{"ID":5443759,"CreatedAt":"2026-07-01T02:07:11.383974684Z","UpdatedAt":"2026-07-03T13:50:35.156039308Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.31693","arxiv_id":"2606.31693","title":"ShopX: A Foundation Model for Intent-to-Item Fulfillment in Agentic Shopping","abstract":"The wave of AI-native applications is moving shopping beyond page- and feed-based browsing toward intent-driven experiences orchestrated by LLM agents. A common design wraps an LLM around existing search and recommendation pipelines, forcing complex intents through low-bandwidth retrieval or ranking interfaces and leaving a gap between language understanding and item-space fulfillment. Generative recommendation gives LLMs a direct item-space interface through semantic IDs (SIDs), but existing models mainly generate candidates for retrieval rather than translate flexible intents into item-space outcomes. We propose ShopX to address this bottleneck by unifying intent understanding, execution planning, and flexible SID-native item-space operations into a single foundation model. We deploy ShopX in agentic shopping workflows through a model-native item-fulfillment framework with a serving harness that defines a model-facing action protocol and exposes support surfaces for context access, catalog grounding, and state management. Within this framework, ShopX plans and composes SID-based item-space operations such as SID beam-search retrieval, listwise ranking, or product bundling. This model-centric design reduces lossy hand-offs between agent orchestration and item-space execution. To build ShopX, we design semantically recoverable, LLM-operable SIDs and a training recipe that equips a general LLM for flexible multi-turn item-space fulfillment while retaining the knowledge and instruction-following abilities needed by a shopping agent. We evaluate the ShopX framework against tool-mediated agentic systems on single- and multi-turn fulfillment tasks derived from anonymized Taobao production logs, showing that model-native fulfillment improves overall framework behavior, especially on complex or ambiguous requests.","short_abstract":"The wave of AI-native applications is moving shopping beyond page- and feed-based browsing toward intent-driven experiences orchestrated by LLM agents. A common design wraps an LLM around existing search and recommendation pipelines, forcing complex intents through low-bandwidth retrieval or ranking interfaces and leav...","url_abs":"https://arxiv.org/abs/2606.31693","url_pdf":"https://arxiv.org/pdf/2606.31693v1","authors":"[\"Jiacheng Chen\",\"Tao Zhang\",\"Manxi Lin\",\"Dunxian Huang\",\"Teng Shi\",\"Honghao Fu\",\"Mengyan Li\",\"Xinming Zhang\",\"Chenchi Zhang\",\"Xuan Lu\",\"Xiaoxiong Du\",\"Haibin Chen\",\"Shaolin Ye\",\"Hao Chang\",\"Xiaoqi Li\",\"Shuwen Xiao\",\"Yujin Yuan\",\"Jingxuan Feng\",\"Shaopan Xiong\",\"Huimin Yi\",\"Ju Huang\",\"Qiu Shen\",\"Ying Chen\",\"Junjun Zheng\",\"Xiangheng Kong\",\"Yuning Jiang\"]","published":"2026-06-30T14:05:28Z","proceeding":"cs.IR","tasks":"[\"cs.IR\",\"cs.AI\",\"cs.CL\"]","methods":"[\"Large Language Model\"]","has_code":false}