Why ReasonScape?
Traditional benchmarks treat models as black boxes, measuring only final outputs. ReasonScape treats them as analyzable information processing systems, revealing cognitive architecture patterns through parametric test generation, spectral analysis, and interactive visualization. This approach eliminates contamination, provides infinite test instances, and enables research-grade analysis of how models actually reason.

Revolutionary Methodology

Treating language models as analyzable information processing systems

3D Difficulty Manifolds

Navigate reasoning landscapes as interactive 3D terrain. Explore how model performance varies across multiple difficulty dimensions simultaneously with enhanced surface analysis.

Token-Frequency Analysis

Apply FFT to tokenized reasoning problems, revealing spectral signatures and validating difficulty parameters through frequency domain analysis of cognitive architectures.

Parametric Test Generation

Generate infinite unique test instances within controlled difficulty manifolds. Eliminate contamination through deterministic coordinate-based seeding and hierarchical sampling.

Statistical Rigor

Excess accuracy correction, truncation handling, and dynamic confidence intervals with Winston methodology ensure meaningful model and task comparisons.

Progressive Evaluation

Hierarchical multi-degree system enables rapid model exploration (Degree 0) scaling through standard assessment (Degree 1) to research-grade precision (Degree 2).

Cognitive Architecture Insights

Reveal patterns invisible to traditional benchmarks through spectral analysis, parametric testing, and interactive visualization of information processing capabilities.

Learn About the Methodology

Twelve Cognitive Domains

Comprehensive assessment across diverse reasoning capabilities

Analysis Tools

Comprehensive visualization and exploration of model reasoning capabilities

ReasonScape Leaderboard

Interactive Leaderboard

  • ReasonScore rankings across multiple reasoning domains with pagination
  • Token efficiency analysis for cost/performance optimization
  • Heatmap visualization with color-coded performance cells showing exactly where models break down
  • Truncation indicators displayed as rising darkness from the bottom of each cell
  • Statistical confidence indicators with 95% confidence intervals
  • Group and manifold filtering for focused analysis
Explore M12X Leaderboard
ReasonScape Explorer

3D Difficulty Manifold Explorer

  • Navigate reasoning landscapes as interactive 3D surfaces
  • Multi-panel analysis: FFT spectral analysis, accuracy plots, token distributions
  • Line projection analysis for systematic parameter studies
  • Cross-model comparison of cognitive architecture patterns
Launch M12X Explorer
Surface Comparison

Comparison Tools

  • Surface comparison: Side-by-side 3D manifold analysis across models
  • Projection comparison: Multi-model performance across parameter sweeps
  • Spectral analysis: Token-frequency domain patterns reveal architectural differences
See Documentation

ReasonScape: Information Processing Evaluation for Large Language Models

Mikhail Ravkine • 2025

ReasonScape introduces a next-generation evaluation methodology that treats language models as analyzable information processing systems. Through parametric test generation, spectral analysis, and interactive visualization, ReasonScape reveals cognitive architecture patterns invisible to traditional benchmarks. The M12X suite provides comprehensive assessment across twelve cognitive domains with progressive difficulty degrees, enabling both rapid model comparison and research-grade analysis.

@software{reasonscape2025, title={ReasonScape: Information Processing Evaluation for Large Language Models}, author={Mikhail Ravkine}, year={2025}, url={https://github.com/the-crypt-keeper/reasonscape} }