{"slug": "generic-triple-latent-compression-with-gated-associative-retrieval", "title": "Generic Triple-Latent Compression with Gated Associative Retrieval", "summary": "Researchers introduced generic triple-latent sequence models that maintain a running token state and compressed pair-memory pathway to capture higher-order token interactions without benchmark-specific parsing. The triple-latent family improved a small Transformer baseline on byte-level WikiText-2 and a tokenizer-based MiniMind language-model benchmark, while a recall-focused gated key-value retrieval extension enhanced associative recall but remained seed-sensitive and significantly slower in the current reference implementation.", "body_md": "# Computer Science > Computation and Language\n\n[Submitted on 17 Apr 2026]\n\n# Title:Generic Triple-Latent Compression with Gated Associative Retrieval\n\n[View PDF](/pdf/2606.05175)\n\n[HTML (experimental)](https://arxiv.org/html/2606.05175v1)\n\nAbstract:We study generic triple-latent sequence models that maintain a running token state and compressed pair-memory pathway to capture higher-order token interactions without benchmark-specific parsing. The triple-latent family improves a small Transformer baseline on byte-level WikiText-2 and on a tokenizer-based MiniMind language-model benchmark, while a recall-focused gated key-value retrieval extension improves associative recall but remains seed-sensitive and much slower in the current reference implementation.\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/))# 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))# 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))# 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/generic-triple-latent-compression-with-gated-associative-retrieval", "canonical_source": "https://arxiv.org/abs/2606.05175", "published_at": "2026-06-05 04:00:00+00:00", "updated_at": "2026-06-05 04:19:58.953424+00:00", "lang": "en", "topics": ["natural-language-processing", "machine-learning", "large-language-models", "neural-networks", "artificial-intelligence"], "entities": ["WikiText-2", "MiniMind"], "alternates": {"html": "https://wpnews.pro/news/generic-triple-latent-compression-with-gated-associative-retrieval", "markdown": "https://wpnews.pro/news/generic-triple-latent-compression-with-gated-associative-retrieval.md", "text": "https://wpnews.pro/news/generic-triple-latent-compression-with-gated-associative-retrieval.txt", "jsonld": "https://wpnews.pro/news/generic-triple-latent-compression-with-gated-associative-retrieval.jsonld"}}