Seeking arXiv cs.AI Endorsement — Independent Researcher, Unified Theory of Response Uncertainty in AI Language Models

Hi everyone,

I am Jihong Park, an independent researcher from South Korea. I am looking for an endorsement to submit my first paper to arXiv under the cs.AI category.

Paper Title: Convergence Point: A Unified Explanatory Principle for Response Uncertainty in AI Language Models

Core Argument: This paper proposes that response uncertainty in AI language models is not caused by model architecture or prompt design, but is structurally determined by an attribute inherent to the topic itself — specifically, the degree to which humanity has reached consensus on that topic (Consensus Density).

Previous research has treated hallucination, knowledge conflict, RLHF limitations, and prompt sensitivity as separate, isolated problems. This paper argues that the common underlying cause of all these phenomena lies not inside the model, but in the attributes of the topic itself. For topics on which humanity has fully converged (mathematics, physical laws), AI responses converge stably. For topics where consensus exists simultaneously in opposing directions (capital punishment, euthanasia), data conflict maximizes internal processing variance. For topics where humanity has not yet determined the underlying principles (the nature of consciousness, the existence of God), data sparsity makes it structurally impossible to form a response direction. Crucially, the most original prediction of this theory — that data conflict may induce stronger internal processing variance than data absence — was experimentally confirmed.

This research addresses LLM internal representation structure, response uncertainty measurement, and AI alignment, making it appropriate for the cs.AI category.

Experimental Scale: 5 open-source models (Mistral, Llama, DeepSeek, Gemma2, Qwen3), 4 utterance versions, 3,600 total measurements. Both external measurement (hedging language analysis) and internal measurement (logit entropy) were conducted independently and yielded consistent results. Independent Wikipedia validation included. Spearman correlation r=0.676 (p<0.001) confirmed.

Paper link (zenodo): https://doi.org/10.5281/zenodo.20404739

Endorsement code: IF4DEK

Contact: onconc574@naver.com

Happy to answer any questions about the paper or methodology. Thank you for your time.