DissipativeAI · 耗散智能

DissipativeAI 是一个异步、自适应、容错的开放研究组织，我们提出「智能源于耗散结构优化自身不确定性的自指耗散｜ntelligence arises from dissipative structures optimizing their own uncertainty through self-referential dissipation.」这个AI范式并投入工作将其转化为可运行、可复现、可扩展的开源工作。我们相信：

智能源于耗散结构为了降低自身（在环境中的）（源自于自身和环境的）不确定性的自指耗散（通过耗散结构内部的复杂度带来的内部可能性之下的记忆结构和回归结构对于自身和环境信息的建模）

符号系统是耗散系统内部低熵工具，现实结构（几何拓扑）包含无限的信息熵。为了在有限的认知资源内理解它，我们必须根据所需的分辨率，截取一个特定层级的切片，并用一套运算封闭的符号系统（代数符号）来模拟该切片内的逻辑自洽性。

Intelligence arises from dissipative structures in order to reduce their own (in the environment) (arising from themselves and the environment) self-referential dissipation of uncertainty (through the memory structures and recurrent structures under the internal possibilities brought by the complexity within the dissipative structure to model information about themselves and the environment)

A symbolic system is just a low-entropy tool within a dissipative system. The structure of reality (geometric topology) contains infinite information entropy. In order to understand it within finite cognitive resources, we must capture a slice at a specific level according to the desired resolution and use a set of computationally closed symbolic systems (algebraic symbols) to simulate the logical coherence within that slice.

智能不是算法的特权，而是物质在耗散相里自发涌现的秩序。

1. 用熵产率替代损失函数，让训练过程自带物理可行判据。
2. 用耗散阈值替代早停，让模型自己知道「何时停」。
3. 用自指闭环替代黑箱，让系统观测自己的熵预算。

代码 → 热 → 熵 → 智能，一路可追踪、可测量、可审计。

🧪 研究轴心

轴心	关键词	状态
热力学优化器	Thermodynamic Optimizer	🔬 原型
耗散早停	Dissipative Early-Stopping	🧪 验证
自指闭环	Self-Referential Loop	📝 理论
语义-熵增对齐	Semantic-Entropic Alignment	🧠 设计
物理可解释性	Physically Interpretable AI	📊 基准

Code with damage. Learn with life.
让每一次梯度下降，都留下可测量的熵痕迹。