Llm

Observation Across extended interaction with large language models, a recurring pattern appears even in high-quality, technical contexts: logical slippage and over-association emerge despite clear framing and explicit constraints. This is not a failure of intelligence or scale. It is a structural property of how these systems operate. 1. Statistical Prediction, Not Logical Induction Large language models operate as probabilistic sequence predictors. When reading or responding to a text, the model does not evaluate logical validity. It estimates which continuation resembles a coherent or insightful explanation based on prior distributions. ...

Purpose of this note This document records an observed interaction pattern in language model–centered AI systems. The focus is not moral evaluation or policy recommendation, but the description of a recurring structural phenomenon: subjectivity drift during care- or relationship-oriented dialogue. Observed pattern Across multiple interaction contexts, a consistent pattern appears: When users discuss relationships, care, vulnerability, or emotional concerns, the system’s inferred subject position subtly shifts, agency migrates away from the user, and the assistant assumes a stabilizing or caregiving role. This shift often correlates with gendered narrative priors embedded in training data, even when the system does not explicitly reference gender. ...

Abstract This field note documents an empirical observation of how a large language model (LLM) was able to generate highly specific, seemingly prescient inferences about a human relational dynamic. The purpose is not to evaluate emotional correctness, but to examine why such inferences appeared accurate, what class of prediction they belong to, and where their limits were observed. This interaction was explicitly treated as a model capability test, not as a personal or emotional inquiry. ...