This paper argues that the operations of a 'Universal Turing Machine' (UTM) and equivalent mechanisms such as the 'Post Canonical System' (PCS) - which are widely accepted as definitions of the concept of `computing' - may be interpreted as *information compression by multiple alignment, unification and search* (ICMAUS).
The motivation for this interpretation is that it suggests ways in which the UTM/PCS model may be augmented in a proposed new computing system designed to exploit the ICMAUS principles as fully as possible. The provision of a relatively sophisticated search mechanism in the proposed 'SP' system appears to open the door to the integration and simplification of a range of functions including unsupervised inductive learning, best-match pattern recognition and information retrieval, probabilistic reasoning, planning and problem solving, and others. Detailed consideration of how the ICMAUS principles may be applied to these functions is outside the scope of this article but relevant sources are cited in this article.
Tencent Media Lab（腾讯媒体实验室）；Northeastern University（东北大学）
Compressing Deep Neural Network (DNN) models to alleviate the storage and computation requirements is essential for practical applications, especially for resource limited devices. Although capable of reducing a reasonable amount of model parameters, previous unstructured or structured weight pruning methods can hardly truly accelerate inference, either due to the poor hardware compatibility of the unstructured sparsity or due to the low sparse rate of the structurally pruned network. Aiming at reducing both storage and computation, as well as preserving the original task performance, we propo…
Language Models Resist Alignment: Evidence From Data Compression
Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；School of Computer Science, Peking University（北京大学计算机科学学院）
Pruning via Merging: Compressing LLMs via Manifold Alignment Based Layer Merging
Harbin Institute of Technology（哈尔滨工业大学）；University of Science and Technology of China（中国科学技术大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Shandong Agricultural University（山东农业大学）；Tencent Inc.（腾讯公司）；Anhui University（安徽大学）
Bridging Efficiency and Transparency: Explainable CoT Compression in Multimodal Large Reasoning Models
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Key Laboratory of Computer Network and Information Integration (SEU), Ministry of Education, China（计算机网络与信息集成重点实验室（SEU））
On the Adversarial Robustness of Large Vision-Language Models under Visual Token Compression
The Hong Kong Polytechnic University, Hong Kong（香港理工大学）；Nanyang Technological University, Singapore（南洋理工大学）；Chongqing University, Chongqing, China（重庆大学）；Research Centre for Privacy and Security Technologies in Future Smart Systems, PolyU（未来智能系统中的隐私与安全技术研究中心）
