Deconstructing LLM Hallucinations via Chaos Dynamics and Biological Homeostasis
lnsip9reg · reddit · 2026-07-14
The author (a dentist and amateur AI researcher) suggests stepping outside traditional computer science to understand the underlying mechanisms and instability of LLMs through the lenses of orbital mechanics and systems biology.
Core Insights:
- The N-body Problem of Context Windows: In the Transformer's attention mechanism, every Token exerts a gravity-like pull on every other Token. As context length grows, this interaction's complexity increases exponentially, making the system highly prone to mathematical chaos.
- Hallucinations as Positive Feedback Loops: An LLM's autoregressive nature means its output immediately becomes the next input. A tiny deviation (generating an incorrect Token) instantly alters the context's gravitational field. The model builds upon this error, continuously amplifying noise until it detaches from facts entirely, resulting in a "hallucination."
- Fragile Software-Level Constraints Cannot Achieve Homeostasis: After-the-fact software guardrails cannot fundamentally prevent system collapse. The author argues that LLM stability shouldn't rely on externally imposed limits, but rather on reshaping the probability distribution landscape and introducing real-time variance-based feedback to achieve systemic "homeostasis."
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