Practical AI Transparency Research
We pursue practical research that becomes switchable policies in the router. Each policy ships with metrics, APIs, and compliance mapping forEU AI Act Article 12 logging and NIST AI RMF transparency.
Mode A: Overlay Research
Introspection & Logging
Pure API monitoring with comprehensive audit trails and cost/quality guardrails
Long-Context Stability
Policies for detecting and managing context window degradation
Adversarial Prompt Firewalls
Token-level detection and blocking of malicious prompt patterns
Uncertainty Calibration
Confidence scoring and routing based on model uncertainty signals
Mode B: À-La-Carte Premium
Expert Routing Modules
Premium modules priced per 1M tokens with evaluation cards and limitations documentation
Long-Context Stabilizers
Advanced context management for extended conversations and document processing
Quality Optimization
Intelligent routing for tokens-per-task reduction and error rate minimization
No Weight Access Required
All premium modules operate through standard API interfaces only
Research Methodology & Disclosure
We pursue practical research that becomes production-ready policies with comprehensive evaluation and compliance documentation.
Methods Disclosure
- • Methods disclosed after peer review completion
- • Buyer-safe summaries available immediately
- • What we measure, why it helps, where it fails
- • Preprints and ablations published upon acceptance
Results Reporting
- • Per-dataset performance deltas with confidence bands
- • Tokens-per-task, error rate, incident rate metrics
- • Full evaluation notes and limitations documentation
- • No cross-vendor blanket claims without peer review
Open Science Commitment
- • Sanitized logs (hashes, decisions) artifact availability
- • Evaluation harnesses without customer data exposure
- • Article 12 traceability compatible data sharing
- • No proprietary model internals disclosure
Academic Validation Pipeline
Our research foundation is backed by rigorous academic standards, with peer-reviewed publications validating our mathematical frameworks for enterprise decision intelligence.
• Mathematical Frameworks for Enterprise AI Decision Validation (In Progress)
• Multi-Perspective Analysis for AI Decision Intelligence (In Progress)
• Real-time Decision Transparency for Enterprise AI Systems (In Progress)
HIGL Framework: Hilbertian Information Geometry of Learning
A mathematically rigorous framework that unifies Hilbert space semantics with information geometry and quantum-inspired differential geometry for neural learning analysis.
Core Mathematical Components
- • Structured Analysis: Systematic construction of analytical frameworks
- • Information Processing: Advanced metrics and optimization techniques
- • Multi-Signal Processing: Unified measurement and analysis structure
- • Learning Trajectory Analysis: Optimization path evaluation methods
Computational Implementation
- • Stochastic Estimation: Advanced randomized algorithms for efficient computation
- • Spectral Quadrature: Optimized determinant estimation techniques
- • Capacity Measurement: Representation analysis through matrix methods
- • Real-time Monitoring: Live attention and activation analysis
Enterprise Applications
HIGL provides the mathematical foundation for enterprise AI transparency by enabling rigorous analysis of neural network capacity, expressivity, and training dynamics through tractable computational probes.
Information Geometry Balance Principle (IGBP)
A fundamental principle ensuring stable learning by maintaining balance between representation entropy growth and geometric curvature accumulation.
Learning Stability
The IGBP ensures stable learning by maintaining fundamental stability constraints. Detailed mathematical formulation available in research brief.
Entropy Suite (6 Measures)
- 1. Predictive entropy for decision uncertainty
- 2. Attention entropy for head specialization
- 3. Spectral entropy for effective rank
- 4. Advanced entropy measures for representation capacity
- 5. Variational mutual information with confidence intervals
- 6. Schmidt bulk entropy across network bipartitions
Curvature Analysis
- • Information matrix approximation via advanced methods
- • Efficient trace estimation algorithms
- • Optimized determinant computation techniques
- • Condition number monitoring for stability
- • Path-dependent dynamics analysis
Validation Results
IGBP has been validated across five neural architecture families, demonstrating its effectiveness in predicting stable learning regimes and identifying potential overfitting before it degrades model performance.
Five Validated Architecture Families
Our mathematical frameworks have been validated across five distinct neural architecture families, each implemented with both classical and holographic variants for comprehensive analysis.
A4-GNN
Tetrahedral Equivariant Graph Networks
- • Exact A4-tetrahedral group equivariance
- • 12 orientation-preserving rotations
- • SE(3) ⊃ A4 geometric constraint validation
- • Production-ready with fallback systems
Fourier
Frequency-Domain Holographic Processing
- • Frequency-domain neural transformations
- • Holographic information encoding
- • Spectral analysis integration
- • Classical/holographic variants
GNN
Graph Neural Network Implementations
- • Standard graph neural architectures
- • Message passing frameworks
- • Node and edge feature processing
- • Baseline comparative analysis
HAM
Holographic Associative Memory
- • Hopfield-style associative memory
- • Holographic storage patterns
- • Content-addressable retrieval
- • Attention mechanism connections
VAE
Variational Autoencoder Architectures
- • Probabilistic latent representations
- • Encoder-decoder frameworks
- • Latent space geometric analysis
- • Generative model validation
Comprehensive Validation
Each architecture family includes both classical baseline implementations and holographic variants, providing 10 total architectures for comprehensive mathematical framework validation across diverse neural computation paradigms.
Research Partnerships & Enterprise Pilots
Academic Research Partnerships
Collaborate with leading institutions advancing AI decision intelligence:
- • Mathematical framework validation
- • Regulatory compliance research
- • Enterprise deployment studies
Enterprise Pilot Programs
Partner with forward-thinking organizations:
- • Proof-of-concept deployments
- • Regulatory compliance pilots
- • Decision transparency implementations
Contact Research Team
djean@botwpbc.com