NeuroGenesysThe research we build on.
1,514 papers by other researchers that our work cites, from continual learning and verification to language models, agents and hardware. Each entry links to the paper on arXiv and to where we use it. 301 are cited on published pages of this site; the rest in the research notes behind our studies. Our own papers are in the archive.
- 2026-11arXiv 2611.02746Replay for LLM Continual Learning with Multi-Teacher On-Policy Distillation
Yiqiao Qiu · Machine learning
Multi-teacher on-policy distillation (MOPD) distills several domain experts into one student and is used in the post-training of recent large language models. It trains on all domains at once, so every expert must be available at the same time.
- 2026-10arXiv 2610.25914FisherEdit: Fisher Information Guided Knowledge Editing for Large Language Models
Yizhou Yang, Jiahong Fu, Zhiwu Wang et al. · Language models & NLP
Sequential knowledge editing in Large Language Models (LLMs) often leads to a degradation of general capabilities due to cumulative parameter interference.
- 2026-10arXiv 2610.22293Towards Continual Learning: Mitigating Forgetting With Subspace Tuning
Arnav Chavan, Kusha Sareen, Rishabh Tiwari et al. · Machine learning
Large language models (LLMs) acquire new skills through fine-tuning, but it can destroy capabilities they already have: catastrophic forgetting remains a central obstacle to continual learning in LLMs.
- 2026-09arXiv 2609.06986Continual Learning Mechanisms Compose for Long-Horizon Memorization
Zheyuan Zhang, Alvin Zhang, Daniel Khashabi et al. · Machine learning
Language models may need to internalize information that arrives over time and retain it through many subsequent updates.
Cited inEWC protects what its Fisher sees: a literature synthesis on elastic weight consolidation, scale and coherenceTo keep what a model knows, control what it is allowed to moveStudy: Elastic weight consolidation deep diveStudy: Fortifying knowledge in continual learningStudy: Seven continual learning methods tested - 2026-06arXiv 2606.26783Reproducibility Study of "AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models"
Ananth K Suresh, Arya Hariharan · Machine learning
Fang et al.
- 2026-06arXiv 2606.03979Language Models Need Sleep: Learning to Self-Modify and Consolidate Memories
Ali Behrouz, Farnoosh Hashemi, Adel Javanmard et al. · Machine learning
The past few decades have witnessed significant advances in the design of machine learning algorithms, from early studies on task-specific shallow models to more general deep Large Language Models (LLMs).
Cited inInvestors are paying for agents that learn on the job. The hard part is knowing what they forgot.To keep what a model knows, control what it is allowed to moveWhat a model lets into long-term memory matters more than how it learnsStudy: Fortifying knowledge in continual learningStudy: Genesys program prior artStudy: Hardware guardian core for continual learningStudy: LLMs modifying open model weightsStudy: Logical coherence of LLM world modelsStudy: Memory world models and source trustStudy: Seven continual learning methods tested - 2026-05arXiv 2605.29495On-Policy Replay for Continual Supervised Fine-Tuning
Yan Chen, Taojie Zhu, Meng Zhang et al. · Machine learning
Continual supervised fine-tuning (SFT) is the de facto recipe for adapting large language models (LLMs) to a stream of downstream tasks, but it suffers from catastrophic forgetting of earlier capabilities.
- 2026-05arXiv 2605.28495Janus-LoRA: A Balanced Low-Rank Adaptation for Continual Learning
Cheng Chen, Pengpeng Zeng, Yuyu Guo et al. · Vision
Low-Rank Adaptation (LoRA) has emerged as a promising paradigm for Continual Learning. It independently updates its low-rank factors ( and ), creating a composite update to the full weight matrix through their interaction.
- 2026-05arXiv 2605.26097Forgetting in Language Models: Capacity, Optimization, and Self-Generated Replay
Martin Marek, Dongkyu Cho, Shikai Qiu et al. · Machine learning
Models trained on a new task typically degrade on prior tasks, a phenomenon known as forgetting. Traditionally, mitigating forgetting has required replaying stored exemplars from prior tasks, which is often impractical.
- 2026-05arXiv 2605.26099Do Language Models Need Sleep? Offline Recurrence for Improved Online Inference
Sangyun Lee, Sean McLeish, Tom Goldstein et al. · Language models & NLP
Transformer-based large language models are increasingly used for long-horizon tasks; however, their attention mechanism scales poorly with context length.
- 2026-05arXiv 2605.07076Self-Consolidating Language Models: Continual Knowledge Incorporation from Context
Zekun Wang, Anant Gupta, Zihan Dong et al. · Language models & NLP
Large language models (LLMs) increasingly receive information as streams of passages, conversations, and long-context workflows. While longer context windows expose more evidence, they do not ensure that useful information is preserved and reused.
- 2026-05arXiv 2605.03229Sparse Memory Finetuning as a Low-Forgetting Alternative to LoRA and Full Finetuning
Prakhar Gupta, Garv Shah, Satyam Goyal et al. · Language models & NLP
Adapting a pretrained language model to a new task often hurts the general capabilities it already had, a problem known as catastrophic forgetting.
- 2026-04arXiv 2604.05248Improving Sparse Memory Finetuning
Satyam Goyal, Anirudh Kanchi, Garv Shah et al. · Machine learning
Large Language Models (LLMs) are typically static after training, yet real-world applications require continual adaptation to new knowledge without degrading existing capabilities.
- 2026-03arXiv 2603.14517Learning to Forget: Sleep-Inspired Memory Consolidation for Resolving Proactive Interference in Large Language Models
Ying Xie · AI & agents
Large language models (LLMs) suffer from proactive interference (PI): outdated information in the context window disrupts retrieval of current values.
- 2026-01arXiv 2602.02543Norm Anchors Make Model Edits Last
Mingda Liu, Zhenghan Zhu, Ze'an Miao et al. · Machine learning
Sequential Locate-and-Edit (L&E) model editing can fail abruptly after many edits.
- 2026-01arXiv 2601.19897Self-Distillation Enables Continual Learning
Idan Shenfeld, Mehul Damani, Jonas Hübotter et al. · Machine learning
Continual learning, enabling models to acquire new skills and knowledge without degrading existing capabilities, remains a fundamental challenge for foundation models.
- 2026-01arXiv 2601.02500GEM-Style Constraints for PEFT with Dual Gradient Projection in LoRA
Brian Tekmen, Jason Yin, Qianqian Tong · Machine learning
Full fine-tuning of Large Language Models (LLMs) is computationally costly, motivating Continual Learning (CL) approaches that utilize parameter-efficient adapters.
- 2025-12arXiv 2512.24695Nested Learning: The Illusion of Deep Learning Architectures
Ali Behrouz, Meisam Razaviyayn, Peilin Zhong et al. · Machine learning
Despite the recent progresses, particularly in developing Language Models, there are fundamental challenges and unanswered questions about how such models can continually learn/memorize, self-improve, and find effective solutions.
- 2025-11arXiv 2511.22367SuRe: Surprise-Driven Prioritised Replay for Continual LLM Learning
Hugo Hazard, Zafeirios Fountas, Martin A. Benfeghoul et al. · Machine learning
Continual learning, one's ability to adapt to a sequence of tasks without forgetting previously acquired knowledge, remains a major challenge in machine learning and a key gap between artificial and human intelligence.
- 2025-10arXiv 2510.18874Retaining by Doing: The Role of On-Policy Data in Mitigating Forgetting
Howard Chen, Noam Razin, Karthik Narasimhan et al. · Proceedings of the 43rd International Conference on Machine Learning (ICML), 2026 · Machine learning
Adapting language models (LMs) to new tasks via post-training carries the risk of degrading existing capabilities -- a phenomenon classically known as catastrophic forgetting.
- 2025-10arXiv 2510.15103Continual Learning via Sparse Memory Finetuning
Jessy Lin, Luke Zettlemoyer, Gargi Ghosh et al. · Language models & NLP
Modern language models are powerful, but typically static after deployment. A major obstacle to building models that continually learn over time is catastrophic forgetting, where updating on new data erases previously acquired capabilities.
Cited inTo keep what a model knows, control what it is allowed to moveStudy: Fortifying knowledge in continual learningStudy: Genesys program prior artStudy: Hardware guardian core for continual learningStudy: Memory world models and source trustStudy: Seven continual learning methods testedStudy: Weight editing to prevent forgetting - 2025-10arXiv 2510.09551Titans Revisited: A Lightweight Reimplementation and Critical Analysis of a Test-Time Memory Model
Gavriel Di Nepi, Federico Siciliano, Fabrizio Silvestri · Machine learning
By the end of 2024, Google researchers introduced Titans: Learning at Test Time, a neural memory model achieving strong empirical results across multiple tasks. However, the lack of publicly available code and ambiguities in the original description hinder reproducibility.
- 2025-09arXiv 2509.04259RL's Razor: Why Online Reinforcement Learning Forgets Less
Idan Shenfeld, Jyothish Pari, Pulkit Agrawal · Machine learning
Comparison of fine-tuning models with reinforcement learning (RL) and supervised fine-tuning (SFT) reveals that, despite similar performance at a new task, RL preserves prior knowledge and capabilities significantly better.
- 2025-08arXiv 2508.04676GeRe: Towards Efficient Anti-Forgetting in Continual Learning of LLM via General Samples Replay
Yunan Zhang, Shuoran Jiang, Mengchen Zhao et al. · Language models & NLP
The continual learning capability of large language models (LLMs) is crucial for advancing artificial general intelligence.
- 2025-08arXiv 2508.01908Revisiting Replay and Gradient Alignment for Continual Pre-Training of Large Language Models
Istabrak Abbes, Gopeshh Subbaraj, Matthew Riemer et al. · Machine learning
Training large language models (LLMs) typically involves pre-training on massive corpora, only to restart the process entirely when new data becomes available.
- 2025-07arXiv 2507.02503Continual Gradient Low-Rank Projection Fine-Tuning for LLMs
Chenxu Wang, Yilin Lyu, Zicheng Sun et al. · Machine learning
Continual fine-tuning of Large Language Models (LLMs) is hampered by the trade-off between efficiency and expressiveness.
- 2025-06arXiv 2506.10943Self-Adapting Language Models
Adam Zweiger, Jyothish Pari, Han Guo et al. · Machine learning
Large language models (LLMs) are powerful but static; they lack mechanisms to adapt their weights in response to new tasks, knowledge, or examples. We introduce Self-Adapting LLMs (SEAL), a framework that enables LLMs to self-adapt by generating their own finetuning data and update directives.
Cited inCoherence is selected, anchored, then pruned: a literature synthesis on logical coherence in LLM world modelsA model rewrote another model's weights. Read what it took away.Study: Consequence consistent updatesStudy: Continual learning order and companion dataStudy: Fortifying knowledge in continual learningStudy: LLMs modifying open model weightsStudy: Logical coherence of LLM world modelsStudy: Seven continual learning methods testedStudy: Weight editing to prevent forgetting - 2025-05arXiv 2505.23735ATLAS: Learning to Optimally Memorize the Context at Test Time
Ali Behrouz, Zeman Li, Praneeth Kacham et al. · Language models & NLP
Transformers have been established as the most popular backbones in sequence modeling, mainly due to their effectiveness in in-context retrieval tasks and the ability to learn at scale.
- 2025-05arXiv 2505.12512Scalable Strategies for Continual Learning with Replay
Truman Hickok · Machine learning
Future deep learning models will be distinguished by systems that perpetually learn through interaction, imagination, and cooperation, blurring the line between training and inference.
- 2025-04arXiv 2504.07097Sculpting Subspaces: Constrained Full Fine-Tuning in LLMs for Continual Learning
Nikhil Shivakumar Nayak, Krishnateja Killamsetty, Ligong Han et al. · Machine learning
Continual learning in large language models (LLMs) is prone to catastrophic forgetting, where adapting to new tasks significantly degrades performance on previously learned ones.
- 2024-12arXiv 2501.00663Titans: Learning to Memorize at Test Time
Ali Behrouz, Peilin Zhong, Vahab Mirrokni · Machine learning
Over more than a decade there has been an extensive research effort on how to effectively utilize recurrent models and attention.
- 2024-12arXiv 2412.09764Memory Layers at Scale
Vincent-Pierre Berges, Barlas Oğuz, Daniel Haziza et al. · Language models & NLP
Memory layers use a trainable key-value lookup mechanism to add extra parameters to a model without increasing FLOPs. Conceptually, sparsely activated memory layers complement compute-heavy dense feed-forward layers, providing dedicated capacity to store and retrieve information cheaply.
- 2024-10arXiv 2410.10179Is Parameter Collision Hindering Continual Learning in LLMs?
Shuo Yang, Kun-Peng Ning, Yu-Yang Liu et al. · Machine learning
Large Language Models (LLMs) often suffer from catastrophic forgetting when learning multiple tasks sequentially, making continual learning (CL) essential for their dynamic deployment.
- 2024-10arXiv 2410.02355AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models
Junfeng Fang, Houcheng Jiang, Kun Wang et al. · 13th International Conference on Learning Representations (ICLR 2025 Oral) · Language models & NLP
Large language models (LLMs) often exhibit hallucinations due to incorrect or outdated knowledge. Hence, model editing methods have emerged to enable targeted knowledge updates.
- 2024-06arXiv 2406.14026Demystifying Language Model Forgetting with Low-rank Example Associations
Xisen Jin, Xiang Ren · Machine learning
Large language models (LLMs) suffer from forgetting of upstream knowledge when fine-tuned. Despite efforts on mitigating forgetting, few have investigated how forgotten upstream examples are dependent on newly learned tasks.
- 2024-03arXiv 2404.00228InfLoRA: Interference-Free Low-Rank Adaptation for Continual Learning
Yan-Shuo Liang, Wu-Jun Li · Machine learning
Continual learning requires the model to learn multiple tasks sequentially. In continual learning, the model should possess the ability to maintain its performance on old tasks (stability) and the ability to adapt to new tasks continuously (plasticity).
- 2024-03arXiv 2403.01244Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal
Jianheng Huang, Leyang Cui, Ante Wang et al. · Language models & NLP
Large language models (LLMs) suffer from catastrophic forgetting during continual learning. Conventional rehearsal-based methods rely on previous training data to retain the model's ability, which may not be feasible in real-world applications.
- 2024-02arXiv 2402.01865What Will My Model Forget? Forecasting Forgotten Examples in Language Model Refinement
Xisen Jin, Xiang Ren · Machine learning
Language models deployed in the wild make errors. However, simply updating the model with the corrected error instances causes catastrophic forgetting -- the updated model makes errors on instances learned during the instruction tuning or upstream training phase.
Cited inA gradient kernel flags contradictions but does not predict forgetting, and steering rehearsal by it backfiresCoherence is selected, anchored, then pruned: a literature synthesis on logical coherence in LLM world modelsTo stop a model forgetting, rehearse what it knew. New context won't do it.Study: Continual learning order and companion dataStudy: Decision signatures for conflict detectionStudy: Logical coherence of LLM world modelsStudy: Seven continual learning methods tested - 2023-12arXiv 2401.08623Wake-Sleep Consolidated Learning
Amelia Sorrenti, Giovanni Bellitto, Federica Proietto Salanitri et al. · Neuroscience & neural computation
We propose Wake-Sleep Consolidated Learning (WSCL), a learning strategy leveraging Complementary Learning System theory and the wake-sleep phases of the human brain to improve the performance of deep neural networks for visual classification tasks in continual learning settings.
- 2023-10arXiv 2310.14152Orthogonal Subspace Learning for Language Model Continual Learning
Xiao Wang, Tianze Chen, Qiming Ge et al. · Language models & NLP
Benefiting from massive corpora and advanced hardware, large language models (LLMs) exhibit remarkable capabilities in language understanding and generation. However, their performance degrades in scenarios where multiple tasks are encountered sequentially, also known as catastrophic forgetting.
Cited inEWC protects what its Fisher sees: a literature synthesis on elastic weight consolidation, scale and coherenceStudy: Continual learning order and companion dataStudy: Elastic weight consolidation deep diveStudy: Fortifying knowledge in continual learningStudy: Genesys program prior artStudy: Hardware guardian core for continual learningStudy: Merging and splitting MoE expertsStudy: Orchestrated specialists and bridge synthesisStudy: Seven continual learning methods tested
Built from every arXiv paper cited in our papers, posts, topic hubs, reports and research notes, with titles, authors, dates and abstracts from the arXiv API (updated 2026-11-12). Summaries are the opening sentences of each paper's own abstract, not our assessment; a citation means we used or checked the work, not that we endorse it. Fields follow each paper's primary arXiv category. Papers cited only outside arXiv (journals, proceedings) are not listed yet. Entries are generated from our citations, so a mistyped ID in a note would list the wrong paper; we remove those as we find them. Report a wrong entry.
