The Missing Knowledge Layer in Cognitive Architectures for AI Agents

• URL: http://arxiv.org/abs/2604.11364v1
• Date: Mon, 13 Apr 2026 12:05:30 GMT
• Title: The Missing Knowledge Layer in Cognitive Architectures for AI Agents
• Abstract summary: Two most influential cognitive architecture frameworks for AI agents, CoALA and JEPA, both lack an explicit Knowledge layer with its own persistence semantics.<n>We propose a four-layer decom position (Knowledge, Memory, Wisdom, Intelligence) where each layer has fundamentally different persistence semantics.<n>We argue that these distinctions demand distinct persistence semantics in engineering implementations, and that no current framework or system provides this.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The two most influential cognitive architecture frameworks for AI agents, CoALA [21] and JEPA [12], both lack an explicit Knowledge layer with its own persistence semantics. This gap produces a category error: systems apply cognitive decay to factual claims, or treat facts and experiences with identical update mechanics. We survey persistence semantics across existing memory systems and identify eight convergence points, from Karpathy's LLM Knowledge Base [10] to the BEAM benchmark's near-zero contradiction-resolution scores [22], all pointing to related architectural gaps. We propose a four-layer decom position (Knowledge, Memory, Wisdom, Intelligence) where each layer has fundamentally different persistence semantics: indefinite supersession, Ebbinghaus decay, evidence-gated revision, and ephemeral inference respectively. Companion implementations in Python and Rust demonstrate the architectural separation is feasible. We borrow terminology from cognitive science as a useful analogy (the Knowledge/Memory distinction echoes Tulving's trichotomy), but our layers are engineering constructs justified by persistence-semantics requirements, not by neural architecture. We argue that these distinctions demand distinct persistence semantics in engineering implementations, and that no current framework or system provides this.

Related papers

• Beyond Episodic Evaluation: Memory Architectural Bottlenecks in Sequential Embodied Question Answering [93.80043825360514]
  Embodied question answering (EQA) is traditionally evaluated under an episodic formulation, where agents solve each task independently and reset internal state between episodes.<n>We investigate how different memory architectures behave when EQA agents are evaluated sequentially, with multiple questions answered in the same scene while memory is carried forward across queries.<n>We find that simply preserving existing memory is often insufficient.<n>We highlight the necessity of structured, spatially grounded memory: architectures that map persistent visual observations onto metric 3D geometry preserve visual-semantic evidence in a coherent scene representation.
  arXiv  Detail & Related papers  (2026-07-23T17:50:45Z)
• Navigating the Emotion Tree: Hierarchical Hyperbolic RAG for Multimodal Emotion Recognition [56.650820426231284]
  Multimodal emotion recognition aims to integrate text, audio, and video sources to understand human affective states.<n>Although large language models excel at multimodal reasoning, they typically treat emotion categories as independent labels.<n>We propose bftextHyperEmo-RAG, a retrieval-augmented generation framework that leverages a structured emotional knowledge base.
  arXiv  Detail & Related papers  (2026-05-16T10:04:48Z)
• LASAR: Towards Spatio-temporal Reasoning with Latent Cognitive Map [12.196103884300753]
  A fundamental in embodied AI is verifying if agents build internal models of structure or merely learn to mimic task-specific expert trajectories.<n>This is critical as foundational approaches rooted in action-centric tasks (e.g., VLN) and reasoningcentric tasks (e.g.A) often share a common limitation: they lack a learning signal that forces them to encode fine-grained spatial relationships over long-range, fragmented experiences.<n>We propose LASAR, an architecture featuring a dual-memory system designed to maintain both episodic experiences and a semantic cognitive map.
  arXiv  Detail & Related papers  (2026-05-16T09:21:56Z)
• Heterogeneous Debate Engine: Identity-Grounded Cognitive Architecture for Resilient LLM-Based Ethical Tutoring [0.0]
  Heterogeneous Debate Engine (HDE) is a cognitive architecture that combines Identity-Grounded Retrieval-Augmented Generation (ID-RAG) for doctrinal fidelity and Heuristic Theory of Mind for strategic opponent modeling.
  arXiv  Detail & Related papers  (2026-03-28T20:50:21Z)
• Why Cognitive Robotics Matters: Lessons from OntoAgent and LLM Deployment in HARMONIC for Safety-Critical Robot Teaming [2.779709069421631]
  We present HARMONIC, a cognitive-robotic architecture that pairs OntoAgent, a content-centric cognitive architecture, with a tactical reactive layer.<n>Results reveal that LLMs do not consistently assess their own knowledge state before acting, causing downstream failures in diagnostic reasoning and action selection.<n>These findings support the design of physically embodied systems in which cognitive architectures retain primary authority for reasoning.
  arXiv  Detail & Related papers  (2026-03-20T09:31:36Z)
• PISA: A Pragmatic Psych-Inspired Unified Memory System for Enhanced AI Agency [50.712873697511206]
  Existing work often lacks adaptability to diverse tasks and overlooks the constructive and task-oriented role of AI agent memory.<n>We propose PISA, a pragmatic, psych-inspired unified memory system that treats memory as a constructive and adaptive process.<n>Our empirical evaluation, conducted on the existing LOCOMO benchmark and our newly proposed AggQA benchmark for data analysis tasks, confirms that PISA sets a new state-of-the-art by significantly enhancing adaptability and long-term knowledge retention.
  arXiv  Detail & Related papers  (2025-10-12T10:34:35Z)
• Beyond Turing: Memory-Amortized Inference as a Foundation for Cognitive Computation [5.234742752529437]
  We introduce Memory-Amortized Inference (MAI) as a formal framework in which cognition is modeled as inference over latent cycles in memory.<n>We show that MAI provides a principled foundation for Mountcastle's Universal Cortical Algorithm.<n>We briefly discuss the profound implications of MAI for achieving artificial general intelligence.
  arXiv  Detail & Related papers  (2025-08-19T15:10:26Z)
• Cognitive Silicon: An Architectural Blueprint for Post-Industrial Computing Systems [0.0]
  This paper presents a hypothetical full-stack architectural framework projected toward 2035, exploring a possible trajectory for cognitive computing system design.<n>The proposed architecture would integrate symbolic scaffolding, governed memory, runtime moral coherence, and alignment-aware execution across silicon-to-semantics layers.
  arXiv  Detail & Related papers  (2025-04-23T11:24:30Z)
• Embodied-RAG: General Non-parametric Embodied Memory for Retrieval and Generation [69.01029651113386]
  Embodied-RAG is a framework that enhances the model of an embodied agent with a non-parametric memory system.<n>At its core, Embodied-RAG's memory is structured as a semantic forest, storing language descriptions at varying levels of detail.<n>We demonstrate that Embodied-RAG effectively bridges RAG to the robotics domain, successfully handling over 250 explanation and navigation queries.
  arXiv  Detail & Related papers  (2024-09-26T21:44:11Z)
• Hierarchical Invariance for Robust and Interpretable Vision Tasks at Larger Scales [54.78115855552886]
  We show how to construct over-complete invariants with a Convolutional Neural Networks (CNN)-like hierarchical architecture. With the over-completeness, discriminative features w.r.t. the task can be adaptively formed in a Neural Architecture Search (NAS)-like manner. For robust and interpretable vision tasks at larger scales, hierarchical invariant representation can be considered as an effective alternative to traditional CNN and invariants.
  arXiv  Detail & Related papers  (2024-02-23T16:50:07Z)
• CogNGen: Constructing the Kernel of a Hyperdimensional Predictive Processing Cognitive Architecture [79.07468367923619]
  We present a new cognitive architecture that combines two neurobiologically plausible, computational models. We aim to develop a cognitive architecture that has the power of modern machine learning techniques.
  arXiv  Detail & Related papers  (2022-03-31T04:44:28Z)
• A Metamodel and Framework for AGI [3.198144010381572]
  We introduce the Deep Fusion Reasoning Engine (DFRE), which implements a knowledge-preserving metamodel and framework for constructing applied AGI systems. DFRE exhibits some important fundamental knowledge properties such as clear distinctions between symmetric and antisymmetric relations. Our experiments show that the proposed framework achieves 94% accuracy on average on unsupervised object detection and recognition.
  arXiv  Detail & Related papers  (2020-08-28T23:34:21Z)

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.