摘要
Abstract
The engineering practice of large language models(LLMs)and Agent systems is currently facing a structural dilemma:seemingly disparate technical approaches,such as LangGraph,reinforcement learning from human feedback(RLHF),and Guardrail,are all optimizing execution efficiency on the same"cognitive map",yet they universally lack a meta-cognitive capability to determine whether the current cognitive map itself remains valid.This study argues that this dilemma is not accidental but a structurally inclined manifestation of the"compression-residual"structure inherent in any finite cognitive system under long-term operation and continuous environmental adaptation.To address this,an architectural theory of cognitive runtime is proposed.The core thesis is that LLMs generate cognitive content,while the cognitive runtime manages the lifecycle of cognitive states.The main contributions include:① proposing"complexity-capability divergence(CCD)"as a working concept for diagnosing the long-term evolutionary trajectory of cognitive systems,and clarifying its distinction from existing concepts such as concept drift and technical debt;② revealing that the fundamental driving force behind the continuous expansion of Harness systems is the misalignment between token loss and business loss objectives,and pointing out that scaling optimizes the accuracy of selection among existing frameworks rather than the meta-selection capability of deciding whether to abandon the current framework;③ presenting a systematic definition of the cognitive runtime—a system layer responsible for creating,maintaining,evaluating,migrating,and reorganizing the lifecycle of cognitive states—and arguing for its plausibility and principled justification as an independent system layer;④ outlining the possible evolutionary path of the cognitive runtime from"state maintenance"to"ontological evolution",and clarifying the role of humans as value boundary setters and paradigm supervisors within this architecture.The core contribution of this paper is not to propose a new Agent framework,but to abstract"cognitive framework switching"as an independently manageable runtime behavior,and thereby to propose the cognitive runtime as a new system layer.This study presents a testable theoretical hypothesis and provides five falsifiable predictions.The mathematical representation and evolutionary algorithms of cognitive state space remain open questions.The aim of this paper is to provide a discussable,revisable,and falsifiable conceptual framework for future research.关键词
人工智能/认知运行时/复杂度-能力背离/认知状态空间/大语言模型/Harness系统/系统架构Key words
artificial intelligence/cognitive runtime/complexity-capability divergence/cognitive state space/large language models/Harness systems/system architecture分类
信息技术与安全科学
