{"id":"https://openalex.org/W7161303093","doi":"https://doi.org/10.48550/arxiv.2605.14621","title":"Do We Really Need External Tools to Mitigate Hallucinations? SIRA: Shared-Prefix Internal Reconstruction of Attribution","display_name":"Do We Really Need External Tools to Mitigate Hallucinations? SIRA: Shared-Prefix Internal Reconstruction of Attribution","publication_year":2026,"publication_date":"2026-05-14","ids":{"openalex":"https://openalex.org/W7161303093","doi":"https://doi.org/10.48550/arxiv.2605.14621"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.14621","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14621","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.14621","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103262975","display_name":"Tian Qin","orcid":"https://orcid.org/0000-0002-3466-6041"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qin, Tian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136203777","display_name":"Junzhe Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Junzhe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136196540","display_name":"Yuqing Shi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Yuqing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100707907","display_name":"Tianshu Zhang","orcid":"https://orcid.org/0000-0002-8891-3626"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Tianshu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136242132","display_name":"Qiang Ju","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ju, Qiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136244283","display_name":"Lijie Wen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wen, Lijie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.23180000483989716,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.23180000483989716,"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/T11094","display_name":"Face Recognition and Perception","score":0.15489999949932098,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T13397","display_name":"Hallucinations in medical conditions","score":0.06729999929666519,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/counterfactual-thinking","display_name":"Counterfactual thinking","score":0.8269000053405762},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.7368999719619751},{"id":"https://openalex.org/keywords/hallucinating","display_name":"Hallucinating","score":0.6366999745368958},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.522599995136261},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.42800000309944153},{"id":"https://openalex.org/keywords/counterfactual-conditional","display_name":"Counterfactual conditional","score":0.42340001463890076},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4185999929904938},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.40369999408721924}],"concepts":[{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.8269000053405762},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.7368999719619751},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7189000248908997},{"id":"https://openalex.org/C2911011789","wikidata":"https://www.wikidata.org/wiki/Q130741","display_name":"Hallucinating","level":2,"score":0.6366999745368958},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.522599995136261},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.506600022315979},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.42800000309944153},{"id":"https://openalex.org/C71889745","wikidata":"https://www.wikidata.org/wiki/Q1783264","display_name":"Counterfactual conditional","level":3,"score":0.42340001463890076},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4185999929904938},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.40369999408721924},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3806000053882599},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.33640000224113464},{"id":"https://openalex.org/C143299363","wikidata":"https://www.wikidata.org/wiki/Q900584","display_name":"Attribution","level":2,"score":0.33000001311302185},{"id":"https://openalex.org/C88626702","wikidata":"https://www.wikidata.org/wiki/Q1128903","display_name":"Continuation","level":2,"score":0.2946000099182129},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.29019999504089355},{"id":"https://openalex.org/C115086926","wikidata":"https://www.wikidata.org/wiki/Q17004651","display_name":"Causal reasoning","level":3,"score":0.2863999903202057},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2856000065803528},{"id":"https://openalex.org/C178253425","wikidata":"https://www.wikidata.org/wiki/Q162668","display_name":"Visual perception","level":3,"score":0.27390000224113464},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.2687000036239624},{"id":"https://openalex.org/C141513077","wikidata":"https://www.wikidata.org/wiki/Q378542","display_name":"Independent and identically distributed random variables","level":3,"score":0.2687000036239624},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C4051589","wikidata":"https://www.wikidata.org/wiki/Q860959","display_name":"Mental image","level":3,"score":0.26019999384880066},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2572000026702881},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.25589999556541443},{"id":"https://openalex.org/C160086991","wikidata":"https://www.wikidata.org/wiki/Q5939193","display_name":"Human visual system model","level":3,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.14621","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14621","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.14621","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14621","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":"article"},"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":{"Large":[0],"vision-language":[1],"models":[2],"(LVLMs)":[3],"often":[4],"hallucinate":[5],"when":[6],"language":[7],"priors":[8],"dominate":[9],"weak":[10],"or":[11,210],"ambiguous":[12],"visual":[13,34,79,111,143,163,174],"evidence.":[14],"Existing":[15],"contrastive":[16,55,202],"decoding":[17,56,105],"methods":[18],"mitigate":[19],"this":[20],"problem":[21],"by":[22,67],"comparing":[23],"predictions":[24,167],"from":[25,31,81],"the":[26,64,69,82,133,172],"original":[27],"image":[28,87],"with":[29,182,218],"those":[30],"externally":[32],"perturbed":[33,211],"inputs,":[35],"but":[36,137],"such":[37],"references":[38],"can":[39],"introduce":[40],"off-manifold":[41],"artifacts":[42],"and":[43,88,109,165,180,184,196,213],"require":[44],"costly":[45],"extra":[46],"forward":[47],"passes.":[48],"We":[49],"propose":[50],"SIRA,":[51],"a":[52,60,93,116,146],"training-free":[53],"internal":[54,148],"framework":[57],"that":[58,101,158,187],"constructs":[59],"counterfactual":[61,117],"reference":[62,149],"inside":[63],"same":[65],"LVLM":[66],"exploiting":[68],"staged":[70],"information":[71,80],"flow":[72],"of":[73,77],"multimodal":[74,99,135],"transformers.":[75],"Instead":[76],"removing":[78],"input,":[83,212],"SIRA":[84,155,188,204],"first":[85],"lets":[86],"text":[89],"tokens":[90,157],"interact":[91],"through":[92],"shared":[94,134],"prefix,":[95],"forming":[96],"an":[97],"aligned":[98],"state":[100],"preserves":[102],"prompt":[103],"interpretation,":[104],"history,":[106],"positional":[107],"structure,":[108],"early":[110],"grounding.":[112],"It":[113],"then":[114],"forks":[115],"branch":[118,131],"in":[119],"later":[120],"transformer":[121],"layers,":[122],"where":[123],"attention":[124],"to":[125,141,215],"image-token":[126],"positions":[127],"is":[128],"masked.":[129],"This":[130],"retains":[132],"context":[136],"lacks":[138],"continued":[139],"access":[140,164],"fine-grained":[142],"evidence,":[144],"yielding":[145],"language-prior-dominated":[147],"for":[150],"token-level":[151],"contrast.":[152],"During":[153],"decoding,":[154],"suppresses":[156],"remain":[159],"strong":[160],"without":[161],"late":[162],"favors":[166],"whose":[168],"advantage":[169],"depends":[170],"on":[171,177],"full":[173],"pathway.":[175],"Experiments":[176],"POPE,":[178],"CHAIR,":[179],"AMBER":[181],"Qwen2.5-VL":[183],"LLaVA-v1.5":[185],"show":[186],"consistently":[189],"reduces":[190],"hallucinations":[191],"while":[192],"preserving":[193],"descriptive":[194],"coverage":[195],"incurring":[197],"lower":[198],"overhead":[199],"than":[200],"two-pass":[201],"decoding.":[203],"requires":[205],"no":[206],"training,":[207],"external":[208],"verifier,":[209],"applies":[214],"open-weight":[216],"LVLMs":[217],"white-box":[219],"inference":[220],"access.":[221]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-16T00:00:00"}
