{"id":"https://openalex.org/W7161800560","doi":"https://doi.org/10.48550/arxiv.2605.19976","title":"RECIPE: Procedural Planning via Grounding in Instructional Video","display_name":"RECIPE: Procedural Planning via Grounding in Instructional Video","publication_year":2026,"publication_date":"2026-05-19","ids":{"openalex":"https://openalex.org/W7161800560","doi":"https://doi.org/10.48550/arxiv.2605.19976"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.19976","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.19976","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.2605.19976","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5044915756","display_name":"Luigi Seminara","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Seminara, Luigi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135384856","display_name":"Antonino Furnari","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Furnari, Antonino","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5082736347","display_name":"Lorenzo Torresani","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Torresani, Lorenzo","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":0.8245999813079834,"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":0.8245999813079834,"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":0.02329999953508377,"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":0.01759999990463257,"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/task","display_name":"Task (project management)","score":0.6593000292778015},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6359999775886536},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.6008999943733215},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5465999841690063},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4925000071525574},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4805999994277954},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.4609000086784363},{"id":"https://openalex.org/keywords/structured-prediction","display_name":"Structured prediction","score":0.43149998784065247},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.39969998598098755},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.3919999897480011}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8521999716758728},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6593000292778015},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6359999775886536},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.6008999943733215},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5760999917984009},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5465999841690063},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4925000071525574},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4805999994277954},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4625000059604645},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4609000086784363},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.43149998784065247},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.39969998598098755},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39399999380111694},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.3919999897480011},{"id":"https://openalex.org/C2776999362","wikidata":"https://www.wikidata.org/wiki/Q2349274","display_name":"Planner","level":2,"score":0.38600000739097595},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.36890000104904175},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.3531999886035919},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.33550000190734863},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.31220000982284546},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.3052999973297119},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.302700012922287},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.29499998688697815},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.2858000099658966},{"id":"https://openalex.org/C2776187449","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Natural language generation","level":3,"score":0.2777999937534332},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.2718000113964081},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.2655999958515167},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.26499998569488525},{"id":"https://openalex.org/C111370547","wikidata":"https://www.wikidata.org/wiki/Q7451120","display_name":"Sensory cue","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.2630000114440918},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2615000009536743},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2612000107765198}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.19976","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.19976","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.2605.19976","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.19976","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":{"Visual":[0,268],"planning":[1],"asks":[2],"a":[3,11,17,22,40,83,98,131,140,144,158,163,186,253],"model":[4],"to":[5,112,151,173,216,222],"generate":[6],"the":[7,136,199,239,249,260,277],"remaining":[8],"steps":[9],"of":[10,58,114,214,252],"procedure":[12],"in":[13,105,124],"natural":[14],"language":[15],"given":[16],"partial":[18],"video":[19,54,92,169],"context":[20],"and":[21,38,75,78,110,166,172,175,208,220,233,241,272],"goal.":[23],"Progress":[24],"on":[25,66,181,230,267,273],"this":[26,122],"task":[27],"is":[28,93,102,108],"bottlenecked":[29],"by":[30,162],"annotation:":[31],"clean":[32,87],"labeled":[33],"datasets":[34],"are":[35],"small,":[36],"domain-narrow,":[37],"encode":[39],"single":[41],"execution":[42],"trajectory":[43],"per":[44],"example,":[45],"even":[46],"though":[47],"many":[48],"valid":[49],"orderings":[50],"exist.":[51],"Large-scale":[52],"instructional":[53],"corpora":[55],"offer":[56],"orders":[57],"magnitude":[59],"more":[60],"procedural":[61,183,194],"content,":[62],"but":[63,95],"supervised":[64,177,228],"fine-tuning":[65,229],"pseudo-labels":[67],"from":[68,90],"their":[69],"noisy":[70,91,137],"ASR":[71,106],"narrations":[72],"propagates":[73],"segmentation":[74],"alignment":[76],"errors":[77],"stays":[79],"single-trajectory.":[80],"We":[81,120,179],"identify":[82],"key":[84],"asymmetry:":[85],"extracting":[86],"step":[88,100],"labels":[89],"hard,":[94],"verifying":[96],"whether":[97],"generated":[99],"sequence":[101],"temporally":[103],"grounded":[104],"transcripts":[107],"cheap":[109],"scales":[111,204],"millions":[113],"videos":[115],"via":[116],"precomputed":[117],"text":[118],"embeddings.":[119],"exploit":[121],"asymmetry":[123],"RECIPE,":[125],"which":[126],"uses":[127],"grounding":[128],"quality":[129],"as":[130,248],"reward":[132],"for":[133,270],"GRPO,":[134],"turning":[135],"corpus":[138],"into":[139],"verifier":[141],"rather":[142],"than":[143],"label":[145],"source.":[146],"The":[147],"framework":[148],"applies":[149],"uniformly":[150],"two":[152],"planner":[153],"input":[154],"configurations":[155],"(Socratic,":[156],"with":[157,211],"textual":[159],"history":[160],"extracted":[161],"frozen":[164],"VLM,":[165],"Video,":[167],"consuming":[168],"tokens":[170],"directly)":[171],"annotated":[174,232],"weakly":[176],"regimes.":[178],"evaluate":[180],"7":[182],"benchmarks":[184],"using":[185],"reference-based":[187],"LLM-as-judge":[188],"protocol":[189],"scoring":[190],"plans":[191,235],"across":[192],"6":[193],"criteria.":[195],"RECIPE-RL":[196],"improves":[197,258],"over":[198,259],"base":[200],"checkpoint":[201],"at":[202,264],"all":[203],"(0.5B,":[205],"3B,":[206],"7B)":[207],"every":[209,265],"benchmark,":[210],"macro-accuracy":[212],"gains":[213],"+7":[215],"+8":[217],"points":[218,224],"in-domain":[219],"up":[221],"+16":[223],"zero-shot.":[225],"It":[226],"outperforms":[227],"both":[231],"pseudo-labeled":[234],"(the":[236],"latter":[237],"degrades":[238],"base)":[240],"remains":[242],"robust":[243],"without":[244],"human":[245],"annotations.":[246],"Used":[247],"proposal":[250],"stage":[251],"prior":[254],"propose-assess-search":[255],"planner,":[256],"it":[257,275],"strongest":[261],"zero-shot":[262],"baseline":[263],"horizon":[266],"Planning":[269],"Assistance,":[271],"COIN":[274],"preserves":[276],"generation":[278],"diversity":[279],"that":[280],"SFT":[281],"collapses.":[282]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-21T00:00:00"}