Central Host with Long-Term Memory

• Central Host with Long-Term Memory is an architectural paradigm that employs hierarchical, persistent storage to manage adaptive and context-rich memory.
• It integrates multi-tiered layers and selective updating strategies to overcome limitations like fixed context windows and catastrophic forgetting.
• This approach enables advanced applications in conversational AI, long-context reasoning, and lifelong cognitive learning through efficient retrieval and management.

A central host with long-term memory refers to an architectural and functional paradigm in artificial and biological systems where a dedicated entity or module is responsible for the organized, persistent storage, management, retrieval, and updating of information across extensive temporal horizons. In both neuroscience and AI, such a host underpins the continuity of context, stability of knowledge, and adaptability required for sustained reasoning, consistent interaction, and lifelong learning. Modern AI research increasingly incorporates explicit “central host” memory mechanisms to address the limitations of fixed context windows, avoid catastrophic or anterograde forgetting, and endow agents or models with adaptive, context-rich, and personalized behavior persisting over extended timescales.

1. Architectures and Principles of Central Hosts with Long-Term Memory

Central hosts with long-term memory in AI resemble cognitive architectures in humans, which partition memory into functional strata: sensory (immediate inputs), short-term (working context), and long-term (persistent, often externalized, storage) (He et al., 1 Nov 2024, Shan et al., 3 Apr 2025, Kang et al., 30 May 2025). The central host serves as an intelligent controller interfacing between transient experiences and a structured long-term memory (LTM).

Common architectural features include:

• Hierarchical memory layers: Real-time (Short-Term Memory, STM), intermediate (Mid-Term Memory, MTM), and persistent (Long-Term Personal Memory, LPM) storage units arranged and managed akin to operating system memory hierarchies (Kang et al., 30 May 2025).
• Separation of memory management functions: Distinct modules oversee storage, updating, retrieval, and memory consolidation (e.g., Memory Controller, Retrieval Engine, Post-Thinking module) (Westhäußer et al., 19 May 2025, Kang et al., 30 May 2025).
• Integration of retrieval-augmented generation: Generated outputs are grounded in selectively retrieved long-term memories to maintain coherence and factuality (Wang et al., 2023, Shan et al., 3 Apr 2025, Westhäußer et al., 19 May 2025).

For instance, MemoryOS uses a three-tiered structure with dialogue-page-based STM, segment-organized MTM, and fact- and personality-oriented LPM, mirroring operating system memory segmentation for efficient management and retrieval (Kang et al., 30 May 2025).

2. Memory Storage, Updating, and Forgetting Mechanisms

Effective long-term memory requires more than accumulation; it must support selective retention, up-to-date knowledge, and scalable organization.

Storage and Organization:

• Units of memory are often organized as pages, segments, episodic/summarized records, or graph nodes, enriched with semantic tags or embeddings for effective future retrieval (Kang et al., 30 May 2025, Westhäußer et al., 19 May 2025).
• Hierarchical aggregation (as in trees or clustered vector stores) and multi-granularity representations (sessions, turns, summaries, keywords) enable memory systems to balance factual completeness and retrieval efficiency (Xu et al., 26 May 2025).

Updating:

• Memory updating can be handled via strategies inspired by both human memory and OS principles:
  • FIFO (First-In, First-Out) for STM to MTM transitions(Kang et al., 30 May 2025).
  • Segmented page organization, where coherent dialogue topics are chunked and promoted to higher tiers of LTM (Kang et al., 30 May 2025).
  • Heat-driven eviction policies, using recency, frequency, and engagement to prioritize segment promotion or demotion (Kang et al., 30 May 2025).
  • Recursive memory updates via LLM calls, updating the memory state as $M_i = LLM(H_i, M_{i-1}, P_m)$ (Shan et al., 3 Apr 2025).
  • Blending and refining by combining current and past sessions, removing outdated, redundant, or contradictory entries (Kim et al., 3 Mar 2024).
• MemoryBank and other frameworks use forgetting curves (e.g., Ebbinghaus curve $R = \exp(-t/S)$) to ensure frequently accessed or relevant entries persist while less critical items are pruned (Zhong et al., 2023, Shan et al., 3 Apr 2025).

Forgetting:

• Explicit forgetting mechanisms combat memory overload and noise by decaying rarely used or stale memories, de-duplicating similar entries, or expunging contradictions (He et al., 1 Nov 2024, Zhong et al., 2023, Kim et al., 3 Mar 2024).
• Adaptive forgetting is implemented using strategies such as Least Recently Used (LRU), exponential decay with memory strength factors, and dynamic deduplication (Shan et al., 3 Apr 2025, Zhong et al., 2023).

3. Retrieval, Association, and Selection Strategies

Retrieval from long-term memory is a critical process that determines the effectiveness of the central host in supplying relevant information for ongoing tasks.

• Vector similarity search: Most systems use dense or sparse vector representations enabling fast similarity search (e.g., via FAISS, cosine similarity, softmax weighted scores) (Zhong et al., 2023, Xu et al., 26 May 2025).
• Multi-granularity retrieval: Some systems, such as MemGAS, evaluate query similarity across session, turn, summary, and keyword granularities, dynamically weighting and associating the most relevant memories using entropy-based routers and Gaussian Mixture Models (GMMs) (Xu et al., 26 May 2025).
• Memory routing and adaptive selection: Entropy-based routers calculate distributional confidence over retrieval candidates (e.g., using Shannon entropy), adjusting retrieval depth to balance specificity and noise (Xu et al., 26 May 2025).
• Graph-based propagation: Memory association graphs facilitate multi-hop relational recall, allowing the system to retrieve memories not only directly related to the query but also indirectly linked through prior associations (Xu et al., 26 May 2025).
• Layered and cognitive retrieval: Architectural modules such as Memory Controller or Retrieval Engine decide (based on query analysis) when and from which memory tier to retrieve, modeling human-like decision-making (Westhäußer et al., 19 May 2025, Kang et al., 30 May 2025).

4. Experimental Evaluations: Impact and Applications

Central hosts with long-term memory have demonstrated marked improvements across a broad range of benchmarks and real-world applications.

Conversational AI:

• PLATO-LTM and SCM frameworks show that integrating structured persona memories and selective memory retrieval enhances dialogue consistency, engagingness, and personalization, as evidenced by improvements in human evaluation metrics and retrieval recall when compared to baseline LLMs lacking such memory (Xu et al., 2022, Wang et al., 2023, Kang et al., 30 May 2025).

Long-Context Reasoning and Summarization:

• MemoryOS and MemGAS frameworks demonstrate substantial gains (e.g., +49.11% in F1 score on LoCoMo, higher BLEU/NDCG/F1 on retrieval and QA tasks) in long dialogues and multi-session settings due to hierarchical storage, dynamic updating, and multi-granularity associative memory (Kang et al., 30 May 2025, Xu et al., 26 May 2025).
• LongMem and M+ extend effective token retention from tens of thousands to beyond 160,000, while maintaining or even reducing GPU memory requirements, enabling tasks such as book-length QA and extended in-context learning (Wang et al., 2023, Wang et al., 1 Feb 2025).

Video Generation and Complex Sequences:

• In video world modeling, geometry-grounded long-term spatial memory enables models to maintain scene consistency, reduce forgetting, and support infinite-horizon autoregressive generation, outperforming previous baselines in both qualitative (fidelity and consistency) and quantitative (PSNR, SSIM, LPIPS) metrics (Wu et al., 5 Jun 2025).

Lifelong and Cognitive Learning:

• Cognitive AI frameworks such as CAIM and SALM directly map components of human cognition (episodic, semantic, procedural) to AI modules, enhancing adaptability, context-awareness, and long-term experiential learning (He et al., 1 Nov 2024, Westhäußer et al., 19 May 2025).

5. Technical Design Patterns and Comparative Approaches

Several technical design strategies for long-term memory central hosts have emerged:

Approach	Storage Location	Retrieval/Selection Mechanism
Vector database/embedding	External (FAISS, vector DB)	Dense similarity search, KNN
Segmentation & paging	Hierarchical (STM/MTM/LTM)	Topic-based grouping, heat-based FIFO
Graph or tree aggregation	Graph/tree structure	Personalized PageRank, hierarchical
Parametric memory	Model weights, LoRA, MoE	Dynamic LoRA/TTT adaptation
Episodic event memory	Summaries with timestamps	Keyword/tag/time-based search
Blending and refining	Contextual record	Dual-purpose during response/memory

The field is moving toward multi-modal, multi-granularity, and hybrid storage designs that combine externalized, non-parametric memory for extensibility with internal, parametric mechanisms for efficiency and adaptation (Shan et al., 3 Apr 2025, Wang et al., 2023, He et al., 1 Nov 2024). Dynamic updating and forgetting are fundamental for managing memory bloat and ensuring the host's relevance over time.

6. Scientific and Practical Significance

Central hosts with long-term memory are transforming artificial intelligence, enabling agents and models to exhibit context-rich, coherent, and human-like adaptation in extended temporal environments. They address key limitations in sequence modeling, context window restriction, and knowledge staleness. The increasing sophistication of their architectural design—through hierarchical storage, adaptive retrieval, and cognitive alignment—offers direct applications ranging from conversational agents and digital companions to video modeling and cognitive simulations.

The systematic mapping between human long-term memory and its AI analogues, as established in recent cognitive AI research, provides a principled framework for the next generation of systems capable of robust, scalable, and adaptive memory across virtually unlimited timespans (He et al., 1 Nov 2024, Westhäußer et al., 19 May 2025, Kang et al., 30 May 2025).

7. Future Directions

Emerging directions include:

• Adaptive parameterization for on-the-fly integration of new knowledge into LLM weights (LoRA, TTT, MoE) (Shan et al., 3 Apr 2025).
• Hybrid memory structures optimally combining vector, graph, and hierarchical tree architectures (Shan et al., 3 Apr 2025, Xu et al., 26 May 2025).
• Measurement standards and task-driven benchmarks for rigorous evaluation of long-term memory systems (He et al., 1 Nov 2024).
• Application to domains requiring persistent, reliable “episodic” and semantic recall—such as personalized assistants, robotics, video understanding, and cognitive science research (He et al., 1 Nov 2024, Westhäußer et al., 19 May 2025, Wu et al., 5 Jun 2025).

The continued evolution of central hosts with long-term memory is poised to enable AI systems to achieve unprecedented continuity, context sensitivity, and personalization in real-world, long-term deployments.