主动涌现式 AI 记忆架构:基于物质记忆择优原理的 AI 软件推理范式革新

Abstract

The retrieval-augmented generation (RAG) systems for large language models suffer from inherent software limitations. Conventional AI inference adopts a model-centric scheduling paradigm, where the large model undertakes all tasks including knowledge matching, correlation mining and memory filtering. This leads to sharply increased inference load, missed retrieval of implicitly associated knowledge and poor activation of cold knowledge as knowledge bases scale up. Drawing on the preference principle of Zhao Futao’s Material Memory Axiom (FT‑MMT), this paper proposes a purely software-based active emergent AI memory architecture natively driven by the FT‑MMT theory, without any hardware modification. The architecture restructures the memory scheduling logic of large models: centralized matching computation and similarity discrimination originally handled by the model are offloaded to distributed software memory units. Via pure software mechanisms including global broadcast of demand signals, autonomous activation of memory units following material preference rules, and cross-unit cascading diffusion emergence, the system automatically converges to an optimal candidate memory set satisfying the FT‑MMT steady-state criterion, while the large model only performs final content integration and inference output. Logical experiments and paradigm analysis demonstrate that the proposed architecture can substantially cut down the software inference load of large models, strengthen the capability to capture implicit knowledge correlations and improve scheduling efficiency for large-scale knowledge bases. This research breaks the conventional paradigm of passive memory scheduling in AI, and offers a novel software implementation path directly derived from the fundamental axioms of material memory for lightweight inference, long-term memory management and autonomous knowledge association of large language models.