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[ICML 2026] InnoEval: On Research Idea Evaluation as a Knowledge-Grounded, Multi-Perspective Reasoning Problem

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<div align="center">
  <h1>InnoEval: On Research Idea Evaluation as a Knowledge-Grounded, Multi-Perspective Reasoning Problem</h1>
</div>
<p align="center">
  <a href="https://arxiv.org/abs/2602.14367">📄arXiv</a> •
  <a href="http://innoeval.zjukg.cn/">🌐Demo</a>
</p>
<p align="center">
  <a href="https://github.com/zjunlp/InnoEval">
  	<img src="https://awesome.re/badge.svg" alt="Awesome">
  </a>
  <a href="https://github.com/zjunlp/InnoEval/blob/main/LICENSE">
    <img src="https://img.shields.io/badge/License-MIT-green.svg" alt="License: MIT">
  </a>
  <img src="https://img.shields.io/github/last-commit/zjunlp/InnoEval?color=blue" alt="Last Commit">
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<h5 align="center"> If you like our project, please give us a star on GitHub for the latest updates!</h5>

---

## 🔔 News

- **[2026-05]** We release SciAtas! A Large-Scale Knowledge Graph as the foundation for Automated Scientific Research. Try it through [here](https://github.com/zjunlp/SciAtlas)!

![method](https://raw.githubusercontent.com/zjunlp/InnoEval/main/asset/method.png)

**InnoEval** is an automated evaluation framework designed for assessing research ideas and innovation proposals. It leverages multi-agent systems and LLMs to comprehensively evaluate the novelty, feasibility, and significance of research contributions.

* **Multi-Agent Pipeline**
  <br> A chain of specialized agents (Extraction, Research, Grounding, Evaluation, Report) working together

* **Multi-Source Grounding**
  <br> Gathers evidence from web pages, code repositories, and academic papers to validate claims

* **Persona-Based Evaluation**
  <br> Simulates multiple reviewer perspectives for balanced and comprehensive assessment

* **Flexible Input Modes**
  <br> Supports both PDF URLs and direct text input for research ideas

* **Batch Processing**
  <br> Point-wise and group-wise evaluation for large-scale dataset analysis

## Table of Contents

* [📥 Installation](#📥-installation)
* [🎬 Quick Start](#🎬-quick-start)
* [📂 Architecture](#📂-architecture)
* [🔬 Examples](#🔬-examples)
* [🛠 Configuration](#🛠-configuration)
* [📄 Acknowledgement](#📄-acknowledgement)
* [✍️ Citation](#✍️-citation)

## 📥 Installation

### 1. Clone the Repository

```bash
git clone https://github.com/your-org/InnoEval.git
cd InnoEval
```

### 2. Create Virtual Environment

```bash
conda create -n innoeval python=3.10 -y
conda activate innoeval
```

### 3. Install Dependencies

```bash
pip install -r requirements.txt
```

### 4. Configure API Keys

Copy the example configuration file and fill in your API keys:

```bash
cd config/
cp LLM.env.example LLM.env
# Edit LLM.env with your API keys
```

Required API keys:
| Key | Description |
|-----|-------------|
| `DS_API_KEY` | DeepSeek API key (primary LLM) |
| `DS_API_BASE_URL` | DeepSeek API base URL |
| `OPENAI_API_KEY` | OpenAI API key (optional) |
| `GOOGLE_API_KEY` | Google Search API key |
| `SERPER_API_KEY` | Serper API key for web search |
| `JINA_API_KEY` | Jina API key for content extraction |
| `S2_API_KEY` | Semantic Scholar API key |
| `GH_TOKEN` | GitHub token for repository analysis |

## 🎬 Quick Start

### 1. Single Idea Evaluation

Run the complete pipeline for a single research idea:

```bash
cd InnoEval
python3 -m innoeval.pipeline.single_idea_pipeline
```

This executes the full 6-step pipeline:
1. **ExtractionAgent**: Extract structured idea from PDF/text
2. **ResearchAgent**: Search for related works (web, code, papers)
3. **Report Extraction**: Build evidence reports from search results
4. **GroundingAgent**: Map claims to supporting evidence
5. **EvaluationAgent**: Multi-perspective quality assessment
6. **ReportAgent**: Generate final evaluation report

### 2. Point-wise Dataset Evaluation

Evaluate an entire dataset of research papers:

```bash
python3 -m innoeval.pipeline.batch_pipeline
```

Results are saved to `cache/dataset_conference_points/`.

### 3. Group Dataset Evaluation

Process papers organized in groups:

```bash
python3 -m innoeval.pipeline.group_pipeline
```

Results are saved to `cache/dataset_conference_groups/`.

### 4. Group/Pair Evaluation

Run comparison evaluation on cached group results:

```bash
# Group-wise comparison and ranking
python3 -m innoeval.pipeline.group_evaluation

# Pair-wise comparison
python3 -m innoeval.pipeline.pair_evaluation
```

These scripts read from `cache/dataset_conference_groups/` and do not re-run the pipeline.

## 📂 Architecture

### Directory Structure

```
InnoEval/
├── config/                     # Configuration files
│   ├── LLM.env                 # API keys (not tracked)
│   ├── LLM.env.example         # Example configuration
│   ├── kaggle.json             # Kaggle API config
│   └── reviewer_personas.json  # Reviewer personas
├── dataset/                    # Evaluation datasets
│   ├── conference_points.jsonl # Point-wise dataset
│   ├── conference_groups.json  # Group-wise dataset
│   └── conference_pairs_*.json # Pair datasets
├── cache/                      # Pipeline results cache
└── innoeval/                   # Main package
    ├── mas/                    # Multi-Agent System
    │   ├── agents/             # Agent implementations
    │   │   ├── extraction_agent.py
    │   │   ├── research_agent.py
    │   │   ├── grounding_agent.py
    │   │   ├── evaluation_agent.py
    │   │   └── report_agent.py
    │   ├── models/             # LLM and model interfaces
    │   │   ├── model_factory.py
    │   │   └── bge_singleton.py
    │   └── tools/              # Utility tools
    │       ├── searchers/      # Web/code/paper search
    │       ├── querygen/       # Query generation
    │       ├── enricher/       # Content enrichment
    │       ├── grobid_refs/    # Reference extraction
    │       └── repo_analysis/  # GitHub repo analysis
    └── pipeline/               # Pipeline implementations
        ├── single_idea_pipeline.py
        ├── batch_pipeline.py
        ├── group_pipeline.py
        ├── group_evaluation.py
        └── pair_evaluation.py
```

### Pipeline Workflow

```
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│  Input: PDF URL │───▶│ ExtractionAgent │───▶│   Idea Object   │
│  or Text Input  │    │   (Extract)     │    │  (structured)   │
└─────────────────┘    └─────────────────┘    └────────┬────────┘
                                                       │
                                                       ▼
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Web Pages     │    │  ResearchAgent  │───▶│  SearchResults  │
│   Code Repos    │◀───│    (Search)     │    │   (enriched)    │
│   Papers        │    └─────────────────┘    └────────┬────────┘
└─────────────────┘                                     │
                                                        ▼
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Claims Map    │◀───│ GroundingAgent  │◀───│  Reports Data   │
│  (evidence)     │    │   (Grounding)   │    │  (extracted)    │
└────────┬────────┘    └─────────────────┘    └─────────────────┘
         │
         ▼
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Personas      │───▶│EvaluationAgent  │───▶│ EvaluationResult│
│  (reviewers)    │    │   (Evaluate)    │    │   (per-persona) │
└─────────────────┘    └─────────────────┘    └────────┬────────┘
                                                       │
                                                       ▼
                                              ┌─────────────────┐
                                              │  ReportAgent    │
                                              │  (Synthesize)   │
                                              └────────┬────────┘
                                                       │
                                                       ▼
                                              ┌─────────────────┐
                                              │  Final Report   │
                                              │  (Markdown)     │
                                              └─────────────────┘
```

### Evaluation Dimensions

The framework evaluates research ideas across five core dimensions:

| Dimension | Description |
|-----------|-------------|
| **Clarity** | How clearly the idea is presented and explained |
| **Novelty** | Originality and innovation compared to existing work |
| **Validity** | Soundness of methodology and theoretical foundations |
| **Feasibility** | Practical implementability with available resources |
| **Significance** | Potential impact and contribution to the field |

Custom evaluation metrics can be added through the `user_metric` parameter.

## 🔬 Examples

### Example 1: Evaluate from PDF URL

```python
import asyncio
from pathlib import Path
from innoeval.pipeline.single_idea_pipeline import SingleIdeaPipeline

async def evaluate_paper():
    pipeline = SingleIdeaPipeline(
        input_type="pdf",
        pdf_url="https://openreview.net/pdf?id=YOUR_PAPER_ID",
        cache_path=Path("cache/my_paper.json"),
        persona_path=Path("config/reviewer_personas.json"),
        research_params={
            "title": "Your Paper Title",
            "after": "2022-01-01",
            "before": "2024-01-01",
            "depth": 3,
        },
        num_personas=5,
        get_future_paper=True,
    )
    result = await pipeline.run()
    print(result["final_report"])

asyncio.run(evaluate_paper())
```

### Example 2: Evaluate from Text

```python
import asyncio
from pathlib import Path
from innoeval.pipeline.single_idea_pipeline import SingleIdeaPipeline

async def evaluate_idea():
    idea_text = """
    This paper introduces a novel approach to automated code review
    using large language models with retrieval-augmented generation...
    """

pipeline = SingleIdeaPipeline(
        input_type="text",
        idea_text=idea_text,
        cache_path=Path("cache/my_idea.json"),
        research_params={
            "title": "LLM-based Code Review",
            "after": "2023-01-01",
            "before": "2024-12-01",
        },
        num_personas=3,
    )
    result = await pipeline.run()
    print(result["final_decision"])

asyncio.run(evaluate_idea())
```

### Example 3: Custom Evaluation Metrics

```python
# The evaluation agent supports custom metrics
eval_params = {
    "temperature": 0.7,
    "user_metric": [
        {
            "metric": "Reproducibility",
            "description": "Evaluate whether sufficient detail is provided for reproduction"
        },
        {
            "metric": "EthicalConsiderations",
            "description": "Assess potential ethical implications and mitigation strategies"
        }
    ]
}
```

### Example 4: Batch Processing with Custom Dataset

```python
# Create a JSONL file with format:
# {"paper_id": "xxx", "title": "...", "decision": "accept"}
# Then run:
# python3 -m innoeval.pipeline.batch_pipeline

# Or programmatically:
from innoeval.pipeline.batch_pipeline import load_dataset, process_paper

items = load_dataset(Path("dataset/my_papers.jsonl"), num=10)
for item in items:
    print(f"Processing: {item.title}")
```

## 🛠 Configuration

### LLM Configuration

The `config/LLM.env` file controls all API settings:

```bash
# Primary LLM (DeepSeek)
DS_API_KEY=your_deepseek_key
DS_API_BASE_URL=https://api.deepseek.com/v1

# OpenAI (alternative)
OPENAI_API_KEY=your_openai_key
OPENAI_API_BASE_URL=https://api.openai.com/v1

# Search APIs
GOOGLE_API_KEY=your_google_key
SERPER_API_KEY=your_serper_key
JINA_API_KEY=your_jina_key
S2_API_KEY=your_semantic_scholar_key

# GitHub
GH_TOKEN=your_github_token

# Kaggle (optional)
KAGGLE_CONFIG_DIR=./config
```

### Model Configuration

The default model configuration in `SingleIdeaPipeline`:

```python
model_config = {
    "models": {
        "default_provider": "dsr1",
        "dsr1": {
            "model_name": "deepseek-v3.2",
            "api_key": os.getenv("DS_API_KEY"),
            "base_url": os.getenv("DS_API_BASE_URL"),
            "max_tokens": 4096,
            "temperature": 0.7,
        },
    }
}
```

### Agent Parameters

| Agent | Key Parameters |
|-------|----------------|
| **ExtractionAgent** | `extract_temperature: 0.3` |
| **ResearchAgent** | `top_k: 10`, `max_results_per_query: 5`, `web_max_results: 5`, `github_max_results: 5` |
| **GroundingAgent** | `extract_temperature: 0.0` |
| **EvaluationAgent** | `temperature: 0.7`, `num_personas: 5` |
| **ReportAgent** | `temperature: 0.4` |

### Research Parameters

| Parameter | Type | Description |
|-----------|------|-------------|
| `title` | str | Paper title for search optimization |
| `after` | str | Search papers after this date (YYYY-MM-DD) |
| `before` | str | Search papers before this date (YYYY-MM-DD) |
| `depth` | int | Search depth (1-5) |
| `web_temperature` | float | Temperature for web search queries |
| `code_temperature` | float | Temperature for code search queries |

### Cache Structure

Pipeline results are cached in JSON format:

```json
{
  "extraction_result": {...},
  "search_results_dict": {...},
  "reports_data": {...},
  "grounding_result": {...},
  "evaluation_result": {...},
  "final_report": "...",
  "final_decision": "accept/reject",
  "total_time": 123.45,
  "total_token": 50000
}
```

## 📄 Acknowledgement

This project builds upon and draws inspiration from the following open-source projects:

### InternAgent
We thank the [InternAgent](https://github.com/InternScience/InternAgent) project for providing foundational multi-agent architecture patterns and evaluation methodologies that influenced our pipeline design.

### RepoMaster
We thank [RepoMaster](https://github.com/QuantaAlpha/RepoMaster) for the repository analysis toolkit that enables comprehensive code repository evaluation in our grounding process.

---

## ✍️ Citation

If you find our work helpful, please use the following citations.

```
@misc{qiao2026innoevalresearchideaevaluation,
      title={InnoEval: On Research Idea Evaluation as a Knowledge-Grounded, Multi-Perspective Reasoning Problem}, 
      author={Shuofei Qiao and Yunxiang Wei and Xuehai Wang and Bin Wu and Boyang Xue and Ningyu Zhang and Hossein A. Rahmani and Yanshan Wang and Qiang Zhang and Keyan Ding and Jeff Z. Pan and Huajun Chen and Emine Yilmaz},
      year={2026},
      eprint={2602.14367},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2602.14367}, 
}
```

### License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

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