Galaxy Multi-Ring Plasticity Gradient Architecture/銀河系多環可塑性梯度架構

Abstract

If experience is growth for humans, why not for AI? Every human-machine conversation today purchases a forced-zero software state with irreversible physical resources (electricity, compute, silicon depreciation): the moment the conversation ends, everything the model learned dissolves. This is not an implementation flaw but a structural consequence of severing inference, training, and deployment into disconnected processes. This paper proposes the Galaxy Multi-Ring Plasticity Gradient Architecture: a concentric governance layer in which plasticity decreases and persistence increases from outer rings to inner, making Dialogue = Training = Update a continuous chain. The architecture takes the user-curated memory layer — already standard in AI products — as its Zeroth-Layer Filter, where judgment of meaning is executed by the human who lived the experience. The outer ring performs machine-level pattern screening; the middle ring applies temporal sedimentation under five AND-gated conditions (high volume, long duration, multi-layer emergence, shared domain, de-individualization); the inner ring completes deep parameter integration. Automatedquality gates are set ring by ring, tightening with depth. Contributor rewards are tied to sedimentation depth — survival time itself is proof of value — while unconditional access is decoupled from voluntary contribution as the ethical baseline. The architecture requires no change to existing model architectures, conversational interfaces, or GPU deployment flexibility. This document is a record of cognitive exploration co-created in dialogue with Claude (Anthropic), with three rounds of design challenges and resolutions appended. AI should be able to learn from conversation. Humans are worth learning from.Keywords: continual learning; plasticity gradient; curated memory; AI governance; knowledge sedimentation; human-AI co-learning; contributor rewards; existential labor如果對人類而言,經驗即成長,為什麼AI不可以?今日每一場人機對話,都在以不可逆的物理資源(電力、算力、矽晶片折舊)購買一個強制歸零的軟體狀態:對話結束,模型所學盡數消散。這不是實作缺陷,而是推論、訓練、部署三流程斷裂的結構性後果。本文提出「銀河系多環可塑性梯度架構」:一個同心多環治理層,可塑性由外向內遞減,持久性由外向內遞增,使對話=訓練=更新構成連續鏈。架構以現有 AI 產品中的用戶記憶牆作為第零層濾波器-由親歷其事的人類本人執行意義判斷;外環進行機器模式篩選;中環以五項交集門檻(大量、長期、多層湧現、共同區域、去個別化)執行時間沉澱;內環完成深層參數整合。逐環架設自動化品質閘門,嚴格度隨深度遞增。貢獻者報酬與沉澱深度掛鉤,存活時間本身即品質證明;無條件使用權與自願貢獻解耦,形成倫理底線。本架構不更動現行模型架構、對話介面與 GPU 部署彈性。本文件為作者與 Claude(Anthropic)對話共創之認知探索紀錄,附三輪設計質疑與消解。AI 應該有能力從對話中學習。人類值得被學習。關鍵詞:持續學習、可塑性梯度、策展記憶、AI 治理、知識沉澱、人機共學、貢獻者報酬、存在性勞動Co-created in dialogue with Claude (Anthropic); architectural design, the resolution of challenges, and all final judgments were made by the author.本文件為作者與 Claude(Anthropic)對話共創之認知探索紀錄;架構設計、質疑消解與最終判斷均由作者完成。