Descriptor-Based Object-Aware Memory Systems: A Comprehensive Review

• URL: http://arxiv.org/abs/2510.27070v2
• Date: Mon, 10 Nov 2025 02:03:06 GMT
• Title: Descriptor-Based Object-Aware Memory Systems: A Comprehensive Review
• Authors: Dong Tong,
• Abstract summary: The security and efficiency of modern computing systems are undermined by the absence of a native architectural mechanism to propagate high-level program semantics.<n>This paper presents a comprehensive survey of the architectural paradigm designed to bridge this semantic gap.<n>By elevating the descriptor to a first-class architectural abstraction, this paradigm enables hardware to dynamically acquire and enforce the rich semantics of software-defined objects.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The security and efficiency of modern computing systems are fundamentally undermined by the absence of a native architectural mechanism to propagate high-level program semantics, such as object identity, bounds, and lifetime, across the hardware/software interface. This paper presents a comprehensive survey of the architectural paradigm designed to bridge this semantic gap: descriptor-based, object-aware memory systems. By elevating the descriptor to a first-class architectural abstraction, this paradigm enables hardware to dynamically acquire and enforce the rich semantics of software-defined objects. This survey systematically charts the evolution and current landscape of this approach. We establish the foundational concepts of memory objects and descriptors and introduce a novel taxonomy of descriptor addressing modes, providing a structured framework for analyzing and comparing diverse implementations. Our unified analysis reveals how this paradigm holistically addresses the intertwined challenges of memory protection, management, and processing. As a culminating case study, we re-examine the CentroID model, demonstrating how its hybrid tagged-pointer encoding and descriptor processing mechanisms embody the path toward practical and efficient object-aware designs. Finally, we outline how the explicit cross-layer communication of object semantics provides a foundational research direction for next-generation cache hierarchies, unified virtual memory, and even 128-bit architectures.

Related papers

• Architecting AgentOS: From Token-Level Context to Emergent System-Level Intelligence [13.062618208633483]
  This paper proposes AgentOS, a holistic conceptual framework that redefines the Large Language Models as a "Reasoning Kernel" governed by structured operating system logic.<n>By mapping classical OS abstractions such as memory paging interrupt handling and process scheduling onto LLM native constructs, this review provides a rigorous roadmap for architecting resilient scalable and self-evolving cognitive environments.
  arXiv  Detail & Related papers  (2026-02-24T14:12:21Z)
• Taxonomy of the Retrieval System Framework: Pitfalls and Paradigms [0.0]
  We discuss how to navigate a complex design space of conflicting trade-offs between efficiency and effectiveness.<n>We identify architectural mitigations for domain generalization failures, lexical blind spots, and the silent degradation of retrieval quality due to temporal drift.<n>By categorizing these limitations and design choices, we provide a comprehensive framework for practitioners to optimize the efficiency-effectiveness frontier in modern neural search systems.
  arXiv  Detail & Related papers  (2026-01-27T23:49:46Z)
• The AI Hippocampus: How Far are We From Human Memory? [77.04745635827278]
  Implicit memory refers to the knowledge embedded within the internal parameters of pre-trained transformers.<n>Explicit memory involves external storage and retrieval components designed to augment model outputs with dynamic, queryable knowledge representations.<n>Agentic memory introduces persistent, temporally extended memory structures within autonomous agents.
  arXiv  Detail & Related papers  (2026-01-14T03:24:08Z)
• CAM: A Constructivist View of Agentic Memory for LLM-Based Reading Comprehension [55.29309306566238]
  Current Large Language Models (LLMs) are confronted with overwhelming information volume when comprehending long-form documents.<n>This challenge raises the imperative of a cohesive memory module, which can elevate vanilla LLMs into autonomous reading agents.<n>We draw inspiration from Jean Piaget's Constructivist Theory, illuminating three traits of the agentic memory -- structured schemata, flexible assimilation, and dynamic accommodation.
  arXiv  Detail & Related papers  (2025-10-07T02:16:30Z)
• Incorporating Hierarchical Semantics in Sparse Autoencoder Architectures [10.919461859475268]
  We introduce a modified SAE architecture that explicitly models a semantic hierarchy of concepts.<n> Application of this architecture to the internal representations of large language models shows both that semantic hierarchy can be learned, and that doing so improves both reconstruction and interpretability.
  arXiv  Detail & Related papers  (2025-06-01T22:20:07Z)
• From Objects to Events: Unlocking Complex Visual Understanding in Object Detectors via LLM-guided Symbolic Reasoning [71.41062111470414]
  Current object detectors excel at entity localization and classification, yet exhibit inherent limitations in event recognition capabilities.<n>We present a novel framework that expands the capability of standard object detectors beyond mere object recognition to complex event understanding.<n>Our key innovation lies in bridging the semantic gap between object detection and event understanding without requiring expensive task-specific training.
  arXiv  Detail & Related papers  (2025-02-09T10:30:54Z)
• Persistent Topological Features in Large Language Models [0.6597195879147556]
  We introduce topological descriptors that measure how topological features, $p$-dimensional holes, persist and evolve throughout the layers.<n>This offers a statistical perspective on how prompts are rearranged and their relative positions changed in the representation space.<n>As a showcase application, we use zigzag persistence to establish a criterion for layer pruning, achieving results comparable to state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-10-14T19:46:23Z)
• Cycle Consistency Driven Object Discovery [75.60399804639403]
  We introduce a method that explicitly optimize the constraint that each object in a scene should be associated with a distinct slot. By integrating these consistency objectives into various existing slot-based object-centric methods, we showcase substantial improvements in object-discovery performance. Our results suggest that the proposed approach not only improves object discovery, but also provides richer features for downstream tasks.
  arXiv  Detail & Related papers  (2023-06-03T21:49:06Z)
• Unsupervised Multi-object Segmentation Using Attention and Soft-argmax [0.6853165736531939]
  We introduce a new architecture for unsupervised object-centric representation learning and multi-object detection and segmentation. We show that this architecture significantly outperforms the state of the art on complex synthetic benchmarks and provide examples of applications to real-world traffic videos.
  arXiv  Detail & Related papers  (2022-05-26T10:58:48Z)
• Pin the Memory: Learning to Generalize Semantic Segmentation [68.367763672095]
  We present a novel memory-guided domain generalization method for semantic segmentation based on meta-learning framework. Our method abstracts the conceptual knowledge of semantic classes into categorical memory which is constant beyond the domains.
  arXiv  Detail & Related papers  (2022-04-07T17:34:01Z)
• Visual Concept Reasoning Networks [93.99840807973546]
  A split-transform-merge strategy has been broadly used as an architectural constraint in convolutional neural networks for visual recognition tasks. We propose to exploit this strategy and combine it with our Visual Concept Reasoning Networks (VCRNet) to enable reasoning between high-level visual concepts. Our proposed model, VCRNet, consistently improves the performance by increasing the number of parameters by less than 1%.
  arXiv  Detail & Related papers  (2020-08-26T20:02:40Z)
• Characterizing an Analogical Concept Memory for Architectures Implementing the Common Model of Cognition [1.468003557277553]
  We propose a new analogical concept memory for Soar that augments its current system of declarative long-term memories. We demonstrate that the analogical learning methods implemented in the proposed memory can quickly learn a diverse types of novel concepts.
  arXiv  Detail & Related papers  (2020-06-02T21:54:03Z)
• Look-into-Object: Self-supervised Structure Modeling for Object Recognition [71.68524003173219]
  We propose to "look into object" (explicitly yet intrinsically model the object structure) through incorporating self-supervisions. We show the recognition backbone can be substantially enhanced for more robust representation learning. Our approach achieves large performance gain on a number of benchmarks, including generic object recognition (ImageNet) and fine-grained object recognition tasks (CUB, Cars, Aircraft)
  arXiv  Detail & Related papers  (2020-03-31T12:22:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.