{"id":"https://openalex.org/W7168388203","doi":"https://doi.org/10.48550/arxiv.2607.12893","title":"MemOps: Benchmarking Lifecycle Memory Operations in Long-Horizon Conversations","display_name":"MemOps: Benchmarking Lifecycle Memory Operations in Long-Horizon Conversations","publication_year":2026,"publication_date":"2026-07-14","ids":{"openalex":"https://openalex.org/W7168388203","doi":"https://doi.org/10.48550/arxiv.2607.12893"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.12893","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.12893","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":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.2607.12893","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5140827959","display_name":"Xixuan Hao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hao, Xixuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140886514","display_name":"Zeyu Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zeyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140800523","display_name":"Zehao Lin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Zehao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140905399","display_name":"Yihang Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Yihang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140889726","display_name":"Ziliang Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Ziliang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140831964","display_name":"Xichong Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Xichong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140917950","display_name":"Yuxuan Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Yuxuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140794978","display_name":"Feiyu Xiong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiong, Feiyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5140864321","display_name":"Zhiyu Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zhiyu","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/T10028","display_name":"Topic Modeling","score":0.6833000183105469,"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":0.6833000183105469,"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/T12031","display_name":"Speech and dialogue systems","score":0.057999998331069946,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.03229999914765358,"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/correctness","display_name":"Correctness","score":0.6657999753952026},{"id":"https://openalex.org/keywords/trace","display_name":"TRACE (psycholinguistics)","score":0.5839999914169312},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5776000022888184},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5166000127792358},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.5120000243186951},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.47450000047683716},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.42910000681877136},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.41130000352859497}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7882000207901001},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.6657999753952026},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.5839999914169312},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5776000022888184},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5166000127792358},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.5120000243186951},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.47450000047683716},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.42910000681877136},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.41130000352859497},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.39469999074935913},{"id":"https://openalex.org/C176649486","wikidata":"https://www.wikidata.org/wiki/Q2308807","display_name":"Memory management","level":3,"score":0.39250001311302185},{"id":"https://openalex.org/C12186640","wikidata":"https://www.wikidata.org/wiki/Q6815743","display_name":"Memory model","level":3,"score":0.390500009059906},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37369999289512634},{"id":"https://openalex.org/C118702147","wikidata":"https://www.wikidata.org/wiki/Q189396","display_name":"Dynamic random-access memory","level":3,"score":0.34610000252723694},{"id":"https://openalex.org/C119907115","wikidata":"https://www.wikidata.org/wiki/Q6815725","display_name":"Memory errors","level":3,"score":0.34380000829696655},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.3102000057697296},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.29589998722076416},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27869999408721924},{"id":"https://openalex.org/C82687282","wikidata":"https://www.wikidata.org/wiki/Q66221","display_name":"Auxiliary memory","level":2,"score":0.2621999979019165},{"id":"https://openalex.org/C88576662","wikidata":"https://www.wikidata.org/wiki/Q18646","display_name":"Episodic memory","level":3,"score":0.2517000138759613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.12893","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.12893","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":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.2607.12893","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.12893","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":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":{"Long-term":[0],"memory":[1,22,45,86,98,128,138,231],"has":[2],"become":[3],"a":[4,34,53,69,72,101,107,123,130,141],"foundational":[5],"capability":[6],"for":[7],"LLM-based":[8],"agents":[9],"that":[10,125,195,201],"accompany":[11],"users":[12],"across":[13],"extended,":[14],"multi-session":[15],"interactions.":[16],"Existing":[17],"benchmarks,":[18],"however,":[19],"evaluate":[20],"such":[21,47],"almost":[23],"exclusively":[24],"through":[25],"downstream":[26],"question":[27],"answering,":[28],"scoring":[29,235],"only":[30],"the":[31,41,50,60],"correctness":[32],"of":[33,44,52,104,109,132,174],"final":[35],"answer.":[36],"This":[37],"black-box":[38],"formulation":[39],"conflates":[40],"heterogeneous":[42],"causes":[43],"failure,":[46],"as":[48,129],"missing":[49],"introduction":[51],"relevant":[54],"fact,":[55],"binding":[56],"an":[57],"operation":[58,168],"to":[59],"wrong":[61],"target,":[62,147],"or":[63,84],"relying":[64],"on":[65,82],"stale":[66],"values":[67],"after":[68],"correction.":[70],"As":[71],"result,":[73],"it":[74],"can":[75],"credit":[76],"correct":[77],"answers":[78],"despite":[79],"their":[80,118],"reliance":[81],"inconsistent":[83],"unsafe":[85],"states.":[87],"In":[88],"this":[89],"paper,":[90],"we":[91],"argue":[92],"that,":[93],"in":[94],"dynamic":[95],"long-horizon":[96],"interactions,":[97],"is":[99],"not":[100],"static":[102],"collection":[103],"facts":[105],"but":[106],"lifecycle":[108,133],"explicit":[110],"operations,":[111],"including":[112],"remembering,":[113],"forgetting,":[114],"updating,":[115],"reflecting,":[116],"and":[117,135,151,165,181,188,216],"compositions.":[119],"We":[120],"introduce":[121],"MemOps,":[122],"benchmark":[124],"reformulates":[126],"conversational":[127],"sequence":[131],"operations":[134,160],"represents":[136],"each":[137],"event":[139],"with":[140,171],"structured":[142],"trace":[143],"specifying":[144],"its":[145],"trigger,":[146],"scope,":[148],"state":[149],"transition,":[150],"supporting":[152],"evidence.":[153],"A":[154],"controllable":[155],"generation":[156],"pipeline":[157],"embeds":[158],"these":[159],"into":[161],"long,":[162],"task-oriented":[163],"conversations":[164],"produces":[166],"gold":[167],"traces":[169],"together":[170],"six":[172],"categories":[173],"operation-level":[175,238],"probes,":[176],"evaluated":[177],"under":[178],"both":[179],"adjacent-evidence":[180],"long-context":[182,217],"settings.":[183],"Across":[184],"long-context,":[185],"retrieval-based,":[186],"parametric":[187],"managed-memory":[189],"systems,":[190],"MemOps":[191],"disentangles":[192],"failure":[193],"modes":[194],"final-answer":[196,234],"accuracy":[197],"alone":[198],"conceals,":[199],"revealing":[200],"current":[202],"systems":[203],"remain":[204,219],"far":[205],"from":[206,233],"uniformly":[207],"reliable.":[208],"For":[209],"instance,":[210],"session-level":[211],"retrieval":[212],"outperforms":[213],"turn-level":[214],"retrieval,":[215],"models":[218],"notably":[220],"weak":[221],"at":[222],"reconstructing":[223],"ordered":[224],"memory-state":[225],"trajectories.":[226],"These":[227],"results":[228],"move":[229],"long-term":[230],"evaluation":[232],"toward":[236],"interpretable,":[237],"diagnosis.":[239]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-16T00:00:00"}
