[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85675-en":3,"doc-seo-85675-105":29,"detail-sidebar-cat-0-en-105":91},{"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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},85675,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Prioritizing Search Space Regions in the Low Autocorrelation Binary Sequences Problem","Low autocorrelation binary sequences problem (LABS) is a difficult combinatorial optimization task with major uses in communications, signal processing, and satellite navigation. The paper introduces a hybrid search framework that blends Thompson sampling with parallel selfavoiding walks to distribute compute adaptively across restriction classes. Partitions are treated as bandit arms so the method concentrates effort on classes yielding higher merit factors while still exploring less-sampled regions. GPU parallelization, shared posterior updates, efficient neighborhood scoring, and a Bloom filter for cycle prevention improve scalability. A two-stage strategy first optimizes constrained partitioned skew-symmetric spaces, then refines candidates in the unrestricted space. Experiments on long sequences report improved best-known results for 35 lengths (450 ≤ L ≤ 527 and L = 573) and a new longest sequence with merit factor above 8.0 at L = 451.","arXiv :2607 .09688v1 [ cs .LG] 17 Jun 2026  \nPRIORITIZING SEARCH SPACE REGIONS IN THE LOW AUTOCORRELATION BINARY SEQUENCES PROBLEM  \n Blaž Pšeninik  \nComputer Architecture and Languages Laboratory  \nFaculty of Electrical Engineering and Computer Science  \nUniversity of Maribor  \n[blaz.psenicnik1@um.si](blaz.psenicnik1@um.si)  \n Borko Boškovi  \nComputer Architecture and Languages Laboratory  \nFaculty of Electrical Engineering and Computer Science  \nUniversity of Maribor  \n[borko.boskovic@um.si](borko.boskovic@um.si)  \n Jan Popi  \nComputer Architecture and Languages Laboratory  \nFaculty of Electrical Engineering and Computer Science  \nUniversity of Maribor  \n[jan.popic1@um.si](jan.popic1@um.si)  \n Janez Brest  \nComputer Architecture and Languages Laboratory  \nFaculty of Electrical Engineering and Computer Science  \nUniversity of Maribor  \n[janez.brest@um.si](janez.brest@um.si)  \nJuly 14, 2026  \nABSTRACT  \nLow autocorrelation binary sequences problem (LABS) is a hard combinatorial optimization challenge with important applications in communications, signal processing, and satellite navigation. This paper proposes a hybrid search framework that combines Thompson sampling with parallel selfavoiding walks to adaptively allocate computational effort across restriction classes of the LABS search space. By modeling partitions as arms in a multi-armed bandit setting, the proposed method dynamically shifts search resources toward partitions that empirically produce higher merit factors while maintaining exploration of less-sampled regions. The approach is further accelerated through GPU-parallel execution, shared posterior updates, efficient neighborhood evaluation, and a Bloom filter for cycle prevention. In addition, we use a two-stage optimization strategy that first searches constrained partitioned skew-symmetric spaces and then refines the best candidates in the unrestricted space. Experiments on long binary sequences show that the proposed method improves the previously best-known results for 35 sequence lengths in the range 450 ≤ L ≤ 527 and for L = 573 . In particular, we report a new longest sequence with merit factor exceeding 8.0, obtained for L = 451 . The results also show that Thompson sampling effectively prioritizes partitions with better observed performance, confirming the value of online, data-driven resource allocation in LABS optimization. Overall, the proposed framework provides a scalable and effective strategy for high-performance merit factor maximization.  \nA PREPRINT-JULY 14, 2026  \nKeywords LABS, merit factor, reinforcement learning, Thompson sampling  \nIntroduction  \nThe study of low autocorrelation binary sequences (LABS) is recognized as a highly challenging computational problem, belonging to the class of hard binary combinatorial problems. It was formally introduced in 1972 by Golay [1] . Earlier groundwork was laid by Littlewood [2], a mathematician, who examined polynomials with coefficients restricted to ±1 on the unit circle in the complex plane, a problem closely related to LABS. Sequences with low autocorrelation are valuable across a range of practical contexts. In digital communications, they help distinguish signals from background noise more effectively [3, 4] and are critical for packet detection and bit alignment [5], especially for low-power Internet of Things receivers. Beyond that, their usefulness extends to areas such as physics [6], chemistry, and cryptography. Abroader overview of additional applications and theoretical developments can be found in the survey literature on this topic [3] . A particularly striking application involved their role in highly accurate interplanetary radar experiments designed to test the curvature of space-time [7] . In global navigation satellite systems (GNSS), sequences with low autocorrelation play a crucial role; when combined with additional desirable properties, they are known as spreading codes [8] . For example, the Global Positioning System L1 C/A ","cbCaipjVFHvxVWDD","https://ap.wps.com/l/cbCaipjVFHvxVWDD","pdf",1075765,1,15,"English","en",105,"# Introduction\n## Low autocorrelation binary sequences and applications\n## Autocorrelation, energy, and optimization criteria","[{\"question\":\"What problem does LABS address, and why is it important?\",\"answer\":\"LABS seeks binary sequences with low autocorrelation, which improves distinguishability of signals from noise in communications and supports critical tasks such as packet detection and alignment. It is also useful in GNSS spreading codes and has broader relevance in areas like physics, chemistry, and cryptography.\"},{\"question\":\"How does the proposed method prioritize different regions of the LABS search space?\",\"answer\":\"It models restriction classes (partitions) as arms in a multi-armed bandit and uses Thompson sampling to shift computation toward partitions that empirically produce higher merit factors, while maintaining exploration of less-sampled partitions.\"},{\"question\":\"What acceleration techniques are used to make the search scalable?\",\"answer\":\"The framework leverages GPU-parallel execution, shared posterior updates, efficient neighborhood evaluation, and a Bloom filter to prevent cycles during parallel self-avoiding walks. It also employs a two-stage optimization workflow across constrained and then unrestricted spaces.\"}]",1784205528,38,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"prioritizing-search-space-regions-in-the-low-autocorrelation-binary-sequences-problem","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/prioritizing-search-space-regions-in-the-low-autocorrelation-binary-sequences-problem/85675/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does LABS address, and why is it important?","Question",{"text":75,"@type":76},"LABS seeks binary sequences with low autocorrelation, which improves distinguishability of signals from noise in communications and supports critical tasks such as packet detection and alignment. It is also useful in GNSS spreading codes and has broader relevance in areas like physics, chemistry, and cryptography.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method prioritize different regions of the LABS search space?",{"text":80,"@type":76},"It models restriction classes (partitions) as arms in a multi-armed bandit and uses Thompson sampling to shift computation toward partitions that empirically produce higher merit factors, while maintaining exploration of less-sampled partitions.",{"name":82,"@type":73,"acceptedAnswer":83},"What acceleration techniques are used to make the search scalable?",{"text":84,"@type":76},"The framework leverages GPU-parallel execution, shared posterior updates, efficient neighborhood evaluation, and a Bloom filter to prevent cycles during parallel self-avoiding walks. 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