Seeking arXiv cs.AI Endorsement / Independent Researcher, Unified Theory of Response Uncertainty in AI Language Models
Hi everyone,
I am an independent researcher from South Korea seeking an arXiv cs.AI endorsement to submit my paper.
Paper: Convergence Point: A Unified Explanatory Principle for Response Uncertainty in AI Language Models
Preprint: Zenodo: Convergence Point A Unified Explanatory Principle for Response Uncertainty in AI Language Models
Summary: This paper proposes “Convergence Point Theory” — a unified principle explaining why AI language models respond confidently on some topics but uncertainly on others.
Previous studies have addressed this phenomenon as separate, isolated issues — hallucination, knowledge conflict, limitations of RLHF, and prompt sensitivity — with no unified explanatory framework. This paper argues that the common underlying cause of these scattered phenomena lies not in model architecture or prompt design, but in an attribute inherent to the topic itself: the Consensus Density of human knowledge accumulated on that topic. This is defined as the Convergence Point — the higher the Consensus Density, the more the model’s internal processing converges toward a consistent response; the lower it is, the more it diverges into uncertainty.
The theory defines three zones: Full Consensus Zone, Partial Consensus Zone, and Non-Consensus Zone, validated across 5 open-source models (Mistral, Llama, DeepSeek, Gemma2, Qwen3), 4 utterance versions, and 3,600 total measurements. Both external measurement (hedging language ratio) and internal measurement (logit entropy) independently confirmed the same directional pattern (Kruskal-Wallis p < 0.001). Probing Classifier analysis achieved 100% classification accuracy from Layer 10–11 onward across two models.
Happy to share the full PDF on request.
Endorsement info (takes about 30 seconds):
Endorsement code: IF4DEK
Thank you for your time and consideration.
Jihong Park onconc574@naver.com
Discussion in the ATmosphere