|
|
--- |
|
|
license: apache-2.0 |
|
|
task_categories: |
|
|
- text-generation |
|
|
- question-answering |
|
|
language: |
|
|
- en |
|
|
tags: |
|
|
- code |
|
|
- benchmark |
|
|
- evaluation |
|
|
- algorithms |
|
|
- systems |
|
|
- machine-learning |
|
|
- security |
|
|
- optimization |
|
|
size_categories: |
|
|
- 100K<n<1M |
|
|
pretty_name: Frontier-CS |
|
|
--- |
|
|
|
|
|
<p align=""> |
|
|
<a href="https://frontier-cs.org"> |
|
|
<img src="assets/logo.png" alt="Frontier-CS Logo" width="2000"/> |
|
|
</a> |
|
|
</p> |
|
|
|
|
|
<h2 align="center"> |
|
|
Evolving Challenges for Evolving Intelligence |
|
|
</h2> |
|
|
|
|
|
<p align="center"> |
|
|
<a href="https://frontier-cs.org"><img src="https://img.shields.io/badge/Website-frontier--cs.org-orange?logo=googlechrome" alt="Website"></a> |
|
|
<a href="https://frontier-cs.org/leaderboard"><img src="https://img.shields.io/badge/Leaderboard-View_Rankings-purple?logo=trophy" alt="Leaderboard"></a> |
|
|
<a href="https://discord.gg/k4hd2nU4UE"><img src="https://img.shields.io/badge/Discord-Join_Community-5865F2?logo=discord&logoColor=white" alt="Discord"></a> |
|
|
<a href="https://deepwiki.com/FrontierCS/Frontier-CS"><img src="https://img.shields.io/badge/DeepWiki-Documentation-blue?logo=bookstack&logoColor=white" alt="DeepWiki"></a> |
|
|
<br> |
|
|
<img src="https://img.shields.io/badge/Research_Problems-63-blue" alt="Research Problems"> |
|
|
<img src="https://img.shields.io/badge/Algorithmic_Problems-118-green" alt="Algorithmic Problems"> |
|
|
</p> |
|
|
|
|
|
## What is Frontier-CS? |
|
|
|
|
|
**Frontier-CS** is an _unsolved_, _open-ended_, _verifiable_, and _diverse_ benchmark for evaluating AI on challenging computer science problems. |
|
|
|
|
|
Think of it as an "exam" for AI, but instead of easy textbook questions, we give problems that are genuinely difficult: ones that researchers struggle with, that have no known optimal solutions, or that require deep expertise to even attempt. |
|
|
|
|
|
## Why Frontier-CS? |
|
|
|
|
|
Current benchmarks are becoming too easy. Models score 90%+ on many existing coding benchmarks, but that doesn't mean they can actually do useful research or solve real-world engineering challenges. |
|
|
|
|
|
**Frontier-CS is different:** |
|
|
|
|
|
| | Traditional Benchmarks | Frontier-CS | |
|
|
| ---------- | ------------------------------------------ | ------------------------------------------------------- | |
|
|
| Difficulty | Often saturated with evolving intelligence | _Unsolved_: no solution has achieved perfect scores | |
|
|
| Problems | Textbook-style, known solutions | _Open-ended_ research & optimization challenges | |
|
|
| Evaluation | Binary pass-or-fail | _Verifiable_ continuous scoring, always room to improve | |
|
|
| Scope | Usually one domain | _Diverse_: systems, ML, algorithms, security, and more | |
|
|
|
|
|
**[Leaderboard →](https://frontier-cs.org/leaderboard)** | Browse example problems at [frontier-cs.org](https://frontier-cs.org) |
|
|
|
|
|
## Getting Started |
|
|
|
|
|
### Installation |
|
|
|
|
|
```bash |
|
|
git clone https://github.com/FrontierCS/Frontier-CS.git |
|
|
cd Frontier-CS |
|
|
|
|
|
# Install dependencies (using uv, recommended) |
|
|
uv sync |
|
|
|
|
|
# Or with pip: |
|
|
pip install -e . |
|
|
``` |
|
|
|
|
|
### Try it yourself |
|
|
|
|
|
Here's [Algorithmic Problem 0](algorithmic/problems/0/statement.txt) - try to beat GPT-5! |
|
|
|
|
|
```bash |
|
|
# Start the judge server |
|
|
cd algorithmic && docker compose up -d |
|
|
|
|
|
# Run the example solution (Human Expert Solution) |
|
|
frontier-eval --algorithmic 0 problems/0/examples/reference.cpp |
|
|
|
|
|
# Run the example solution (GPT-5 Thinking Solution) |
|
|
frontier-eval --algorithmic 0 problems/0/examples/gpt5.cpp |
|
|
|
|
|
# Try you own solution! |
|
|
frontier-eval --algorithmic 0 <your_solution.cpp> |
|
|
``` |
|
|
|
|
|
<p align="center"> |
|
|
<img src="assets/teaser.png" alt="Example Problem" width="800"/> |
|
|
</p> |
|
|
|
|
|
### Research Problems |
|
|
|
|
|
```bash |
|
|
# List all problems |
|
|
frontier-eval --list |
|
|
|
|
|
# Evaluate a generated solution locally for flash_attn problem (requires Docker) |
|
|
frontier-eval flash_attn <your_solution.py> |
|
|
|
|
|
# Evaluate on cloud (requires SkyPilot) |
|
|
frontier-eval flash_attn <your_solution.py> --skypilot |
|
|
``` |
|
|
|
|
|
See [research/README.md](research/README.md) for full documentation. |
|
|
|
|
|
### Algorithmic Problems |
|
|
|
|
|
```bash |
|
|
# Start the judge server |
|
|
cd algorithmic && docker compose up -d |
|
|
|
|
|
# Evaluate a solution |
|
|
frontier-eval --algorithmic 1 <your_solution.cpp> |
|
|
``` |
|
|
#### Raw Score |
|
|
Frontier-CS supports unbounded scoring for algorithmic problems, enabling open-ended evaluation compatible with algorithm evolution frameworks such as OpenEvolve. |
|
|
|
|
|
```bash |
|
|
# Get unbounded score (without clipping to 100) |
|
|
frontier-eval --algorithmic --unbounded 1 <your_solution.cpp> |
|
|
``` |
|
|
|
|
|
#### Note |
|
|
1. We currently support C++17 only for algorithmic problem solutions. |
|
|
2. Reference solutions and hidden tests are withheld; full evaluation and leaderboard inclusion require submission. |
|
|
|
|
|
See [algorithmic/README.md](algorithmic/README.md) for full documentation. |
|
|
|
|
|
### Python API |
|
|
|
|
|
```python |
|
|
from frontier_cs import FrontierCSEvaluator |
|
|
|
|
|
evaluator = FrontierCSEvaluator() |
|
|
|
|
|
# Evaluate a research problem |
|
|
result = evaluator.evaluate("research", problem_id="flash_attn", code=my_code) |
|
|
print(f"Score: {result.score}") |
|
|
|
|
|
# Evaluate an algorithmic problem |
|
|
result = evaluator.evaluate("algorithmic", problem_id=1, code=cpp_code) |
|
|
print(f"Score: {result.score}") |
|
|
|
|
|
# Get unbounded score for algorithmic problems |
|
|
result = evaluator.evaluate("algorithmic", problem_id=1, code=cpp_code, unbounded=True) |
|
|
print(f"Score (bounded): {result.score}") |
|
|
print(f"Score (unbounded): {result.score_unbounded}") |
|
|
``` |
|
|
|
|
|
## Submitting Results |
|
|
|
|
|
We release partial test cases so you can develop and debug locally. For full evaluation and leaderboard inclusion, submit your solutions to qmang@berkeley.edu, or wenhao.chai@princeton.edu, or zhifei.li@berkeley.edu following the instructions in [SUBMIT.md](SUBMIT.md). |
|
|
|
|
|
Questions? Join our [Discord](https://discord.gg/k4hd2nU4UE) |
|
|
|
|
|
## Acknowledgments |
|
|
|
|
|
Some problems are adapted from [ALE-bench](https://github.com/SakanaAI/ALE-Bench) and [AI-Driven Research for Systems (ADRS)](https://ucbskyadrs.github.io/). |
|
|
|
|
|
## Citing Us |
|
|
|
|
|
If you use Frontier-CS in your research, please cite: |
|
|
|
|
|
```bibtex |
|
|
|
|
|
``` |
|
|
|
