[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83979-en":3,"doc-seo-83979-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83979,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","ResonatorLM Causal Resonant Field Mixing for Efficient Long-Context Language Modeling","ResonatorLM is a physics-derived long-context language modeling architecture that replaces the transformer attention mechanism. Token sequences are treated as a single driven one-dimensional latent field, and attention dot products are replaced with causal functions of damped resonators using learned resonant kernels. Training preserves parallel computation, while decoding relies on compact recurrent states for efficient inference. Experiments on standard long-context benchmarks show decode speedups up to 6.47x at 32K tokens and accuracy improvements on WikiText, alongside large kernel-reference speed gains.","arXiv :2607 .05583v2 [ cs .CL] 9 Jul 2026  \nResonatorLM: Causal Resonant Field Mixing for Efficient Long-Context Language Modeling  \nArchie Chaudhury 1[0009−0001−1427−0664]  \nAxionic Labs  \n[archie@axioniclabs.ai](archie@axioniclabs.ai)  \nAbstract. Contemporary language models are dominated by the transformer architecture, which leverages self-attention mechanisms to enable more efficient, parallelized training across a wide set of documents and corpora. This has allowed transformers to effectively model data across a wide range of modalities and contexts. However, transformers, along with their conventional counterparts such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs), often struggle to maintain efficiency when processing long contexts. We introduce ResonatorLM, anew mechanism that replaces attention with a physics-derived alternative.  \nResonatorLM treats token sequences as a single, driven one-dimensional latent field and replaces attention dot products with causal functions of damped resonators. We implement ResonatorLM on a traditional network architecture and test it on standard long-context modeling tasks. We find that in a small, 6M matched setting, training and prefill speedups increase with sequence length, decode speed reaches 6.47x compared to that of a standard, optimized transformer at 32K tokens, and accuracy reaches 61.31 percent (compared to 55.32 percent) on WikiText.  \nKeywords: long-context language modeling · efficient decoding · sequence modeling · alternative to attention  \n1 Introduction  \nConsistent, efficient long-range sequence modeling has been the primary goal of generative model architectures. Transformers have emerged as the current, most widely adopted state-of-the-art approach for modeling multimodal tasks with long contexts [14] . The adoption of transformers has led to the creation of generalized language models (LMs), which have shown themselves to be capable of modeling text and language across a wide range of use cases. Transformers, and by extension the majority of modern LMs, depend on the attention mechanism to efficiently parallelize training on large sets of data [23, 3, 15] . Despite the widespread adoption of transformers, their core computational profile remains inefficient and expensive at long context lengths [20] .  \nRecent alternatives have tried to achieve stronger results, mostly by modifying the attention mechanism rather than departing from it entirely. Linear and kernelized methods approximate attention with feature maps or low-rank structure  \n2 A. Chaudhury  \n[16, 6] . Other long-context architectures use implicit convolutional filters or statespace recurrences to obtain linear-time sequence updates, including Hyena, S4, and Mamba [19, 13, 11] . These models show that explicit quadratic attention isnot necessarily needed for strong sequence modeling; yet, they are still inherently derived from the existing attention-based architecture.  \nWe introduce ResonatorLM, a language model architecture that replaces selfattention with causal resonant field mixing. ResonatorLM models a token sequence as a driven latent field and uses learned resonant kernels to transmit context across positions. This design preserves parallel computation during training and uses compact recurrent states during decoding for efficient long-context inference. This allows ResonatorLM to be significantly more efficient at language modeling, especially in long-context scenarios. Implementing ResonatorLM with a standardized language modeling architecture and benchmarking against a matched transformer, we find that it is both faster and more accurate. In a 6M-parameter WikiText-2 character setting, it reaches a decode speedup of 6.47x at 32K tokens while outperforming the baseline on both perplexity and accuracy. In a separate experiment focusing solely on a kernel reference benchmark, ResonatorLM reaches a 440.29x speedup at 8K tokens and 575.86x at 32K tokens.  \n2 Related","cbCaicSXWw1mD926","https://ap.wps.com/l/cbCaicSXWw1mD926","pdf",486671,5,1,12,"English","en",105,"# Introduction\n# Related Work","[{\"question\":\"What problem does ResonatorLM target in long-context language modeling?\",\"answer\":\"It targets the inefficiency and high computational cost of attention-based transformers when processing long sequences.\"},{\"question\":\"How does ResonatorLM replace self-attention?\",\"answer\":\"It treats the token sequence as a driven one-dimensional latent field and replaces attention dot products with causal functions of damped resonators and learned resonant kernels.\"},{\"question\":\"What performance benefits are reported for ResonatorLM?\",\"answer\":\"In a small 6M matched setting, training and prefill speedups grow with sequence length; decode speed reaches 6.47x at 32K tokens, and accuracy reaches 61.31% on WikiText, improving over the baseline.\"}]",1784191820,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"resonatorlm-causal-resonant-field-mixing-for-efficient-long-context-language-modeling","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/resonatorlm-causal-resonant-field-mixing-for-efficient-long-context-language-modeling/83979/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-07-25","2026-07-16",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does ResonatorLM target in long-context language modeling?","Question",{"text":76,"@type":77},"It targets the inefficiency and high computational cost of attention-based transformers when processing long sequences.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does ResonatorLM replace self-attention?",{"text":81,"@type":77},"It treats the token sequence as a driven one-dimensional latent field and replaces attention dot products with causal functions of damped resonators and learned resonant kernels.",{"name":83,"@type":74,"acceptedAnswer":84},"What performance benefits are reported for ResonatorLM?",{"text":85,"@type":77},"In a small 6M matched setting, training and prefill speedups grow with sequence length; decode speed reaches 6.47x at 32K tokens, and accuracy reaches 61.31% on WikiText, improving over the baseline.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,110,115,120,122,127,130,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":20,"slug":137},19,"General","general"]