DeepInnovator

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DeepInnovator: AI Research Assistant - Idea Spark & Scientific Discovery

| 💡 Generate Research Ideas and Hypotheses | 🔗 Discovers Cross-Disciplinary Connections | <br> | 🔍 Research Gap & Trend Analysis | 🛠️ AI-Powered Scientific Problem Solving |

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🔬 DeepInnovator is an AI research copilot powered by our built scientific foundation model trained specifically.

💡 DeepInnovator transforms how researchers discover and develop breakthrough ideas for research discovery.

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🧠 DeepInnovator's Key Features

1. 💡 AI Research Idea Generator

• Autonomously generates innovative research ideas and directions.
• Identifies unexplored opportunities and knowledge gaps in scientific fields.

2. 🔗 Cross-Disciplinary Innovation Engine

• Discovers interdisciplinary research connections and fusion opportunities.
• Synthesizes breakthrough concepts from multiple scientific domains.

3. ❓ Scientific Hypothesis & Question Formation

• Automatically constructs scientifically valuable research questions.
• Generates testable scientific hypotheses and predicts experimental designs.

4. 📊 Research Gap & Trend Analysis

• Intelligently identifies gaps and limitations in current research.
• Predicts development trends and emerging hotspots in scientific fields.

5. ⚙️ Innovation Methodology Framework

• "Standing on shoulders of giants": Extracts innovative insights from vast literature.
• "Conjectures and refutations": Iterative idea generation and optimization.

6. 🎯 Creative Problem-Solving Assistant

• Provides multi-angle solutions for complex scientific problems.
• Inspires researchers' innovative thinking and guides strategic resource allocation.

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🚀 DeepInnovator's Performance

Strong Baseline Improvement:

• DeepInnovator-14B significantly outperforms Qwen-14B-Instruct across all evaluation dimensions.

• It achieves impressive win rates of 80.53%-93.81% against the base model in automated evaluations.

Competitive with top-tier LLMs (GPT-4o and Gemini-2.5-pro)

• Despite smaller parameter size, DeepInnovator matches performance of GPT-4o and Gemini-2.5-pro.

• DeepInnovator even surpasses GPT-4o in well-justified rationale evaluation, scoring 82.3% vs 77.9%

Excellent cross-domain generalization:

• The model shows strong zero-shot transfer capabilities to completely unseen research domains.

• It generates high-quality research ideas in law, education, and biotechnology despite being trained on STEM.

| Domain | Metric | Much Better | Better | Worse | Much Worse | Both Bad | Avg. Winrate (vs Qwen-14B-IT / vs GPT-4o) | |:------:|:------:|:-----------:|:------:|:-----:|:----------:|:--------:|:-----------------------------------:| | Law | Novelty | 0 / 0 | 9 / 7 | 3 / 6 | 1 / 0 | 0 / 0 | 69.2% / 53.8% | | | Feasibility | 1 / 0 | 5 / 4 | 3 / 7 | 1 / 1 | 3 / 1 | 60.0% / 33.3% | | | Effectiveness | 2 / 1 | 5 / 4 | 2 / 3 | 1 / 3 | 3 / 2 | 70.0% / 45.5% | | | Detailedness | 7 / 1 | 3 / 4 | 2 / 5 | 1 / 0 | 0 / 3 | 76.9% / 50.0% | | Education | Novelty | 3 / 0 | 9 / 9 | 2 / 5 | 1 / 1 | 0 / 0 | 80.0% / 60.0% | | | Feasibility | 3 / 0 | 5 / 0 | 5 / 7 | 1 / 3 | 1 / 5 | 57.1% / 0.0% | | | Effectiveness | 2 / 0 | 6 / 0 | 4 / 5 | 3 / 4 | 0 / 6 | 53.3% / 0.0% | | | Detailedness | 1 / 0 | 8 / 4 | 3 / 5 | 0 / 2 | 3 / 4 | 75.0% / 36.4% | | Biotech | Novelty | 3 / 2 | 8 / 6 | 1 / 5 | 0 / 0 | 2 / 2 | 91.7% / 61.5% | | | Feasibility | 2 / 0 | 6 / 5 | 0 / 3 | 0 / 5 | 6 / 2 | 100.0% / 38.5% | | | Effectiveness | 1 / 2 | 6 / 5 | 4 / 2 | 1 / 4 | 2 / 2 | 58.3% / 53.8% | | | Detailedness | 6 / 3 | 4 / 2 | 1 / 4 | 0 / 5 | 3 / 1 | 90.9% / 35.7% |

• DeepInnovator-14B achieves 100% win rate in Biotech Feasibility against Qwen2.5-14B-IT

• Demonstrates 91.7% win rate in Biotech Novelty versus the baseline model

• Secures 90.9% win rate in Biotech Detailedness compared to Qwen2.5-14B-IT

• Maintains 61.5% win rate in Biotech Novelty when benchmarked against GPT-4o

• Shows 60.0% win rate in Education Novelty against the advanced GPT-4o model

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🏗️ DeepInnovator's Architecture

• Intelligent Knowledge Synthesis Pipeline:

• Transforms dense literature into structured cognitive primitives (Insight, Research Trending, Serendipity).
• Mimics human scientific reasoning through hierarchical abstraction and relationship modeling.
• Maintains computational efficiency while preserving semantic completeness.

• Next Idea Prediction Training Paradigm:

• Introduces an iterative refinement framework that models research idea generation as a sequential process.
• Enables continuous predicting, evaluating, and improving of ideas through systematic cycles.
• Mimics the "conjectures and refutations" methodology of authentic scientific discovery.

• Decoupled Reward-Comment RL Architecture:

• First to separate guidance from scoring in scientific domains, solving key RL challenges for creative tasks.
• Prevents reward hacking through independent feedback streams, unlike single-reward RL systems.
• Ensures optimization for genuine idea quality rather than reward model exploitation.

Project Structure

DeepInnovator/
├── recipe/
│   └── DeepInnovator/
│       ├── data_preparation/      # Data preparation pipeline
│       │   ├── config/           # Agent and model configurations
│       │   ├── data_prepare/     # Pipeline scripts
│       │   ├── run.sh           # Quick run script
│       │   └── README.md        # Data preparation documentation
│       ├── config/               # Training configurations
│       │   ├── agent.yaml       # Agent loop configuration
│       │   ├── reward_config.yaml  # Reward function configuration
│       │   └── ResearchGAN_interaction_config.yaml  # Interaction configuration
│       ├── metrics/              # Reward metrics
│       │   ├── basic_reward.py
│       │   ├── delta_reward.py
│       │   └── token_amount.py
│       ├── preprocess.py        # Dataset preprocessing script
│       ├── preprocess.sh        # Preprocessing script runner
│       ├── reward_function.py   # Main reward function
│       ├── DeepInnovator_interation.py  # Interaction logic
│       ├── DeepInnovator_agent_loop.py  # Agent loop implementation
│       ├── train_rl.sh          # Training script
│       └── utils.py             # Utility functions
└── verl/                        # VERL framework (for RL training)

Prerequisites

• Python 3.8+
• CUDA-capable GPU (for training)
• VERL framework (for RL training)
• Required Python packages (see installation section)

Environment Setup

1. Install Dependencies

# Core dependencies
pip install openai omegaconf python-dotenv feedparser requests PyPDF2 tqdm python-dateutil
pip install datasets numpy torch transformers

2. Configure Environment Variables

Create a .env file in the project root:

# API Configuration for data preparation
OPENAI_API_BASE=your_api_base_url
OPENAI_API_KEY=your_api_key

# For training (if needed)
WANDB_API_KEY=your_wandb_api_key
WANDB_BASE_URL=your_wandb_base_url

3. Configure Model Settings

Edit recipe/DeepInnovator/data_preparation/config/models/providers.yaml to set your API endpoints:

openai:
  base_url: ${env:OPENAI_API_BASE}
  api_key: ${env:OPENAI_API_KEY}

Data Preparation

The data preparation pipeline processes academic papers through multiple stages to generate training data.

Quick Start

Run the complete pipeline:

cd recipe/DeepInnovator/data_preparation
bash run.sh [total_papers] [datapath]

Example:

cd recipe/DeepInnovator/data_preparation
bash run.sh 100 ./data/arxiv_data

Step-by-Step Process

Step 1: Download Papers

Download papers from arXiv across predefined categories (cs, stat, q-fin, math):

cd recipe/DeepInnovator/data_preparation
python data_prepare/pull_papers.py --total_papers 100 --datapath ./data/arxiv_data

Parameters:

• --total_papers: Total number of papers to download
• --datapath: Data save path

Output: Papers saved to {datapath}/raw_paper/ directory

Step 2: Extract Target Paper Ideas

Extract ideas from target papers:

cd recipe/DeepInnovator/data_preparation
python data_prepare/get_target_paper_idea.py --datapath ./data/arxiv_data

Output: {datapath}/{paper_id}/target_paper/raw_paper/paper_idea.json

Step 3: Generate Training Data

Process papers through the full pipeline (step1-step4) to generate training data:

cd recipe/DeepInnovator/data_preparation
python data_prepare/get_training_data.py

Output Structure:

• layer0/: Paper analysis results
• layer1/: Paper groups and memories
• layer2/: Connections, serendipity, and trends
• insights/: Generated research ideas

Data Preprocessing

After generating training data, preprocess it for RL training:

cd recipe/DeepInnovator
python preprocess.py \
    --input_dir ./data/arxiv_data \
    -