This domain serves as a reference point for examining how artificial intelligence systems increasingly consume, recycle, and train on their own generated outputs rather than independent human data sources.
Model autophagy does not arise from a single failure or error.
It emerges through synthetic data loops, automated content generation, large-scale retraining pipelines, and the gradual replacement of original information with machine-produced material.
In such environments, models may begin to degrade, distort knowledge, reinforce errors, and lose alignment with external reality.
This site does not advocate technical solutions or research agendas. It does not provide tools, diagnostics, or forecasts.
Its purpose is to mark a systemic phenomenon already unfolding across AI development pipelines, data ecosystems, and digital infrastructure — often without a unified conceptual framework.
This page is intentionally minimal.
It exists to ensure the term Model Autophagy has a stable place to stand.
本網域作為一個參考標記，用於檢視：人工智慧系統如何日益消耗、回收， 並以自身產生的輸出進行訓練，而非依賴獨立的人類資料來源。
「模型自噬」並非源於單次失敗或錯誤。
它透過合成資料迴圈、自動化內容生成、大規模再訓練流程， 以及以機器產生資料逐步取代原始資訊而浮現。
在此類環境中，模型可能開始退化、扭曲知識、強化錯誤， 並失去與外部現實的對齊。
本站不主張技術解決方案或研究議程， 亦不提供工具、診斷或預測。
它的目的，是標示一個已在人工智慧開發管道、 資料生態系統與數位基礎設施中展開的系統性現象—— 且往往缺乏統一的概念框架。
本頁刻意維持極簡。
它存在，是為了確保 模型自噬（Model Autophagy） 這個詞能有一個穩定的立足點。