{"id":"https://openalex.org/W7160323613","doi":"https://doi.org/10.48550/arxiv.2605.01882","title":"Chart-FR1: Visual Focus-Driven Fine-Grained Reasoning on Dense Charts","display_name":"Chart-FR1: Visual Focus-Driven Fine-Grained Reasoning on Dense Charts","publication_year":2026,"publication_date":"2026-05-03","ids":{"openalex":"https://openalex.org/W7160323613","doi":"https://doi.org/10.48550/arxiv.2605.01882"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.01882","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.01882","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.2605.01882","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135163242","display_name":"Hongkun Pan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pan, Hongkun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135338883","display_name":"Yuwei Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Yuwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135408039","display_name":"Wanyi Hong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hong, Wanyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133950493","display_name":"Shenghui Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Shenghui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017844022","display_name":"Qitong Yan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yan, Qitong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135346045","display_name":"Yi \u6613 Yang \u6768","orcid":"https://orcid.org/0000-0003-0395-3889"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Yi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135311373","display_name":"Rufei Han","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Rufei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135318527","display_name":"Changju Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Changju","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135368722","display_name":"Minfeng Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Minfeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135389981","display_name":"Dongming Han","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Dongming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135299237","display_name":"Wei Chen","orcid":"https://orcid.org/0000-0002-6203-8966"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Wei","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":0.45399999618530273,"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":0.45399999618530273,"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/T10799","display_name":"Data Visualization and Analytics","score":0.10100000351667404,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.07090000063180923,"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/visual-reasoning","display_name":"Visual reasoning","score":0.7430999875068665},{"id":"https://openalex.org/keywords/chart","display_name":"Chart","score":0.5443999767303467},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5379999876022339},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5058000087738037},{"id":"https://openalex.org/keywords/reasoning-system","display_name":"Reasoning system","score":0.45669999718666077},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.43160000443458557},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.38260000944137573},{"id":"https://openalex.org/keywords/visual-perception","display_name":"Visual perception","score":0.3691999912261963}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7551000118255615},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.7430999875068665},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6934000253677368},{"id":"https://openalex.org/C190812933","wikidata":"https://www.wikidata.org/wiki/Q28923","display_name":"Chart","level":2,"score":0.5443999767303467},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5379999876022339},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5058000087738037},{"id":"https://openalex.org/C89288958","wikidata":"https://www.wikidata.org/wiki/Q7301504","display_name":"Reasoning system","level":2,"score":0.45669999718666077},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.43160000443458557},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4097999930381775},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.38260000944137573},{"id":"https://openalex.org/C178253425","wikidata":"https://www.wikidata.org/wiki/Q162668","display_name":"Visual perception","level":3,"score":0.3691999912261963},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3686000108718872},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.33219999074935913},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.3100000023841858},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29809999465942383},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2799000144004822},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.257099986076355},{"id":"https://openalex.org/C2780878386","wikidata":"https://www.wikidata.org/wiki/Q1659648","display_name":"Visual language","level":2,"score":0.2540000081062317},{"id":"https://openalex.org/C20162079","wikidata":"https://www.wikidata.org/wiki/Q1151406","display_name":"Case-based reasoning","level":2,"score":0.25369998812675476},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2524999976158142},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2524999976158142}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.01882","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.01882","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.2605.01882","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.01882","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multimodal":[0],"large":[1],"language":[2],"models":[3],"(MLLMs)":[4],"have":[5],"shown":[6],"considerable":[7],"potential":[8],"in":[9,43,177,211],"chart":[10,85,196,203,212],"understanding":[11,213],"and":[12,30,94,127,153,214],"reasoning":[13,67,86,97,116,164,197],"tasks.":[14],"However,":[15],"they":[16],"still":[17],"struggle":[18],"with":[19,141,188],"high":[20],"information":[21,55,149],"density":[22],"(HID)":[23],"charts":[24],"characterized":[25],"by":[26,113],"multiple":[27,202],"subplots,":[28],"legends,":[29],"dense":[31],"annotations":[32],"due":[33],"to":[34,69,89,118,173,193],"three":[35],"major":[36],"challenges:":[37],"(1)":[38],"limited":[39],"fine-grained":[40,84,111,195],"perception":[41,112],"results":[42],"the":[44,57,70,175],"omission":[45],"of":[46,59,64,72],"critical":[47],"visual":[48,54,73,106,120,148,168],"cues;":[49],"(2)":[50],"redundant":[51,147],"or":[52],"noisy":[53],"undermines":[56],"performance":[58],"multimodal":[60],"reasoning;":[61],"(3)":[62],"lack":[63],"adaptive":[65,95,155],"deep":[66,96],"relative":[68],"amount":[71],"information.":[74],"To":[75],"tackle":[76],"these":[77],"challenges,":[78],"we":[79,102,133,182],"present":[80],"a":[81,105,136,185],"novel":[82],"focus-driven":[83,137],"model,":[87],"Chart-FR1,":[88],"improve":[90],"perception,":[91],"focusing":[92,107],"efficiency,":[93],"on":[98,131,201],"HID":[99,180],"charts.":[100],"Specifically,":[101],"propose":[103],"Focus-CoT,":[104],"chain-of-thought":[108],"that":[109,145,159,206],"enhances":[110],"explicitly":[114],"linking":[115],"steps":[117],"key":[119],"cues,":[121],"such":[122],"as":[123,166],"local":[124],"image":[125],"regions":[126],"OCR":[128],"signals.":[129],"Building":[130],"this,":[132],"introduce":[134],"Focus-GRPO,":[135],"reinforcement":[138],"learning":[139],"algorithm":[140],"an":[142,154,189],"information-efficiency":[143],"reward":[144],"compresses":[146],"for":[150,179],"efficient":[151],"focusing,":[152],"KL":[156],"penalty":[157],"mechanism":[158],"enables":[160],"flexible":[161],"control":[162],"over":[163],"depth":[165],"more":[167],"cues":[169],"are":[170],"discovered.":[171],"Furthermore,":[172],"fill":[174],"gap":[176],"benchmarks":[178,204],"charts,":[181],"build":[183],"HID-Chart,":[184],"challenging":[186],"benchmark":[187],"information-density":[190],"metric":[191],"designed":[192],"evaluate":[194],"capabilities.":[198],"Extensive":[199],"experiments":[200],"demonstrate":[205],"Chart-FR1":[207],"outperforms":[208],"state-of-the-art":[209],"MLLMs":[210],"reasoning.":[215],"Code":[216],"is":[217],"available":[218],"at":[219],"https://github.com/phkhub/Chart-FR1.":[220]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-06T00:00:00"}
