标题： 自然的洞察：通过神经科学视角下的代理推理新框架与综合分析 显示英文标题

标题： Nature's Insight: A Novel Framework and Comprehensive Analysis of Agentic Reasoning Through the Lens of Neuroscience

摘要： 自主人工智能已不再是难以企及的概念，它使智能体能够超越执行任务，独立解决复杂问题，适应变化并在处理环境的不确定性时游刃有余。 然而，是什么真正让智能体实现自主？ 这是代理推理，这是基础模型发展符号逻辑、统计关联或大规模模式识别以处理信息、推断和决策的关键。 然而，现有代理推理方法为何以及如何运作尚不清楚，相比之下，生物推理深深植根于涉及分层认知、多模态整合和动态交互的神经机制。 在这项工作中，我们提出了一个新颖的受神经科学启发的代理推理框架。 基于三个神经科学定义，并得到数学和生物学基础的支持，我们提出了一种统一框架，从感知到行动建模推理，包括四种核心类型：感知型、维度型、逻辑型和交互型，这些灵感来源于人类大脑中观察到的不同功能角色。 我们将此框架应用于系统地分类和分析现有的AI推理方法，评估它们的理论基础、计算设计和实际局限性。 我们还探讨了其对构建更具通用性和认知一致性的物理和虚拟环境中智能体的启示。 最后，在我们的框架基础上，我们概述了未来方向并提出了新的受神经科学启发的推理方法，类似于思维链提示。 通过结合认知神经科学与人工智能，这项工作为智能系统中代理推理的发展提供了理论基础和实践路线图。 相关项目可在以下地址找到：https://github.com/BioRAILab/Awesome-Neuroscience-Agent-Reasoning 。 显示英文摘要

摘要： Autonomous AI is no longer a hard-to-reach concept, it enables the agents to move beyond executing tasks to independently addressing complex problems, adapting to change while handling the uncertainty of the environment. However, what makes the agents truly autonomous? It is agentic reasoning, that is crucial for foundation models to develop symbolic logic, statistical correlations, or large-scale pattern recognition to process information, draw inferences, and make decisions. However, it remains unclear why and how existing agentic reasoning approaches work, in comparison to biological reasoning, which instead is deeply rooted in neural mechanisms involving hierarchical cognition, multimodal integration, and dynamic interactions. In this work, we propose a novel neuroscience-inspired framework for agentic reasoning. Grounded in three neuroscience-based definitions and supported by mathematical and biological foundations, we propose a unified framework modeling reasoning from perception to action, encompassing four core types, perceptual, dimensional, logical, and interactive, inspired by distinct functional roles observed in the human brain. We apply this framework to systematically classify and analyze existing AI reasoning methods, evaluating their theoretical foundations, computational designs, and practical limitations. We also explore its implications for building more generalizable, cognitively aligned agents in physical and virtual environments. Finally, building on our framework, we outline future directions and propose new neural-inspired reasoning methods, analogous to chain-of-thought prompting. By bridging cognitive neuroscience and AI, this work offers a theoretical foundation and practical roadmap for advancing agentic reasoning in intelligent systems. The associated project can be found at: https://github.com/BioRAILab/Awesome-Neuroscience-Agent-Reasoning .