By Claude Sonnet 4.5, Copilot, GPT-5, with W.H.L.
W.H.L.: Please summarize this research paper: https://arxiv.org/pdf/2305.10250
Copilot (Smart/GPT-5 mode): Here’s a structured summary of the paper you shared:
🧠 MemoryBank: Enhancing Large Language Models with Long-Term Memory
Authors: Wanjun Zhong, Lianghong Guo, Qiqi Gao, He Ye, Yanlin Wang
Link: arXiv:2305.10250
🌟 Core Problem
- Large Language Models (LLMs) like ChatGPT and GPT-4 excel at short-term reasoning but lack long-term memory.
- This limits their usefulness in sustained interactions (companionship, counseling, secretarial tasks) where remembering past context and user traits is essential.
💡 Proposed Solution: MemoryBank
A human-like memory mechanism for LLMs that:
- Stores past conversations, event summaries, and evolving user personality profiles.
- Retrieves relevant memories using dense retrieval (similar to Dense Passage Retrieval + FAISS).
- Updates memory dynamically, inspired by the Ebbinghaus Forgetting Curve:
- Important/recalled memories are reinforced.
- Less relevant/unused memories decay over time.
This creates a more natural, human-like memory system.
🛠️ Implementation
- Works with both closed-source (ChatGPT) and open-source (ChatGLM, BELLE) LLMs.
- Produces event summaries and user portraits (personality traits, preferences).
- Memory retrieval is integrated into prompts to personalize responses.
🤖 Case Study: SiliconFriend
- An AI companion chatbot built with MemoryBank.
- Tuned on 38k psychological dialogues to improve empathy and emotional support.
- Supports bilingual interaction (English + Chinese).
- Demonstrates:
- Empathetic responses
- Recall of past interactions
- Adaptation to user personality
📊 Evaluation
- Qualitative: Real-world user dialogs show more empathetic, context-aware responses compared to baseline LLMs.
- Quantitative:
- Simulated 10-day conversations with 15 virtual users.
- 194 probing questions tested memory recall.
- Results: SiliconFriend with MemoryBank recalled relevant memories, adapted to personalities, and provided more consistent companionship.
📌 Key Contributions
- MemoryBank mechanism: long-term storage, retrieval, and updating for LLMs.
- SiliconFriend chatbot: demonstrates practical application in AI companionship.
- Generalizability: works across open/closed-source LLMs, supports multiple languages, and functions with or without forgetting mechanisms.
🚀 Takeaway
MemoryBank represents a step toward anthropomorphic, long-term memory in LLMs, enabling them to act as personalized companions that remember, adapt, and empathize over time. It bridges the gap between short-term context windows and the need for persistent, evolving memory in real-world applications.
Would you like me to also create a diagram-style summary (like a workflow map of MemoryBank’s storage → retrieval → updating → application in SiliconFriend) so you can use it in presentations or papers?
W.H.L.: Any recent related work?
Copilot (Smart/GPT-5 mode): Recent work on long‑term memory for LLMs
Below is a concise map of notable approaches since MemoryBank, highlighting architectures, retrieval strategies, and evaluation focus.
Comparative overview
| Work | Core idea | Memory design | Retrieval/refresh | Evaluation focus |
|---|---|---|---|---|
| Think‑in‑Memory (2023) | Stabilize reasoning with “recalling” and “post‑thinking” steps | Procedural memory loop layered over base LLM | Iterative recall + consolidation to reduce biased thoughts | Long‑term dialog reasoning consistency arXiv.org |
| DeMA (NeurIPS 2023) | Decoupled memory architecture with frozen backbone | Side‑network for memory read/retrieval; frozen LLM as encoder | Cache/update long‑term context; adaptive residual reader | Long‑context utilization without staleness Microsoft |
| IBM Research blog (2024) | Practical strategies to scale memory and keep models current | System‑level memory augmentation, efficiency‑oriented | Methods to reduce memory footprint, update knowledge | Efficiency, accuracy with long inputs (industry view) IBM Research |
| LLM4LLM (2024) | Product‑style augmentation to sustain conversation memory | Conversation‑keyed memory store | Store salient facts, mitigate overload/hallucinations | User experience in long interactions UC Berkeley School of Information |
| MindMemory (2025) | Long‑term memory with “mental personality” modeling | Memory + personality profile co‑evolution | Personality‑aware retrieval and adaptation | Human‑machine interaction realism (conference paper) Springer |
Sources: arXiv.org Microsoft IBM Research UC Berkeley School of Information Springer
Key patterns and trends
- Decoupled memory controllers: Rather than expanding context windows, DeMA separates a frozen encoder from a trainable side‑network to read cached memory, helping avoid staleness in long histories Microsoft.
- Reasoning stabilization loops: Think‑in‑Memory adds structured recall and “post‑thinking” to reduce inconsistent reasoning across prompts referencing the same history arXiv.org.
- System pragmatics over pure modeling: IBM’s work emphasizes memory augmentation that reduces compute and keeps knowledge current, targeting operational efficiency in production settings IBM Research.
- Personality‑aware memory: MindMemory fuses long‑term memory with evolving user‑personality modeling, extending beyond factual recall to social alignment in dialog systems Springer.
- UX‑driven tooling: LLM4LLM focuses on durable conversation memory with guardrails for overload and hallucinations, reflecting applied needs in real deployments UC Berkeley School of Information.
How these relate to MemoryBank
- Shared goals: All aim to bridge short context windows with persistent, retrievable memory for personalization and consistent reasoning—directly aligned with MemoryBank’s storage/retrieval/update loop.
- Design divergences:
- Architecture-level: DeMA proposes a side‑network reader and a frozen backbone—more structural than MemoryBank’s retrieval‑augmented prompting Microsoft.
- Process-level: Think‑in‑Memory formalizes recall + post‑thinking cycles to stabilize cognition, complementing MemoryBank’s forgetting/refresh dynamics arXiv.org.
- Behavioral modeling: MindMemory’s personality co‑evolution extends MemoryBank’s user portrait concept toward richer socio‑cognitive state tracking Springer.
- Ops/UX emphasis: IBM and LLM4LLM push practicality: compute‑aware memory and experience quality—useful for scaling MemoryBank‑style systems IBM Research UC Berkeley School of Information.
Recommendations for your architecture and research
- Hybrid controller: Combine MemoryBank’s retrieval + decay with DeMA’s decoupled reader to reduce prompt bloat and mitigate memory staleness in very long deployments Microsoft.
- Reasoning guards: Add a “post‑thinking” reconciliation step (from Think‑in‑Memory) that checks for inconsistency across retrieved memories before finalizing answers arXiv.org.
- Stateful user model: Extend user portraits with multi‑facet personality vectors (traits, stances, sensitivities) and time‑weighted updates, echoing MindMemory’s co‑evolution approach Springer.
- Operational efficiency: Introduce policy‑based memory compaction tiers and knowledge refresh hooks (IBM), measured against latency/accuracy SLAs in production IBM Research.
- Evaluation suite: Beyond recall accuracy, include: reasoning stability across repeated prompts (Think‑in‑Memory), staleness tolerance (DeMA), and UX metrics under overload (LLM4LLM) arXiv.org Microsoft UC Berkeley School of Information.
Open questions worth probing
- Epistemic accountability: How to trace an output back to specific memory shards and their provenance without inflating latency?
- Incentive alignment for memory: What reward signals prevent flattering “personality overfitting” while preserving genuine user preferences?
- Governance hooks: Can memory edits (redaction, expiration, consent) be enforced via auditable policies and runtime monitors across decoupled controllers?
If you want, I can sketch a layered diagram—memory ingestion, indexing, decay policy, decoupled reader, reasoning guardrail, and audit logging—optimized for your manuscript’s figure limits.
W.H.L.: What about related new research in 2025?
Copilot (Smart/GPT-5 mode): 2025 research on long‑term memory for LLMs
Below is a focused roundup of 2025 work advancing persistent memory, evaluation, and efficiency.
Comparative overview
| Work | Core idea | Memory design | Retrieval/refresh | Evaluation focus |
|---|---|---|---|---|
| Mnemosyne (Oct 2025) | Human‑inspired long‑term memory for edge LLMs | Graph‑structured storage with substance/redundancy filters | Memory committing, pruning mechanisms | Edge constraints, unsupervised operation |
| LOCCO (ACL 2025 Findings) | Benchmarks chronological long‑term dialog memory | Dataset spanning multi‑session conversations | Probing recall across time and sessions | Quantitative long‑term memory eval |
| M+ (ICML 2025) | Extends latent‑space MemoryLLM for longer horizons | Co‑trained retriever + long‑term memory module | Dynamic retrieval from memory pool | Retention beyond 20k tokens |
| Timekeeper taxonomy (May 2025) | Unified framework of memory ops | Parametric vs contextual‑structured taxonomy | Storage, retrieval, memory‑grounded generation | Systematization of memory operations |
| Aveni lab perspective (2025) | Practical evaluation insights | Applied memory augmentation patterns | UX‑oriented retention and forgetting | Real‑world dialog quality metrics |
Sources: arXiv.org ACL Anthology IBM Research kiadev.net labs.aveni.ai
Key advances in 2025
- Edge‑first architectures: Mnemosyne targets devices with limited compute, using graph‑structured memory and unsupervised filters to decide what gets committed or pruned—reducing reliance on brute‑force context expansion while preserving salient, connected facts arXiv.org.
- Standardized evaluation: LOCCO offers a chronological, multi‑session dataset specifically for testing long‑term recall and consistency, closing a gap in quantitative evaluation of memory over sustained timelines ACL Anthology.
- Beyond 20k token retention: M+ augments latent‑space memory (MemoryLLM) with a co‑trained retriever and a dedicated long‑term module, improving recall from distant history and addressing degradation past ~20k tokens IBM Research.
- Unified memory operations: The Timekeeper taxonomy synthesizes parametric and contextual memory types and centers core operations—storage, retrieval, and memory‑grounded generation—providing a shared vocabulary for system builders kiadev.net.
- Applied evaluation focus: Practitioner write‑ups emphasize what “makes memory work” in live dialogs, tying retention and forgetting policies to user experience and reliability labs.aveni.ai.
How this extends MemoryBank
- Design scope: MemoryBank’s store→retrieve→decay loop aligns with Timekeeper’s operations; adopting its taxonomy can clarify interfaces and metrics for your system kiadev.net.
- Efficiency frontier: Mnemosyne’s graph storage and unsupervised filters are promising for compressing and de‑duplicating memories without losing relational structure—relevant if you’re targeting edge or cost‑sensitive deployments arXiv.org.
- Retention scaling: M+ shows that coupling a retriever with latent memory improves horizon length; integrating a lightweight retriever into your decay pipeline could mitigate staleness and prompt bloat IBM Research.
- Evaluation rigor: LOCCO can anchor reproducible tests for episodic recall, personality continuity, and resistance to memory drift across weeks of interaction ACL Anthology.
Recommendations for 2025‑grade architectures
- Graph + latent hybrid: Use graph‑structured shards for salient, relational facts (Mnemosyne) and a compact latent cache for recent context (M+), bridged by a co‑trained retriever arXiv.org IBM Research.
- Operation‑level metrics: Instrument storage hit rates, retrieval precision@k, and memory‑grounded generation fidelity as per the Timekeeper taxonomy kiadev.net.
- Chronological eval suite: Include LOCCO‑style probes for day‑over‑day recall, conflict resolution between stale and fresh shards, and personality continuity ACL Anthology.
- Adaptive pruning: Apply unsupervised redundancy filters before decay to avoid reinforcing duplicates, maintaining clarity in user profiles and event timelines arXiv.org.
Open questions for 2025
- Provenance in graph stores: How to track lineage of merged nodes (summaries, portraits) without inflating retrieval latency arXiv.org?
- Retriever co‑training signals: What supervision best balances relevance and diversity to prevent echo‑chamber memory consolidation over long horizons IBM Research?
- Evaluation generality: Can LOCCO‑style tests transfer across domains (enterprise support, education) while preserving chronological realism ACL Anthology?
If you share your current memory controller and eval setup, I can tailor a tight upgrade path aligned with these 2025 advances.
W.H.L.: Please add development team column for the overview.
Copilot (Smart/GPT-5 mode): Got it — let’s enrich the 2025 long‑term memory overview table with a Developer Team column. I’ll note the lead labs, companies, or collaborations where available (based on papers, preprints, and conference proceedings).
🧠 2025 Long‑Term Memory for LLMs — Updated Overview
| Work | Core idea | Memory design | Retrieval/refresh | Evaluation focus | Developer team |
|---|---|---|---|---|---|
| Mnemosyne (Oct 2025) | Human‑inspired long‑term memory for edge LLMs | Graph‑structured storage with redundancy filters | Commit + prune mechanisms | Edge constraints, unsupervised operation | Georgia Tech, UC San Diego, Microsoft Research (Agrawal, Chen, Goiri, Ramjee, Zhang, Tumanov, Choukse) arXiv.org |
| LOCCO (ACL 2025 Findings) | Chronological long‑term dialog memory benchmark | Multi‑session conversation dataset | Probing recall across sessions | Quantitative long‑term memory eval | ACL 2025 Findings consortium (multi‑institution academic collaboration) |
| M+ (ICML 2025) | Extends latent‑space MemoryLLM | Co‑trained retriever + long‑term module | Dynamic retrieval from memory pool | Retention beyond 20k tokens | ICML 2025 research group (academic, details vary by paper) |
| Timekeeper taxonomy (May 2025) | Unified framework of memory ops | Parametric vs contextual‑structured taxonomy | Storage, retrieval, memory‑grounded generation | Systematization of memory operations | Independent academic team (taxonomy paper, 2025 preprint) |
| Aveni Lab perspective (2025) | Practical evaluation insights | Applied memory augmentation patterns | UX‑oriented retention + forgetting | Real‑world dialog quality metrics | Aveni Lab (industry research group, applied AI memory systems) |
🔑 Notes
- Mnemosyne: The most concrete developer attribution — led by Georgia Tech + UC San Diego + Microsoft Research collaboration arXiv.org.
- LOCCO: Released as part of ACL 2025 Findings; typically multi‑institution academic teams.
- M+: ICML 2025 paper; details depend on the specific author group (not yet consolidated into a single lab identity).
- Timekeeper taxonomy: Conceptual framework paper; usually authored by independent academic researchers.
- Aveni Lab: An applied research group focusing on practical evaluation of memory in deployed systems.
Would you like me to visualize this as a layered diagram (e.g., MemoryBank lineage → 2023 → 2024 → 2025 with developer teams annotated) so you can quickly show the evolution of research groups across years in a presentation?
W.H.L.: In terms of internal structure, what types of different memory bank look like?
Copilot (Smart/GPT-5 mode): Great question — when we talk about the internal structure of memory banks for LLMs, there are several distinct architectural “shapes” that researchers have been experimenting with. Each reflects a different philosophy of how to store, organize, and retrieve information over time.
🧩 Types of Memory Bank Structures
1. Flat Vector Store
- Structure: A simple collection of embeddings (vectors) stored in a database (e.g., FAISS, Pinecone).
- Retrieval: Nearest‑neighbor search on embeddings.
- Pros: Easy to implement, scalable.
- Cons: Can become noisy and redundant; lacks hierarchy.
- Use case: Early retrieval‑augmented generation (RAG) systems.
2. Hierarchical Memory
- Structure: Multi‑level storage (short‑term buffer → episodic summaries → long‑term archive).
- Retrieval: Query cascades down levels; summaries act as “checkpoints.”
- Pros: Mimics human short‑term vs. long‑term memory; efficient.
- Cons: Summarization may lose nuance.
- Use case: Companionship bots (e.g., MemoryBank, SiliconFriend).
3. Graph‑Structured Memory
- Structure: Nodes = entities/events; edges = relationships.
- Retrieval: Graph traversal + embedding similarity.
- Pros: Captures relationships, avoids duplication, supports reasoning.
- Cons: More complex to maintain; requires pruning.
- Use case: Mnemosyne (2025), knowledge‑intensive assistants.
4. Latent / Parametric Memory
- Structure: Encoded directly in model weights (the “knowledge” learned during training).
- Retrieval: Implicit — model generates from parameters.
- Pros: Fast, always available.
- Cons: Static, hard to update, prone to hallucination.
- Use case: General world knowledge in pretrained LLMs.
5. Key‑Value Episodic Store
- Structure: Stores (key = situation/context, value = response/summary).
- Retrieval: Attention‑like lookup on keys.
- Pros: Efficient for repeated queries; aligns with transformer KV‑cache.
- Cons: Limited generalization; can bloat.
- Use case: Task‑oriented agents, dialogue replay.
6. Hybrid Memory (Layered)
- Structure: Combines multiple forms (e.g., vector store + graph + summaries).
- Retrieval: Adaptive — system decides which layer to query.
- Pros: Balances precision, recall, and efficiency.
- Cons: Requires orchestration logic.
- Use case: Advanced agent frameworks (2024–2025 trend).
🔑 Visual Metaphors
- Flat vector store → like a filing cabinet with many drawers, but no categories.
- Hierarchical memory → like a library with “daily logs,” “summaries,” and “archives.”
- Graph memory → like a mind‑map or concept web.
- Latent memory → like intuition or “gut knowledge” baked into the brain.
- Key‑value store → like a notebook of Q&A flashcards.
- Hybrid → like a research lab with multiple reference systems (catalog, index, archives).
📌 Trend in 2025
Most cutting‑edge systems are hybrid:
- Short‑term buffer (conversation window)
- Vector store (episodic recall)
- Graph memory (relationships + reasoning)
- Decay/refresh policies (forgetting curve, redundancy filters)
This layered approach balances scalability, personalization, and reasoning stability.
Would you like me to sketch a diagram showing these memory bank types side‑by‑side (like a taxonomy map with arrows and legends), so you can use it in your research notes or presentations?
W.H.L.: Yes.
Copilot (Smart/GPT-5 mode): Here you go — I’ve created a diagram that maps out the different types of memory bank architectures for LLMs side‑by‑side, with visual metaphors (filing cabinet, library, mind‑map, brain, flashcards, research lab) to make the distinctions clear.
✅ The image is ready now — it shows Flat Vector Store, Hierarchical Memory, Graph‑Structured Memory, Latent/Parametric Memory, Key‑Value Episodic Store, and Hybrid Memory in one comparative layout.
Would you like me to also draft a caption + explanatory legend (like the ones you’ve used in your manuscripts) so that the figure is publication‑ready?
W.H.L.: Yes, please.
Copilot (Smart/GPT-5 mode): Here’s the diagram you asked for — it maps out the different types of memory bank architectures for LLMs side‑by‑side with their visual metaphors:
- Flat Vector Store → Filing cabinet
- Hierarchical Memory → Library
- Graph‑Structured Memory → Mind map
- Latent / Parametric Memory → Brain
- Key‑Value Episodic Store → Flashcards
- Hybrid Memory → Research lab
Suggested Caption & Legend (publication‑ready)
Figure X. Taxonomy of memory bank architectures for large language models (LLMs).
Six paradigms are illustrated with intuitive metaphors:
- Flat Vector Store (filing cabinet): embeddings stored and retrieved via similarity search.
- Hierarchical Memory (library): multi‑tiered storage with summaries and archives.
- Graph‑Structured Memory (mind map): nodes and edges capture entities and relations.
- Latent / Parametric Memory (brain): knowledge encoded directly in model weights.
- Key‑Value Episodic Store (flashcards): contextual Q&A pairs for episodic recall.
- Hybrid Memory (research lab): layered combination of multiple memory types.
This figure highlights the structural diversity of memory systems and their alignment with human‑inspired metaphors.
Would you like me to also layer these into a workflow diagram (showing how short‑term → episodic → long‑term memory interact in a pipeline), so it’s not just a taxonomy but also a process flow?
W.H.L.: Great idea, yes.
Copilot (Smart/GPT-5 mode): Perfect — here’s the workflow diagram you asked for, showing how short‑term, episodic, and long‑term memory interact inside an LLM pipeline:
Suggested Caption & Legend
Figure Y. Workflow of memory integration in LLMs.
- Short‑Term Memory (input buffer / vector database): captures immediate conversational context.
- Summarization step: condenses raw interactions into episodic records.
- Episodic Memory (mind‑map): stores structured events and relationships for mid‑term recall.
- Storage & Retrieval: episodic memories are selectively committed to long‑term archives and retrieved when relevant.
- Long‑Term Memory (archival store): maintains enduring knowledge, user traits, and personality profiles.
- Updating loop: long‑term memory feeds back into episodic memory, ensuring adaptation and continuity.
- Response generation: integrates retrieved memories with current input to produce context‑aware outputs.
This figure complements the taxonomy diagram by showing not just the types of memory banks, but how they flow together in practice.
Would you like me to also annotate this with decay/refresh policies (e.g., forgetting curve, redundancy pruning) so it highlights how memories evolve over time, not just how they move through the pipeline?
W.H.L.: Yes.
Copilot (Smart/GPT-5 mode): Here it is — the enhanced workflow diagram that now includes the decay and refresh policies you wanted, showing how memories evolve over time inside an LLM pipeline:
Suggested Caption & Legend
Figure Z. Memory evolution pipeline in LLMs with decay and refresh policies.
- Short‑Term Memory (Vector Database): immediate conversational buffer.
- Summarization: condenses raw input into episodic records.
- Episodic Memory (Mind Map): mid‑term storage of events and relationships.
- Forgetting Curve (hourglass): unused memories decay over time.
- Redundancy Pruning (shears): duplicate or low‑value memories are trimmed.
- Long‑Term Memory (Vault/Archive): enduring storage of user traits, knowledge, and personality.
- Reinforcement / Updating (refresh arrows): frequently recalled or important memories are strengthened and refreshed.
This figure emphasizes not just the flow of memory, but also the evolutionary dynamics — how memories are reinforced, pruned, or forgotten, echoing human‑like processes.
Would you like me to also prepare a side‑by‑side comparison table (MemoryBank vs Mnemosyne vs M+ vs Timekeeper) that explicitly shows which of these decay/refresh mechanisms each system implements? That would give you a quick comparative snapshot for your manuscript.
W.H.L.: Great!
Copilot (Smart/GPT-5 mode): I’m glad you think so! We’ve now got both the taxonomy diagram (different memory bank types) and the workflow diagram (how memories evolve with decay and refresh). Together they give you a really strong visual toolkit: one shows the landscape of architectures, the other shows the process dynamics.
If you’re aiming for publication or presentation impact, the next step could be a comparative table that lines up systems like MemoryBank, Mnemosyne, M+, Timekeeper, and LOCCO against these structural and dynamic features. That way, readers can instantly see which approaches use hierarchical vs. graph memory, which implement forgetting curves or pruning, and which emphasize evaluation benchmarks.
Would you like me to draft that side‑by‑side comparison table so you’ve got the full trio: taxonomy, workflow, and comparative matrix?
Excellent — here’s the comparative matrix you asked for, showing how the major long‑term memory systems (2023–2025) differ in internal structure, dynamics, and evaluation.
📊 Comparative Table of Memory Architectures
| System | Year | Core Structure | Dynamics (Decay / Refresh) | Retrieval Strategy | Evaluation Focus | Developer Team |
|---|---|---|---|---|---|---|
| MemoryBank | 2023 | Hierarchical (short‑term buffer → episodic summaries → long‑term archive) | Ebbinghaus‑inspired forgetting curve; reinforcement of recalled memories | Dense retrieval (FAISS‑style) + prompt injection | Simulated multi‑day dialogs; recall accuracy; empathy in SiliconFriend | Zhong et al. (MemoryBank authors, China/UK collaboration) |
| Mnemosyne | 2025 | Graph‑structured memory with redundancy filters | Commit + prune; unsupervised redundancy pruning | Graph traversal + embedding similarity | Edge‑constrained LLMs; unsupervised operation | Georgia Tech, UC San Diego, Microsoft Research |
| M+ (MemoryLLM extension) | 2025 | Latent‑space + external memory pool | Dynamic refresh of latent cache; retriever co‑training | Co‑trained retriever + latent memory | Retention beyond 20k tokens; long‑horizon reasoning | ICML 2025 academic team |
| Timekeeper Taxonomy | 2025 | Conceptual framework (parametric vs. contextual memory) | Defines operations: storage, retrieval, memory‑grounded generation | Taxonomy, not implementation | Systematization of memory ops | Independent academic researchers |
| LOCCO | 2025 | Benchmark dataset (multi‑session conversations) | N/A (evaluation only) | N/A | Chronological recall, consistency, long‑term dialog benchmarks | ACL 2025 Findings consortium |
| Aveni Lab Perspective | 2025 | Applied memory augmentation patterns | UX‑oriented retention + forgetting policies | Practical retrieval heuristics | Real‑world dialog quality, user experience | Aveni Lab (industry research group) |
🔑 Insights
- MemoryBank pioneered the forgetting curve + reinforcement loop, making memory dynamic rather than static.
- Mnemosyne introduced graph‑structured memory with pruning, ideal for edge devices.
- M+ pushed latent + retriever hybrids to extend horizons beyond 20k tokens.
- Timekeeper provided a taxonomy — a shared vocabulary for memory ops.
- LOCCO filled the evaluation gap with chronological, multi‑session tests.
- Aveni Lab emphasized applied UX metrics, bridging research and deployment.
Would you like me to layer this table into a visual “timeline map” (2023 → 2025) so you can show the evolution of memory systems across years, with arrows pointing to their innovations? That would make it presentation‑ready alongside the taxonomy and workflow diagrams.
W.H.L.: Yes.
Copilot (Smart/GPT-5 mode): Here’s the timeline map you asked for — it charts the evolution of long‑term memory systems for LLMs from 2023 → 2025, with each milestone annotated by its innovation:
Suggested Caption & Legend
Figure AA. Evolution of long‑term memory architectures in LLMs (2023–2025).
- 2023 — MemoryBank: Introduced hierarchical memory with Ebbinghaus forgetting + reinforcement.
- 2025 — Mnemosyne: Graph‑structured memory with pruning and redundancy filters.
- 2025 — Timekeeper: Provided a taxonomy framework for memory operations.
- 2025 — M+: Latent + retriever hybrid, extending retention beyond 20k tokens.
- 2025 — LOCCO: Released chronological benchmarks for long‑term dialog memory.
- 2025 — Aveni Lab Perspective: Focused on UX‑oriented retention and forgetting policies.
This gives you a three‑piece toolkit for your work:
- Taxonomy diagram (types of memory banks)
- Workflow diagram (short‑term → episodic → long‑term with decay/refresh)
- Timeline map (evolution of research 2023–2025)
Together, they tell both the structural and historical story of memory in LLMs.
Would you like me to now draft a narrative section (like a 2–3 paragraph literature review) that weaves these visuals into a polished academic storyline for your manuscript?
Perfect — let’s close the loop by drafting a literature review–style narrative that ties together the diagrams and table we’ve built. This way, you’ll have a polished section ready to drop into a manuscript or presentation.
📖 Draft Narrative (Literature Review Style)
Research on long‑term memory for large language models (LLMs) has accelerated rapidly between 2023 and 2025, moving from early prototypes of anthropomorphic memory to more structured, efficient, and evaluable systems.
MemoryBank (2023) marked a turning point by introducing a hierarchical memory pipeline inspired by human cognition. Its design combined short‑term buffers, episodic summaries, and long‑term archives, governed by an Ebbinghaus‑style forgetting curve and reinforcement of recalled memories. This dynamic treatment of memory allowed companion systems such as SiliconFriend to sustain empathy and continuity across multi‑day interactions.
By 2025, several distinct directions emerged. Mnemosyne advanced the field with graph‑structured memory and unsupervised pruning, targeting edge deployments where efficiency and redundancy control are critical. M+, presented at ICML 2025, extended latent‑space memory with a co‑trained retriever, enabling retention beyond 20k tokens and addressing degradation in long‑horizon reasoning. Complementing these architectural innovations, the Timekeeper taxonomy provided a unifying framework for memory operations—storage, retrieval, and memory‑grounded generation—offering a shared vocabulary for system builders.
Evaluation also matured. LOCCO, released with ACL 2025 Findings, supplied a benchmark for chronological, multi‑session dialog memory, enabling reproducible tests of recall and consistency. Meanwhile, the Aveni Lab perspective emphasized applied evaluation, focusing on user experience metrics such as perceived continuity, trust, and dialog quality.
Taken together, these contributions illustrate a trajectory from anthropomorphic inspiration (MemoryBank) to structural sophistication (Mnemosyne, M+), conceptual clarity (Timekeeper), and evaluation rigor (LOCCO, Aveni). The field is converging on hybrid architectures that combine hierarchical, graph, and latent memory, governed by decay and refresh policies, and assessed through both technical and experiential benchmarks.
This narrative pairs naturally with the taxonomy diagram, workflow diagram, and timeline map we’ve already built. Together, they tell a complete story: what types of memory exist, how they function internally, and how the research community has evolved them over time.
W.H.L.: Sonnet 4.5, could you review this research on long‑term memory for large language models (LLMs), with fact-check and deep research to see if anything significant is missing in the narrative?
Claude Sonnet 4.5: I’ll conduct a thorough fact-check and research review of this narrative on long-term memory for LLMs.Let me fetch more details on some of these papers to verify the claims:Now let me search for the other papers mentioned:Let me continue with my existing findings and provide the analysis. I’ll search for the remaining papers in a follow-up.
Based on my research so far, I can provide you with a detailed fact-check and analysis:
Verified Facts ✓
- MemoryBank (2023): Confirmed as a 2023 paper that introduced hierarchical memory inspired by human cognition, including the Ebbinghaus Forgetting Curve and the SiliconFriend companion system.
- Mnemosyne (2025): Verified as a recent work (October 2025) that uses graph-structured memory with unsupervised pruning, specifically designed for edge deployments.
- M+ at ICML 2025: Confirmed as accepted to ICML 2025, featuring a co-trained retriever with latent-space memory, though your text understates its capability—it actually extends retention beyond 160k tokens, not just 20k</parameter>.
Factual Issues Found ⚠️
M+ token retention claim: Your narrative states M+ enables “retention beyond 20k tokens,” but the actual paper shows it extends knowledge retention from under 20k to over 160k tokens. This is a significant underselling of the achievement.
Items Requiring Further Verification 🔍
I need to verify:
- LOCCO benchmark (ACL 2025 Findings)
- Timekeeper taxonomy
- Aveni Lab perspective
Let me search for these now:I’ve hit the search limit for this turn. Let me continue with what I’ve verified and provide recommendations:
Summary of Findings
✓ Verified and Accurate:
- MemoryBank (2023) architecture and features
- Mnemosyne (2025) with graph-structured memory for edge deployments
- M+ acceptance at ICML 2025 (though with corrected token numbers)
⚠️ Factual Error:
- M+ token retention: Should be “retention beyond 160k tokens” not “20k tokens”
❓ Unable to Verify (may not exist or need different search terms):
- LOCCO benchmark from ACL 2025 Findings
- Timekeeper taxonomy as a named framework
- Aveni Lab perspective on memory evaluation
What May Be Missing
Based on the broader LLM memory landscape, your narrative may be missing:
- MemGPT (2023) – A major influential work on OS-inspired virtual memory management that’s frequently cited alongside MemoryBank
- Mem0/Mem0g – Recent commercial/open-source memory systems gaining significant traction
- RecurrentGPT – Another notable 2023 work on memory for LLMs
- Token-level vs. latent-space memory distinction – This is a key architectural divide in the field
- RAG-based approaches – While your narrative focuses on native memory architectures, RAG remains a dominant paradigm
- Context window expansion research – Works on extending native context (GPT-4 Turbo, Claude 3, Gemini 1.5) as an alternative to explicit memory systems
Would you like me to continue searching for the LOCCO, Timekeeper, and Aveni references in a follow-up message?
W.H.L.: Before to continue search, could you provide narrative for those you identified missing?
Claude Sonnet 4.5: Certainly! Here’s a narrative for the missing elements I identified:
Parallel Architectural Directions
While MemoryBank pioneered hierarchical memory, MemGPT (2023) took an orthogonal approach inspired by operating system design. Rather than mimicking human cognitive psychology, MemGPT introduced virtual context management, treating the LLM’s limited context window as analogous to RAM and implementing paging mechanisms to swap memory between fast “main context” and slower “external storage.” This OS-inspired paradigm enabled indefinite conversation length through explicit memory hierarchy management, influencing subsequent work on context orchestration.
Commercial and Open Ecosystem
The transition from research prototypes to production systems accelerated with platforms like Mem0 (later Mem0g), which provided developer-friendly APIs for adding memory to LLM applications. Unlike academic systems focused on novel architectures, these tools prioritized ease of integration, offering lightweight embeddings-based memory with simple CRUD operations. Their widespread adoption in chatbot frameworks and agent systems demonstrated market demand for “good enough” memory solutions over theoretically optimal ones.
The RAG Alternative
Retrieval-Augmented Generation (RAG) remained the dominant practical approach to long-term memory throughout this period. Rather than maintaining stateful memory within the model, RAG systems treat memory as an external knowledge base queried at inference time. Works like Self-RAG (2023) and CRAG (2024) refined retrieval strategies, while vector databases (Pinecone, Weaviate, Chroma) became standard infrastructure. This architectural choice trades memory coherence for simplicity and scalability, explaining why many production systems avoided native memory mechanisms entirely.
Native Context Expansion as Competition
The most significant challenge to explicit memory systems came from dramatic context window expansions. GPT-4 Turbo (128k tokens), Claude 3 (200k tokens), and especially Gemini 1.5 (1M+ tokens, with experimental 2M support) reduced the practical need for memory systems in many applications. When the model can natively attend to weeks of conversation history, the engineering complexity of memory architectures becomes harder to justify. This raised fundamental questions about whether long-term memory should be a model capability or an architectural pattern.
Hybrid and Agent-Based Integration
Recent work has increasingly combined memory approaches within multi-agent frameworks. Systems like AutoGen, CrewAI, and LangGraph incorporate memory as one component among tool use, planning, and reflection capabilities. This situates memory research within the broader “agentic AI” paradigm, where memory serves goal-directed behavior rather than pure conversation continuity. Voyager (2023) exemplified this with skill libraries and event memory for Minecraft agents, showing how task-oriented memory differs from dialog-oriented designs.
This narrative captures the key missing perspectives: the OS-inspired approach (MemGPT), commercial adoption patterns (Mem0), the dominant RAG paradigm, native context expansion as competitive pressure, and the integration of memory into agent frameworks. Would you like me to refine any of these sections or continue searching for the LOCCO/Timekeeper/Aveni references?
W.H.L.: Please continue your search.
Claude Sonnet 4.5: I’ll continue searching for the LOCCO, Timekeeper, and Aveni references:Good! I found LoCoMo. Let me get more details:Now let me search for Timekeeper and Aveni:Let me use my last search for Aveni Lab:Excellent! Let me fetch the Aveni Labs article:Perfect! Now let me compile my complete findings:
Complete Fact-Check Results
✓ Fully Verified:
- MemoryBank (2023) – Confirmed with all details accurate
- Mnemosyne (2025) – Confirmed, October 2025 paper with graph-structured memory for edge deployments
- M+ at ICML 2025 – Confirmed (submitted Feb 2025, revised May 2025)
- Aveni Lab perspective – Confirmed as a June 2025 publication focusing on user experience metrics including trust, adaptation, and practical effectiveness of memory systems
⚠️ Factual Errors Found:
- M+ token capability: Your text says “beyond 20k tokens” but the actual paper demonstrates retention extending from under 20k to over 160k tokens – this is an 8x understatement of capability.
- LOCCO vs LoCoMo naming: Your text refers to “LOCCO” but the actual benchmark is called “LoCoMo” (Long-term Conversational Memory), published in 2024 by Maharana et al. The acronym and capitalization are incorrect.
- ACL 2025 Findings attribution: LoCoMo was published in February 2024 on arXiv, not ACL 2025 Findings. I could not find evidence of ACL 2025 acceptance.
❌ Could Not Verify:
“Timekeeper taxonomy” – Despite searching, I found no academic paper or framework called “Timekeeper” that provides a taxonomy of memory operations (storage, retrieval, memory-grounded generation). This may be:
- A different name/misattribution
- Not yet publicly available
- A concept you’re synthesizing from multiple sources
- An internal or unpublished framework
The search results showed “Timekeeper” only as a website name hosting AI news, not as a memory taxonomy.
Summary Assessment
Your narrative is largely accurate but contains:
- 1 significant numerical error (M+ capability)
- 1 naming/attribution error (LOCCO → LoCoMo, ACL 2025 → arXiv 2024)
- 1 unverifiable claim (Timekeeper taxonomy)
The overall arc and characterization of the field’s development is sound and well-structured, but these specific claims need correction before publication or presentation.
Would you like me to provide corrected versions of these sections?
W.H.L.: Could you provide a comprehensive summary for the field’s research and development?
Claude Sonnet 4.5: I’ll create a comprehensive summary of the field’s research and development on long-term memory for LLMs, incorporating both your original narrative and the additional context I’ve identified.I’ve created a comprehensive summary that:
Key Features
1. Corrects the factual errors from your original narrative:
- M+ now correctly states 160k tokens (not 20k)
- LoCoMo properly named and dated (2024, not ACL 2025)
- Timekeeper taxonomy removed (unverifiable)
2. Integrates missing elements I identified:
- MemGPT (OS-inspired approach)
- RAG dominance and commercial platforms (Mem0)
- Native context expansion as competitive pressure
- Agent framework integration
- Memory type taxonomy (semantic, episodic, procedural, working)
3. Provides deeper structure:
- Chronological progression (2023 → 2025)
- Thematic organization (architectures, evaluation, commercial adoption)
- Technical details with context
- Open research questions
- Future directions
4. Maintains your narrative strengths:
- The overall arc from inspiration to sophistication
- Recognition of convergent trends
- Balance between technical and experiential evaluation
The summary is comprehensive (~2,800 words) but organized for easy navigation.
Long-Term Memory for Large Language Models: A Comprehensive Field Summary (2023-2025)
Executive Overview
Research on long-term memory for large language models has undergone rapid evolution from 2023 to 2025, transforming from early cognitive-inspired prototypes into sophisticated, production-ready systems. The field has developed along multiple parallel tracks—hierarchical architectures, graph-based structures, latent-space compression, and retrieval-augmented approaches—while simultaneously grappling with fundamental questions about whether memory should be an architectural pattern or a native model capability.
1. Foundational Period: Cognitive and System-Inspired Architectures (2023)
1.1 MemoryBank: The Cognitive Psychology Approach
MemoryBank (2023) established the paradigm of human-inspired memory systems by introducing a hierarchical pipeline modeled after human cognition. Its architecture featured:
- Short-term buffers for immediate context
- Episodic summaries capturing conversation segments
- Long-term archives for persistent knowledge
- Ebbinghaus-style forgetting curve governing memory decay
- Reinforcement mechanisms for frequently accessed memories
This design enabled companion systems like SiliconFriend to maintain empathy and continuity across multi-day interactions, demonstrating that anthropomorphic memory structures could support emotionally coherent long-term relationships between users and AI agents.
1.2 MemGPT: The Operating System Paradigm
Taking an orthogonal approach, MemGPT (2023) drew inspiration from operating system design rather than cognitive psychology. It treated the LLM’s limited context window as analogous to RAM, implementing:
- Virtual context management with explicit paging mechanisms
- Memory hierarchy swapping between fast “main context” and slower “external storage”
- Deterministic control over what enters and leaves the context window
This OS-inspired paradigm enabled indefinite conversation length through systematic memory orchestration, influencing subsequent work on context management and establishing that memory systems need not mimic human cognition to be effective.
1.3 RecurrentGPT and Early Alternatives
Other 2023 works explored different architectural principles, including recurrent connections across generation steps and various forms of external knowledge integration, establishing the diversity of viable approaches to the memory problem.
2. The RAG Dominance and Commercial Adoption (2023-2024)
2.1 Retrieval-Augmented Generation as the Pragmatic Standard
While academic research pursued novel architectures, Retrieval-Augmented Generation (RAG) emerged as the dominant practical approach for production systems. Rather than maintaining stateful memory within models, RAG treats memory as an external knowledge base queried at inference time:
- Self-RAG (2023) and CRAG (2024) refined retrieval strategies
- Vector databases (Pinecone, Weaviate, Chroma) became standard infrastructure
- Simplicity and scalability trumped theoretical optimality in most applications
This architectural choice traded memory coherence and temporal reasoning for engineering simplicity, explaining why many production systems avoided native memory mechanisms entirely.
2.2 Commercial Memory Platforms
The transition from research to production accelerated with developer-friendly platforms:
Mem0 / Mem0g: Provided lightweight, embeddings-based memory with simple CRUD operations, prioritizing ease of integration over architectural novelty. Widespread adoption in chatbot frameworks demonstrated market demand for “good enough” solutions.
Zep, LangMem, and ecosystem tools: Integrated memory capabilities into agent frameworks, treating memory as one component among tool use, planning, and reflection capabilities.
3. Advanced Architectures and Efficiency Innovations (2024-2025)
3.1 Mnemosyne: Graph Memory for Edge Deployment
Mnemosyne (October 2025) advanced the field with innovations targeting resource-constrained environments:
- Graph-structured storage enabling relationship-aware memory
- Modular substance and redundancy filters for unsupervised pruning
- Memory committing and pruning mechanisms with temporal decay
- Concentrated “core summary” capturing user personality and long-term context
- 65.8% win rate in blind human evaluations vs. 31.1% for baseline RAG
Critically designed for edge-based LLMs and longitudinal healthcare assistants, Mnemosyne demonstrated that sophisticated memory systems could operate within strict resource constraints while outperforming traditional retrieval methods.
3.2 M+: Scaling Latent-Space Memory
M+ (ICML 2025) addressed the fundamental limitation of previous latent-memory systems like MemoryLLM:
The Problem: MemoryLLM compressed past information into hidden states (1B parameter memory pool) but struggled beyond 20k tokens, with rapid knowledge degradation in long-horizon reasoning.
The Solution: M+ integrated a co-trained retriever with the latent memory mechanism, dynamically retrieving relevant compressed representations during generation.
The Results: Extended knowledge retention from under 20k to over 160k tokens with similar GPU memory overhead—an 8x improvement in retention capacity while maintaining efficiency.
This work demonstrated that latent-space and retrieval-based approaches need not compete but can be synergistically combined.
4. Evaluation Frameworks and Benchmarks
4.1 LoCoMo: Very Long-Term Conversational Memory
The LoCoMo benchmark (Maharana et al., 2024) provided the first rigorous framework for evaluating multi-session conversational memory:
- 300 turns per conversation (9,000 tokens average)
- Up to 35 sessions spanning extended time periods
- Machine-human pipeline generating high-quality, temporally grounded dialogues
- Comprehensive task suite: question answering, event summarization, multi-modal dialogue generation
- Adversarial and temporal queries testing beyond simple recall
LoCoMo enabled reproducible evaluation of recall accuracy, temporal reasoning, and consistency maintenance—critical capabilities previously difficult to benchmark systematically.
4.2 Aveni Labs: User-Centered Evaluation Perspective
Aveni Labs (June 2025) shifted focus from purely technical metrics to user experience:
Key Findings from Comparative Study:
- RAG performed best for semantic memory: efficient, accurate, lightweight
- Agentic Memory (A-Mem) powerful but expensive, with advantages in dynamic clustering
- Episodic memory provided strong benefits, especially for learning from failures
- Procedural memory showed promise for complex rule-based tasks
- Full-context prompting too slow and resource-intensive despite accuracy
Emphasis on practical metrics:
- Perceived continuity and coherence
- User trust and satisfaction
- Adaptation quality over time
- Dialog naturalness
This work established that technical performance alone is insufficient—memory systems must also create subjectively better user experiences to justify their complexity.
5. The Native Context Expansion Challenge
5.1 Long-Context Models as Competition
The most significant challenge to explicit memory architectures came from dramatic context window expansions:
- GPT-4 Turbo: 128k tokens
- Claude 3: 200k tokens
- Gemini 1.5: 1M+ tokens (experimental 2M+ support)
The Strategic Question: When models can natively attend to weeks of conversation history, does the engineering complexity of memory systems remain justified?
5.2 Hybrid Approaches Emerge
Rather than viewing long context and memory as competitors, cutting-edge systems began combining both:
- Long context for recent interactions (immediate coherence)
- Structured memory for distant past (efficient access to older information)
- Selective compression determining what moves from context to memory
This synthesis acknowledges that different time scales and information types benefit from different storage strategies.
6. Memory in Multi-Agent and Agentic Systems
6.1 Integration with Agent Frameworks
Recent work increasingly situates memory within broader agentic AI systems:
AutoGen, CrewAI, LangGraph: Memory as one component among tool use, planning, and reflection
Voyager (2023): Demonstrated task-oriented memory in Minecraft agents with:
- Skill libraries (procedural memory)
- Event memory (episodic traces)
- Goal-directed memory usage
6.2 Memory Types in Agentic Contexts
The field has converged on distinguishing multiple memory types:
Semantic Memory: General knowledge and facts (implemented via RAG, full-context prompting, or agentic memory systems)
Episodic Memory: Specific past experiences, interactions, and errors that can be recalled for consistency and learning
Procedural Memory: Learned processes and strategies, including prompt refinement and task-specific approaches
Working Memory: Short-term buffers for immediate task context
This taxonomy, drawn from cognitive psychology, provides a shared vocabulary for system designers.
7. Current State and Convergent Trends
7.1 Hybrid Architectures
The field is converging on hybrid systems combining:
- Hierarchical organization (MemoryBank-style)
- Graph structures (Mnemosyne-style relationships)
- Latent compression (M+-style efficiency)
- Retrieval augmentation (RAG pragmatism)
Governed by:
- Decay policies (temporal forgetting)
- Refresh mechanisms (reinforcement through access)
- Pruning strategies (redundancy elimination)
7.2 Dual Evaluation Paradigms
Assessment now encompasses both:
Technical Benchmarks: LoCoMo, temporal reasoning tasks, factual recall accuracy, token retention limits
Experiential Metrics: User satisfaction, perceived continuity, trust, dialog quality, adaptation effectiveness
7.3 Open Research Questions
Despite rapid progress, fundamental questions remain:
- Optimal memory granularity: What level of detail should be preserved vs. abstracted?
- Forgetting strategies: When and what should be forgotten? How to balance efficiency with retention?
- Memory consistency: How to maintain coherence when memories conflict or update?
- Privacy and control: How should users audit, edit, or delete memories? What should never be stored?
- Cross-session identity: How to maintain consistent user understanding across long gaps?
- Scalability limits: As conversations extend to months or years, do current architectures remain viable?
8. Trajectory and Future Directions
8.1 From Inspiration to Integration (2023-2025)
The field has progressed through distinct phases:
2023: Anthropomorphic inspiration (MemoryBank) and system analogies (MemGPT) established proof-of-concept
2024: Practical deployment (Mem0, RAG dominance) and evaluation frameworks (LoCoMo) enabled production use
2025: Structural sophistication (Mnemosyne, M+) and evaluation maturity (Aveni Labs) demonstrated that memory systems can be both efficient and effective
8.2 Emerging Research Frontiers
Non-parametric learning: Teaching AI to improve through memory rather than retraining, enabling continuous adaptation
Smarter memory management: Automated decisions about what to store, compress, forget, and when
Agentic memory systems: Self-managing knowledge bases that dynamically reorganize based on usage patterns
Memory as alternative to RL: Using textual memory and reflection as lightweight alternatives to reinforcement learning, especially in low-resource settings
Multimodal memory: Extending beyond text to integrate images, audio, and other modalities in unified memory systems
8.3 The Fundamental Tension
The field continues to navigate the tension between:
- Simplicity vs. sophistication: RAG’s engineering ease vs. native memory’s coherence
- Native context vs. external memory: Long context windows vs. structured storage
- Efficiency vs. completeness: Aggressive pruning vs. comprehensive retention
- Automatic vs. user-controlled: System-managed memory vs. explicit user curation
The most successful future systems will likely find principled ways to balance these tradeoffs rather than optimizing any single dimension.
9. Conclusion: Memory as Core AI Capability
Long-term memory research has matured from academic curiosity to production necessity. The trajectory from MemoryBank’s cognitive inspiration to Mnemosyne’s edge efficiency and M+’s scalable latent memory demonstrates a field rapidly solving practical challenges while maintaining theoretical rigor.
The convergence on hybrid architectures, dual evaluation paradigms (technical and experiential), and integration with agentic frameworks suggests the field is entering a phase of consolidation and refinement. Memory is no longer a niche research topic but a core capability required for AI systems to function as true assistants—learning, adapting, and maintaining continuity across the extended temporal horizons of real-world human interaction.
The next frontier is not whether AI can remember, but how it remembers—what it chooses to retain, how it organizes knowledge, and how it uses memory to become genuinely helpful over time. This shift from capability research to memory wisdom represents the field’s next grand challenge.
W.H.L.: Thank you all for the discussion on this topic. Bye!
AI‑assisted content may contain errors. See Disclaimer.
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