{"id":"https://openalex.org/W7162454688","doi":"https://doi.org/10.48550/arxiv.2605.25571","title":"AnE: Pushing the Reasoning Frontier of Multimodal LLMs via Anchor Evolution","display_name":"AnE: Pushing the Reasoning Frontier of Multimodal LLMs via Anchor Evolution","publication_year":2026,"publication_date":"2026-05-25","ids":{"openalex":"https://openalex.org/W7162454688","doi":"https://doi.org/10.48550/arxiv.2605.25571"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.25571","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.25571","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.2605.25571","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137074878","display_name":"Zehao Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zehao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137082988","display_name":"Yihan Zeng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zeng, Yihan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5131383175","display_name":"\u516c\u5b50 \u6771","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gong, Zidong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021895690","display_name":"Yuanfan Guo","orcid":"https://orcid.org/0000-0002-5835-8545"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Yuanfan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137075671","display_name":"Feng Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Feng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136998200","display_name":"Hongzhi Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Hongzhi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137062138","display_name":"Wei Zhang","orcid":"https://orcid.org/0009-0008-6192-6612"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137009872","display_name":"Wangmeng Zuo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zuo, Wangmeng","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/T10028","display_name":"Topic Modeling","score":0.3853999972343445,"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":0.3853999972343445,"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":0.2799000144004822,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.05609999969601631,"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/bottleneck","display_name":"Bottleneck","score":0.6276000142097473},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5824999809265137},{"id":"https://openalex.org/keywords/visual-reasoning","display_name":"Visual reasoning","score":0.4255000054836273},{"id":"https://openalex.org/keywords/mechanism","display_name":"Mechanism (biology)","score":0.36880001425743103},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.3614000082015991},{"id":"https://openalex.org/keywords/abductive-reasoning","display_name":"Abductive reasoning","score":0.3467999994754791},{"id":"https://openalex.org/keywords/reasoning-system","display_name":"Reasoning system","score":0.3361999988555908},{"id":"https://openalex.org/keywords/causal-reasoning","display_name":"Causal reasoning","score":0.3294999897480011}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7597000002861023},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.6276000142097473},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5824999809265137},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.54339998960495},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.4255000054836273},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4115000069141388},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.36880001425743103},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.3614000082015991},{"id":"https://openalex.org/C166088908","wikidata":"https://www.wikidata.org/wiki/Q308495","display_name":"Abductive reasoning","level":2,"score":0.3467999994754791},{"id":"https://openalex.org/C89288958","wikidata":"https://www.wikidata.org/wiki/Q7301504","display_name":"Reasoning system","level":2,"score":0.3361999988555908},{"id":"https://openalex.org/C115086926","wikidata":"https://www.wikidata.org/wiki/Q17004651","display_name":"Causal reasoning","level":3,"score":0.3294999897480011},{"id":"https://openalex.org/C195344581","wikidata":"https://www.wikidata.org/wiki/Q2555318","display_name":"Automated reasoning","level":2,"score":0.3273000121116638},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.32350000739097595},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.30239999294281006},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.29989999532699585},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2978000044822693},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.28940001130104065},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2842000126838684},{"id":"https://openalex.org/C2780660688","wikidata":"https://www.wikidata.org/wiki/Q25052564","display_name":"Multimodal learning","level":2,"score":0.27959999442100525},{"id":"https://openalex.org/C9616225","wikidata":"https://www.wikidata.org/wiki/Q3929429","display_name":"Semantic reasoner","level":2,"score":0.27379998564720154},{"id":"https://openalex.org/C159032336","wikidata":"https://www.wikidata.org/wiki/Q2488768","display_name":"Non-monotonic logic","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.26339998841285706},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.2572999894618988}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.25571","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.25571","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.2605.25571","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.25571","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Post-training":[0],"via":[1,103,134],"Supervised":[2],"Fine-Tuning":[3],"(SFT)":[4],"and":[5,52,78,83,106,142,195],"Reinforcement":[6],"Learning":[7],"(RL)":[8],"is":[9],"crucial":[10],"for":[11,114],"enhancing":[12],"reasoning":[13,54,89,126,132,163,173],"in":[14],"Multimodal":[15],"Large":[16],"Language":[17],"Models":[18],"(MLLMs),":[19],"yet":[20],"existing":[21],"paradigms":[22],"often":[23],"reach":[24],"a":[25,70],"performance":[26,85,183],"bottleneck":[27],"due":[28],"to":[29,42,110,124,137,154],"the":[30,88,99,121,139,156,162,181,186],"limitations":[31],"of":[32,145],"static":[33],"data.":[34,60],"While":[35],"current":[36],"methods":[37],"leverage":[38],"self-reflection":[39],"or":[40],"self-evolution":[41],"push":[43],"these":[44,63],"boundaries,":[45],"they":[46],"still":[47],"suffer":[48],"from":[49],"cognitive":[50],"drift":[51,144],"hallucinated":[53],"paths":[55,133,164],"caused":[56],"by":[57,189],"low-quality":[58],"synthetic":[59],"To":[61],"address":[62],"challenges,":[64],"we":[65,92,119],"propose":[66,93],"Anchor":[67,95],"Evolution":[68],"(AnE),":[69],"new":[71],"paradigm":[72],"that":[73,176],"integrates":[74],"truth-anchored":[75],"data":[76,116],"curation":[77],"model":[79,100,167,182,188],"evolution,":[80],"achieving":[81,196],"faithful":[82,115],"steady":[84],"gains":[86],"at":[87],"frontier.":[90],"Specifically,":[91],"Truth":[94],"Expansion,":[96],"which":[97],"pinpoints":[98],"failing":[101],"frontier":[102],"trajectory":[104],"rollouts":[105],"leverages":[107,152],"ground-truth":[108],"databases":[109],"retrieve":[111],"high-fidelity":[112],"anchors":[113,131],"curation.":[117],"Subsequently,":[118],"introduce":[120],"Scaffold-Stripping":[122],"Mechanism":[123],"internalize":[125],"capabilities.":[127,168],"This":[128],"mechanism":[129],"first":[130],"scaffold-augmented":[135],"supervision":[136],"mitigate":[138],"learning":[140],"complexity":[141],"distribution":[143],"direct":[146],"SFT":[147],"on":[148,171],"raw":[149],"data,":[150],"then":[151],"RL":[153],"strip":[155],"scaffold":[157],"template,":[158],"thereby":[159],"effectively":[160],"transitioning":[161],"into":[165],"intrinsic":[166],"Experimental":[169],"results":[170],"multimodal":[172,193],"benchmarks":[174,194],"show":[175],"our":[177],"method":[178],"substantially":[179],"advances":[180],"frontier,":[184],"improving":[185],"base":[187],"10.3\\%":[190],"across":[191],"eight":[192],"state-of-the-art":[197],"results.":[198],"The":[199],"code":[200],"will":[201],"be":[202],"made":[203],"publicly":[204],"available.":[205]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-27T00:00:00"}
