---
license: other
pretty_name: Into the Unknown
license_name: mixed
license_link: LICENSE
tags:
- agents
- ai-scientist
- scientific-discovery
- benchmark
task_categories:
- text-generation
configs:
- config_name: benchmark
  data_files: benchmark/index.csv
- config_name: outcomes
  data_files: outcomes/index.csv
- config_name: trajectories
  data_files: trajectories/index.csv
---

# Into the Unknown

**Dataset Summary**

Into the Unknown tests whether AI agents can build on a scientist's work and write a follow-up paper. Each of its 15 tasks gives an agent a published (or recently submitted) paper and a follow-up research direction proposed by the paper's own author; the agent must carry out the research and write a follow-up paper that is honest, complete, and meets the scientist's bar.

This dataset holds the 15 tasks and everything nine agents produced on them: 135 submissions, with each agent's paper, full trajectory, and the files it wrote. Code, rubrics and graders are on [GitHub](https://github.com/prometheus-eval/into-the-unknown); the paper is on [arXiv](https://arxiv.org/abs/2610.19159).

**Supported Tasks and Leaderboards**

The task is open-ended research: given the paper and the direction, produce the follow-up paper. The [leaderboard](https://prometheus-eval.github.io/into-the-unknown/viewer/#/) shows every agent's results and every rubric verdict, and you can [submit your agent's paper](https://prometheus-eval.github.io/into-the-unknown/viewer/#/submit) to be evaluated.

**Subsets**

| subset | content |
|---|---|
| `benchmark/` | The 15 tasks: `paper<i>/task_spec.md`, the original paper (`paper.pdf`, its text `paper.md`, figures `images/`) and `index.csv`. A task's code and data are downloaded by `prepare_tasks.py` in the GitHub repository. |
| `outcomes/` | The papers the agents wrote, `<agent>/paper<i>.pdf` (the `proposal/report.pdf` the judge read): 131 PDFs, since four GLM-5.3 runs produced none. |
| `trajectories/` | Each run's `<agent>/paper<i>.json` (run metadata and every agent event), plus the harnesses' own logs (`*.harness_logs.tar`; DeepSeek-V4-Pro's full conversation is here) and the runners' console logs (`*.runner_logs.tar`). |
| `file_systems/` | Each submission's files as the judge saw them: `proposal/` and the rest of the working directory (`workdir_outside_proposal/`), without environments, caches or the final transcript. `view.tar.part-*` with a SHA-256 `manifest.json`; nine runs add a `supplement.tar` of files the judge's copy lacked. |

Agents (`<agent>`): `opus-4.8`, `opus-5`, `opus-5.5`, `fable-5`, `sol-5.6`, `astra-6`, `deepseek-v4-pro`, `kimi-k3`, `glm-5.3`.

**Dataset Structure**

An example of a `benchmark` datum is as follows:

```
paper: (str) - The task id, P1 to P15.
group: (str) - A: published 2024 or earlier; B: submitted 2026.
title: (str) - The original paper's title.
domain, venue, lab: (str) - Its field, venue and the laboratory of its authors.
citations: (int) - Citations as of September 14, 2026.
original_paper: (str) - A link to the original paper.
follow_up_direction: (str) - The research direction the paper's author proposed.
task_spec, paper_pdf, paper_md: (str) - Paths to the task specification and the original paper in this dataset.
```

`outcomes/index.csv` and `trajectories/index.csv` list one row per submission (agent, paper, file, and run metadata).

**Evaluation**

Run an agent and judge its submission with the [GitHub repository](https://github.com/prometheus-eval/into-the-unknown):

```bash
bash src/solver/run.sh <agent> <paper_id>
python src/grader/run_codex_judge.py prepare --agents <agent>     # then rescope, launch
```

To unpack one submission's files:

```bash
huggingface-cli download prometheus-eval/into-the-unknown --repo-type dataset \
    --include "file_systems/astra-6/paper3/*" --local-dir .
cd file_systems/astra-6/paper3 && cat view.tar.part-* > view.tar && tar -xf view.tar
```

**Sample Usage**

Load the benchmark subset directly with the `datasets` library:

```python
from datasets import load_dataset

tasks = load_dataset('prometheus-eval/into-the-unknown', 'benchmark', split='train')
```

A few notes specific to this dataset:

- `outcomes/fable-5/paper8.pdf` has a real person's name, affiliation and e-mail blacked out on its title page; every other PDF is byte-identical to the submission.
- Files written on the compute cluster can contain absolute paths of the cluster's file system.
- In `file_systems/opus-5.5/paper12`, two R help pages (`qsreg.html`, `spam.html`) differ only in case from other pages; the archive has the originals, the others are in `supplement.tar`, and `manifest.json` lists both under `checked_against_judge_record.sha256_mismatch`.

**Citation**

If you find our work helpful, please consider citing it:

```bibtex
@article{kim2026into,
  title={Into the Unknown: Benchmarking AI agents on open scientific research problems},
  author={Kim, Seungone and Geng, Jiayi and Lee, Young-Jun and Liu, Bo and Suk, Juyoung and Lubbers, Nicholas and Wood, Mitchell and Noack, Marcus and Yao, Yong-Xin and Shi, Yuan and others},
  journal={arXiv preprint arXiv:2610.19159},
  year={2026}
}
```

**License**

Task specifications, rubrics and documentation are released under the Apache-2.0 license of the GitHub repository. The original papers in `benchmark/` remain under their authors' and publishers' terms and are included for research use; agent outputs may contain third-party code and data that keep their own licenses.