{"id":"https://openalex.org/W7151378831","doi":"https://doi.org/10.48550/arxiv.2604.03647","title":"Stabilizing Unsupervised Self-Evolution of MLLMs via Continuous Softened Retracing reSampling","display_name":"Stabilizing Unsupervised Self-Evolution of MLLMs via Continuous Softened Retracing reSampling","publication_year":2026,"publication_date":"2026-04-04","ids":{"openalex":"https://openalex.org/W7151378831","doi":"https://doi.org/10.48550/arxiv.2604.03647"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.03647","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03647","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2604.03647","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5106737308","display_name":"Yunyao Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Yunyao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133096951","display_name":"Zhengxian Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Zhengxian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124956232","display_name":"Zhuohong Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Zhuohong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133108615","display_name":"Hangrui Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Hangrui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133082979","display_name":"Zirui Liao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liao, Zirui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122108610","display_name":"XiangWen Deng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Deng, Xiangwen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133142016","display_name":"Zhifang Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Zhifang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101315397","display_name":"Senyuan Shi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Senyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133072008","display_name":"Haoqian Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Haoqian","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":0.5289000272750854,"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":0.5289000272750854,"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":0.18170000612735748,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.03530000150203705,"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/correctness","display_name":"Correctness","score":0.7389000058174133},{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.5044000148773193},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.4526999890804291},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.41290000081062317},{"id":"https://openalex.org/keywords/binary-code","display_name":"Binary code","score":0.4025999903678894},{"id":"https://openalex.org/keywords/voting","display_name":"Voting","score":0.396699994802475},{"id":"https://openalex.org/keywords/perturbation","display_name":"Perturbation (astronomy)","score":0.3939000070095062}],"concepts":[{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.7389000058174133},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6894999742507935},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5465999841690063},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.5044000148773193},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.4526999890804291},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.41290000081062317},{"id":"https://openalex.org/C63435697","wikidata":"https://www.wikidata.org/wiki/Q864135","display_name":"Binary code","level":3,"score":0.4025999903678894},{"id":"https://openalex.org/C520049643","wikidata":"https://www.wikidata.org/wiki/Q189760","display_name":"Voting","level":3,"score":0.396699994802475},{"id":"https://openalex.org/C177918212","wikidata":"https://www.wikidata.org/wiki/Q803623","display_name":"Perturbation (astronomy)","level":2,"score":0.3939000070095062},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.37310001254081726},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.36079999804496765},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.34700000286102295},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33230000734329224},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.31929999589920044},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3181000053882599},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.2800999879837036},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27390000224113464},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.26080000400543213}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.03647","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03647","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2604.03647","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03647","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"the":[1,9,34,39,46,53,57,62,83,91,115,131,146],"unsupervised":[2,162],"self-evolution":[3,25,163],"of":[4,11,56,93,149],"Multimodal":[5],"Large":[6],"Language":[7],"Models,":[8],"quality":[10],"feedback":[12],"signals":[13],"during":[14],"post-training":[15],"is":[16,169],"pivotal":[17],"for":[18],"stable":[19],"and":[20],"effective":[21],"learning.":[22],"However,":[23],"existing":[24],"methods":[26],"predominantly":[27],"rely":[28],"on":[29,114,151,164],"majority":[30],"voting":[31],"to":[32,89],"select":[33],"most":[35],"frequent":[36],"output":[37],"as":[38,154],"pseudo-golden":[40],"answer,":[41],"which":[42,104],"may":[43],"stem":[44],"from":[45,86],"model's":[47],"intrinsic":[48],"biases":[49],"rather":[50],"than":[51],"guaranteeing":[52],"objective":[54],"correctness":[55],"reasoning":[58,95,120,147],"paths.":[59,96],"To":[60],"counteract":[61],"degradation,":[63],"we":[64,75,98],"propose":[65,99],"Continuous":[66],"Softened":[67,100],"Retracing":[68,78],"reSampling":[69],"(CSRS)":[70],"in":[71,161],"MLLM":[72],"self-evolution.":[73],"Specifically,":[74],"introduce":[76],"a":[77],"Re-inference":[79],"Mechanism":[80],"(RRM)":[81],"that":[82,142],"model":[84,132],"re-inferences":[85],"anchor":[87],"points":[88],"expand":[90],"exploration":[92],"long-tail":[94],"Simultaneously,":[97],"Frequency":[101],"Reward":[102],"(SFR),":[103],"replaces":[105],"binary":[106],"rewards":[107],"with":[108,124],"continuous":[109],"signals,":[110],"calibrating":[111],"reward":[112],"based":[113],"answers'":[116],"frequency":[117],"across":[118],"sampled":[119],"sets.":[121],"Furthermore,":[122],"incorporated":[123],"Visual":[125],"Semantic":[126],"Perturbation":[127],"(VSP),":[128],"CSRS":[129,143],"ensures":[130],"prioritizes":[133],"mathematical":[134],"logic":[135],"over":[136],"visual":[137],"superficiality.":[138],"Experimental":[139],"results":[140,160],"demonstrate":[141],"significantly":[144],"enhances":[145],"performance":[148],"Qwen2.5-VL-7B":[150],"benchmarks":[152],"such":[153],"MathVision.":[155],"We":[156],"achieve":[157],"state-of-the-art":[158],"(SOTA)":[159],"geometric":[165],"tasks.":[166],"Our":[167],"code":[168],"avaible":[170],"at":[171],"https://github.com/yyy195/CSRS.":[172]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-08T00:00:00"}
