Agentic DesignPatterns and tools for production AI agents

Loading...

🔬

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

In 30 seconds

What: Standardized benchmark with 201 ML research tasks from top conferences and automated evaluation across methodology, implementation, and analysis dimensions.
When to use: Assessing whether your research agent can handle open-ended ML problems comparable to published conference work across multiple subfields.
Watch out: Scores reflect narrow task performance; agents may overfit to benchmark patterns without generalizing to novel research directions outside these 9 areas.

Loading technique guide…

Patterns

Loading...

Loading Agentic Design…

Agentic DesignPatterns and tools for production AI agents

Expert help Pricing

Design Patterns & Techniques

Loading...

🔬

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

In 30 seconds

What: Standardized benchmark with 201 ML research tasks from top conferences and automated evaluation across methodology, implementation, and analysis dimensions.
When to use: Assessing whether your research agent can handle open-ended ML problems comparable to published conference work across multiple subfields.
Watch out: Scores reflect narrow task performance; agents may overfit to benchmark patterns without generalizing to novel research directions outside these 9 areas.

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

Official Paper & Resources

MLR-Bench: Evaluating AI Agents on Open-Ended Machine Learning Research (arXiv:2505.19955)

MLR-Bench HTML Version

Literature Review by AI Paper Reviews

Related Research Benchmarks

ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code

EXP-Bench: Can AI Conduct AI Research Experiments?

MLRC-Bench: Machine Learning Research Challenges

ResearchTown: Human Research Community Simulator

Conference & Workshop Sources

NeurIPS Workshop Papers Database

ICLR Workshop Proceedings

ICML Workshop Papers

Machine Learning Research Best Practices (MLSys)

Evaluation & Review Standards

OpenReview Platform for Scientific Peer Review

Guidelines for Reproducible Research (Nature)

ML Conference Review Criteria (NeurIPS)

Scientific Writing and Review Standards (IEEE)

Loading technique guide…

Patterns

Design Patterns & Techniques

Loading...

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

Official Paper & Resources

MLR-Bench: Evaluating AI Agents on Open-Ended Machine Learning Research (arXiv:2505.19955)

MLR-Bench HTML Version

Literature Review by AI Paper Reviews

Related Research Benchmarks

ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code

EXP-Bench: Can AI Conduct AI Research Experiments?

MLRC-Bench: Machine Learning Research Challenges

ResearchTown: Human Research Community Simulator

Conference & Workshop Sources

NeurIPS Workshop Papers Database

ICLR Workshop Proceedings

ICML Workshop Papers

Machine Learning Research Best Practices (MLSys)

Evaluation & Review Standards

OpenReview Platform for Scientific Peer Review

Guidelines for Reproducible Research (Nature)

ML Conference Review Criteria (NeurIPS)

Scientific Writing and Review Standards (IEEE)

MLR-Bench (MLR-Bench) - Agentic Design