{"id":"https://openalex.org/W7138043306","doi":"https://doi.org/10.48550/arxiv.2603.14864","title":"Shopping Companion: Benchmarking and Training LLM Agents for Long-Horizon Preference-Grounded E-Commerce Tasks","display_name":"Shopping Companion: Benchmarking and Training LLM Agents for Long-Horizon Preference-Grounded E-Commerce Tasks","publication_year":2026,"publication_date":"2026-03-16","ids":{"openalex":"https://openalex.org/W7138043306","doi":"https://doi.org/10.48550/arxiv.2603.14864"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.14864","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14864","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.14864","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125553616","display_name":"Zijian Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Zijian","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/A5129702455","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/A5129724704","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":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.6062999963760376,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10203","display_name":"Recommender Systems and Techniques","score":0.6062999963760376,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.055399999022483826,"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/T10028","display_name":"Topic Modeling","score":0.04879999905824661,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.722100019454956},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6784999966621399},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6567999720573425},{"id":"https://openalex.org/keywords/bundle","display_name":"Bundle","score":0.5898000001907349},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.5637999773025513},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.459199994802475}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7609999775886536},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.722100019454956},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6784999966621399},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6567999720573425},{"id":"https://openalex.org/C2778134712","wikidata":"https://www.wikidata.org/wiki/Q1047307","display_name":"Bundle","level":2,"score":0.5898000001907349},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.5637999773025513},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.4593000113964081},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.459199994802475},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38109999895095825},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3698999881744385},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33379998803138733},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.30239999294281006},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.25600001215934753},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.2551000118255615}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.14864","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14864","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.14864","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14864","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"e-commerce,":[1],"LLM":[2],"agents":[3],"show":[4],"promise":[5],"for":[6,40,53,131],"shopping":[7,44,54,72],"tasks":[8,73],"such":[9,148],"as":[10,149],"recommendations,":[11],"budget":[12],"management,":[13],"and":[14,46,108,176],"bundle":[15],"deals,":[16],"where":[17],"accurately":[18],"capturing":[19],"user":[20,115],"preferences":[21],"from":[22],"long-horizon":[23,139],"conversations":[24],"is":[25,29],"critical.":[26],"However,":[27],"progress":[28],"limited":[30],"by":[31,105],"two":[32,71,94],"key":[33],"challenges:":[34],"(1)":[35],"the":[36,48,59,157,180],"absence":[37],"of":[38,50,84,97,111,159,182],"benchmarks":[39],"evaluating":[41],"long-term":[42],"preference-aware":[43],"tasks,":[45],"(2)":[47],"lack":[49],"fine-grained":[51],"supervision":[52,130],"agent":[55],"training.":[56],"To":[57,117],"fill":[58],"benchmark":[60,69],"gap,":[61],"we":[62,122],"introduce":[63],"Shopping":[64],"Companion":[65],"Bench,":[66],"a":[67,81],"novel":[68],"comprising":[70],"that":[74,127,144],"require":[75],"cross-session":[76],"preference":[77,106,174],"memory,":[78],"grounded":[79],"in":[80,138,172],"product":[82,112],"pool":[83],"over":[85],"1.2":[86],"million":[87],"real-world":[88],"items.":[89],"Our":[90],"analysis":[91],"further":[92],"identifies":[93],"major":[95],"sources":[96],"failure":[98,120],"on":[99],"this":[100],"benchmark:":[101],"cascading":[102],"errors":[103],"caused":[104],"hallucination,":[107],"insufficient":[109],"verification":[110],"attributes":[113],"against":[114],"requirements.":[116],"address":[118],"these":[119],"modes,":[121],"design":[123],"annotation-free,":[124],"tool-wise":[125],"rewards":[126],"provide":[128],"process":[129],"each":[132],"tool":[133],"call,":[134],"alleviating":[135],"reward":[136,184],"sparsity":[137],"tasks.":[140],"Experimental":[141],"results":[142],"demonstrate":[143],"even":[145],"state-of-the-art":[146],"models":[147],"GPT-5":[150],"achieve":[151],"success":[152],"rates":[153],"below":[154],"70%,":[155],"highlighting":[156],"difficulty":[158],"our":[160,163,183],"benchmark.":[161],"Notably,":[162],"fine-tuned":[164],"lightweight":[165],"4B":[166],"model":[167],"consistently":[168],"outperforms":[169],"strong":[170],"baselines":[171],"both":[173],"capture":[175],"task":[177],"performance,":[178],"suggesting":[179],"effectiveness":[181],"design.":[185]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-18T00:00:00"}
