Ongoing Research
Our research addresses the fundamental problems facing AI today, from reproducibility to verifiability. We're not just building models—we're redefining what's possible.
Ensuring AI systems produce identical outputs for identical inputs, eliminating randomness and unpredictability.
Progress
65%
Problems We're Solving:
Non-deterministic behavior in neural networks
Random seed dependencies
Floating-point precision inconsistencies
Hardware-dependent computations
Key Milestones:
Mathematical framework for deterministic computation
Hardware abstraction layer prototype
Deterministic inference engine (v0.1)
Developing mathematical proofs that AI outputs reflect truth and logical reasoning, not randomness or bias.
Progress
40%
Problems We're Solving:
Lack of mathematical guarantees for AI outputs
Black box decision-making
Inability to prove correctness
Hallucination and fact fabrication
Key Milestones:
Formal verification framework design
Proof-of-concept for verifiable reasoning
Mathematical proof system architecture
Creating systems where the same computation produces identical results across different environments and time.
Progress
55%
Problems We're Solving:
Environment-dependent results
Non-reproducible training processes
Version drift in dependencies
Time-dependent computations
Key Milestones:
Reproducibility testing framework
Deterministic training pipeline
Environment isolation system
Building AI systems with reliability as a foundational principle, not an afterthought.
Progress
75%
Problems We're Solving:
Reliability as an add-on feature
No built-in safety mechanisms
Fragile system designs
Lack of graceful degradation
Key Milestones:
Reliability-first design patterns
Self-healing system architecture
Production-ready reliability framework
Protecting against unpredictable and harmful AI behavior through built-in safety mechanisms.
Progress
60%
Problems We're Solving:
Unpredictable AI behavior
Adversarial vulnerabilities
Lack of safety guarantees
Uncontrolled AI outputs
Key Milestones:
Safety constraint system
Adversarial robustness framework
Output validation and filtering
Transforming AI from probabilistic chance to scientific certainty through rigorous methodology.
Progress
35%
Problems We're Solving:
Trust deficit in AI systems
Lack of scientific rigor
No reproducibility standards
Uncertainty in AI decisions
Key Milestones:
Trust metrics framework
Scientific methodology for AI
Reproducibility standards
Non-deterministic outputs for identical inputs
Inconsistent behavior across different hardware
Unpredictable performance degradation
Lack of reproducibility in training and inference
Black box decision-making processes
Inability to mathematically prove correctness
Hallucination and fact fabrication
Lack of explainability and interpretability
Adversarial attacks and vulnerabilities
Uncontrolled or harmful outputs
Bias and discrimination in AI systems
Lack of safety guarantees
Exponential computational requirements
Energy consumption concerns
Difficulty scaling to larger models
Inefficient resource utilization
Poor out-of-distribution performance
Overfitting to training data
Lack of domain transferability
Fragile to distribution shifts
AI alignment with human values
Ethical decision-making frameworks
Fairness and equity concerns
Transparency and accountability
Exploring quantum computing principles to enhance classical AI reliability and verification.
AI systems that can mathematically prove their own correctness in real-time.
Establishing industry-wide standards for AI reliability, similar to safety standards in aviation.
AI systems that understand cause-and-effect relationships, not just correlations.
Reliable AI systems that maintain consistency across distributed networks.
Discovering fundamental laws that govern intelligence, similar to physical laws.