{"id":"https://openalex.org/W7225146497","doi":"https://doi.org/10.5281/zenodo.23919658","title":"Where Should Elapsed Time Enter a Pretrained Model?","display_name":"Where Should Elapsed Time Enter a Pretrained Model?","publication_year":2026,"publication_date":"2026-11-03","ids":{"openalex":"https://openalex.org/W7225146497","doi":"https://doi.org/10.5281/zenodo.23919658"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.23919658","doi":"https://doi.org/10.5281/zenodo.23919658","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.23919658","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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"},"volume":null,"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.23919658","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139630631","display_name":"Aliasgar Khimani","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Khimani, Aliasgar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5139630631"],"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/T10028","display_name":"Topic Modeling","score":0.21570000052452087,"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/T10028","display_name":"Topic Modeling","score":0.21570000052452087,"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/T10906","display_name":"AI-based Problem Solving and Planning","score":0.1412000060081482,"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/T13904","display_name":"Artificial Intelligence Applications","score":0.07280000299215317,"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/elapsed-time","display_name":"elapsed time","score":0.7319999933242798},{"id":"https://openalex.org/keywords/llm-based-agents","display_name":"LLM agents","score":0.7310000061988831},{"id":"https://openalex.org/keywords/agent-memory","display_name":"agent memory","score":0.46799999475479126},{"id":"https://openalex.org/keywords/fine-tuning-pre-trained-models","display_name":"pre-trained model fine-tuning","score":0.6200000047683716},{"id":"https://openalex.org/keywords/lora-adapters","display_name":"LoRA adapters","score":0.8970000147819519}],"concepts":[{"id":"https://openalex.org/C113954288","wikidata":"https://www.wikidata.org/wiki/Q186885","display_name":"Timestamp","level":2,"score":0.6843000054359436},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6507999897003174},{"id":"https://openalex.org/C2779182362","wikidata":"https://www.wikidata.org/wiki/Q17126187","display_name":"Session (web analytics)","level":2,"score":0.5954999923706055},{"id":"https://openalex.org/C98184364","wikidata":"https://www.wikidata.org/wiki/Q1780131","display_name":"Argument (complex analysis)","level":2,"score":0.5429999828338623},{"id":"https://openalex.org/C177284502","wikidata":"https://www.wikidata.org/wiki/Q1005390","display_name":"Adapter (computing)","level":2,"score":0.4900999963283539},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46810001134872437},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.35830000042915344},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3560999929904938},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.3294000029563904},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.32359999418258667},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.31380000710487366},{"id":"https://openalex.org/C151637689","wikidata":"https://www.wikidata.org/wiki/Q5318064","display_name":"Dwell time","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.26759999990463257},{"id":"https://openalex.org/C2776141515","wikidata":"https://www.wikidata.org/wiki/Q1274479","display_name":"Repetition (rhetorical device)","level":2,"score":0.2551000118255615}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.5281/zenodo.23919658","doi":"https://doi.org/10.5281/zenodo.23919658","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.23919658","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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"},{"id":"doi:10.5281/zenodo.23919659","doi":"https://doi.org/10.5281/zenodo.23919659","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.23919659","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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"},{"id":"doi:10.5281/zenodo.23921544","doi":"https://doi.org/10.5281/zenodo.23921544","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.23921544","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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"}],"best_oa_location":{"id":"doi:10.5281/zenodo.23919658","doi":"https://doi.org/10.5281/zenodo.23919658","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.23919658","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7212312662","https://openalex.org/W7202213811","https://openalex.org/W4402670271","https://openalex.org/W7166803750","https://openalex.org/W7148299077","https://openalex.org/W4411443540","https://openalex.org/W4402671902","https://openalex.org/W4416865542","https://openalex.org/W7214909346","https://openalex.org/W7221161822"],"abstract_inverted_index":{"Large":[0],"language":[1],"model":[2,89],"(LLM)":[3],"agents":[4],"keep":[5],"a":[6,12,47,67,88,131,220,226],"record":[7],"of":[8,19,76,90,133,149,157,178],"what":[9],"happened":[10],"in":[11,142],"session":[13],"but":[14],"have":[15,109],"no":[16],"built-in":[17],"sense":[18],"how":[20],"long":[21],"ago":[22],"it":[23,175],"happened,":[24],"and":[25,70,86,136,152,172,188,235],"agent":[26,101,159],"frameworks":[27],"supply":[28],"time,":[29,185],"if":[30],"at":[31,244],"all,":[32],"as":[33,170],"timestamps":[34],"written":[35,60],"into":[36,54,61],"the":[37,52,62,77,83,91,104,146,179,199,211,236],"text.":[38],"This":[39],"paper":[40],"asks":[41],"where":[42],"elapsed":[43,168,200],"time":[44,169],"should":[45],"enter":[46],"pretrained":[48],"model.":[49],"It":[50],"sorts":[51],"options":[53],"three":[55],"levels:":[56],"text-timed":[57],"(time":[58,65,72],"is":[59,66,73,139,243],"tokens),":[63],"time-conditioned":[64],"separate":[68],"input)":[69],"state-level":[71],"an":[74],"argument":[75],"operators":[78],"that":[79,108,189,207],"update":[80],"or":[81],"read":[82],"model's":[84],"memory),":[85],"calls":[87],"third":[92],"kind":[93],"chronoaware.":[94],"A":[95],"measurement":[96],"study":[97],"on":[98,154,174,232],"one":[99,186],"user's":[100],"transcripts":[102],"motivates":[103],"question:":[105],"among":[106],"subagents":[107],"run":[110],"equally":[111],"long,":[112],"their":[113],"activity":[114],"predicts":[115],"which":[116],"will":[117],"finish":[118],"within":[119],"five":[120],"minutes":[121],"(AUC":[122],"0.101":[123],"above":[124],"chance,":[125],"95%":[126],"CI":[127],"[0.057,":[128],"0.162]).":[129],"Qwen3.5-2B,":[130],"hybrid":[132],"Gated":[134],"DeltaNet":[135],"attention":[137],"layers,":[138],"then":[140],"adapted":[141],"eight":[143],"ways":[144],"with":[145,161],"same":[147],"number":[148],"trainable":[150],"parameters":[151],"trained":[153,231],"four":[155,180],"kinds":[156],"time-dependent":[158],"decision,":[160],"pass/fail":[162],"criteria":[163],"fixed":[164],"before":[165],"training.":[166],"Writing":[167],"text":[171],"fine-tuning":[173],"did":[176],"best;":[177],"pre-specified":[181],"hypotheses":[182],"about":[183],"in-model":[184],"passes,":[187],"pass":[190],"does":[191,217],"not":[192,218],"show":[193,219],"better":[194],"extrapolation.":[195],"Exploratory":[196],"diagnostics":[197],"find":[198],"gap":[201],"linearly":[202],"decodable":[203],"from":[204],"most":[205],"models":[206],"receive":[208],"it,":[209],"including":[210],"shuffled-gap":[212],"control,":[213],"so":[214],"decodability":[215],"alone":[216],"learned":[221],"clock.":[222],"The":[223],"released":[224],"model,":[225],"LoRA":[227],"adapter":[228],"for":[229],"Qwen3.5-2B":[230],"synthetic":[233],"sessions":[234],"public":[237],"BPI":[238],"Challenge":[239],"2012":[240],"log":[241],"only,":[242],"https://huggingface.co/NovusEdge/sundial-qwen3.5-2b-T-int.":[245]},"counts_by_year":[],"updated_date":"2026-11-05T12:25:35.769452","created_date":"2026-11-04T00:00:00"}