本研究旨在将 Lorenzo Magnani 的生态认知计算主义置于当前关于信息、计算和认知的更广泛研究背景中。传统上，认知被认为是人类独有的，并且是大脑活动的结果。然而，近期研究表明，认知是所有生命形式的基本特征，涵盖从单细胞到复杂多细胞生物及其网络。尽管如此，关于认知的文献和普遍理解仍在很大程度上以人脑为中心，从而导致概念空白和不一致。本文提出多种计算（信息处理）方法，包括一种关于认知的信息计算方法：在该方法中，相对于正在观察和认知的主体，自然结构表征信息，而自然结构上的动态过程被视为计算。我们将认知建模为一个由并发形态计算构成的网络，这些计算由跨越物理、化学和生物领域的自组装、自组织和自创生过程驱动。我们考察了将形态计算、形态发生、能动性、基底认知、扩展进化综合论和主动推断联系起来的近期发现。我们建立了与 Magnani 的生态认知计算主义以及无知实体的计算驯化思想之间的联系。新的理论与应用洞见对传统认知计算模型的边界提出质疑。传统模型优先考虑符号处理，往往忽视不同组织层次上主体物理具身化的内在约束与潜能。更好地从信息计算角度理解认知具身性，对于推动生物学、进化研究、人工智能、机器人学、医学等领域的发展至关重要。
英文摘要：
This study aims to place Lorenzo Magnanis Eco-Cognitive Computationalism within the broader context of current work on information, computation, and cognition. Traditionally, cognition was believed to be exclusive to humans and a result of brain activity. However, recent studies reveal it as a fundamental characteristic of all life forms, ranging from single cells to complex multicellular organisms and their networks. Yet, the literature and general understanding of cognition still largely remain human-brain-focused, leading to conceptual gaps and incoherency. This paper presents a variety of computational (information processing) approaches, including an info-computational approach to cognition, where natural structures represent information and dynamical processes on natural structures are regarded as computation, relative to an observing cognizing agent. We model cognition as a web of concurrent morphological computations, driven by processes of self-assembly, self-organisation, and autopoiesis across physical, chemical, and biological domains. We examine recent findings linking morphological computation, morphogenesis, agency, basal cognition, extended evolutionary synthesis, and active inference. We establish a connection to Magnanis Eco-Cognitive Computationalism and the idea of computational domestication of ignorant entities. Novel theoretical and applied insights question the boundaries of conventional computational models of cognition. The traditional models prioritize symbolic processing and often neglect the inherent constraints and potentialities in the physical embodiment of agents on different levels of organization. Gaining a better info-computational grasp of cognitive embodiment is crucial for the advancement of fields such as biology, evolutionary studies, artificial intelligence, robotics, medicine, and more.
RAIL: Rethinking Auditory Intelligence in Large Audio-Language Models with a CHC-Grounded Benchmark
School of Computing and Information Systems, The University of Melbourne（墨尔本大学计算与信息系统学院）；Faculty of Psychology and Educational Sciences, Alexandru Ioan Cuza University of Iași（亚历山德鲁伊万库扎大学心理学与教育科学学院）；School of Electronic Information, Wuhan University（武汉大学电子信息学院）；School of Public Health, The University of Hong Kong（香港大学公共卫生学院）；School of Computer Science, The University of Auckland（奥克兰大学计算机科学学院）；Department of Data Science and Artificial Intelligence, Monash University（莫纳什大学数据科学与人工智能系）
Neurosymbolic Systems of Perception & Cognition: The Role of Attention
Cisco Systems（思科系统公司）；Icelandic Institute for Intelligent Machines（冰岛智能机器研究所）；Reykjavik University（雷克雅未克大学）；Temple University（天普大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）；Stockholm University（斯德哥尔摩大学）
Rethinking the Understanding Ability across LLMs through Mutual Information
Department of Industrial and Systems Engineering（工业与系统工程系）；University of Houston（休斯敦大学）；Bakar Computational Health Sciences Institute（巴卡计算健康科学研究所）；UCSF
Generativism: Toward a Learning Theory for the Age of Generative Artificial Intelligence
Department of Education and Human Services, College of Education, Lehigh University（教育与人类服务系，教育学院，莱维大学）；Department of Community and Global Health, College of Health, Lehigh University（社区与全球健康系，健康学院，莱维大学）
EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning
College of Cyber Security, Jinan University（暨南大学网络空间安全学院）；School of Management, Jinan University（暨南大学管理学院）；School of Computer, Sun Yat-sen University（中山大学计算机学院）；School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）；Guangdong Key Laboratory of Data Security and Privacy Preserving（广东省数据安全与隐私保护重点实验室）
Rethinking KV Cache Eviction via a Unified Information-Theoretic Objective
College of Computer Science, Sichuan University, Chengdu, China（四川大学计算机学院，成都，中国）；Institute of High Performance Computing, A STAR, Singapore（高性能计算研究院，新加坡）
Complex Systems Lab, Universitat Pompeu Fabra（复杂系统实验室，庞培法布拉大学）；Institució Catalana de la Recerca i Estudis Avançats (ICREA)（加泰罗尼亚高级研究与教育机构）；Institut de Biologia Evolutiva, CSIC-UPF（生物进化研究所，CSIC-UPF）；Santa Fe Institute（圣菲研究所）；School of Computing, Australian National University（澳大利亚国立大学计算机学院）；ISEM, Université de Montpellier, CNRS, IRD（蒙彼利埃大学ISEM，CNRS，IRD）；Allen Discovery Center, Tufts University（塔夫茨大学艾伦发现中心）；Wyss Institute for Biologically Inspired Engineering, Harvard University（哈佛大学生物启发工程研究所）
A Novel Biologically Mechanism-Based Visual Cognition Model--Automatic Extraction of Semantics, Formation of Integrated Concepts and Re-selection Features for Ambiguity
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Science（中国科学院数学与系统科学研究院应用数学研究所）
