Our Testing Philosophy
Scientific rigor meets real-world applicability in every benchmark we create
Core Principles
🔬Scientific Rigor
Every benchmark is grounded in peer-reviewed research and validated methodologies. We design tests that are statistically significant, methodologically sound, and scientifically defensible.
🔄Reproducibility
All tests are deterministic and version-controlled. We provide complete documentation, source code, and test data so that results can be independently verified and reproduced.
🪟Transparency
Everything is open source: prompts, rubrics, scoring algorithms, and infrastructure. No black boxes, no hidden evaluation criteria, no proprietary scoring methods.
🎯Real-World Relevance
Our benchmarks test capabilities that matter in production. We focus on practical applications, edge cases, and scenarios that developers and researchers actually encounter.
Test Design Framework
Multi-Layer Complexity
Our tests are designed with graduated difficulty levels to distinguish between models at different capability tiers. Each benchmark includes:
- •Baseline tests: Validate fundamental capabilities
- •Intermediate tests: Challenge with realistic complexity
- •Advanced tests: Push boundaries of current capabilities
Diverse Evaluation Metrics
We move beyond simple accuracy scores to capture nuanced performance characteristics:
Statistical Significance
We run sufficient test iterations to ensure statistical validity. Results include confidence intervals, standard deviations, and significance testing where appropriate. No single-run conclusions.
Benchmark Standards
Open Source Requirement
Every benchmark we publish must be completely open source, including:
- ✓Complete test suite source code
- ✓All prompts and evaluation rubrics
- ✓Test data generation procedures
- ✓Scoring and analysis algorithms
- ✓Infrastructure and deployment code
Peer-Reviewable Design
Each benchmark includes comprehensive documentation explaining:
- →Research foundation and theoretical basis
- →Methodology and evaluation criteria
- →Known limitations and potential biases
- →Validation procedures and results
Version Control
All benchmarks are semantically versioned. Breaking changes, improvements, and bug fixes are clearly documented. Historical results remain accessible for longitudinal analysis.
Case Study: RLM Benchmark
Our Recursive Language Models benchmark exemplifies these principles in practice:
Research Foundation
Based on the MIT CSAIL paper "Recursive Language Models" (arXiv:2512.24601), implementing their approach for handling contexts 100x beyond native model limits.
Three-Tier Testing
Tests at 10K, 50K, and 100K+ token contexts with both RLM and traditional approaches, providing clear performance comparisons.
Multi-Dimensional Metrics
Tracks accuracy, latency, token usage, cost per query, and compression effectiveness across different context lengths.
Full Transparency
Complete source code, test questions, scoring rubrics, and infrastructure available on GitHub with MIT license.
Future Benchmarks
We're actively developing additional benchmarks to evaluate emerging LLM capabilities:
Multi-Modal Understanding
Evaluating vision-language models on complex image reasoning tasks
Code Generation Quality
Testing beyond syntax correctness to maintainability and design
Reasoning Chains
Measuring multi-step logical reasoning and problem decomposition
Safety & Alignment
Evaluating robustness against adversarial inputs and edge cases
Have a benchmark idea?
We welcome proposals for new benchmarks that align with our methodology. Join us in advancing the science of LLM evaluation.