[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"kb-article-naver-s-tailored-llm-and-multimodal-ai-search-how-ai-tab-is-redefining-the-search-to-action-journey-en":3,"ArticleBody_hTLlr0yhjzYaNjB1v3agLq68lJ1m5mX3qo6mwrQKhg":219},{"article":4,"relatedArticles":188,"locale":66},{"id":5,"title":6,"slug":7,"content":8,"htmlContent":9,"excerpt":10,"category":11,"tags":12,"metaDescription":10,"wordCount":13,"readingTime":14,"publishedAt":15,"sources":16,"sourceCoverage":58,"transparency":60,"seo":63,"language":66,"featuredImage":67,"featuredImageCredit":68,"isFreeGeneration":72,"trendSlug":73,"trendSnapshot":74,"niche":83,"geoTakeaways":86,"geoFaq":95,"entities":105},"6a4a6750170b534e3d08e1ef","Naver’s Tailored LLM and Multimodal AI Search: How AI Tab Is Redefining the Search-to-Action Journey","naver-s-tailored-llm-and-multimodal-ai-search-how-ai-tab-is-redefining-the-search-to-action-journey","## From 27 Years of Search to an AI-Native Experience\n\n[Naver](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FNaver) is refactoring 27 years of search infrastructure, logs, and UGC from Blog, Café, Shopping, and Place into an AI-native stack that connects a query directly to buying, booking, or visiting.[1] This is end-to-end orchestration over a mature content and commerce ecosystem, not just retrieval.[1]  \n\n**AI Tab** is the flagship interface for this shift:\n\n- Generative, conversational surface on mobile and PC\n- Anchored at the search bar and [Green Dot](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FGreen_Dot_Corporation) entry points[5]\n- Default “search brain” for text, voice, image, and location queries[5]\n\nImpact at scale:[5]\n\n- Naver main site: ~50M daily visitors  \n- AI Tab beta: 4M+ cumulative users  \n- Product and location card CTRs: 20%+  \n- Heavy users (11+ uses):  \n  - 2.7× more product clicks  \n  - 2× more location clicks vs. one-time users  \n\nThis reflects a shift from benchmark scores to “practical AI” tuned for hundreds of millions of daily queries.[3][4] For natural-language queries like “restaurants with convenient parking,” the system must interpret nuanced reviews, not just tags like “parking available.”[3][4]\n\n**Key takeaway:** Naver is rewiring search as a continuous journey from intent → understanding → concrete action at massive scale, not building a generic chatbot.[1][3]  \n\n\n## Inside Naver’s Product-Native LLM and Harness Engineering\n\nNaver’s product-native LLM is a lightweight model based on HyperCLOVA X, optimized for search, shopping, and reservation flows rather than generic NLP tasks.[1][4]\n\nCore design choices:\n\n- **Domain-tuned data:**  \n  - Trained on Naver logs and service usage  \n  - High-quality content from search, shopping, places, lifestyle via filters and pipelines[3]\n- **Architecture:**  \n  - Transformer foundation with web-scale pretraining[6][7]  \n  - Mixture-of-Experts (MoE): activates only part of the model per request for faster, cheaper inference under heavy traffic[1][3]\n- **Training focus:**  \n  - >2× reinforcement learning resources vs. HyperCLOVA X to optimize real-world performance[1]  \n  - Clarity-focused RL: model prefers clarifying questions over confident guesses, cutting hallucinations by up to 30%[1][4]\n\nPerformance vs. HyperCLOVA X:[1][4]\n\n- >2× faster responses  \n- Up to 3× lower operating cost  \n- Significantly fewer hallucinations  \n\nThe LLM is trained with **agentic AI** capabilities to act across Naver services like Maps and reservations:[4][5]\n\n- Example flow: “Find a kid-friendly brunch place near Gangnam with easy parking” → refine via chat → transition into real-time booking in the same guided experience.[5]\n\nBeneath the model, a **harness engineering** layer turns the LLM into a robust service:[3][8]\n\n- Prompt and tool orchestration  \n- Evaluation frameworks and routing logic  \n- Safety filters and observability between the raw model and live endpoints[8]\n\nResearch on efficient inference, such as speculative decoding in EdgeLLM (up to 9.3× faster token generation on constrained devices), underscores why runtime behavior is as critical as parameter count.[7] Naver’s MoE, small models, and orchestration adopt this “do more with less, fast” philosophy at cloud scale.[1][3][7]\n\n**Implementation lens:** For ML\u002Fproduct teams, this translates to domain-specific data pipelines, MoE architectures, RL for interaction quality, and a strong orchestration tier for evaluation, routing, and tooling.[1][3][8]  \n\n\n## Multimodal AI Search: OmniSearch, [Smart Lens](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FGoogle_Contact_Lens), and AI Tab\n\nNaver defines multimodal search as jointly processing text and images so a single model, **OmniSearch**, can align keywords and pictures with relevant content across Blog, Café, Shopping, Knowledge iN, and News.[2] This “multimodal document search” ranks documents by how well they match both textual and visual signals.[2]\n\nExample: sneakers[2]\n\n- User uploads or captures an image without knowing the product name  \n- System returns matched items, reviews, and styling ideas  \n- Results appear as a “reviews and styles searched by image” block for quick decision-making  \n\nMultimodal document search uses **smart thumbnail** technology:[2][3]\n\n- Automatically crops images to the most relevant region  \n- Improves scanability and click-through by emphasizing the clearest visual cue  \n- This visual understanding increasingly feeds into AI Tab\n\n**Smart Lens**, Naver’s multimodal search tool, is placed beside the AI Tab button in the Green Dot interface, with music search integrated as well.[5] Voice, images, and text now converge into a single conversational, action-oriented surface, shrinking the gap between “what is this?” and “book\u002Fbuy this now.”[2][5]\n\nAs models learn to jointly interpret photos, maps, menus, and long-tail reviews, AI Tab can act as a proactive agent for local discovery:[3]\n\n- Instead of only answering “Which café?”, it can offer:  \n  - A few candidates  \n  - Live wait times  \n  - Reservation options aligned with user preferences  \n\n**Key takeaway:** Multimodality grounds language in real-world objects, places, and behaviors so search becomes context-rich and immediately actionable.[2][3][5]  \n\n\n## Conclusion: A Playbook for Service-First Generative AI\n\nNaver’s AI stack—product-native LLM, harness engineering, and multimodal models—shows how to build generative AI around service quality rather than model size.[1][3] By tuning to Korean user behavior, deep-linking into commerce and local services, and prioritizing efficiency, Naver converts complex intents into real-world actions at massive scale.[3][5]\n\n**Practical lesson:** Domain-specific tuning, efficiency-first infrastructure, and tight integration with real product surfaces often beat chasing generic, maximal models.[1][3][8]","\u003Ch2>From 27 Years of Search to an AI-Native Experience\u003C\u002Fh2>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FNaver\" class=\"wiki-link\" target=\"_blank\" rel=\"noopener\">Naver\u003C\u002Fa> is refactoring 27 years of search infrastructure, logs, and UGC from Blog, Café, Shopping, and Place into an AI-native stack that connects a query directly to buying, booking, or visiting.\u003Ca href=\"#source-1\" class=\"citation-link\" title=\"View source [1]\">[1]\u003C\u002Fa> This is end-to-end orchestration over a mature content and commerce ecosystem, not just retrieval.\u003Ca href=\"#source-1\" class=\"citation-link\" title=\"View source [1]\">[1]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cp>\u003Cstrong>AI Tab\u003C\u002Fstrong> is the flagship interface for this shift:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Generative, conversational surface on mobile and PC\u003C\u002Fli>\n\u003Cli>Anchored at the search bar and \u003Ca href=\"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FGreen_Dot_Corporation\" class=\"wiki-link\" target=\"_blank\" rel=\"noopener\">Green Dot\u003C\u002Fa> entry points\u003Ca href=\"#source-5\" class=\"citation-link\" title=\"View source [5]\">[5]\u003C\u002Fa>\u003C\u002Fli>\n\u003Cli>Default “search brain” for text, voice, image, and location queries\u003Ca href=\"#source-5\" class=\"citation-link\" title=\"View source [5]\">[5]\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>Impact at scale:\u003Ca href=\"#source-5\" class=\"citation-link\" title=\"View source [5]\">[5]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Naver main site: ~50M daily visitors\u003C\u002Fli>\n\u003Cli>AI Tab beta: 4M+ cumulative users\u003C\u002Fli>\n\u003Cli>Product and location card CTRs: 20%+\u003C\u002Fli>\n\u003Cli>Heavy users (11+ uses):\n\u003Cul>\n\u003Cli>2.7× more product clicks\u003C\u002Fli>\n\u003Cli>2× more location clicks vs. one-time users\u003C\u002Fli>\n\u003C\u002Ful>\n\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>This reflects a shift from benchmark scores to “practical AI” tuned for hundreds of millions of daily queries.\u003Ca href=\"#source-3\" class=\"citation-link\" title=\"View source [3]\">[3]\u003C\u002Fa>\u003Ca href=\"#source-4\" class=\"citation-link\" title=\"View source [4]\">[4]\u003C\u002Fa> For natural-language queries like “restaurants with convenient parking,” the system must interpret nuanced reviews, not just tags like “parking available.”\u003Ca href=\"#source-3\" class=\"citation-link\" title=\"View source [3]\">[3]\u003C\u002Fa>\u003Ca href=\"#source-4\" class=\"citation-link\" title=\"View source [4]\">[4]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cp>\u003Cstrong>Key takeaway:\u003C\u002Fstrong> Naver is rewiring search as a continuous journey from intent → understanding → concrete action at massive scale, not building a generic chatbot.\u003Ca href=\"#source-1\" class=\"citation-link\" title=\"View source [1]\">[1]\u003C\u002Fa>\u003Ca href=\"#source-3\" class=\"citation-link\" title=\"View source [3]\">[3]\u003C\u002Fa>\u003C\u002Fp>\n\u003Ch2>Inside Naver’s Product-Native LLM and Harness Engineering\u003C\u002Fh2>\n\u003Cp>Naver’s product-native LLM is a lightweight model based on HyperCLOVA X, optimized for search, shopping, and reservation flows rather than generic NLP tasks.\u003Ca href=\"#source-1\" class=\"citation-link\" title=\"View source [1]\">[1]\u003C\u002Fa>\u003Ca href=\"#source-4\" class=\"citation-link\" title=\"View source [4]\">[4]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cp>Core design choices:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>\u003Cstrong>Domain-tuned data:\u003C\u002Fstrong>\n\u003Cul>\n\u003Cli>Trained on Naver logs and service usage\u003C\u002Fli>\n\u003Cli>High-quality content from search, shopping, places, lifestyle via filters and pipelines\u003Ca href=\"#source-3\" class=\"citation-link\" title=\"View source [3]\">[3]\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Architecture:\u003C\u002Fstrong>\n\u003Cul>\n\u003Cli>Transformer foundation with web-scale pretraining\u003Ca href=\"#source-6\" class=\"citation-link\" title=\"View source [6]\">[6]\u003C\u002Fa>\u003Ca href=\"#source-7\" class=\"citation-link\" title=\"View source [7]\">[7]\u003C\u002Fa>\u003C\u002Fli>\n\u003Cli>Mixture-of-Experts (MoE): activates only part of the model per request for faster, cheaper inference under heavy traffic\u003Ca href=\"#source-1\" class=\"citation-link\" title=\"View source [1]\">[1]\u003C\u002Fa>\u003Ca href=\"#source-3\" class=\"citation-link\" title=\"View source [3]\">[3]\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Training focus:\u003C\u002Fstrong>\n\u003Cul>\n\u003Cli>\n\u003Cblockquote>\n\u003Cp>2× reinforcement learning resources vs. HyperCLOVA X to optimize real-world performance\u003Ca href=\"#source-1\" class=\"citation-link\" title=\"View source [1]\">[1]\u003C\u002Fa>\u003C\u002Fp>\n\u003C\u002Fblockquote>\n\u003C\u002Fli>\n\u003Cli>Clarity-focused RL: model prefers clarifying questions over confident guesses, cutting hallucinations by up to 30%\u003Ca href=\"#source-1\" class=\"citation-link\" title=\"View source [1]\">[1]\u003C\u002Fa>\u003Ca href=\"#source-4\" class=\"citation-link\" title=\"View source [4]\">[4]\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>Performance vs. HyperCLOVA X:\u003Ca href=\"#source-1\" class=\"citation-link\" title=\"View source [1]\">[1]\u003C\u002Fa>\u003Ca href=\"#source-4\" class=\"citation-link\" title=\"View source [4]\">[4]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>\n\u003Cblockquote>\n\u003Cp>2× faster responses\u003C\u002Fp>\n\u003C\u002Fblockquote>\n\u003C\u002Fli>\n\u003Cli>Up to 3× lower operating cost\u003C\u002Fli>\n\u003Cli>Significantly fewer hallucinations\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>The LLM is trained with \u003Cstrong>agentic AI\u003C\u002Fstrong> capabilities to act across Naver services like Maps and reservations:\u003Ca href=\"#source-4\" class=\"citation-link\" title=\"View source [4]\">[4]\u003C\u002Fa>\u003Ca href=\"#source-5\" class=\"citation-link\" title=\"View source [5]\">[5]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Example flow: “Find a kid-friendly brunch place near Gangnam with easy parking” → refine via chat → transition into real-time booking in the same guided experience.\u003Ca href=\"#source-5\" class=\"citation-link\" title=\"View source [5]\">[5]\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>Beneath the model, a \u003Cstrong>harness engineering\u003C\u002Fstrong> layer turns the LLM into a robust service:\u003Ca href=\"#source-3\" class=\"citation-link\" title=\"View source [3]\">[3]\u003C\u002Fa>\u003Ca href=\"#source-8\" class=\"citation-link\" title=\"View source [8]\">[8]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Prompt and tool orchestration\u003C\u002Fli>\n\u003Cli>Evaluation frameworks and routing logic\u003C\u002Fli>\n\u003Cli>Safety filters and observability between the raw model and live endpoints\u003Ca href=\"#source-8\" class=\"citation-link\" title=\"View source [8]\">[8]\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>Research on efficient inference, such as speculative decoding in EdgeLLM (up to 9.3× faster token generation on constrained devices), underscores why runtime behavior is as critical as parameter count.\u003Ca href=\"#source-7\" class=\"citation-link\" title=\"View source [7]\">[7]\u003C\u002Fa> Naver’s MoE, small models, and orchestration adopt this “do more with less, fast” philosophy at cloud scale.\u003Ca href=\"#source-1\" class=\"citation-link\" title=\"View source [1]\">[1]\u003C\u002Fa>\u003Ca href=\"#source-3\" class=\"citation-link\" title=\"View source [3]\">[3]\u003C\u002Fa>\u003Ca href=\"#source-7\" class=\"citation-link\" title=\"View source [7]\">[7]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cp>\u003Cstrong>Implementation lens:\u003C\u002Fstrong> For ML\u002Fproduct teams, this translates to domain-specific data pipelines, MoE architectures, RL for interaction quality, and a strong orchestration tier for evaluation, routing, and tooling.\u003Ca href=\"#source-1\" class=\"citation-link\" title=\"View source [1]\">[1]\u003C\u002Fa>\u003Ca href=\"#source-3\" class=\"citation-link\" title=\"View source [3]\">[3]\u003C\u002Fa>\u003Ca href=\"#source-8\" class=\"citation-link\" title=\"View source [8]\">[8]\u003C\u002Fa>\u003C\u002Fp>\n\u003Ch2>Multimodal AI Search: OmniSearch, \u003Ca href=\"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FGoogle_Contact_Lens\" class=\"wiki-link\" target=\"_blank\" rel=\"noopener\">Smart Lens\u003C\u002Fa>, and AI Tab\u003C\u002Fh2>\n\u003Cp>Naver defines multimodal search as jointly processing text and images so a single model, \u003Cstrong>OmniSearch\u003C\u002Fstrong>, can align keywords and pictures with relevant content across Blog, Café, Shopping, Knowledge iN, and News.\u003Ca href=\"#source-2\" class=\"citation-link\" title=\"View source [2]\">[2]\u003C\u002Fa> This “multimodal document search” ranks documents by how well they match both textual and visual signals.\u003Ca href=\"#source-2\" class=\"citation-link\" title=\"View source [2]\">[2]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cp>Example: sneakers\u003Ca href=\"#source-2\" class=\"citation-link\" title=\"View source [2]\">[2]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>User uploads or captures an image without knowing the product name\u003C\u002Fli>\n\u003Cli>System returns matched items, reviews, and styling ideas\u003C\u002Fli>\n\u003Cli>Results appear as a “reviews and styles searched by image” block for quick decision-making\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>Multimodal document search uses \u003Cstrong>smart thumbnail\u003C\u002Fstrong> technology:\u003Ca href=\"#source-2\" class=\"citation-link\" title=\"View source [2]\">[2]\u003C\u002Fa>\u003Ca href=\"#source-3\" class=\"citation-link\" title=\"View source [3]\">[3]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Automatically crops images to the most relevant region\u003C\u002Fli>\n\u003Cli>Improves scanability and click-through by emphasizing the clearest visual cue\u003C\u002Fli>\n\u003Cli>This visual understanding increasingly feeds into AI Tab\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>\u003Cstrong>Smart Lens\u003C\u002Fstrong>, Naver’s multimodal search tool, is placed beside the AI Tab button in the Green Dot interface, with music search integrated as well.\u003Ca href=\"#source-5\" class=\"citation-link\" title=\"View source [5]\">[5]\u003C\u002Fa> Voice, images, and text now converge into a single conversational, action-oriented surface, shrinking the gap between “what is this?” and “book\u002Fbuy this now.”\u003Ca href=\"#source-2\" class=\"citation-link\" title=\"View source [2]\">[2]\u003C\u002Fa>\u003Ca href=\"#source-5\" class=\"citation-link\" title=\"View source [5]\">[5]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cp>As models learn to jointly interpret photos, maps, menus, and long-tail reviews, AI Tab can act as a proactive agent for local discovery:\u003Ca href=\"#source-3\" class=\"citation-link\" title=\"View source [3]\">[3]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Instead of only answering “Which café?”, it can offer:\n\u003Cul>\n\u003Cli>A few candidates\u003C\u002Fli>\n\u003Cli>Live wait times\u003C\u002Fli>\n\u003Cli>Reservation options aligned with user preferences\u003C\u002Fli>\n\u003C\u002Ful>\n\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>\u003Cstrong>Key takeaway:\u003C\u002Fstrong> Multimodality grounds language in real-world objects, places, and behaviors so search becomes context-rich and immediately actionable.\u003Ca href=\"#source-2\" class=\"citation-link\" title=\"View source [2]\">[2]\u003C\u002Fa>\u003Ca href=\"#source-3\" class=\"citation-link\" title=\"View source [3]\">[3]\u003C\u002Fa>\u003Ca href=\"#source-5\" class=\"citation-link\" title=\"View source [5]\">[5]\u003C\u002Fa>\u003C\u002Fp>\n\u003Ch2>Conclusion: A Playbook for Service-First Generative AI\u003C\u002Fh2>\n\u003Cp>Naver’s AI stack—product-native LLM, harness engineering, and multimodal models—shows how to build generative AI around service quality rather than model size.\u003Ca href=\"#source-1\" class=\"citation-link\" title=\"View source [1]\">[1]\u003C\u002Fa>\u003Ca href=\"#source-3\" class=\"citation-link\" title=\"View source [3]\">[3]\u003C\u002Fa> By tuning to Korean user behavior, deep-linking into commerce and local services, and prioritizing efficiency, Naver converts complex intents into real-world actions at massive scale.\u003Ca href=\"#source-3\" class=\"citation-link\" title=\"View source [3]\">[3]\u003C\u002Fa>\u003Ca href=\"#source-5\" class=\"citation-link\" title=\"View source [5]\">[5]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cp>\u003Cstrong>Practical lesson:\u003C\u002Fstrong> Domain-specific tuning, efficiency-first infrastructure, and tight integration with real product surfaces often beat chasing generic, maximal models.\u003Ca href=\"#source-1\" class=\"citation-link\" title=\"View source [1]\">[1]\u003C\u002Fa>\u003Ca href=\"#source-3\" class=\"citation-link\" title=\"View source [3]\">[3]\u003C\u002Fa>\u003Ca href=\"#source-8\" class=\"citation-link\" title=\"View source [8]\">[8]\u003C\u002Fa>\u003C\u002Fp>\n","From 27 Years of Search to an AI-Native Experience\n\nNaver is refactoring 27 years of search infrastructure, logs, and UGC from Blog, Café, Shopping, and Place into an AI-native stack that connects a q...","trend-radar",[],803,4,"2026-07-05T14:24:32.893Z",[17,22,26,30,34,38,42,46,50,54],{"title":18,"url":19,"summary":20,"type":21},"Naver turns 27 years of search into AI with tailored LLM, SLMs, multimodal","https:\u002F\u002Fbiz.chosun.com\u002Fen\u002Fen-it\u002F2026\u002F07\u002F05\u002FDEHN3S3BRRCIBA5XDIA3LZGAEQ\u002F","Ko Sung-min\nStaff writer, Economy Chosun\nPublished 2026.07.05. 08:00\n\n\"The search infrastructure and know-how accumulated over the past 27 years, the vast content from blogs and cafes, and diverse ser...","kb",{"title":23,"url":24,"summary":25,"type":21},"NAVER Search Unveils “Multimodal Document Search” based on Multimodal AI Model","https:\u002F\u002Fnavercorp.com\u002Fen\u002Fmedia\u002FpressReleasesDetail?seq=31075","NAVER Search is launching a new search service called “multimodal document search,” which will integrate “OmniSearch,” a multimodal AI model developed by NAVER Search. NAVER developed “OmniSearch” in ...",{"title":27,"url":28,"summary":29,"type":21},"Naver Unveils 3 Core AI Search Technologies, Shifts Focus from Benchmarks to Real-World Service","https:\u002F\u002Ffinance.biggo.com\u002Fnews\u002F0a4908d6-c1d9-4d51-a619-e17f46e5ce44","Naver has unveiled a blueprint focused on 'practical AI' for its service users, prioritizing real-world application over AI model size or global benchmark competition. The strategy, announced at the '...",{"title":31,"url":32,"summary":33,"type":21},"Naver unveils AI search strategy and Product Native Giant Language Model","https:\u002F\u002Fwww.mk.co.kr\u002Fen\u002Fit\u002F12089804","Naver presented a blueprint to create a model optimized for daily services for Koreans, not artificial intelligence (AI) that does everything well. Beyond growing model sizes (parameters) or sticking ...",{"title":35,"url":36,"summary":37,"type":21},"Naver Officially Launches AI-Powered Interactive Search 'AI Tab'","https:\u002F\u002Fm.ajupress.com\u002Famp\u002F20260626085670804","By Shin Hye An\nPosted: June 26, 2026, 08:56\nUpdated: June 26, 2026, 08:56\n\nNaver announced the official launch of its generative artificial intelligence (AI)-based interactive search service, 'AI Tab,...",{"title":39,"url":40,"summary":41,"type":21},"Large Language Models (LLMs) Tutorial","https:\u002F\u002Fnexla.com\u002Fai-infrastructure\u002Fllms\u002F","Large language models (LLMs) are AI implementations that generate text. They are trained on terabytes of data from the internet and private sources, from which the AI learns statistical correlations. ...",{"title":43,"url":44,"summary":45,"type":21},"Edgellm: Fast on-device llm inference with speculative decoding — D Xu, W Yin, H Zhang, X Jin, Y Zhang… - … Mobile Computing, 2024 - ieeexplore.ieee.org","https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10812936\u002F","Abstract:\nGenerative tasks, such as text generation and question answering, are essential for mobile applications. Given their inherent privacy sensitivity, executing them on devices is demanded. Nowa...",{"title":47,"url":48,"summary":49,"type":21},"Leveraging Large Language Models to build Enterprise AI","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=BTYL-kLmhFE","Leveraging Large Language Models to build Enterprise AI\n\nToronto Machine Learning Series (TMLS)\n\nOct 31, 2024\n\nDescription\nLeveraging Large Language Models to build Enterprise AI\n\nSpeakers: Rohit Saha...",{"title":51,"url":52,"summary":53,"type":21},"Augmenting orbital debris identification with neo4j-enabled graph-based retrieval-augmented generation for multimodal large language models — DS Roll, Z Kurt, Y Li, WL Woo - Sensors, 2025 - mdpi.com","https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F25\u002F11\u002F3352","Augmenting Orbital Debris Identification with Neo4j-Enabled Graph-Based Retrieval-Augmented Generation for Multimodal Large Language Models\n\nby\n\nDaniel S. Roll\n\nZeyneb Kurt\n\nYulei Li\n\nWai Lok Woo\n\nNot...",{"title":55,"url":56,"summary":57,"type":21},"OpenClaw: A Physical AI Terminal for LLMs","https:\u002F\u002Fwww.startuphub.ai\u002Fai-news\u002Fartificial-intelligence\u002F2026\u002Fopenclaw-a-physical-ai-terminal-for-llms","Lech Kalinowski from Callstack introduces OpenClaw, a physical AI terminal for LLM agents, featuring a dual-display system and local backend interaction.\n\nJun 28 at 10:03 PM 8 min read\n\nLech Kalinowsk...",{"totalSources":59},10,{"generationDuration":61,"kbQueriesCount":59,"confidenceScore":62,"sourcesCount":59},123996,100,{"metaTitle":64,"metaDescription":65},"Naver AI Tab: Tailored LLMs Power Search-to-Action","Explore how Naver's AI Tab turns search into action with a product-native LLM linking queries to purchases, bookings, and visits — see key impact now.","en","https:\u002F\u002Fimages.unsplash.com\u002Fphoto-1763110305836-17790330be78?ixid=M3w4OTczNDl8MHwxfHNlYXJjaHw0Nnx8YXJ0aWZpY2lhbCUyMGludGVsbGlnZW5jZSUyMHRlY2hub2xvZ3l8ZW58MXwwfHx8MTc4MzI2MTAwOHww&ixlib=rb-4.1.0&w=1200&h=630&fit=crop&crop=entropy&auto=format,compress&q=60",{"photographerName":69,"photographerUrl":70,"unsplashUrl":71},"Immo Wegmann","https:\u002F\u002Funsplash.com\u002F@tinkerman?utm_source=coreprose&utm_medium=referral","https:\u002F\u002Funsplash.com\u002Fphotos\u002Fsmartphone-with-ai-text-in-jeans-pocket-CSb9Ehxb65A?utm_source=coreprose&utm_medium=referral",true,"naver-s-tailored-llm-and-multimodal-ai-search",{"score":75,"type":76,"sourceCount":77,"topSourceDomains":78,"detectedAt":82,"mentionsLast7Days":14},90,"spiking",5,[79,80,81],"biz.chosun.com","mk.co.kr","digitaltoday.co.kr","2026-07-05T00:13:44.164Z",{"key":84,"name":85,"nameEn":85},"ai-engineering","AI Engineering & LLM Ops",[87,89,91,93],{"text":88},"Naver has refactored 27 years of search logs and UGC into an AI-native stack that connects queries directly to buying, booking, or visiting through the AI Tab, which serves as the default search brain for text, voice, image, and location queries.",{"text":90},"AI Tab reached 4M+ beta users within deployment and runs on a platform with ~50M daily visitors; product and location card CTRs exceed 20%, and heavy users (11+ uses) generate 2.7× more product clicks and 2× more location clicks than one-time users.",{"text":92},"The product-native LLM, derived from HyperCLOVA X, delivers >2× faster responses, up to 3× lower operating cost, and up to 30% fewer hallucinations via clarity-focused RL and Mixture-of-Experts inference.",{"text":94},"Multimodal OmniSearch and Smart Lens jointly process text and images to rank documents by combined visual and textual relevance, enabling image-driven shopping flows, smart thumbnails for higher scanability, and direct transitions from discovery to booking or purchase.",[96,99,102],{"question":97,"answer":98},"What fundamentally differentiates Naver’s AI Tab from a generic conversational chatbot?","Naver’s AI Tab is an end-to-end, service-first orchestration layer that transforms intent into concrete actions rather than acting as a standalone conversational agent. 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