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arxiv: 2305.10250 · v3 · submitted 2023-05-17 · 💻 cs.CL · cs.AI

Recognition: 3 theorem links

· Lean Theorem

MemoryBank: Enhancing Large Language Models with Long-Term Memory

Wanjun Zhong , Lianghong Guo , Qiqi Gao , He Ye , Yanlin Wang
Authors on Pith no claims yet

Pith reviewed 2026-05-16 07:13 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords MemoryBanklong-term memorylarge language modelsEbbinghaus Forgetting CurveAI companion chatbotpersonality adaptationSiliconFriend
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The pith

MemoryBank equips large language models with a long-term memory system modeled on human forgetting.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Large language models struggle with sustained interactions because they lack built-in mechanisms to retain and recall details across many conversations. MemoryBank adds a memory system that lets models retrieve relevant past information, update it continuously, and synthesize it to understand and adapt to a user's personality. The mechanism draws on the Ebbinghaus Forgetting Curve to decide what to reinforce or let fade based on time and importance, aiming for selective, human-like preservation. Tests in the SiliconFriend chatbot for long-term companionship show improved recall and empathetic responses in both real and simulated multi-turn dialogs. The approach works with both closed-source models like ChatGPT and open-source ones like ChatGLM.

Core claim

MemoryBank enables models to summon relevant memories from past interactions, continually evolve through memory updates, and adapt to user personality by synthesizing information, using an updating rule inspired by the Ebbinghaus Forgetting Curve that forgets and reinforces based on elapsed time and relative significance.

What carries the argument

MemoryBank, a memory updating mechanism that selectively forgets and reinforces entries according to time elapsed and significance, modeled after the Ebbinghaus Forgetting Curve.

Load-bearing premise

Applying the Ebbinghaus Forgetting Curve to AI memory will let the system selectively keep important details without dropping critical information or creating inconsistencies.

What would settle it

A controlled test that runs the same long conversation sequence with and without MemoryBank, then measures whether the model correctly recalls specific user facts stated early in the sequence after many turns.

read the original abstract

Revolutionary advancements in Large Language Models have drastically reshaped our interactions with artificial intelligence systems. Despite this, a notable hindrance remains-the deficiency of a long-term memory mechanism within these models. This shortfall becomes increasingly evident in situations demanding sustained interaction, such as personal companion systems and psychological counseling. Therefore, we propose MemoryBank, a novel memory mechanism tailored for LLMs. MemoryBank enables the models to summon relevant memories, continually evolve through continuous memory updates, comprehend, and adapt to a user personality by synthesizing information from past interactions. To mimic anthropomorphic behaviors and selectively preserve memory, MemoryBank incorporates a memory updating mechanism, inspired by the Ebbinghaus Forgetting Curve theory, which permits the AI to forget and reinforce memory based on time elapsed and the relative significance of the memory, thereby offering a human-like memory mechanism. MemoryBank is versatile in accommodating both closed-source models like ChatGPT and open-source models like ChatGLM. We exemplify application of MemoryBank through the creation of an LLM-based chatbot named SiliconFriend in a long-term AI Companion scenario. Further tuned with psychological dialogs, SiliconFriend displays heightened empathy in its interactions. Experiment involves both qualitative analysis with real-world user dialogs and quantitative analysis with simulated dialogs. In the latter, ChatGPT acts as users with diverse characteristics and generates long-term dialog contexts covering a wide array of topics. The results of our analysis reveal that SiliconFriend, equipped with MemoryBank, exhibits a strong capability for long-term companionship as it can provide emphatic response, recall relevant memories and understand user personality.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes MemoryBank, a novel memory mechanism for LLMs that incorporates an Ebbinghaus Forgetting Curve-inspired update to selectively preserve or forget memories based on time elapsed and significance. It enables relevant memory recall, continuous updates, and adaptation to user personality through synthesis of past interactions. The mechanism is demonstrated via the SiliconFriend chatbot for long-term companionship, with qualitative evaluation on real-user dialogs and quantitative evaluation on ChatGPT-simulated dialogs covering diverse topics and traits, claiming improved empathy, recall accuracy, and personality comprehension.

Significance. If the selective memory update proves effective and generalizable, MemoryBank could meaningfully improve LLM performance in sustained, personalized interactions such as AI companions or counseling applications by providing a more human-like long-term memory system. The approach is noted as compatible with both closed-source and open-source models and includes psychological dialog tuning for empathy. However, the evaluation does not isolate the contribution of the forgetting curve, limiting the strength of the claims.

major comments (2)
  1. [Quantitative evaluation] Quantitative evaluation section: the protocol generates long-term contexts via ChatGPT role-play but performs no ablation that removes or replaces the Ebbinghaus time-and-significance decay rule, and includes no baselines that retain full conversation history or apply standard vector retrieval. Consequently, any reported gains in empathy, recall, or personality understanding cannot be attributed to the proposed memory update rather than prompt length, retrieval quality, or base LLM in-context learning.
  2. [MemoryBank mechanism] MemoryBank mechanism description: the integration of the forgetting curve parameters (free parameters noted in the design) into retrieval and synthesis steps lacks sufficient implementation detail for closed-source versus open-source models, making it impossible to verify the claim of selective preservation without introducing critical forgetting errors.
minor comments (1)
  1. [Abstract] Abstract: the description of quantitative metrics for 'empathy', 'recall', and 'personality understanding' is not specified, hindering assessment of the reported results.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback on our manuscript. We appreciate the opportunity to clarify and strengthen our work. Below, we address each major comment in detail.

read point-by-point responses

  1. Referee: [Quantitative evaluation] Quantitative evaluation section: the protocol generates long-term contexts via ChatGPT role-play but performs no ablation that removes or replaces the Ebbinghaus time-and-significance decay rule, and includes no baselines that retain full conversation history or apply standard vector retrieval. Consequently, any reported gains in empathy, recall, or personality understanding cannot be attributed to the proposed memory update rather than prompt length, retrieval quality, or base LLM in-context learning.

    Authors: We agree that isolating the contribution of the Ebbinghaus-inspired forgetting curve is important for validating our claims. In the revised manuscript, we will include additional ablation studies: one removing the time-and-significance decay rule, and comparisons against baselines that use the full conversation history without selective forgetting and standard vector-based retrieval without our memory bank. These experiments will help attribute the observed improvements in empathy, recall accuracy, and personality comprehension specifically to the proposed memory update mechanism. revision: yes

  2. Referee: [MemoryBank mechanism] MemoryBank mechanism description: the integration of the forgetting curve parameters (free parameters noted in the design) into retrieval and synthesis steps lacks sufficient implementation detail for closed-source versus open-source models, making it impossible to verify the claim of selective preservation without introducing critical forgetting errors.

    Authors: We will enhance the description of the MemoryBank mechanism by providing detailed pseudocode and step-by-step explanations of how the forgetting curve parameters are computed and integrated into both the retrieval and synthesis processes. For closed-source models such as ChatGPT, the parameters influence the prompt construction to prioritize or deprioritize memories, while for open-source models like ChatGLM, they can be used to filter or weight the context directly. This additional detail will allow readers to verify the selective preservation without risking critical forgetting errors. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper introduces MemoryBank as a novel architecture with an Ebbinghaus-inspired memory update rule defined independently of the target capabilities (relevant recall, personality adaptation). No equations, fitted parameters, or self-citations are shown that reduce the claimed outcomes to inputs by construction. The mechanism is presented as an additive proposal rather than a renaming or tautological re-derivation of prior results. Evaluation details (simulated dialogs) do not alter the self-contained nature of the derivation itself.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

The proposal introduces MemoryBank as a new entity and relies on the assumption that psychological forgetting curves apply directly to digital memory management in LLMs.

free parameters (1)
  • forgetting curve parameters
    The Ebbinghaus Forgetting Curve likely involves parameters for decay rate and significance that may be fitted or chosen to control memory updates.
axioms (1)
  • domain assumption The Ebbinghaus Forgetting Curve can be adapted to model memory retention in LLMs.
    Invoked in the memory updating mechanism description to permit forgetting and reinforcement based on time and significance.
invented entities (1)
  • MemoryBank no independent evidence
    purpose: To provide long-term memory for LLMs via storage, update, and recall
    New system proposed in the paper to address deficiency in long-term memory.

pith-pipeline@v0.9.0 · 5583 in / 1283 out tokens · 46042 ms · 2026-05-16T07:13:57.247705+00:00 · methodology

discussion (0)

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