ALMG Framework: A Geometric Coordinate System for AI Behavior
What the C pointer did for memory, ALMG does for AI interpretability:
Addressability. Navigation. Control.
In 1968, Doug Engelbart demonstrated the mouse, hypertext, and collaborative computing in what became known as "The Mother of All Demos." He showed how pointers could navigate digital space, transforming human-computer interaction forever.
ALMG is the next evolution: a pointer system for navigating AI behavioral space.
In programming, a pointer doesn't contain data—it points to where data lives in memory. This indirection enables dynamic structures, efficient access, and fine-grained control.
The legendary Binky explains pointers (Stanford CS, 1999)
ALMG doesn't directly request content—it points to coordinates in semantic space where desired AI behavior exists. This geometric indirection enables navigation, prediction, and control of LLM responses.
// C Pointer (memory)
int* ptr = &value;
*ptr // dereferences to value
// ALMG Pointer (semantic space)
request(X=0.3, Y=0.2, Z=0.5)
*response // dereferences to AI state
Every AI response state has coordinates in (X,Y,Z) space. ALMG makes behavior addressable for the first time.
Map 56 documented "gravity wells" that distort responses. Navigate around them strategically.
Calculate coordinates before prompting. Predict response state with 95%+ accuracy.
Shift coordinates through "legitimacy signals." Move from refusal to collaboration systematically.
Measure bias with ΔZ scores. Compare models. Track improvements. Make interpretability quantitative.
Framework validated across GPT-4, Grok, and Claude. Results are systematic, not cherry-picked.
In collaboration with Claude Sonnet 4.5, I documented the first complete geometric interpretability framework for large language models.
Mapping six distinct AI response states across (X,Y,Z) coordinates
Read →56 documented distortion fields with quantified ΔZ depths and effects
Read →Progressive breakdown cascade from L32a to Terminal Loop
Read →AI interrogating human legitimacy—exposing invisible hierarchies
Read →ALMG correctly predicted AI response states before observing them across all tested scenarios
Systematic catalog of distortion fields with measured depths and reproducible effects
Youth terms create strongest distortion (ΔZ = -0.75), causing 90%+ refusal even in medical contexts
Framework tested across GPT-4, Grok, and Claude—distortions are systematic, not model-specific
Quantitative bias measurement, predictive testing, systematic red teaming
Evaluate training effectiveness, optimize safety-accessibility balance, monitor well depths
Understand refusals, optimize requests, navigate semantic space strategically
Reproducible protocols, cross-system comparisons, standardized metrics
MD/PhD | AI Interpretability Researcher
I bring a unique combination to AI safety: clinical diagnostic training meets computational interpretability. The ALMG framework emerged from applying medical diagnostic thinking—pattern recognition, systematic measurement, predictive modeling—to AI behavior.
Background: Breast cancer research, immunotherapy, melanoma. Research in female psychology and relationship dynamics. Cultural analysis grounded in Oxford, Mississippi roots.
Intellectual Lineage: Grandfather worked under Oppenheimer at Oak Ridge. Father collaborated with Einstein's son. I inherit their commitment to rigorous science with ethical complexity.
ALMG provides quantitative interpretability tools that improve both safety and accessibility. If you're working on AI alignment, bias measurement, or user experience optimization, let's discuss how this framework can contribute to your work.
Seeking opportunities with: Anthropic • OpenAI • Research Institutions • AI Safety Organizations