{"id":"https://openalex.org/W7133225864","doi":"https://doi.org/10.48550/arxiv.2602.23716","title":"ProductResearch: Training E-Commerce Deep Research Agents via Multi-Agent Synthetic Trajectory Distillation","display_name":"ProductResearch: Training E-Commerce Deep Research Agents via Multi-Agent Synthetic Trajectory Distillation","publication_year":2026,"publication_date":"2026-02-27","ids":{"openalex":"https://openalex.org/W7133225864","doi":"https://doi.org/10.48550/arxiv.2602.23716"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.23716","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5127838882","display_name":"Jiangyuan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jiangyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009789784","display_name":"Kejun Xiao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao, Kejun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073188782","display_name":"Huaipeng Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Huaipeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127828283","display_name":"Tao Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Tao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5127842910","display_name":"Xiaoyi Zeng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zeng, Xiaoyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.25546166,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.07769999653100967,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.07769999653100967,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.07609999924898148,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.06210000067949295,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6547999978065491},{"id":"https://openalex.org/keywords/supervisor","display_name":"Supervisor","score":0.6313999891281128},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5415999889373779},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5182999968528748},{"id":"https://openalex.org/keywords/implementation","display_name":"Implementation","score":0.491100013256073},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.46779999136924744},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.4442000091075897},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.4440999925136566}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.65829998254776},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6547999978065491},{"id":"https://openalex.org/C2779110517","wikidata":"https://www.wikidata.org/wiki/Q1240788","display_name":"Supervisor","level":2,"score":0.6313999891281128},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5415999889373779},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5182999968528748},{"id":"https://openalex.org/C26713055","wikidata":"https://www.wikidata.org/wiki/Q245962","display_name":"Implementation","level":2,"score":0.491100013256073},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48750001192092896},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.46779999136924744},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.4442000091075897},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.4440999925136566},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.4320000112056732},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41620001196861267},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3382999897003174},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3370000123977661},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.287200003862381},{"id":"https://openalex.org/C19351080","wikidata":"https://www.wikidata.org/wiki/Q1395034","display_name":"New product development","level":2,"score":0.28220000863075256},{"id":"https://openalex.org/C41550386","wikidata":"https://www.wikidata.org/wiki/Q529909","display_name":"Multi-agent system","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.2628999948501587},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.25920000672340393},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.257999986410141},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.2578999996185303}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.23716","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.23716","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.23716","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.23716","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"Language":[1],"Model":[2],"(LLM)-based":[3],"agents":[4,128],"show":[5,135],"promise":[6],"for":[7,22,58,129,182],"e-commerce":[8,61],"conversational":[9],"shopping,":[10],"yet":[11],"existing":[12],"implementations":[13],"lack":[14],"the":[15,27,162],"interaction":[16],"depth":[17],"and":[18,78,106,158,170,179],"contextual":[19],"breadth":[20],"required":[21],"complex":[23,130],"product":[24,98],"research.":[25],"Meanwhile,":[26],"Deep":[28],"Research":[29,88],"paradigm,":[30],"despite":[31],"advancing":[32],"information":[33],"synthesis":[34],"in":[35,95,153],"web":[36],"search,":[37],"suffers":[38],"from":[39,75],"domain":[40],"gaps":[41],"when":[42],"transferred":[43],"to":[44,70,90],"e-commerce.":[45],"We":[46],"propose":[47],"ProductResearch,":[48],"a":[49,67,79,87,109,137],"multi-agent":[50,115,172],"framework":[51,65],"that":[52,82,113,136],"synthesizes":[53],"high-fidelity,":[54],"long-horizon":[55],"tool-use":[56],"trajectories":[57,93,102],"training":[59,121,175],"robust":[60],"shopping":[62,73,131,185],"agents.":[63],"The":[64],"employs":[66],"User":[68],"Agent":[69,81,89],"infer":[71],"nuanced":[72],"intents":[74],"behavioral":[76],"histories,":[77],"Supervisor":[80],"orchestrates":[83],"iterative":[84],"collaboration":[85],"with":[86],"generate":[91],"synthetic":[92,144,173],"culminating":[94],"comprehensive,":[96],"insightful":[97],"research":[99,156,168],"reports.":[100],"These":[101],"are":[103],"rigorously":[104],"filtered":[105],"distilled":[107],"through":[108],"reflective":[110],"internalization":[111],"process":[112],"consolidates":[114],"supervisory":[116],"interactions":[117],"into":[118],"coherent":[119],"single-role":[120],"examples,":[122],"enabling":[123],"effective":[124,178],"fine-tuning":[125],"of":[126,164],"LLM":[127],"inquiries.":[132],"Extensive":[133],"experiments":[134],"compact":[138],"MoE":[139],"model":[140,152],"fine-tuned":[141],"on":[142],"our":[143],"data":[145],"achieves":[146],"substantial":[147],"improvements":[148],"over":[149],"its":[150],"base":[151],"response":[154],"comprehensiveness,":[155],"depth,":[157],"user-perceived":[159],"utility,":[160],"approaching":[161],"performance":[163],"frontier":[165],"proprietary":[166],"deep":[167],"systems":[169],"establishing":[171],"trajectory":[174],"as":[176],"an":[177],"scalable":[180],"paradigm":[181],"enhancing":[183],"LLM-based":[184],"assistance.":[186]},"counts_by_year":[],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2026-03-03T00:00:00"}