PLDR-LLMs Reason At Self-Organized Criticality
#PLDR-LLMs #self-organized criticality #reasoning #large language models #complexity #emergent behavior #AI architecture
📌 Key Takeaways
- PLDR-LLMs exhibit reasoning at self-organized criticality, a state of optimal complexity.
- This suggests their reasoning processes operate near a critical point between order and chaos.
- The finding may explain the emergent reasoning capabilities in large language models.
- It implies potential for more efficient and robust AI reasoning architectures.
📖 Full Retelling
arXiv:2603.23539v1 Announce Type: new
Abstract: We show that PLDR-LLMs pretrained at self-organized criticality exhibit reasoning at inference time. The characteristics of PLDR-LLM deductive outputs at criticality is similar to second-order phase transitions. At criticality, the correlation length diverges, and the deductive outputs attain a metastable steady state. The steady state behaviour suggests that deductive outputs learn representations equivalent to scaling functions, universality cla
🏷️ Themes
AI Reasoning, Complex Systems
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Original Source
arXiv:2603.23539v1 Announce Type: new
Abstract: We show that PLDR-LLMs pretrained at self-organized criticality exhibit reasoning at inference time. The characteristics of PLDR-LLM deductive outputs at criticality is similar to second-order phase transitions. At criticality, the correlation length diverges, and the deductive outputs attain a metastable steady state. The steady state behaviour suggests that deductive outputs learn representations equivalent to scaling functions, universality cla
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