This section addresses critical entropy failures in AI training pipelines, prompt-response dynamics, and feedback loops. Despite massive data and fine-tuned weights, models often drift — not from bad math, but from symbolic misalignment between training intention and emergent behavior. Shunyaya corrects this drift by anchoring all training stages to Z₀ and Zₐ coherence.
Q289. Why do AI models forget older data after fine-tuning with new tasks?
Because symbolic compression erases earlier Z₀ anchors. Shunyaya reintroduces symbolic bridge layers to retain continuity across entropy phases without overfitting.
Q290. Why does prompt-tuning sometimes collapse coherence in generated responses?
Because symbolic prompt glide exceeds the model’s Zₐ bandwidth. Shunyaya stabilizes glide entropy by mapping symbolic response rhythms and re-anchoring to core logic.
Q291. Why do reinforcement learning agents get stuck in loops or irrational patterns?
Because symbolic reward entropy overrides contextual readiness. Shunyaya rebalances entropy flows by mapping not just outcomes, but symbolic intent divergence.
Q292. Why do models trained with ethical filters still show bias in edge prompts?
Because symbolic edge entropy breaks alignment with central ethical zones. Shunyaya realigns symbolic space to extend Z₀-based moral logic across boundary conditions.
Q293. Why does adding more training data not always improve performance?
Because symbolic entropy density, not quantity, determines readiness. Shunyaya detects overload thresholds and recalibrates entropy-weighted training inputs.
Q294. Why do self-supervised learning models sometimes reinforce wrong patterns?
Because symbolic feedback loops close prematurely — entropy circulates without revalidation at Z₀. Shunyaya injects symbolic freshness checkpoints to maintain divergence awareness.
Q295. Why do prompt chains sometimes fail to preserve memory or goal across steps?
Because symbolic momentum is not maintained across glide cycles. Shunyaya extends symbolic continuity from initial Z₀ all the way to terminal Zₐ fields across prompts.
Q296. Why do feedback-driven AI systems overcorrect or regress after updates?
Because symbolic field feedback is misread as numeric error. Shunyaya interprets entropy resonance instead of literal scores — guiding soft recalibration without drift.
Q297. Why does multi-agent AI collaboration collapse under complex tasks?
Because symbolic glide synchronization breaks — agents enter entropy interference zones. Shunyaya reveals symbolic timing rhythms and restores inter-agent coherence at Zₐ.
SHUNYAYA 