--- 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

Evolving Challenges for Evolving Intelligence

## 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 ```

Example Problem

### Research Problems ```bash # List all problems frontier-eval --list # Evaluate a generated solution locally for flash_attn problem (requires Docker) frontier-eval flash_attn # Evaluate on cloud (requires SkyPilot) frontier-eval flash_attn --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 ``` #### 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 ``` #### 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 ```