Title: Hippocampal-like Sequential Editing for Continual Knowledge Updates in Large Language Models
Venue: NeurIPS 2025
Accepted: yes
Keywords: Large Language Models

Hippocampal Mechanisms

Machine Unlearning

Catastrophic Forgetting

Hallucination Mitigation

Long-term Editing Memory
Abstract: Large language models (LLMs) are now pivotal in real-world applications. Model editing has emerged as a promising paradigm for efficiently modifying LLMs without full retraining. However, current editing approaches face significant limitations due to parameter drift, which stems from inconsistencies between newly edited knowledge and the model's existing knowledge. In sequential editing scenarios, cumulative drifts progressively lead to model collapse characterized by general capability degradation and balance between acquiring new knowledge and catastrophic forgetting of existing knowledge. Drawing inspiration from the hippocampal trisynaptic circuit for continual memorizing and forgetting, we propose a Hippocampal-like Sequential Editing (HSE) framework that designs the unlearning of obsolete knowledge, domain-specific knowledge update separation and replay for edited knowledge. Specifically, the HSE framework designs three core mechanisms: (1) Machine unlearning selectively erases outdated knowledge to facilitate integration of new information, (2) Fisher Information Matrix-guided parameter updates prevents cross-domain knowledge interference, and (3) Parameter replay consolidates long-term editing memory through lightweight and global replay of editing data in a parametric form. Theoretical analysis demonstrates that HSE achieves smaller generalization error bounds, more stable convergence and higher computational efficiency. Experimental results validate its effective balance between acquiring new knowledge and mitigating catastrophic forgetting, maintaining or even slightly enhancing general capabilities. In practical applications, experiments confirm its effectiveness in multi-domain hallucination mitigation, healthcare knowledge injecting, and societal bias reduction.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work addresses the problem of continual learning for LLMs, and proposes a new sequential editing framework inspired by neuroscience research on the hippocampus. The proposed framework unfolds in three main components: a forgetting mechanism, to delete old/stale/wrong knowledge, a parameter importance estimate, to mitigate interference problems between updates and existing knowledge, and a method to promote long-term memory editing, by a replay mechanism.\nThe proposed framework is evaluated on editing tasks and compared to a wide range of SOTA methods, with results showing that it outperforms existing techniques. Additional experiments are briefly described, but fully reported in the Appendix.\n\nSTRENGTHS AND WEAKNESSES:\n- Strengths\n\n  * Continual learning is a very timely and important topic, and I greatly appreciate the authors approach of rooting their ideas in the neuroscience literature. \n\n  * The proposed methods, despite their complexity (e.g., machine unlearning is a field per-se, and research is on-going to improve on currently available methods), are nicely combined into a unified framework, which appears to be assessed fairly and compared to several approaches from the state of the art\n\n  * The appendix contains several additional experimental results (e.g., an ablation study to assess the benefits of individual components of the proposed method) which confer additional solidity to the claims by the authors\n\n- Weaknesses\n\n  * The mathematical rigor of this work should be improved. I had a really hard time following the mathematica details and notation used in this work, as it is inconsistent, and hinders a proper understanding of the details of the proposed methods. The general idea is quite clear, but the notation needs some polishing. Also, please double check some equations, as discussed in the questions below.\n\n  * The overall framework presented by the authors is fairly complex, and requires computation of the (inverse of) Fisher information matrix, as well as methods to determine and locate ``objects’’ to forget, update matrices and so on. I could find some discussions about complexity in appendix E.6, but I am not sure the sequential edit complexity of $n$ facts is really linear.\n\n  * The distinction between the LEM and the ER modules is not sufficiently clear to me. This is evaluated in the ablation study in appendix E.5, and they are considered as separate entities, whereas in Sec. 2.5 they are presented as a unified mechanism.\n\n  * Despite impressive results from the experimental evaluation of the proposed method, I am not convinced by the experimental protocol. The key ideas developed in this work are individually discussed in appendix E.5, but I think an extended version of the ablation should include more insights on the role of individual components. For example, the FIM module does not seem to play a major role, whereas the LEM + ER modules are the main responsible for improved performance.\n\nQUESTIONS:\n* Concerning Memory-directed Active Forgetting via Machine Unlearning: the fact that you point to the neuroscience literature indicating that humans tend to forget dissonant facts, thus inspiring your design of a forgetting mechanism is in contrast with other literature [1], which suggests that instead humans retain both obsolete and new knowledge. In other words, it seems that the human brain appends, rather than replaces, obsolete knowledge. Do you think “append only updates” are feasible within an LLM?\n\n[1] Vincent Van Veen, Marie K Krug, Jonathan W Schooler, and Cameron S Carter. Neural activity\npredicts attitude change in cognitive dissonance. Nature neuroscience, 12(11):1469–1474, 2009.\n\n* Again, in Sec. 2.3, once you establish the key ideas (which are extremely difficult to follow in technical details, as the mathematical notation requires some polishing and disambiguation), you move on to prove generalization bounds. Is this the right way to assess the benefits of active forgetting? Isn’t there a better way to measure the effectiveness of the machine unlearning method you propose?\n\n* In Sec. 2.4., what does $\\delta_{e_1,e_2}$ mean? Is this a single parameter update related to the ingestion of two consecutive edits? \n\n* In Sec. 2.4, can you double check expression (9)? Which independence assumptions did you use? Can you explain the relation between equation (6), $p(e_2|\\delta)$? \n\n* The whole derivation for expression (15) is also relying on extremely strong assumptions: can you elaborate on their validity? For example, why do you need to assume that the optimal weight update is zero? Is this to have a Gaussian approximation with zero mean? Also, from equation (14) to (15), shouldn’t we use the FIM inverse?\n\n* In equation (16), concerning the \"MAF\" loss, how do you determine what you should forget? Is this $o_1$ and $o_2$ to be replaced with the new objects? How do you locate the weights \"holding\" those objects?\n\n* In Section 2.5, I am not sure to fully grasp the relation between $\\delta$ and $\\Delta$. My understanding is that $\\Delta$ is not a matrix of learnable parameters.\n\n* In appendix E.5, Table 2, results indicate the role of each proposed module. Can you comment on why the FIM module does not seem to play a major role, whereas the LEM module is the main responsible for improved performance, even if ER is active.\n\nLIMITATIONS:\nYes, in the appendix.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-18"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a biologically inspired framework for continual reinforcement learning that mimics hippocampal memory mechanisms. The authors introduce a Hippocampal-like Sequential Replay (HSR) approach with three key components: (1) an active forgetting module to selectively erase outdated knowledge (analogous to hippocampal forgetting), (2) a Fisher information-guided update strategy to separate domain-specific knowledge and prevent interference (similar to Elastic Weight Consolidation using Fisher importance), and (3) a replay mechanism that consolidates long-term memories by replaying past experiences or parameter updates (inspired by hippocampal sequential replay during rest). The paper provides theoretical analysis showing improved stability (bounded parameter drift and convergence guarantees) and smaller generalization error bounds for the learning process. Empirically, the method is evaluated on sequential RL tasks, demonstrating improved retention of old tasks and integration of new tasks, with higher overall performance than baselines that either lack replay or use standard experience replay. In summary, HSR aims to balance acquiring new knowledge with mitigating catastrophic forgetting by incorporating neuroscience-inspired sequential replay into continual RL.\n\nSTRENGTHS AND WEAKNESSES:\nQuality: The paper is technically solid with a comprehensive approach that includes both theoretical guarantees and extensive experiments. The theoretical contribution (lemmas and corollaries) offers insights into why the method should remain stable (e.g. an upper bound on parameter norm growth and improved convergence). The empirical evaluation spans multiple sequential learning benchmarks, showing that HSR significantly outperforms existing continual learning baselines in preventing performance degradation. However, there are some quality concerns: the experimental section, while thorough, focuses on relatively limited domains/tasks (e.g. simpler or well-structured environments). It is not fully clear how the approach scales to more complex or high-dimensional RL tasks (the paper only briefly mentions results on certain benchmarks). Some important baseline comparisons (e.g. directly against standard experience replay methods or other regularization-based CL methods) are present, but a deeper analysis isolating each component’s contribution is confined to an ablation study. Overall, the execution is solid, but the scope of experiments could be broader, and a few important details (like hyperparameter settings and computational costs) are left to the appendix, making it harder to assess reproducibility at a glance.\n\nClarity: \nStrengths:\nThe paper is generally well-organized and clearly written. The motivation for each component is explained via the hippocampal analogy, and the authors include helpful diagrams/figures and an ablation table to illustrate the contribution of each module (e.g. showing the benefit of the long-term memory replay vs. a standard experience replay buffer). The methodology is described with sufficient mathematical detail, and important claims are theoretically justified. \n\nWeakness: \nOne minor weakness in clarity is that the method involves many moving parts and new terminology (AF, Fisher update, LTM replay, etc.), which can be heavy for readers – some sections (especially the theoretical derivations) are dense. The connection between the biological inspiration and the algorithm is explained, but could be more intuitively illustrated (e.g. a schematic of how information flows through the modules would help). In summary, clarity is good, but the paper could be made more accessible by simplifying certain explanations and highlighting the main intuition behind the approach.\n\nSignificance: Continual learning in reinforcement learning is an important problem, and a method to mitigate catastrophic forgetting has practical significance. The idea of using hippocampal-like sequential replay is intriguing and could inspire further research bridging neuroscience and AI. The proposed HSR framework shows tangible improvements on the tested tasks – for instance, it maintains high performance on earlier tasks while learning new ones, unlike naive fine-tuning that quickly forgets. That said, the significance is somewhat limited by the evaluation scope. The tasks and settings presented, while demonstrating the method’s potential, do not fully convince that HSR would scale to very complex real-world continual RL scenarios (e.g. lifelong learning in rich 3D environments or continuous control with high-dimensional observations). The improvements, though consistent, appear incremental over strong baselines rather than revolutionary. Additionally, some components of HSR (such as Fisher-guided updates or experience replay) are adaptations of known techniques, so the conceptual advance is in how they are combined. In summary, the work is a useful contribution to continual RL but may be viewed as a specialized approach; its broader impact will depend on validation in more diverse settings.\n\nOriginality: The paper combines ideas from neuroscience (hippocampal memory processes) with machine learning techniques for continual learning. This interdisciplinary approach gives the work a novel flavor. In particular, the introduction of a parametric replay mechanism for long-term memory consolidation is a creative twist – rather than storing raw past experiences only, the method replays knowledge in a more abstract form (in the LLM version of this framework, a “parameter replay” was proposed). The active forgetting module for selective unlearning is also interesting and less common in typical RL algorithms. However, many elements of the solution are built on prior work: using Fisher information to protect important weights is essentially the idea from Elastic Weight Consolidation, and experience replay for continual learning is a known baseline approach. The paper’s novelty lies in the particular combination and the biological framing, rather than wholly new algorithms. It would be fair to say the work is an incremental improvement that integrates existing techniques (regularization, replay, etc.) under a unifying bio-inspired framework. This is valuable, but the originality is moderate – the authors should clearly differentiate HSR from prior approaches (e.g. how their “sequential replay” differs from standard replay buffers or recent hippocampal-inspired models).\n\nQUESTIONS:\n1. How does your sequential replay compare quantitatively to standard experience replay? Clarifying this would show the added value of the hippocampal-inspired design.\n\n2. What assumptions underlie your theoretical results? Do the bounds apply to deep networks or mainly simplified cases? And how well do they match empirical trends?\n\n3. How would HSR handle more complex tasks or longer task sequences? Are there expected bottlenecks with memory use or Fisher computation as task complexity increases?\n\n4. Why is the parametric memory more effective than ER? Does it prioritize important updates? Also, how sensitive is HSR to hyperparameters like forgetting rate or Fisher strength?\n\n5. Have you observed replay patterns similar to those in the hippocampus (e.g., forward/reverse replay, reward-focused)? Even anecdotal observations would strengthen the biological analogy.\n\nLIMITATIONS:\nPartially discussed. Scalability, computational cost, and applicability to more complex domains remain unclear.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a novel framework, Hippocampal-like Sequential Editing (HSE), for continual model editing of large language models (LLMs). Drawing inspiration from the hippocampal trisynaptic circuit, the authors design three key mechanisms: (1) memory-directed active forgetting via machine unlearning, (2) domain-specific parameter stabilization using the Fisher Information Matrix (FIM), and (3) long-term memory consolidation through parametric replay. The method shows impressive empirical results, outperforming strong baselines in sequential editing benchmarks, and maintaining or even improving generalization after up to 1,000 edits.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The biological inspiration is well-grounded and mapped coherently to algorithmic modules.\n\n2. The method achieves strong empirical performance across multiple LLMs and domains, including real-world tasks like bias reduction and medical knowledge injection.\n\n3. The use of a closed-form low-rank update and FIM regularization provides theoretical and practical benefits in terms of convergence and generalization error bounds.\n\nWeaknesses:\n\n1. Limited editing locality and adaptability: The method focuses editing solely on the second linear layer in early transformer FFNs. This assumes fixed knowledge localization and may not generalize well to more distributed or higher-layer semantic representations.\n\n2. Restricted capacity for complex edits: The use of low-rank additive updates (i.e., rank-one updates per edit) limits the model’s capacity to encode complex or structurally entangled knowledge edits, such as causal reasoning or multi-hop inferences.\n\n3. Fisher matrix overhead: While the FIM-based update regulation is theoretically sound, computing and storing FIMs—even in diagonal or approximate form—can become computationally expensive as the number of edits increases.\n\n4. Key-value encoding assumption: The method assumes that factual edits can be well-represented in a key-value format. This restricts applicability to structured knowledge (e.g., triples or QA pairs), and limits extension to more general-purpose LLM behaviors.\n\n5. Replay via covariance only: The replay mechanism uses covariance matrices instead of actual examples or activations. This lightweight design may accumulate approximation error over time, especially under domain shifts or noisy edits.\n\nQUESTIONS:\nSee Weakness\n\nLIMITATIONS:\nSee Weakness", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a Hippocampal-like Sequential Editing (HSE) framework, aiming to address the issues of parameter drift and model collapse in the continuous knowledge update of Large Language Models (LLMs). Inspired by the continuous memory and forgetting mechanisms of the hippocampal trisynaptic circuit, the framework designs three core mechanisms: machine unlearning of obsolete knowledge, Fisher information matrix-guided parameter updates to prevent cross-domain interference, and parameter replay to consolidate long-term editing memory. Theoretical analysis shows that it has smaller generalization error bounds, more stable convergence, and higher computational efficiency. Experimental results verify its effectiveness in balancing the acquisition of new knowledge and catastrophic forgetting, as well as maintaining or even enhancing the general capabilities of the model.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. Inspired by the continuous memory and forgetting mechanisms of the hippocampal trisynaptic circuit, this paper proposes the HSE framework, which designs three unique core mechanisms, providing new ideas and methods for solving the problem of continuous knowledge updates in LLMs.\n\n2. This paper conducts experiments on multiple benchmark datasets and practical application scenarios, and the results show that HSE performs excellently in balancing the acquisition of new knowledge and catastrophic forgetting, as well as maintaining or even enhancing the general capabilities of the model.\n\nWeaknesses:\n\n1. The HSE framework involves multiple hyperparameters (such as the Fisher information matrix coefficient λi, the convergence factor αi, etc.). Although the paper provides specific settings, it does not systematically explore the sensitivity of these hyperparameters in different scenarios and the optimal adjustment strategies, which may affect its flexibility and robustness in practical applications.\n\n2. A related method that first forgets old knowledge and then updates new knowledge has not been discussed and compared:\n\n[1] Ni, Shiwen, et al. \"Forgetting before Learning: Utilizing Parametric Arithmetic for Knowledge Updating in Large Language Models.\" Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.\n\nQUESTIONS:\nSee above\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}