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>
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experiments/conditions/c5_random_perspective.py
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178
experiments/conditions/c5_random_perspective.py
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"""
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Condition 5: Random-Perspective Control
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Uses random words as "perspectives" instead of domain experts.
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Tests whether the benefit from expert perspectives comes from
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domain knowledge or simply from any perspective shift.
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"""
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import sys
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import json
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import random
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from pathlib import Path
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# Add backend to path for imports
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sys.path.insert(0, str(Path(__file__).parent.parent.parent / "backend"))
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from typing import List, Dict, Any
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from app.services.llm_service import ollama_provider, extract_json_from_response
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from experiments.config import (
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MODEL, TEMPERATURE, EXPERT_COUNT, IDEAS_PER_EXPERT,
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PROMPT_LANGUAGE, RANDOM_SEED, DATA_DIR
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)
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def load_random_words() -> List[str]:
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"""Load the random word pool from data file."""
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words_file = DATA_DIR / "random_words.json"
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with open(words_file, "r", encoding="utf-8") as f:
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data = json.load(f)
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return data.get("words", [])
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def get_random_perspective_prompt(
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query: str,
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perspective_word: str,
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idea_count: int,
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lang: str = "en"
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) -> str:
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"""Generate prompt for random-perspective idea generation."""
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if lang == "en":
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return f"""/no_think
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Generate {idea_count} creative and innovative ideas for "{query}" inspired by the concept of "{perspective_word}".
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Requirements:
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1. Each idea should draw inspiration from "{perspective_word}" - its qualities, characteristics, or associations
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2. Think about how concepts related to "{perspective_word}" could improve or reimagine "{query}"
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3. Ideas should be specific and actionable (15-30 words each)
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4. Be creative in connecting "{perspective_word}" to "{query}"
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Return JSON only:
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{{"ideas": ["idea 1", "idea 2", "idea 3", ...]}}
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Generate exactly {idea_count} ideas inspired by "{perspective_word}"."""
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else:
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return f"""/no_think
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為「{query}」生成 {idea_count} 個創意點子，靈感來自「{perspective_word}」這個概念。
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要求：
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1. 每個點子要從「{perspective_word}」獲得靈感——它的特質、特徵或聯想
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2. 思考與「{perspective_word}」相關的概念如何改進或重新想像「{query}」
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3. 點子要具體可行（每個 15-30 字）
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4. 創意地連接「{perspective_word}」和「{query}」
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只回傳 JSON：
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{{"ideas": ["點子1", "點子2", "點子3", ...]}}
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生成正好 {idea_count} 個受「{perspective_word}」啟發的點子。"""
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async def generate_ideas(
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query: str,
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model: str = None,
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temperature: float = None,
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word_count: int = None,
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ideas_per_word: int = None,
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lang: str = None,
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seed: int = None
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) -> Dict[str, Any]:
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"""
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Generate ideas using random word perspectives (C5 control).
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Args:
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query: The object/concept to generate ideas for
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model: LLM model to use
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temperature: Generation temperature
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word_count: Number of random words to use (matches expert count)
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ideas_per_word: Ideas to generate per word
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lang: Language for prompts
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seed: Random seed for reproducibility
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Returns:
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Dict with ideas and metadata
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"""
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model = model or MODEL
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temperature = temperature or TEMPERATURE
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word_count = word_count or EXPERT_COUNT
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ideas_per_word = ideas_per_word or IDEAS_PER_EXPERT
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lang = lang or PROMPT_LANGUAGE
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seed = seed or RANDOM_SEED
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# Load word pool and sample random words
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word_pool = load_random_words()
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# Use seeded random for reproducibility
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# Create a unique seed per query to get different words for different queries
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# but same words for same query across runs
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query_seed = seed + hash(query) % 10000
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rng = random.Random(query_seed)
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selected_words = rng.sample(word_pool, min(word_count, len(word_pool)))
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all_ideas = []
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word_details = []
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for word in selected_words:
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prompt = get_random_perspective_prompt(
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query=query,
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perspective_word=word,
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idea_count=ideas_per_word,
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lang=lang
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)
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response = await ollama_provider.generate(
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prompt=prompt,
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model=model,
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temperature=temperature
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)
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result = extract_json_from_response(response)
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ideas = result.get("ideas", [])
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# Tag ideas with perspective word source
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for idea in ideas:
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all_ideas.append({
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"idea": idea,
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"perspective_word": word
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})
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word_details.append({
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"word": word,
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"ideas_generated": len(ideas)
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})
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return {
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"condition": "c5_random_perspective",
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"query": query,
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"ideas": [item["idea"] for item in all_ideas],
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"ideas_with_source": all_ideas,
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"idea_count": len(all_ideas),
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"metadata": {
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"model": model,
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"temperature": temperature,
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"prompt_language": lang,
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"word_count": word_count,
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"ideas_per_word": ideas_per_word,
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"random_seed": seed,
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"query_seed": query_seed,
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"selected_words": selected_words,
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"word_details": word_details,
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"word_pool_size": len(word_pool),
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"mechanism": "random_perspective_control"
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}
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}
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# For testing
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if __name__ == "__main__":
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import asyncio
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async def test():
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result = await generate_ideas("Chair")
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print(f"Generated {result['idea_count']} ideas from {len(result['metadata']['selected_words'])} random words:")
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print(f" Words used: {', '.join(result['metadata']['selected_words'])}")
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print(f" Seed: {result['metadata']['random_seed']}, Query seed: {result['metadata']['query_seed']}")
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print("\nSample ideas:")
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for i, item in enumerate(result['ideas_with_source'][:5], 1):
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print(f" {i}. [{item['perspective_word']}] {item['idea']}")
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asyncio.run(test())
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