{"id":"https://openalex.org/W7161021885","doi":"https://doi.org/10.48550/arxiv.2605.12131","title":"Rollout Cards: A Reproducibility Standard for Agent Research","display_name":"Rollout Cards: A Reproducibility Standard for Agent Research","publication_year":2026,"publication_date":"2026-05-12","ids":{"openalex":"https://openalex.org/W7161021885","doi":"https://doi.org/10.48550/arxiv.2605.12131"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.12131","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.12131","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.2605.12131","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5121024481","display_name":"Charlie Masters","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Masters, Charlie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136037854","display_name":"Ziyuan Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Ziyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5073739308","display_name":"Stefano V. Albrecht","orcid":"https://orcid.org/0000-0002-8735-1465"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Albrecht, Stefano V.","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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.1868000030517578,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.1868000030517578,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T11986","display_name":"Scientific Computing and Data Management","score":0.18140000104904175,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.15520000457763672,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/headline","display_name":"Headline","score":0.6037999987602234},{"id":"https://openalex.org/keywords/audit","display_name":"Audit","score":0.5867999792098999},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5302000045776367},{"id":"https://openalex.org/keywords/reproducibility","display_name":"Reproducibility","score":0.5151000022888184},{"id":"https://openalex.org/keywords/publication","display_name":"Publication","score":0.4943999946117401},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.4602000117301941},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.33959999680519104}],"concepts":[{"id":"https://openalex.org/C2778689934","wikidata":"https://www.wikidata.org/wiki/Q1313396","display_name":"Headline","level":2,"score":0.6037999987602234},{"id":"https://openalex.org/C199521495","wikidata":"https://www.wikidata.org/wiki/Q181487","display_name":"Audit","level":2,"score":0.5867999792098999},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5458999872207642},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5302000045776367},{"id":"https://openalex.org/C9893847","wikidata":"https://www.wikidata.org/wiki/Q1425625","display_name":"Reproducibility","level":2,"score":0.5151000022888184},{"id":"https://openalex.org/C41458344","wikidata":"https://www.wikidata.org/wiki/Q732577","display_name":"Publication","level":2,"score":0.4943999946117401},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.4602000117301941},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36959999799728394},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36890000104904175},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.33959999680519104},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.33739998936653137},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.33660000562667847},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.31940001249313354},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.3034999966621399},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30079999566078186},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.2883000075817108},{"id":"https://openalex.org/C34585555","wikidata":"https://www.wikidata.org/wiki/Q1368723","display_name":"Learning curve","level":2,"score":0.2824000120162964},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.2770000100135803},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.26649999618530273},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C184356942","wikidata":"https://www.wikidata.org/wiki/Q830382","display_name":"Best practice","level":2,"score":0.2574000060558319}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.12131","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.12131","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.2605.12131","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.12131","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":[{"score":0.43149539828300476,"id":"https://metadata.un.org/sdg/5","display_name":"Gender equality"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Reproducibility":[0],"problems":[1],"that":[2,74,131,191],"have":[3],"long":[4],"affected":[5],"machine":[6],"learning":[7,10,226],"and":[8,69,136,142,167,187,228,246],"reinforcement":[9,225],"are":[11],"now":[12],"surfacing":[13],"in":[14,152,160,207],"agent":[15,123],"research:":[16],"papers":[17],"compare":[18],"systems":[19],"by":[20,201],"reported":[21,46,115,146,199],"scores":[22,30,47,200],"while":[23],"leaving":[24],"the":[25,40,118,133,138,194],"rollout":[26,55,112,127,134,150],"records":[27],"behind":[28,145],"those":[29],"difficult":[31],"to":[32,248],"inspect.":[33],"For":[34],"agentic":[35],"tasks,":[36],"this":[37],"matters":[38],"because":[39],"same":[41],"behaviour":[42],"can":[43,96,197],"receive":[44],"different":[45,51,58],"when":[48],"evaluations":[49],"select":[50],"parts":[52],"of":[53,65,120,212],"a":[54,62,217],"or":[56,82,102],"apply":[57],"reporting":[59,94,140,195],"rules.":[60],"In":[61],"structured":[63],"audit":[64],"50":[66],"popular":[67],"training":[68],"evaluation":[70],"repositories,":[71],"we":[72],"find":[73],"none":[75],"report":[76],"how":[77],"many":[78],"runs":[79],"failed,":[80],"errored,":[81],"were":[83],"skipped":[84],"alongside":[85],"headline":[86],"scores.":[87,147],"We":[88,110,125,148,215],"also":[89],"document":[90],"37":[91],"cases":[92],"where":[93],"rules":[95],"change":[97,198],"task-success":[98],"rates,":[99],"cost/token":[100],"accounting,":[101],"timing":[103],"measurements":[104],"for":[105,122,234],"fixed":[106],"evidence,":[107],"sometimes":[108],"dramatically.":[109],"treat":[111],"records,":[113],"not":[114,177],"scores,":[116],"as":[117],"unit":[119],"reproducibility":[121],"research.":[124,251],"introduce":[126],"cards:":[128],"publication":[129],"bundles":[130],"preserve":[132],"record":[135],"declare":[137],"views,":[139],"rules,":[141],"drops":[143],"manifests":[144],"validate":[149],"cards":[151],"two":[153],"settings.":[154],"First,":[155],"four":[156],"partial":[157],"public":[158],"releases":[159],"tool":[161,237],"safety,":[162,245],"multi-agent":[163,243],"systems,":[164],"theorem":[165],"proving,":[166],"search":[168,247],"let":[169],"us":[170],"compute":[171],"analyses":[172],"their":[173],"original":[174],"reports":[175],"did":[176],"include.":[178],"Second,":[179],"re-grading":[180],"preserved":[181],"benchmark":[182],"outputs":[183],"across":[184],"short-answer,":[185],"code-generation,":[186],"tool-use":[188],"tasks":[189],"shows":[190],"changing":[192],"only":[193],"rule":[196],"20.9":[202],"absolute":[203],"percentage":[204],"points":[205],"and,":[206],"some":[208],"cases,":[209],"invert":[210],"rankings":[211],"frontier":[213],"models.":[214],"release":[216],"reference":[218],"implementation":[219],"integrated":[220],"into":[221],"Ergon,":[222],"an":[223],"open-source":[224],"gym,":[227],"publicly":[229],"publish":[230],"Ergon-produced":[231],"rollout-card":[232],"exports":[233],"benchmarks":[235],"spanning":[236],"use,":[238],"software":[239],"engineering,":[240],"web":[241],"interaction,":[242],"coordination,":[244],"support":[249],"future":[250]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-14T00:00:00"}