SciCode

SciCode
Overview
Full name	Scientific Code Benchmark
Abbreviation	SciCode
Description	A research coding benchmark curated by scientists for realistic scientific problem-solving
Release date	2024-07
Latest version	1.0
Benchmark updated	2025-01
Authors	Minyang Tian, Luyu Gao, Et al.
Organization	Princeton University, Carnegie Mellon University
Technical Details
Type	Scientific Computing, Code Generation
Modality	Text (Code)
Task format	Code synthesis for scientific problems
Number of tasks	80 main problems (338 subproblems)
Total examples	338
Evaluation metric	Success rate, Correctness
Domains	Physics, Math, Biology, Chemistry, Materials Science, Ecology
Languages	Python (primarily)
Performance
Human performance	Not reported
Baseline	<1% (random) Property "Baseline score" (as page type) with input value "" contains invalid characters or is incomplete and therefore can cause unexpected results during a query or annotation process.
SOTA score	7.7%
SOTA model	OpenAI o1-preview
SOTA date	2024-10
Saturated	No
Resources
Website	Official website
Paper	Paper
GitHub	Repository
Dataset	Download
License	MIT

SciCode is a challenging artificial intelligence benchmark designed to evaluate large language models' capabilities in generating code for solving realistic scientific research problems. Released in July 2024 through collaboration between Princeton University, Carnegie Mellon University, and scientists from 16 diverse natural science sub-fields, SciCode represents a paradigm shift from exam-like questions to real research problems that scientists encounter in their everyday workflow.

Overview

SciCode addresses a critical gap in evaluating AI systems' ability to assist with scientific research by presenting problems drawn from actual scripts that scientists use in their research, many of which have been used in published papers. Unlike traditional coding benchmarks that focus on algorithmic challenges or software engineering tasks, SciCode requires deep integration of domain knowledge, mathematical reasoning, and computational implementation.

Motivation

The development of SciCode was motivated by several key observations:

• Existing benchmarks fail to capture the complexity of real scientific computing
• Scientists need AI assistants capable of implementing research-grade computational methods
• Current models struggle with problems requiring deep domain expertise
• The gap between exam-style problems and actual research implementation
• Need for benchmarking progress toward AI systems that can meaningfully assist scientific discovery

The benchmark specifically targets the evaluation of models' ability to transform scientific concepts and mathematical formulations into working computational code.

Technical Specifications

Domain Coverage

SciCode covers 6 main scientific domains with 16 specialized subdomains:

Main Domain	Subdomains	Problem Count	Key Topics
Physics	Computational Physics, Optics, Condensed Matter	~60	Simulations, quantum mechanics, statistical physics
Mathematics	Numerical Linear Algebra, PDEs, Optimization	~50	Matrix computations, differential equations, algorithms
Chemistry	Quantum Chemistry, Computational Chemistry	~50	Molecular dynamics, electronic structure
Biology	Ecology, Bioinformatics, Systems Biology	~50	Population dynamics, sequence analysis
Materials Science	Semiconductor Materials, Crystallography	~40	Band structure, material properties
Earth Science	Geophysics, Climatology	~38	Climate models, seismic analysis

Problem Structure

Each main problem in SciCode is decomposed into multiple subproblems:

Component	Description	Count
Main Problems	Complete scientific challenges	80
Subproblems	Decomposed implementation steps	338
Test Cases	Scientist-annotated validations	~1000
Gold Solutions	Reference implementations	338

Problem Characteristics

Characteristic	Description	Example
Knowledge Recall	Retrieving domain-specific facts	Physical constants, equations
Mathematical Reasoning	Deriving and manipulating formulas	Solving differential equations
Algorithm Design	Creating computational approaches	Numerical integration methods
Code Synthesis	Implementing solutions in code	Python implementations
Validation	Verifying correctness	Comparing with analytical solutions

Evaluation Methodology

Evaluation Settings

SciCode offers two primary evaluation configurations:

Setting	Description	Background Knowledge	Difficulty
Standard	No additional context provided	None	Highest
With Background	Scientist-annotated context included	Domain-specific hints	Moderate

Scoring System

Metric	Description	Calculation
Overall Success Rate	Percentage of correctly solved problems	(Solved problems / Total) × 100%
Domain Success Rate	Performance per scientific field	(Domain solved / Domain total) × 100%
Subproblem Accuracy	Correctness at subproblem level	(Correct subproblems / 338) × 100%
Test Case Pass Rate	Percentage of passing test cases	(Passed tests / Total tests) × 100%

Validation Process

SciCode employs rigorous validation: 1. **Test Case Execution**: Running generated code against scientist-created test cases 2. **Numerical Verification**: Checking numerical accuracy within specified tolerances 3. **Output Format Validation**: Ensuring correct data structures and formats 4. **Performance Checks**: Verifying computational efficiency where relevant

Performance Analysis

Current Leaderboard (2024-2025)

Rank	Model	Success Rate (Standard)	Success Rate (w/ Background)	Organization
1	OpenAI o1-preview	7.7%	15.2%	OpenAI
2	OpenAI o1-mini	5.8%	11.3%	OpenAI
3	Claude 3.5 Sonnet	4.6%	9.8%	Anthropic
4	GPT-4o	3.9%	8.7%	OpenAI
5	DeepSeek-R1	~3.5%	~8.2%	DeepSeek
6	DeepSeek-V3	~3.2%	~7.8%	DeepSeek
7	GPT-4 Turbo	2.8%	6.5%	OpenAI
8	Claude 3 Opus	2.5%	5.9%	Anthropic

Performance Insights

Domain-Specific Performance

Domain	Best Model Performance	Average Performance	Difficulty Rating
Mathematics	12%	5%	High
Physics	8%	3%	Very High
Chemistry	6%	2%	Very High
Biology	7%	3%	High
Materials Science	5%	2%	Very High
Earth Science	4%	1.5%	Very High

Key Challenges

• **Domain Knowledge Gap**: Models lack deep scientific understanding
• **Mathematical Complexity**: Difficulty with advanced mathematical derivations
• **Implementation Details**: Struggle with numerical methods and algorithms
• **Integration Challenge**: Combining multiple concepts into working solutions

Notable Problems

Example Categories

Category	Example Problem	Required Knowledge
Quantum Mechanics	Solving Schrödinger equation numerically	Wave functions, numerical methods
Molecular Dynamics	Simulating protein folding	Force fields, integration algorithms
Climate Modeling	Implementing radiative transfer	Atmospheric physics, numerical schemes
Population Dynamics	Predator-prey models	Differential equations, ecology
Crystal Structures	Computing band structures	Solid state physics, linear algebra
Signal Processing	Implementing FFT variants	Mathematics, algorithms

Nobel Prize Methods

SciCode includes problems based on Nobel Prize-winning scientific methods, highlighting the benchmark's connection to groundbreaking research:

• Density Functional Theory calculations
• Monte Carlo simulations
• Molecular dynamics implementations
• Quantum mechanical computations

Implementation

Installation and Setup

```bash

1. Clone the repository

git clone https://github.com/scicode-bench/SciCode cd SciCode

1. Install dependencies

pip install -r requirements.txt

1. Download dataset

python download_data.py ```

Running Evaluations

```python

1. Basic evaluation

from scicode import SciCodeBench

1. Initialize benchmark

benchmark = SciCodeBench()

1. Evaluate without background

results_standard = benchmark.evaluate(

   model='gpt-4',
   setting='standard'

)

1. Evaluate with background

results_background = benchmark.evaluate(

   model='gpt-4',
   setting='with_background'

) ```

Integration with Frameworks

```python

1. Using inspect_ai (as of 2025-01)

from inspect_ai import eval from scicode.inspect import scicode_suite

1. Run evaluation

results = eval(

   scicode_suite(),
   model="openai/gpt-4"

)

1. Using OpenCompass

from opencompass.benchmarks import SciCode

evaluator = SciCode() score = evaluator.eval(model_output) ```

Scientific Workflow Alignment

Research Process Mapping

Research Stage	SciCode Component	Skills Tested
Literature Review	Background understanding	Knowledge recall
Theory Development	Mathematical formulation	Reasoning
Method Design	Algorithm selection	Problem-solving
Implementation	Code writing	Programming
Validation	Testing and verification	Debugging

Real-World Applications

SciCode problems reflect actual scientific computing tasks:

• **Simulation**: Physical system modeling
• **Data Analysis**: Processing experimental data
• **Optimization**: Parameter fitting and optimization
• **Visualization**: Scientific plotting and analysis
• **Numerical Methods**: Implementing computational algorithms

Significance and Impact

Research Applications

Application	Purpose	Value
AI for Science	Evaluating scientific AI assistants	Progress tracking
Model Development	Identifying capability gaps	Targeted improvement
Education	Assessing teaching assistants	Educational tools
Collaboration	Human-AI scientific partnerships	Integration planning

Scientific Community Impact

• **Standardization**: Common benchmark for scientific AI evaluation
• **Interdisciplinary**: Bridges AI and natural sciences
• **Practical Focus**: Emphasizes real research problems
• **Quality Assurance**: Scientist-validated problems and solutions
• **Future Direction**: Guides development of scientific AI systems

Limitations and Challenges

Current Limitations

Limitation	Description	Impact
Low Success Rates	Best models solve <8%	Limited practical utility
Python Focus	Primarily Python implementations	Language diversity
Static Dataset	Fixed problem set	Potential overfitting
Domain Coverage	Limited to 16 subfields	Scope constraints
Evaluation Cost	Computationally intensive	Resource requirements

Future Directions

1. **Expanded Coverage**: More scientific domains and subfields 2. **Multi-language Support**: Beyond Python implementations 3. **Interactive Problems**: Multi-step research workflows 4. **Collaborative Tasks**: Team science scenarios 5. **Dynamic Updates**: Continuously adding new problems 6. **Human Baselines**: Expert scientist performance metrics

Related Benchmarks

• HumanEval: General code generation
• MATH: Mathematical problem solving
• GPQA: Graduate-level science questions
• GSM8K: Grade school math problems
• CodeContests: Competitive programming
• ML-Bench: Machine learning implementation
• SWE-bench: Software engineering tasks

Significance

SciCode represents a crucial step toward AI systems capable of meaningful scientific assistance. Its extremely low success rates even for state-of-the-art models highlight the significant gap between current AI capabilities and the needs of scientific research. The benchmark's focus on real research problems provides:

• Clear metrics for progress toward scientific AI
• Understanding of domain-specific challenges
• Guidance for developing research-capable AI systems
• Bridge between AI and scientific communities
• Realistic assessment of AI readiness for scientific discovery

See Also

• Scientific Computing
• Computational Science
• AI for Science
• Code Generation
• Numerical Methods
• Research Automation
• Domain-Specific AI