{"id":"https://openalex.org/W7166840362","doi":"https://doi.org/10.48550/arxiv.2606.31504","title":"SimpleSearch-VL: A Simple Recipe for Multimodal Agentic Deep Search","display_name":"SimpleSearch-VL: A Simple Recipe for Multimodal Agentic Deep Search","publication_year":2026,"publication_date":"2026-06-30","ids":{"openalex":"https://openalex.org/W7166840362","doi":"https://doi.org/10.48550/arxiv.2606.31504"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.31504","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.31504","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":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.2606.31504","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139839858","display_name":"Ming Dai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dai, Ming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139725179","display_name":"Zhihong Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Zhihong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139785248","display_name":"Jinjie Gu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Jinjie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004604326","display_name":"Jiedong Zhuang","orcid":"https://orcid.org/0000-0003-0551-5911"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhuang, Jiedong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139840885","display_name":"Yefeng Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yefeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139821973","display_name":"Wankou Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Wankou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139746852","display_name":"Jian Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139800167","display_name":"Chunhua Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Chunhua","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.7698000073432922,"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"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.7698000073432922,"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":0.06449999660253525,"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/T11574","display_name":"Artificial Intelligence in Games","score":0.03009999915957451,"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/process","display_name":"Process (computing)","score":0.6086999773979187},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.4977000057697296},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4943000078201294},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.4643999934196472},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4490000009536743},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.41100001335144043},{"id":"https://openalex.org/keywords/recipe","display_name":"Recipe","score":0.40139999985694885},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.38909998536109924}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7840999960899353},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.6086999773979187},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6003000140190125},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.4977000057697296},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4943000078201294},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47609999775886536},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.4643999934196472},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4490000009536743},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.41100001335144043},{"id":"https://openalex.org/C2778671685","wikidata":"https://www.wikidata.org/wiki/Q219239","display_name":"Recipe","level":2,"score":0.40139999985694885},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.38909998536109924},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.3749000132083893},{"id":"https://openalex.org/C113843644","wikidata":"https://www.wikidata.org/wiki/Q901882","display_name":"Interface (matter)","level":4,"score":0.311599999666214},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.3057999908924103},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.2874999940395355},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.28450000286102295},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.2644999921321869},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.2621000111103058},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.25380000472068787},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.31504","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.31504","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":"doi:10.48550/arxiv.2606.31504","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.31504","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":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":{"We":[0],"present":[1],"SimpleSearch-VL,":[2],"an":[3],"efficient,":[4],"reliable,":[5],"and":[6,56,77,92,110,121,128],"practical":[7],"framework":[8],"for":[9,125],"multimodal":[10],"agentic":[11,117,140],"search.":[12],"Its":[13],"core":[14],"idea":[15],"is":[16],"to":[17,52,70,80],"improve":[18],"the":[19,72,81,97,126],"agent's":[20],"own":[21],"search-and-verification":[22],"process":[23],"rather":[24],"than":[25],"scaling":[26],"data,":[27,113],"tools,":[28],"or":[29],"auxiliary":[30],"model":[31,134],"components.":[32],"For":[33,60,84],"efficiency,":[34],"Factorized":[35],"Adaptive":[36],"Rollout":[37],"(FAR)":[38],"improves":[39,115],"sampling":[40],"efficiency":[41],"by":[42,119],"forming":[43],"more":[44],"informative":[45],"training":[46],"groups":[47],"while":[48],"using":[49,67],"redundant":[50],"samples":[51],"mitigate":[53],"long-tail":[54],"latency":[55],"expose":[57],"hard":[58],"samples.":[59],"reliability,":[61],"SimpleSearch-VL":[62,86,114],"performs":[63,93],"evidence-verified":[64],"reasoning,":[65],"explicitly":[66],"chain-of-thought":[68],"verification":[69],"assess":[71],"relevance":[73],"of":[74],"retrieved":[75],"visual":[76],"textual":[78],"cues":[79],"original":[82],"context.":[83],"practicality,":[85],"keeps":[87],"a":[88],"lightweight":[89],"tool":[90],"interface":[91],"webpage":[94],"self-summary":[95],"within":[96],"agent,":[98],"requiring":[99],"no":[100],"additional":[101],"external":[102],"dependencies.":[103],"With":[104],"only":[105],"5K":[106],"supervised":[107],"tool-interleaved":[108],"trajectories":[109],"2K":[111],"RL":[112],"Qwen3-VL":[116],"baselines":[118],"15.8":[120],"16.0":[122],"average":[123],"points":[124],"8B":[127],"30B-A3B":[129],"variants,":[130],"respectively.":[131],"The":[132],"SimpleSearch-VL-30B-A3B":[133],"further":[135],"achieves":[136],"performance":[137],"competitive":[138],"with":[139],"Gemini-3-Pro.":[141]},"counts_by_year":[],"updated_date":"2026-07-02T06:18:51.028212","created_date":"2026-07-02T00:00:00"}
