx

@@ -107,9 +107,9 @@ export function StateInspectionTab() {
         <Stat label="Completed" value={research.completedResearch.length} />
         <Stat label="Points" value={research.researchPoints.toFixed(1)} />
         <Stat label="Active" value={research.activeResearch?.researchId ?? 'None'} />
-        <Stat label="Models" value={models.trainedModels.length} />
-        <Stat label="Training" value={models.activeTraining?.modelName ?? 'None'} />
-        <Stat label="Deployed" value={models.trainedModels.filter(m => m.isDeployed).length} />
+        <Stat label="Models" value={models.baseModels.length} />
+        <Stat label="Training" value={models.activeTrainingPipelines.filter(p => p.status === 'active').length} />
+        <Stat label="Deployed" value={models.baseModels.filter(m => m.isDeployed).length} />
       </Section>
     </div>
   );