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/config.py

@@ -0,0 +1,72 @@
+"""
+Experiment configuration for 5-condition idea generation study.
+"""
+
+from typing import Literal
+from pathlib import Path
+
+# Paths
+EXPERIMENTS_DIR = Path(__file__).parent
+DATA_DIR = EXPERIMENTS_DIR / "data"
+RESULTS_DIR = EXPERIMENTS_DIR / "results"
+DOCS_DIR = EXPERIMENTS_DIR / "docs"
+
+# LLM Settings
+MODEL = "qwen3:8b"
+TEMPERATURE = 0.9
+
+# Expert Settings
+EXPERT_COUNT = 4
+EXPERT_SOURCE: Literal["curated", "llm", "dbpedia", "wikidata"] = "curated"
+KEYWORDS_PER_EXPERT = 1
+
+# Language Settings
+PROMPT_LANGUAGE: Literal["en", "zh"] = "en"
+
+# Attribute Settings
+FIXED_CATEGORIES = ["Functions", "Usages", "User Groups", "Characteristics"]
+
+# Deduplication Settings
+DEDUP_THRESHOLD = 0.85
+DEDUP_METHOD: Literal["embedding", "llm"] = "embedding"
+
+# Reproducibility
+RANDOM_SEED = 42
+
+# Idea Generation Settings
+IDEAS_PER_EXPERT = 5  # For C2 and C5
+IDEAS_DIRECT = 20     # For C1
+
+# Condition Names
+CONDITIONS = [
+    "c1_direct",
+    "c2_expert_only",
+    "c3_attribute_only",
+    "c4_full_pipeline",
+    "c5_random_perspective",
+]
+
+# Condition Display Names
+CONDITION_NAMES = {
+    "c1_direct": "C1: Direct Generation",
+    "c2_expert_only": "C2: Expert-Only",
+    "c3_attribute_only": "C3: Attribute-Only",
+    "c4_full_pipeline": "C4: Full Pipeline",
+    "c5_random_perspective": "C5: Random-Perspective",
+}
+
+# Summary Config Dict (for logging/reporting)
+EXPERIMENT_CONFIG = {
+    "model": MODEL,
+    "temperature": TEMPERATURE,
+    "expert_count": EXPERT_COUNT,
+    "expert_source": EXPERT_SOURCE,
+    "keywords_per_expert": KEYWORDS_PER_EXPERT,
+    "prompt_language": PROMPT_LANGUAGE,
+    "random_seed": RANDOM_SEED,
+    "categories": FIXED_CATEGORIES,
+    "dedup_threshold": DEDUP_THRESHOLD,
+    "dedup_method": DEDUP_METHOD,
+    "ideas_per_expert": IDEAS_PER_EXPERT,
+    "ideas_direct": IDEAS_DIRECT,
+}