{
  "@context": {
    "@vocab": "http://schema.org/",
    "prov": "http://www.w3.org/ns/prov#",
    "schema": "http://schema.org/",
    "dc": "http://purl.org/dc/terms/"
  },
  "@type": "ScholarlyArticle",
  "headline": "The Missing Knowledge Layer in Cognitive Architectures for AI Agents",
  "author": {
    "@type": "Person",
    "name": "Michaël Roynard",
    "affiliation": [
      {
        "@type": "Organization",
        "name": "Scalian DS",
        "address": {
          "@type": "PostalAddress",
          "addressLocality": "Toulouse",
          "addressCountry": "France"
        }
      },
      {
        "@type": "Organization",
        "name": "LAAS-CNRS, Université de Toulouse",
        "address": {
          "@type": "PostalAddress",
          "addressLocality": "Toulouse",
          "addressCountry": "France"
        }
      }
    ],
    "email": "michael.roynard@scalian.com",
    "identifier": "0000-0001-8469-4131"
  },
  "publisher": {
    "@type": "Organization",
    "name": "Scalian DS and LAAS-CNRS, Université de Toulouse"
  },
  "datePublished": "2026-04-13",
  "abstract": "This paper identifies a missing Knowledge layer in cognitive architectures for AI agents, arguing that existing frameworks conflate knowledge with memory, leading to category errors in persistence semantics. It proposes a four-layer decomposition (Knowledge, Memory, Wisdom, Intelligence) each with distinct persistence semantics: indefinite supersession, Ebbinghaus decay, evidence-gated revision, and ephemeral inference respectively. Companion implementations in Python and Rust demonstrate feasibility. The paper surveys existing systems and benchmarks, highlighting the architectural gap and providing convergence evidence from multiple independent sources.",
  "keywords": [
    "Cognitive architecture",
    "AI agents",
    "Memory systems",
    "Knowledge representation",
    "Persistence semantics",
    "LLM agents"
  ],
  "articleBody": "The paper identifies a category error in AI agent memory systems conflating knowledge and memory, proposes a four-layer decomposition with distinct persistence semantics, and validates it with convergence evidence and companion implementations.",
  "hasPart": [
    {
      "@type": "DefinedTermSet",
      "name": "Four-layer cognitive architecture decomposition",
      "description": "Defines four distinct cognitive layers for AI agents with unique persistence semantics and update mechanisms.",
      "hasDefinedTerm": [
        {
          "@type": "DefinedTerm",
          "name": "Knowledge",
          "description": "Factual, structural, and permanent truths about the world. Persistence is indefinite with supersession update semantics. Shared across agents with provenance tracking."
        },
        {
          "@type": "DefinedTerm",
          "name": "Memory",
          "description": "Experiential, per-agent, ephemeral by default, subject to Ebbinghaus decay. Uses bi-temporal event sourcing with scoped contexts."
        },
        {
          "@type": "DefinedTerm",
          "name": "Wisdom",
          "description": "Durable behavioral patterns learned from experience. Updated via evidence-gated revision with stability tiers. Multi-source ownership."
        },
        {
          "@type": "DefinedTerm",
          "name": "Intelligence",
          "description": "Ephemeral capacity to reason, plan, and act. Exists only at inference time and orchestrates other layers."
        },
        {
          "@type": "DefinedTerm",
          "name": "Supersession",
          "description": "An update mechanism where new claims replace older ones but preserve provenance and historical queries."
        },
        {
          "@type": "DefinedTerm",
          "name": "Ebbinghaus decay",
          "description": "A forgetting curve model applied to Memory layer to simulate natural decay of experiential information."
        },
        {
          "@type": "DefinedTerm",
          "name": "Evidence-gated revision",
          "description": "Update mechanism for Wisdom layer where behavioral directives are revised only after crossing evidence thresholds."
        },
        {
          "@type": "DefinedTerm",
          "name": "Bi-temporal event sourcing",
          "description": "Memory storage model using four timestamps per fact to distinguish system and real-world validity periods."
        },
        {
          "@type": "DefinedTerm",
          "name": "Provenance",
          "description": "Metadata tracking the source, time, and evidence supporting knowledge claims."
        },
        {
          "@type": "DefinedTerm",
          "name": "Category error",
          "description": "The conflation of knowledge and memory leading to inappropriate application of decay or update mechanisms."
        }
      ]
    },
    {
      "@type": "HowTo",
      "name": "Implementing the Four-Layer Cognitive Architecture",
      "description": "Steps to realize the four-layer architecture with distinct persistence semantics for AI agents.",
      "hasPart": [
        {
          "@type": "HowToStep",
          "position": 1,
          "name": "Separate Knowledge and Memory Layers",
          "text": "Design distinct storage systems for Knowledge (permanent facts with supersession) and Memory (ephemeral experiences with decay)."
        },
        {
          "@type": "HowToStep",
          "position": 2,
          "name": "Implement Persistence Semantics",
          "text": "Apply indefinite supersession for Knowledge, Ebbinghaus decay for Memory, evidence-gated revision for Wisdom, and ephemeral orchestration for Intelligence."
        },
        {
          "@type": "HowToStep",
          "position": 3,
          "name": "Use Bi-temporal Event Sourcing for Memory",
          "text": "Store memory events with four timestamps to distinguish system and real-world validity, enabling temporal conflict resolution."
        }
      ]
    },
    {
      "@type": "Question",
      "name": "What is the main category error identified in current AI agent memory systems?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "The main category error is conflating knowledge with memory, leading to applying cognitive decay to factual claims that should be permanent, or treating facts and experiences with identical update mechanics."
      }
    },
    {
      "@type": "Question",
      "name": "What are the four layers proposed in the cognitive architecture?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "The four layers are Knowledge (permanent facts), Memory (ephemeral experiences), Wisdom (durable behavioral patterns), and Intelligence (ephemeral reasoning)."
      }
    },
    {
      "@type": "Question",
      "name": "How does the Knowledge layer differ from the Memory layer in persistence semantics?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Knowledge has indefinite persistence with supersession updates, preserving provenance and historical claims, while Memory is ephemeral and subject to Ebbinghaus decay, reflecting natural forgetting."
      }
    },
    {
      "@type": "Question",
      "name": "What is supersession in the context of the Knowledge layer?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Supersession is an update mechanism where new claims replace older ones but both are preserved with provenance, allowing historical queries and preventing loss of factual information."
      }
    },
    {
      "@type": "Question",
      "name": "Why is evidence-gated revision important for the Wisdom layer?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Because behavioral directives should only be revised when sufficient corroborating evidence accumulates, preventing premature or incorrect updates and ensuring durable, reliable wisdom."
      }
    },
    {
      "@type": "Question",
      "name": "What role does Intelligence play in the four-layer architecture?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Intelligence is the ephemeral capacity to reason, plan, and act during inference time, orchestrating queries and application of Knowledge, Memory, and Wisdom without persistent storage."
      }
    },
    {
      "@type": "Question",
      "name": "How do the companion implementations demonstrate the feasibility of the architecture?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "They provide separate Python and Rust systems implementing Knowledge and Memory layers with distinct persistence semantics, deterministic operations without LLM or network dependencies, and pass extensive tests."
      }
    },
    {
      "@type": "Question",
      "name": "What empirical evidence supports the four-layer decomposition?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Convergence evidence from multiple independent sources including DeepMind's Cognitive Framework, BEAM benchmark results showing near-zero contradiction resolution, and practitioner community signals support the architectural gap and the need for distinct persistence semantics."
      }
    },
    {
      "@type": "Question",
      "name": "How does the four-layer model address temporal reasoning and contradiction resolution better than flat memory stores?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "By separating Knowledge and Memory with distinct persistence semantics and using bi-temporal event sourcing and supersession, the model enables correct temporal ordering and filtering of facts, improving contradiction resolution and temporal reasoning."
      }
    },
    {
      "@type": "Question",
      "name": "What are the limitations of the proposed architecture and study?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "The study is a position paper with limited empirical evaluation, relies partly on non-peer-reviewed sources, and the Knowledge/Wisdom boundary remains a design choice. The architecture is a framework, not a formal model with provable guarantees."
      }
    },
    {
      "@type": "Question",
      "name": "What future work is suggested to advance this research?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Scaling pilot evaluations to full benchmarks with learned routing, developing a memory-architecture-specific benchmark testing persistence correctness, and implementing closed-loop consolidation pipelines from Memory to Wisdom."
      }
    },
    {
      "@type": "Question",
      "name": "How do CoALA and JEPA frameworks compare to the four-layer decomposition?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "CoALA names semantic and episodic memory but does not operationalize persistence distinctions, while JEPA lacks a Knowledge layer and persistence semantics. The four-layer model explicitly defines persistence semantics per layer."
      }
    }
  ]
}