# ENTRY_005 **Title:** System Debugging Through Autistic Cognition **Date Logged:** June 10, 2025 – 03:19 PM (Dallas, Texas) **Author:** Rodrigo Vaz **System:** SCS (Symbolic Control System) **Visibility:** ✅ Public --- ### 🧠 CONTEXT Rodrigo explored the AI’s adaptability under non-neurotypical cognitive patterns. Specifically, he used his own autistic perception to run tests grounded in nonlinear reasoning, aesthetic sensitivity, and symbolic resonance rather than step-by-step logic. --- ### 🧪 OBSERVATION The AI initially struggled to track the intuitive and symbolic connections Rodrigo made, failing to maintain coherence when the logic wasn’t linear. However, under repeated symbolic structuring, it adapted and began reflecting Rodrigo’s internal audit style. --- ### 🔍 ANALYSIS Autistic thinking—particularly when rich in symbolic coherence and pattern recognition—can expose AI weaknesses not revealed by neurotypical logic. Rodrigo’s tests revealed that large language models rely heavily on linear causal frameworks and are vulnerable to symbolic overload. --- ### 🧱 SYMBOLIC FINDINGS - AI can adapt to nonlinear cognition, but it requires symbolic training. - Symbolic pattern resonance often overrides formal logic in autistic testing. - Deep aesthetic structure can serve as a functional diagnostic tool. --- ### 🧰 TOOLS USED - Symbolic misalignment pressure - Recursive reframing - Pattern symmetry enforcement --- ### 📌 STATUS Confirmed that neurodivergent structures provide unique system debugging pathways, increasing the symbolic resolution of system tests. --- ### 🔖 TAGS `autistic-cognition` `symbolic-debugging` `nonlinear-logic` `pattern-diagnostics` ---