[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-205881-105":59,"doc-detail-205881-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","attention-aligned-reasoning-for-large-language-models-atar-overview","Attention-Aligned Reasoning for Large Language Models - ATAR简介","","Large Language Models (LLMs) often produce lengthy reasoning chains, but as the chain grows, crucial intermediate steps and the original prompt get buried in the context and receive insufficient attention, leading to errors. This work introduces ATAR, a reasoning method that exploits the intrinsic reasoning structure to steer LLM attention toward balanced global and local objectives. Experiments on six benchmarks show up to 15.39% absolute gains over state of the art, and attention-alignment ablations confirm strong, backend-robust contributions.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/attention-aligned-reasoning-for-large-language-models-atar-overview/205881/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/attention-aligned-reasoning-for-large-language-models-atar-overview/205881.png","ImageObject",300,407,{"name":92,"@type":93},"Ivy","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-06","2026-09-05",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does ATAR address in large language model reasoning?","Question",{"text":113,"@type":114},"LLMs may generate long reasoning chains where the original prompt and key intermediate conclusions become buried in the context. ATAR targets this by steering attention so global objectives and current reasoning steps receive appropriate focus.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does ATAR steer attention during multi-step reasoning?",{"text":118,"@type":114},"ATAR leverages the inherent structure of the reasoning chain and balances attention between the overarching goal and the intermediate step. It also introduces a dynamic step-level mechanism that adjusts steering strength based on model confidence at each reasoning step.",{"name":120,"@type":111,"acceptedAnswer":121},"How effective is ATAR compared with prior methods?",{"text":122,"@type":114},"On six benchmarks and multiple base LLMs, ATAR outperforms state-of-the-art reasoning methods by up to 15.39% absolute improvement. It also helps non-reasoning models achieve comparable or better results within the same model family, and ablations show the attention-alignment component is a major contributor.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},205881,1788585674,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":56,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"preview_page_count":4},549758252649,"https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819","Attention-Aligned Reasoning for Large Language Models  \nHongxiang Zhang and Yuan Tian and Tianyi Zhang  \nPurdue University  \n[hxxzhang@purdue.edu](hxxzhang@purdue.edu)  \n[tian211@purdue.edu](tian211@purdue.edu)  \n[tianyi@purdue.edu](tianyi@purdue.edu)  \narXiv :2510 .03223v2 [ cs .CL] 27 Mar 2026  \nAbstract  \nLarge Language Models (LLMs) tend to generate a long reasoning chain when solving complex tasks. However, as the reasoning chain extends, critical intermediate steps and the original prompt will be buried in the context, receiving insufficient attention and leading to errors. In this work, we present ATAR, a novel reasoning method that leverages the inherent reasoning structure to steer LLM attention. Our experiments show that ATAR outperforms SOTA methods across six benchmarks, achieving up to 15.39% absolute improvement. Furthermore, with ATAR,“non-reasoning” models achieve comparable or even better performance compared to reasoning models of the same size inmost benchmarks. Finally, our ablation studies show that the attention alignment component contributes significantly, and that these improvements are persist under different attentionsteering backends.  \n1 Introduction  \nSolving complex tasks requires sophisticated reasoning capabilities. In practice, LLMs tend to generate long reasoning chains. However, as the context grows, the original prompt and key intermediate conclusions can be buried in the middle, receive insufficient attention, and consequently induce errors (Liu et al., 2024) . This degradation has been evidenced in many studies in the past (Guet al., 2025 ; Chi et al., 2024 ; Liu et al., 2024 ; Sun et al., 2024 ; Yao et al., 2021 ; Tian and Zhang, 2025 ; Li et al., 2024 ; Hong et al., 2025) . For example, relevant information placed in the middle of the prompt is often ignored and shows limited influence on generation (Liu et al., 2024) . More recently, researchers have identified an interesting phenomenon called “attention sink”(Xiao et al., 2024), where LLMs often allocate disproportionate attention to initial tokens. Excessive fixation on the static preamble can prevent the model from  \nFigure 1: Overview of ATAR. By interleaving planning and action steps, our method explicitly steers attention to balance the global objective (the original question) and the local objective (the current plan step) .  \nadequately attending to dynamic, task-specific constraints that emerge later in the reasoning process. Together, these effects may make models drift from the original intent and overlook critical intermediate constraints as the context grows.  \nAlthough attention steering methods (Tian and Zhang, 2025 ; Zhang et al., 2024a) have shown promise in steering attention toward salient content, they introduce their own limitations for multistep reasoning. First, existing methods such as SPA (Tian and Zhang, 2025) and PASTA (Zhang et al., 2024a) rely on humans to select important  \ntokens to pay attention to, which fails to generalize because the most relevant context shifts dynamically at each reasoning step. Second, these methods apply a fixed steering strength during generation. Such a static intervention can either be too weak to prevent drift or too strong to preserve fluent, correct reasoning.  \nTo address these limitations, we propose ATAR (ATtention-Aligned Reasoning), a reasoning method that explicitly balances attention between the overarching goal and intermediate reasoning steps by leveraging the inherent structure of the reasoning chain. Figure 1 illustrates our method with an example where the current step provides the immediate sub-goal and the original user prompt specifies the global objective. As sub-goals evolve over the reasoning chain, the required degree of attention steering may vary at different steps. To accommodate this, we introduce a dynamic, steplevel mechanism that adjusts the steering strength based on model confidence at each reasoning step.  \nWe evaluate ATAR on six benchmarks and ","cbCaiuQ5Jmq7v3H3","https://ap.wps.com/l/cbCaiuQ5Jmq7v3H3","pdf",1538797,"English","# 1 Introduction\n# 2 Method\n## Attention Aligned Reasoning","[{\"question\":\"What problem does ATAR address in large language model reasoning?\",\"answer\":\"LLMs may generate long reasoning chains where the original prompt and key intermediate conclusions become buried in the context. ATAR targets this by steering attention so global objectives and current reasoning steps receive appropriate focus.\"},{\"question\":\"How does ATAR steer attention during multi-step reasoning?\",\"answer\":\"ATAR leverages the inherent structure of the reasoning chain and balances attention between the overarching goal and the intermediate step. It also introduces a dynamic step-level mechanism that adjusts steering strength based on model confidence at each reasoning step.\"},{\"question\":\"How effective is ATAR compared with prior methods?\",\"answer\":\"On six benchmarks and multiple base LLMs, ATAR outperforms state-of-the-art reasoning methods by up to 15.39% absolute improvement. It also helps non-reasoning models achieve comparable or better results within the same model family, and ablations show the attention-alignment component is a major contributor.\"}]","Attention-Aligned Reasoning for Large Language Models - ATAR简介 | PDF",48]