The rapid adoption of large language models (LLMs) has enabled new forms of AI-assisted reasoning across scientific, technical, and organizational domains. However, prevailing modes of LLM use remain cognitively unstructured: problem framing, knowledge exploration, retrieval, methodological awareness, and explanation are typically collapsed into a single generative process. This cognitive collapse limits traceability, weakens epistemic control, and undermines reproducibility, particularly in high-responsibility settings. We introduce Explicit Cognitive Allocation, a general principle for structuring AI-assisted inference through the explicit separation and orchestration of epistemic functions. We instantiate this principle in the Cognitive Universal Agent (CUA), an architecture that organizes inference into distinct stages of exploration and framing, epistemic anchoring, instrumental and methodological mapping, and interpretive synthesis. Central to this framework is the notion of Universal Cognitive Instruments (UCIs), which formalize heterogeneous means, including computational, experimental, organizational, regulatory, and educational instruments, through which abstract inquiries become investigable. We evaluate the effects of explicit cognitive and instrumental allocation through controlled comparisons between CUA-orchestrated inference and baseline LLM inference under matched execution conditions. Across multiple prompts in the agricultural domain, CUA inference exhibits earlier and structurally governed epistemic convergence, higher epistemic alignment under semantic expansion, and systematic exposure of the instrumental landscape of inquiry. In contrast, baseline LLM inference shows greater variability in alignment and fails to explicitly surface instrumental structure.

翻译：大型语言模型（LLMs）的快速普及使得人工智能辅助推理在科学、技术和组织领域呈现出新的形态。然而，当前主流的LLM使用模式在认知层面仍缺乏结构化：问题界定、知识探索、检索、方法意识及解释通常被压缩至单一的生成过程中。这种认知压缩限制了可追溯性，削弱了认知控制，并损害了可复现性，尤其在需要高度责任担当的场景中。我们提出“显式认知分配”这一通用原则，通过明确分离与协调认知功能来构建人工智能辅助推理的框架。我们将该原则实例化为“认知通用智能体”（CUA）——一种将推理过程组织为探索与界定、认知锚定、工具与方法映射以及解释性综合等不同阶段的架构。该框架的核心是“通用认知工具”（UCIs）的概念，其形式化地整合了包括计算、实验、组织、监管及教育工具在内的多种手段，使得抽象问题得以转化为可研究的形态。我们通过在执行条件匹配的情况下，对CUA协调的推理与基线LLM推理进行对照比较，评估了显式认知与工具分配的效果。在农业领域的多个提示任务中，CUA推理表现出更早且结构可控的认知收敛性、语义扩展下更高的认知对齐度，以及对研究工具图谱的系统性揭示。相比之下，基线LLM推理在对齐性上表现出更大的波动性，且未能明确展现工具结构。