{"id":"https://openalex.org/W7162092613","doi":"https://doi.org/10.48550/arxiv.2605.21954","title":"MLLMs Know When Before Speaking: Revealing and Recovering Temporal Grounding via Attention Cues","display_name":"MLLMs Know When Before Speaking: Revealing and Recovering Temporal Grounding via Attention Cues","publication_year":2026,"publication_date":"2026-05-21","ids":{"openalex":"https://openalex.org/W7162092613","doi":"https://doi.org/10.48550/arxiv.2605.21954"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.21954","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21954","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":"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.2605.21954","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011937977","display_name":"Dazhao Du","orcid":"https://orcid.org/0000-0001-6230-4526"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Du, Dazhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136747048","display_name":"Liao Duan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Duan, Liao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136793983","display_name":"Jian Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Jian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136805343","display_name":"Tao Han","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Tao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136781650","display_name":"Yujia Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yujia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101694199","display_name":"Eric Liu","orcid":"https://orcid.org/0000-0002-7046-9264"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Eric","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136764775","display_name":"Xi Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136779198","display_name":"Song Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Song","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":0.911300003528595,"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":0.911300003528595,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.013399999588727951,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.006399999838322401,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/timestamp","display_name":"Timestamp","score":0.7182999849319458},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.652899980545044},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.650600016117096},{"id":"https://openalex.org/keywords/interval","display_name":"Interval (graph theory)","score":0.6449000239372253},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.635699987411499},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.6003999710083008},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.5637000203132629},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.5503000020980835},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.4902999997138977}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7358999848365784},{"id":"https://openalex.org/C113954288","wikidata":"https://www.wikidata.org/wiki/Q186885","display_name":"Timestamp","level":2,"score":0.7182999849319458},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.652899980545044},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.650600016117096},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.6449000239372253},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.635699987411499},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6003999710083008},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.5637000203132629},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5503000020980835},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4902999997138977},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4697999954223633},{"id":"https://openalex.org/C2777402240","wikidata":"https://www.wikidata.org/wiki/Q6783436","display_name":"Masking (illustration)","level":2,"score":0.4560999870300293},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4343000054359436},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.382999986410141},{"id":"https://openalex.org/C168993435","wikidata":"https://www.wikidata.org/wiki/Q6501125","display_name":"Ground","level":2,"score":0.3587000072002411},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.34689998626708984},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.33250001072883606},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.32670000195503235},{"id":"https://openalex.org/C2776544517","wikidata":"https://www.wikidata.org/wiki/Q189447","display_name":"Unexpected events","level":2,"score":0.3176000118255615},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3089999854564667},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.30149999260902405},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.3000999987125397},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.298799991607666},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2985999882221222},{"id":"https://openalex.org/C157657479","wikidata":"https://www.wikidata.org/wiki/Q2367247","display_name":"Closed captioning","level":3,"score":0.28200000524520874},{"id":"https://openalex.org/C162670838","wikidata":"https://www.wikidata.org/wiki/Q6057295","display_name":"Interval temporal logic","level":3,"score":0.2727999985218048},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.2531999945640564}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.21954","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21954","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":"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.2605.21954","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21954","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":"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":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.7675652503967285}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Video":[0],"temporal":[1,60],"grounding":[2],"(VTG),":[3],"which":[4,115],"localizes":[5],"the":[6,73,91,102,106,126,133,169,177],"start":[7],"and":[8,78,167,198,207],"end":[9],"times":[10],"of":[11,23,76,112,215],"a":[12,20,80,109,162],"queried":[13],"event":[14],"in":[15],"an":[16,151],"untrimmed":[17],"video,":[18],"is":[19,86],"key":[21,84],"test":[22],"whether":[24],"multimodal":[25],"large":[26],"language":[27],"models":[28],"(MLLMs)":[29],"understand":[30],"not":[31],"only":[32],"what":[33],"happens":[34],"but":[35,96,145],"also":[36],"when":[37,100],"it":[38,172],"happens.":[39],"Although":[40],"modern":[41],"MLLMs":[42,77,88],"describe":[43],"video":[44,187],"content":[45],"fluently,":[46],"their":[47],"timestamp":[48],"predictions":[49],"remain":[50],"unreliable,":[51],"while":[52],"existing":[53],"remedies":[54],"either":[55],"require":[56],"costly":[57],"post-training":[58],"on":[59,64,125,209],"annotations":[61],"or":[62,189],"rely":[63],"coarse":[65],"training-free":[66],"heuristics.":[67],"In":[68,105],"this":[69,98,140,184],"work,":[70],"we":[71,116],"probe":[72],"cross-modal":[74],"attention":[75,113,124,137,160,190],"uncover":[79],"perception-generation":[81],"gap.":[82],"Our":[83],"finding":[85],"that":[87],"often":[89],"know":[90],"target":[92],"interval":[93,141,171],"during":[94],"prefill,":[95],"lose":[97],"signal":[99,166],"generating":[101],"final":[103],"answer.":[104],"prefill":[107,159],"stage,":[108],"sparse":[110],"set":[111],"heads,":[114],"call":[117],"\\emph{Temporal":[118],"Grounding":[119],"Heads}":[120],"(TG-Heads),":[121],"concentrates":[122],"query-to-video":[123],"ground-truth":[127],"interval.":[128],"During":[129],"autoregressive":[130],"decoding,":[131],"however,":[132],"answer":[134],"tokens":[135],"shift":[136],"away":[138],"from":[139],"toward":[142],"visually":[143],"salient":[144],"query-irrelevant":[146],"segments.":[147],"This":[148],"observation":[149],"motivates":[150],"inference-time":[152],"read-then-regenerate":[153],"framework.":[154],"We":[155,174],"first":[156],"convert":[157],"TG-Head":[158],"into":[161],"debiased":[163],"frame-level":[164],"relevance":[165],"extract":[168],"high-attention":[170],"highlights.":[173],"then":[175],"re-invoke":[176],"MLLM":[178],"with":[179,213],"visual":[180],"context":[181],"restricted":[182],"to":[183,192,217],"interval,":[185],"using":[186],"cropping":[188],"masking":[191],"suppress":[193],"distractors.":[194],"Without":[195],"parameter":[196],"updates":[197],"architectural":[199],"changes,":[200],"our":[201],"framework":[202],"consistently":[203],"improves":[204],"MiMo-VL-7B,":[205],"Qwen3-VL-8B,":[206],"TimeLens-8B":[208],"three":[210],"VTG":[211],"benchmarks,":[212],"gains":[214],"up":[216],"+3.5":[218],"mIoU.":[219],"The":[220],"project":[221],"website":[222],"can":[223],"be":[224],"found":[225],"at":[226],"https://ddz16.github.io/mllmsknowwhen.github.io/.":[227]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-23T00:00:00"}
