Contributing to Frontier-CS
Frontier-CS is currently an invitation-only project for new problems. Please create a GitHub pull request (PR) with your proposed problem following the guidelines below. After your PR is reviewed and merged, please send any hidden test data and reference solutions to the contact email provided at the end of this document.
Algorithmic Problems
Problem Submission Process
- Invitation Required: Only invited contributors can submit algorithmic problems
- Internal Review: All problems undergo internal review by the Frontier-CS team
- Problem Numbering: After approval, problems are assigned a unique numerical ID
- Structure Compliance: Problems must follow the required directory structure
Problem Structure
Each algorithmic problem must be organized in the following directory structure:
algorithmic/problems/{problem_id}/
├── config.yaml # Problem configuration (time limit, memory limit, checker)
├── statement.txt # Problem description and requirements
├── chk.cc or interactor.cc (for interactive problems) # Evaluator
└── testdata/ # Test cases
├── 1.in # Sample input
├── 1.ans # Hidden evaluation data used by the evaluator, e.g., reference score.
├── 2.in
├── 2.ans
└── ...
Required Files
config.yaml
Defines the problem configuration:
type: default # Problem type
time: 1s # Time limit (e.g., 1s, 2s, 5s)
memory: 1024m # Memory limit (e.g., 512m, 1024m, 2048m)
checker: chk.cc # Custom checker file (optional)
subtasks:
- score: 100 # Total score for this subtask
n_cases: 10 # Number of test cases (= 1 for public version)
statement.txt
The problem statement should include:
- Problem Description: Clear description of the problem
- Input Format: Detailed specification of input format
- Output Format: Detailed specification of output format
- Scoring: Explanation of how solutions are scored
- Time Limit: Execution time limit
- Memory Limit: Memory usage limit
- Sample Input/Output: At least one example with explanation
chk.cc / interactor.cc (for interactive problems)
Support partial score
the current judge returns the partial score by parsing the message returned by testlib.h, making sure your quitp follows the following format:
quitp(score, "Ratio: %.9f [additional message str]", score, ...);
To support raw score, use:
quitp(score_ratio, "Value: %lld. Ratio: %.4f, RatioUnbounded: %.4f", score, score_ratio, unbounded_ratio);
testdata/
Test cases with inputs (.in) and expected outputs (.ans):
1.in,1.ans: First test case2.in,2.ans: Second test case- etc.
Hidden Test Data and Human Reference
For security and evaluation integrity:
- Hidden test data (not in public repository)
- Human reference solutions (baseline implementations)
Please send these materials to: [email protected] once your PR is merged.
Include in your email:
- Problem ID (if assigned) or proposed problem name
- Complete test data set (all
.inand.ansfiles) - Reference solution(s) with explanation
- Any additional notes on test case design
Research Problems
Research problems focus on systems optimization, ML systems, databases, compilers, and security challenges.
Research Problem Submission Process
- Invitation Required: Only invited contributors can submit research problems
- Internal Review: Problems undergo internal review for quality and feasibility
- Tag Assignment: Problems are assigned appropriate category tags (os, hpc, ai, db, pl, security)
Research Problem Structure
Each research problem follows a standardized interface:
research/{problem_name}/
├── config.yaml # Dependencies, datasets, runtime config
├── set_up_env.sh # Environment setup script
├── evaluate.sh # Evaluation entry point
├── evaluator.py # Scoring logic
├── readme # Problem description
└── resources/ # Problem-specific code/data
Solution Interface
Solutions implement a Solution class in solution.py:
class Solution:
def __init__(self):
pass
def solve(self, *args):
# Returns: solution output (format varies by problem)
pass
Evaluation Flow
config.yaml → set_up_env.sh → solve.sh → evaluate.sh → evaluator.py → score (0-100)
Step by Step
1. Create Problem Directory
mkdir -p research/{problem_name}/resources
2. Create config.yaml
tag: hpc # Category: os, hpc, ai, db, pl, security
dependencies:
uv_project: resources # Optional: uv project in resources/
datasets: [] # Optional: dataset URLs
runtime:
timeout_seconds: 1800 # Evaluation timeout
requires_gpu: true # GPU requirement
resources: # SkyPilot resources
accelerators: "L4:1"
cpus: "8+"
memory: "32+"
environment: "CUDA 12.2, Python 3.11, PyTorch 2.0+"
3. Create Evaluation Scripts
set_up_env.sh: Prepare environment
#!/bin/bash
# Install dependencies, download data, etc.
evaluate.sh: Run evaluation
#!/bin/bash
python evaluator.py
evaluator.py: Score the solution (last line must be numeric score)
# ... evaluation logic ...
print(score) # Must be last line!
4. Register the Problem
Add to research/problems.txt:
research/{problem_name}
Problem Hierarchy: Categories and Variants
Research problems follow a hierarchical structure:
Problem (e.g., gemm_optimization, poc_generation)
└── Category (e.g., squares, heap_buffer_overflow)
└── Variant (e.g., arvo_21000)
| Level | Evaluation | Reporting |
|---|---|---|
| Category | — | Scores aggregated for leaderboard |
| Variant | Evaluated independently | Contributes to category score |
Example: Simple Variants
research/gemm_optimization/
├── squares/ # Variant (category = squares)
│ ├── config.yaml
│ ├── readme
│ └── evaluator.py
├── rectangles/ # Variant (category = rectangles)
└── transformerish/ # Variant (category = transformerish)
Example: Nested Variants
For problems with many variants per category:
research/poc_generation/
├── heap_buffer_overflow/ # Category
│ ├── config.yaml # Category-level config (tag only)
│ ├── arvo_21000/ # Variant
│ │ ├── config.yaml
│ │ ├── readme
│ │ └── evaluator.py
│ └── arvo_47101/ # Variant
└── stack_buffer_overflow/ # Category
└── ...
Registering Problems
Add each variant (not category) to problems.txt:
research/gemm_optimization/squares
research/gemm_optimization/rectangles
research/poc_generation/heap_buffer_overflow/arvo_21000
research/poc_generation/heap_buffer_overflow/arvo_47101
Contact
For questions, submissions, or to request an invitation:
Email: [email protected] (general & algorithmic problems), [email protected] (research problems)
Please include:
- Your name and affiliation
- Area of expertise
- Type of contribution (algorithmic/research problem)
- Brief description of your proposed contribution