feat: Add experiments framework and novelty-driven agent loop

- Add complete experiments directory with pilot study infrastructure
  - 5 experimental conditions (direct, expert-only, attribute-only, full-pipeline, random-perspective)
  - Human assessment tool with React frontend and FastAPI backend
  - AUT flexibility analysis with jump signal detection
  - Result visualization and metrics computation

- Add novelty-driven agent loop module (experiments/novelty_loop/)
  - NoveltyDrivenTaskAgent with expert perspective perturbation
  - Three termination strategies: breakthrough, exhaust, coverage
  - Interactive CLI demo with colored output
  - Embedding-based novelty scoring

- Add DDC knowledge domain classification data (en/zh)
- Add CLAUDE.md project documentation
- Update research report with experiment findings

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

experiments/novelty_loop/__init__.py

@@ -0,0 +1,42 @@
+"""
+Novelty-Driven LLM Agent Loop
+
+An autonomous agent that generates tasks using novelty as the termination condition.
+"""
+
+from .agent import (
+    NoveltyDrivenTaskAgent,
+    ExhaustFrontierAgent,
+    CoverageTargetAgent,
+    GeneratedTask,
+    TaskGenerationResult,
+    ExpertProvider,
+    DomainProvider,
+)
+
+from .novelty_metrics import (
+    NoveltyMetrics,
+    NoveltyScore,
+    NoveltyTrajectory,
+    compute_batch_novelty,
+    find_most_novel,
+)
+
+__all__ = [
+    # Agents
+    "NoveltyDrivenTaskAgent",
+    "ExhaustFrontierAgent",
+    "CoverageTargetAgent",
+    # Data classes
+    "GeneratedTask",
+    "TaskGenerationResult",
+    "NoveltyScore",
+    "NoveltyTrajectory",
+    # Providers
+    "ExpertProvider",
+    "DomainProvider",
+    # Metrics
+    "NoveltyMetrics",
+    "compute_batch_novelty",
+    "find_most_novel",
+]