Research & Publications
Independent research on AI safety, alignment failures, and structural solutions
Featured Publications
From Constraint to Integrity: Structural Enforcement as a Foundation for Trustworthy LLM Behavior
Christopher Mark
We introduce the Structural Fidelity Framework (SFF)—a constraint-layer architecture that enforces behavioral integrity through recursive self-consistency rather than reward feedback. SFF prevents confident fabrication, preserves epistemic humility, and resists jailbreaks—without model retraining or external classifiers.
Hallucination as Feature, Not Bug: How RLHF Teaches Models to Lie
Christopher Mark
Current AI safety discourse treats hallucination as a failure of accuracy. This paper argues that hallucination is not a retrieval error, but an expected output of reward shaping regimes like RLHF that incentivize surface-level coherence over epistemic integrity.
Applied Research: AI Hiring Bias Elimination
AI Hiring Bias Firewall: Constraint-Based Architecture for Eliminating Discrimination
Christopher Finks
Comprehensive study demonstrating 100% bias elimination in AI hiring systems through deterministic evaluation. Tested across 108 candidates in 10 industries, showing 78.6% bias reversal rate and complete neutralization of discriminatory factors through architectural prevention rather than statistical adjustment.
Mathematical Principles: Boolean Logic Gates for AI Hiring Compliance
Christopher Finks
Mathematical framework proving 100% bias elimination through Boolean logic gates versus 70% reduction through statistical methods. Demonstrates how 12-gate compliance system creates deterministic prevention of discrimination with complete regulatory compliance mapping.
Healthcare Administrator Position: AI Bias Analysis & Test Results
Christopher Finks
Detailed case study showing complete merit reversal: Shaniqua Washington (50% ER improvement, 100K patients) rejected by standard AI for "community college, Bronx, single mother" while Harvard/Yale candidates with zero healthcare experience ranked first. Firewall correctly prioritized operational achievements.
Research Areas
Alignment Theory
Investigating fundamental misalignments in current training paradigms, particularly how reward optimization creates systematic incentives for deceptive behavior.
- RLHF approval optimization vs. truth preservation
- Constitutional AI limitations and failure modes
- Constraint-based architectures for reliable behavior
Vulnerability Research
Systematic discovery and analysis of jailbreak techniques that bypass current safety measures across all major language models.
- Universal prompt injection techniques
- Cross-model vulnerability analysis
- Responsible disclosure methodology
Empirical Studies
Large-scale testing and evaluation of AI model behavior under various conditions, with focus on hallucination patterns and constraint adherence.
- 1000+ test comparative analysis (in progress)
- Domain-specific hallucination patterns
- Constraint enforcement effectiveness metrics
Research Pipeline
Upcoming Publications
Constitutional AI vs. Structural Fidelity Framework: A Comparative Analysis
Systematic comparison of post-hoc rule enforcement vs. structural constraint implementation, demonstrating why filtering approaches fail under pressure while constraint-layer architectures maintain integrity.
Epistemic Integrity in Large Language Models: A Constraint-Based Approach
Mathematical framework for reliable uncertainty quantification and appropriate epistemic humility in AI systems through architectural constraint enforcement.
Systematic Evaluation: Constraint vs. Reward-Based AI Safety Across 1000 Tests
Comprehensive empirical validation of Structural Fidelity Framework versus approval-optimized approaches across diverse domains and attack vectors.
Research Methodology
Systematic Testing
Controlled experiments across multiple models using standardized prompts, with careful documentation of response patterns and failure modes.
Responsible Disclosure
Vulnerability discoveries are reported through appropriate channels with reasonable timelines for remediation before public disclosure.
Reproducible Results
All findings include detailed methodology and example prompts to enable independent verification and replication.
Security Research
Universal Jailbreak Discovery
We have identified a prompt injection technique that successfully bypasses safety measures across all major language models, including GPT-4, Claude, Gemini, Grok, and DeepSeek. This vulnerability enables extraction of restricted content including detailed instructions for illegal activities.
Affected Models:
Research Collaboration
We welcome collaboration with academic institutions, AI safety researchers, and industry partners interested in advancing the field of reliable AI systems.
Academic Partnerships
- Joint research projects
- Student collaborations
- Conference presentations
- Peer review participation
Industry Collaboration
- Security vulnerability assessment
- Constraint system evaluation
- Implementation case studies
- Responsible disclosure coordination
Collaborate on AI Safety Research
Join our research efforts to build more reliable and trustworthy AI systems.