{"slug": "continual-learning-mechanisms-compose-for-long-horizon-memorization", "title": "Continual Learning Mechanisms Compose for Long-Horizon Memorization", "summary": "A September 7, 2026 arXiv paper on machine learning reports that composing complementary continual learning mechanisms raises average final retention on a 100-task long-horizon memorization benchmark from 1.2% under naive sequential fine-tuning to 34.9%, a 28-fold improvement. The authors' best method combines data, function, and weight anchors with merged LoRA, ranks among the top 3 methods across three distinct 100-task memorization datasets, and uses task-level successive halving plus a factorial experiment to measure individual and interaction effects. The data anchor and merged LoRA produced the largest average gains and interacted super-additively on all three datasets.", "body_md": "# Computer Science > Machine Learning\n\n  [Submitted on 7 Sep 2026]\n\n# Title:Continual Learning Mechanisms Compose for Long-Horizon Memorization\n\n[View PDF](https://arxiv.org/pdf/2609.06986)\n\n[HTML (experimental)](https://arxiv.org/html/2609.06986v1)\n\nAbstract:Language models may need to internalize information that arrives over time and retain it through many subsequent updates. To study this challenge, we introduce long-horizon memorization, a setting in which a model learns 100 query-answer tasks through continual supervised fine-tuning without retaining earlier training examples or receiving task identifiers at inference. Sequential updates cause catastrophic forgetting, and no single continual learning mechanism we evaluate maintains strong retention at this horizon. We hypothesize that mechanisms addressing complementary sources of forgetting will be more effective when composed. We organize these compositions along two design dimensions. Data, function, and weight anchors specify what prior information each update should preserve, while low-rank allocation rules determine where successive updates are retained. To test this hypothesis systematically, we construct three distinct 100-task memorization datasets. We introduce task-level successive halving to search the combinatorial design space and use a factorial experiment to measure individual and interaction effects. Our best method combines all three anchors with merged LoRA, ranks among the top 3 methods in all datasets, and raises average final retention from 1.2% under naive sequential fine-tuning to 34.9%, a 28-fold improvement. The data anchor and merged LoRA provide the largest average gains and interact super-additively on all three datasets. Together, these results show that composing complementary mechanisms substantially improves long-horizon memorization beyond what any individual mechanism achieves.\n    \n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer \n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers \n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps \n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations \n\n*(*[What are Smart Citations?](https://www.scite.ai/))\n# Code, Data and Media Associated with this Article\n\nalphaXiv \n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers \n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub \n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub \n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face \n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast \n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))\n# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower \n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender \n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\nIArxiv Recommender\n\n*(*[What is IArxiv?](https://iarxiv.org/about))\n# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/continual-learning-mechanisms-compose-for-long-horizon-memorization", "canonical_source": "https://arxiv.org/abs/2609.06986", "published_at": "2026-09-16 05:20:05+00:00", "updated_at": "2026-09-16 05:37:44.956799+00:00", "lang": "en", "topics": ["machine-learning", "large-language-models", "ai-research", "artificial-intelligence"], "entities": ["arXiv", "LoRA"], "alternates": {"html": "https://wpnews.pro/news/continual-learning-mechanisms-compose-for-long-horizon-memorization", "markdown": "https://wpnews.pro/news/continual-learning-mechanisms-compose-for-long-horizon-memorization.md", "text": "https://wpnews.pro/news/continual-learning-mechanisms-compose-for-long-horizon-memorization.txt", "jsonld": "https://wpnews.pro/news/continual-learning-mechanisms-compose-for-long-horizon-memorization.jsonld"}}