---
license: apache-2.0
language:
- en
task_categories:
- question-answering
- text-generation
pretty_name: DeepSynth Bench
annotations_creators:
- expert-annotators
source_datasets:
- original
paper:
  title: "A Benchmark for Deep Information Synthesis"
  conference: "ICLR 2026"
---

# DEEPSYNTH: A Benchmark for Deep Information Synthesis

<div align="center">
<img src="assets/octopus_logo.png" alt="DEEPSYNTH Bench" width="400"/>
</div>


<div align="center">
   <strong>Published at ICLR 2026</strong> &nbsp;|&nbsp;
  <a href="https://openreview.net/pdf?id=0Dhpt9aY3n">📄 Paper</a> &nbsp;|&nbsp;
  <a href="https://github.com/agentdeepsynthesis/deepsynth-bench">💻 Code</a> &nbsp;|&nbsp;
  <a href="https://agentdeepsynthesis.github.io/deepsynth.github.io/">🌐 Project Page</a>

![Image](assets/deepsynth_figure1.gif)
</div>



## Overview

**DEEPSYNTH-Bench** is a challenging benchmark for evaluating *deep information synthesis* — the ability of AI systems to integrate, reason over, and consolidate multi-source information into precise, structured answers.

Unlike benchmarks focused on retrieval or single-hop reasoning, DEEPSYNTH-Bench requires models to:
- Chain multiple reasoning steps across heterogeneous sources
- Produce structured JSON outputs with specific keys and values
- Demonstrate analytical depth, not just surface-level extraction

The benchmark includes a public **dev set of 40 tasks** with gold answers, full decompositions, and intermediate steps for iterative development, and a **test set of 80 tasks** (questions only) for clean evaluation — **120 tasks in total**.

---

## Repository Structure


```
deepsynth-bench/
├── README.md                  # This dataset card
├── data/
│   ├── test.jsonl   # Full test set (80 tasks)
│   └── dev.jsonl    # Dev/Lite split for prototyping ((40 tasks))
├── evaluation/
│   ├── evaluate.py            # Evaluation script (F1, EM, LLM-Judge)
│   └── llm_judge_prompt.txt   # Prompt used for LLM-as-a-judge metric
├── assets/
│   └── octopus_logo.png       # Project logo
└── LICENSE                    # CC-BY-4.0
```

---

## Dataset Files

| File | Split | Size | Description |
|------|-------|------|-------------|
| `dev.json` | Dev | 40 tasks | Questions, gold answers, reasoning plans, and full decompositions with intermediate steps |
| `test.json` | Test | 80 tasks | Questions only — submit answers for evaluation |

---

## Loading the Data

```python
import json
from huggingface_hub import hf_hub_download

# Dev set — includes gold answers
dev_path = hf_hub_download(
    repo_id="DeepSynthesisTeam/deepsynth-bench",
    filename="data/dev.json",
    repo_type="dataset"
)
with open(dev_path, "r") as f:
    dev_set = json.load(f)

# Test set — questions only
test_path = hf_hub_download(
    repo_id="DeepSynthesisTeam/deepsynth-bench",
    filename="data/test.json",
    repo_type="dataset"
)
with open(test_path, "r") as f:
    test_set = json.load(f)
```

---

## Prediction Format

Model predictions should be a JSON file mapping task IDs to answer dictionaries:

```json
{
  "001": {"Sweden": 1.2, "Finland": 0.8},
  "002": {"Brunei": -0.67, "Singapore": -0.34}
}
```

---

## Evaluation

Evaluation scripts are available in the [GitHub repository](https://github.com/agentdeepsynthesis/deepsynth-bench).

| Metric | Description |
|--------|-------------|
| **Exact Match (EM)** | All keys and values must be exactly correct |
| **F1 Score** | Partial credit for correct key-value pairs |
| **LLM Judge** | Semantic equivalence; allows small numerical margins (1–5.5%) |

```bash
# Clone the repository to access evaluation scripts
git clone https://github.com/agentdeepsynthesis/deepsynth-bench.git
cd deepsynth-bench

# Run EM + F1 evaluation
python scripts/evaluation/eval_static_score.py your_predictions.json

# Run LLM-as-judge evaluation
python scripts/evaluation/llm_judge.py your_predictions.json
```

---

## 🧩 Decompositions & Validation Schemas

### Decomposition Files (`decompositions/*.json`)

Each file (e.g., `001.json`) maps the logical sub-steps required to solve the corresponding question. These decompositions support step-by-step evaluation and can be used to guide or audit model reasoning chains.

### Validation Schemas (`intermediate_answers_schemas/`)

Each decomposition has a matching JSON Schema (e.g., `001.schema.json`) that defines the expected format for intermediate answer fields. Use these to programmatically validate whether a model's intermediate outputs conform to the expected structure.

---


## Citation

If you use DEEPSYNTH-Bench in your research, please cite:

```bibtex
@inproceedings{paul-etal-2026-deepinfosynth,
  title = {A Benchmark for Deep Information Synthesis},
  author = {Paul, Debjit and Murphy, Daniel and Gritta, Milan and Cardenas, Ronald and Prokhorov, Victor and Bolliger, Lena Sophia and Toker, Aysim and Miles, Roy and Oncescu, Andreea-Maria and Sivakumar, Jasivan Alex and Borchert, Philipp and Elezi, Ismail and Zhang, Meiru and Lee, Ka Yiu and Zhang, Guchun and Wang, Jun and Lampouras, Gerasimos},
  booktitle = {The Fourteenth International Conference on Learning Representations},
  month = apr,
  year = {2026},
}
```


### License
We follow Apache License Version 2.0. Please see the [License](LICENSE) file for more information.

Disclaimer: This open source project is not an official Huawei product, Huawei is not expected to provide support for this project.