{"id":"https://openalex.org/W7139094439","doi":"https://doi.org/10.48550/arxiv.2603.17295","title":"Directing the Narrative: A Finetuning Method for Controlling Coherence and Style in Story Generation","display_name":"Directing the Narrative: A Finetuning Method for Controlling Coherence and Style in Story Generation","publication_year":2026,"publication_date":"2026-03-18","ids":{"openalex":"https://openalex.org/W7139094439","doi":"https://doi.org/10.48550/arxiv.2603.17295"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.17295","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.17295","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.2603.17295","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129824245","display_name":"Jianzhang Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Jianzhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130086773","display_name":"Yijing Tian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tian, Yijing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130041075","display_name":"Jiwang Qu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qu, Jiwang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129947924","display_name":"Chuang Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Chuang","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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.5580999851226807,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.5580999851226807,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.17479999363422394,"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/T11574","display_name":"Artificial Intelligence in Games","score":0.11710000038146973,"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/consistency","display_name":"Consistency (knowledge bases)","score":0.597000002861023},{"id":"https://openalex.org/keywords/narrative","display_name":"Narrative","score":0.5652999877929688},{"id":"https://openalex.org/keywords/coherence","display_name":"Coherence (philosophical gambling strategy)","score":0.5346999764442444},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.4948999881744385},{"id":"https://openalex.org/keywords/identity","display_name":"Identity (music)","score":0.4925999939441681},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.4876999855041504}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7052000164985657},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.597000002861023},{"id":"https://openalex.org/C199033989","wikidata":"https://www.wikidata.org/wiki/Q1318295","display_name":"Narrative","level":2,"score":0.5652999877929688},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5565000176429749},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.5346999764442444},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.4948999881744385},{"id":"https://openalex.org/C2778355321","wikidata":"https://www.wikidata.org/wiki/Q17079427","display_name":"Identity (music)","level":2,"score":0.4925999939441681},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.4876999855041504},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.4618000090122223},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3732999861240387},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3499999940395355},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.33070001006126404},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.32519999146461487},{"id":"https://openalex.org/C81081738","wikidata":"https://www.wikidata.org/wiki/Q55542","display_name":"Lossless compression","level":3,"score":0.2971000075340271},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27459999918937683},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C2776035091","wikidata":"https://www.wikidata.org/wiki/Q7928819","display_name":"Viewpoints","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.17295","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.17295","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.2603.17295","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.17295","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":{"Story":[0],"visualization":[1],"requires":[2],"generating":[3],"sequential":[4],"imagery":[5],"that":[6,67,116,144],"aligns":[7],"semantically":[8],"with":[9,27,107,155],"evolving":[10],"narratives":[11],"while":[12,170],"maintaining":[13],"rigorous":[14],"consistency":[15,70],"in":[16,159,165],"character":[17],"identity":[18,31,87,129],"and":[19,30,55,110,128,163],"visual":[20,126],"style.":[21],"However,":[22],"existing":[23],"methodologies":[24],"often":[25],"struggle":[26],"subject":[28],"inconsistency":[29],"drift,":[32],"particularly":[33],"when":[34],"depicting":[35],"complex":[36],"interactions":[37],"or":[38],"extended":[39],"narrative":[40,111],"arcs.":[41],"To":[42],"address":[43],"these":[44],"challenges,":[45],"we":[46,60,97],"propose":[47],"a":[48,65,148],"cohesive":[49],"two-stage":[50],"framework":[51],"designed":[52],"for":[53],"robust":[54],"consistent":[56],"story":[57],"generation.":[58,173],"First,":[59],"introduce":[61],"Group-Shared":[62],"Attention":[63],"(GSA),":[64],"mechanism":[66],"fosters":[68],"intrinsic":[69],"by":[71,131],"enabling":[72],"lossless":[73],"cross-sample":[74],"information":[75],"flow":[76],"within":[77],"attention":[78],"layers.":[79],"This":[80],"allows":[81],"the":[82,140],"model":[83],"to":[84,103],"structurally":[85],"encode":[86],"correspondence":[88],"across":[89],"frames":[90],"without":[91],"relying":[92],"on":[93,118,139],"external":[94],"encoders.":[95],"Second,":[96],"leverage":[98],"Direct":[99],"Preference":[100],"Optimization":[101],"(DPO)":[102],"align":[104],"generated":[105],"outputs":[106],"human":[108],"aesthetic":[109],"standards.":[112],"Unlike":[113],"conventional":[114],"methods":[115],"rely":[117],"conflicting":[119],"auxiliary":[120],"losses,":[121],"our":[122,145],"approach":[123],"simultaneously":[124],"enhances":[125],"fidelity":[127],"preservation":[130],"learning":[132],"from":[133],"holistic":[134],"preference":[135],"data.":[136],"Extensive":[137],"evaluations":[138],"ViStoryBench":[141],"benchmark":[142],"demonstrate":[143],"method":[146],"establishes":[147],"new":[149],"state-of-the-art,":[150],"significantly":[151],"outperforming":[152],"strong":[153],"baselines":[154],"gains":[156],"of":[157],"+10.0":[158],"Character":[160],"Identity":[161],"(CIDS)":[162],"+18.7":[164],"Style":[166],"Consistency":[167],"(CSD),":[168],"all":[169],"preserving":[171],"high-fidelity":[172]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-20T00:00:00"}
