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MLR-Bench(MLR-Bench)
Comprehensive benchmark for evaluating AI agents on open-ended machine learning research tasks from top ML conferences.
Complexity: highEvaluation and Monitoring
🎯 30-Second Overview
Pattern: Comprehensive benchmark with 201 real-world ML research tasks from top-tier conferences
Why: Evaluates complete research pipeline from idea generation to paper writing with automated and human validation
Key Insight: Current SOTA models excel at ideas and writing but struggle with coding, limiting scientific innovation
⚡ Quick Implementation
1Clone:git clone MLR-Bench repository from GitHub
2Setup:Install dependencies and configure MLR-Agent scaffold
3Select:Choose from 201 research tasks across 9 ML domains
4Execute:Run agent through 4-stage research pipeline
5Evaluate:Use MLR-Judge for automated assessment
Example: python run_mlr_bench.py --task llm_safety --agent claude_code --stages all
📋 Do's & Don'ts
✅Test across all 9 ML research domains for comprehensive assessment
✅Use MLR-Judge automated evaluation with structured review rubrics
✅Follow the complete 4-stage research pipeline (idea → code → analysis → paper)
✅Validate results with human expert reviewers from major conferences
✅Focus on coding agent capabilities - major bottleneck identified
❌Skip proper environment setup for research task execution
❌Ignore coding failures - they prevent downstream research quality
❌Rely only on automated evaluation without human validation
❌Test on single domain - research requires cross-domain capabilities
❌Overlook novelty and significance in favor of technical soundness only
🚦 When to Use
Use When
- • Evaluating AI research automation capabilities
- • Testing scientific discovery and innovation potential
- • Benchmarking against real-world research tasks
- • Assessing complete research pipeline performance
- • Academic and industry R&D agent development
Avoid When
- • Simple coding or data analysis tasks only
- • Non-research domain evaluation
- • Quick capability demonstration needs
- • Resource-constrained environments (requires full research stack)
- • Domains outside core ML research areas
📊 Key Metrics
Overall Research Quality
Composite score across 5 evaluation dimensions
Stage-wise Performance
Success rate in idea, code, analysis, writing stages
Domain-specific Scores
Performance across 9 ML research areas
MLR-Judge Alignment
Correlation with human expert reviewers
Innovation Index
Novelty and significance of generated research
Technical Soundness
Code quality and experimental validity
💡 Top Use Cases
Research Automation Assessment: Evaluating AI agents on complete ML research pipeline from idea to publication
Scientific Discovery Benchmarking: Testing innovation capabilities across LLMs, AI4Science, ML Theory domains
Academic Agent Development: Building AI researchers capable of conducting workshop-level research
Research Productivity Tools: Measuring effectiveness of AI assistance in scientific research workflows
Conference Review Simulation: Training agents to conduct peer review and research evaluation
References & Further Reading
Deepen your understanding with these curated resources
Contribute to this collection
Know a great resource? Submit a pull request to add it.
🔬
MLR-Bench(MLR-Bench)
Comprehensive benchmark for evaluating AI agents on open-ended machine learning research tasks from top ML conferences.
Complexity: highEvaluation and Monitoring
🎯 30-Second Overview
Pattern: Comprehensive benchmark with 201 real-world ML research tasks from top-tier conferences
Why: Evaluates complete research pipeline from idea generation to paper writing with automated and human validation
Key Insight: Current SOTA models excel at ideas and writing but struggle with coding, limiting scientific innovation
⚡ Quick Implementation
1Clone:git clone MLR-Bench repository from GitHub
2Setup:Install dependencies and configure MLR-Agent scaffold
3Select:Choose from 201 research tasks across 9 ML domains
4Execute:Run agent through 4-stage research pipeline
5Evaluate:Use MLR-Judge for automated assessment
Example: python run_mlr_bench.py --task llm_safety --agent claude_code --stages all
📋 Do's & Don'ts
✅Test across all 9 ML research domains for comprehensive assessment
✅Use MLR-Judge automated evaluation with structured review rubrics
✅Follow the complete 4-stage research pipeline (idea → code → analysis → paper)
✅Validate results with human expert reviewers from major conferences
✅Focus on coding agent capabilities - major bottleneck identified
❌Skip proper environment setup for research task execution
❌Ignore coding failures - they prevent downstream research quality
❌Rely only on automated evaluation without human validation
❌Test on single domain - research requires cross-domain capabilities
❌Overlook novelty and significance in favor of technical soundness only
🚦 When to Use
Use When
- • Evaluating AI research automation capabilities
- • Testing scientific discovery and innovation potential
- • Benchmarking against real-world research tasks
- • Assessing complete research pipeline performance
- • Academic and industry R&D agent development
Avoid When
- • Simple coding or data analysis tasks only
- • Non-research domain evaluation
- • Quick capability demonstration needs
- • Resource-constrained environments (requires full research stack)
- • Domains outside core ML research areas
📊 Key Metrics
Overall Research Quality
Composite score across 5 evaluation dimensions
Stage-wise Performance
Success rate in idea, code, analysis, writing stages
Domain-specific Scores
Performance across 9 ML research areas
MLR-Judge Alignment
Correlation with human expert reviewers
Innovation Index
Novelty and significance of generated research
Technical Soundness
Code quality and experimental validity
💡 Top Use Cases
Research Automation Assessment: Evaluating AI agents on complete ML research pipeline from idea to publication
Scientific Discovery Benchmarking: Testing innovation capabilities across LLMs, AI4Science, ML Theory domains
Academic Agent Development: Building AI researchers capable of conducting workshop-level research
Research Productivity Tools: Measuring effectiveness of AI assistance in scientific research workflows
Conference Review Simulation: Training agents to conduct peer review and research evaluation
References & Further Reading
Deepen your understanding with these curated resources
Contribute to this collection
Know a great resource? Submit a pull request to add it.