{"id":"https://openalex.org/W7167887005","doi":"https://doi.org/10.48550/arxiv.2607.08716","title":"Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents","display_name":"Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents","publication_year":2026,"publication_date":"2026-07-09","ids":{"openalex":"https://openalex.org/W7167887005","doi":"https://doi.org/10.48550/arxiv.2607.08716"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.08716","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.08716","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.2607.08716","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5140410825","display_name":"Yifan Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Yifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140385965","display_name":"Lizhu Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Lizhu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140389211","display_name":"Yuhang Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Yuhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078914856","display_name":"M Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Mingyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140431210","display_name":"Bo Peng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng, Bo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140438047","display_name":"Serena Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Serena","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140422497","display_name":"Xiangjun Fan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fan, Xiangjun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5140451393","display_name":"Zhuokai Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Zhuokai","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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.2574999928474426,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.2574999928474426,"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.06589999794960022,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12607","display_name":"Personal Information Management and User Behavior","score":0.05130000039935112,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.6205000281333923},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6164000034332275},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6123999953269958},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.4652999937534332},{"id":"https://openalex.org/keywords/intervention","display_name":"Intervention (counseling)","score":0.4578999876976013},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.39590001106262207},{"id":"https://openalex.org/keywords/task-analysis","display_name":"Task analysis","score":0.33559998869895935}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6452999711036682},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.6205000281333923},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6164000034332275},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6123999953269958},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.4652999937534332},{"id":"https://openalex.org/C2780665704","wikidata":"https://www.wikidata.org/wiki/Q959298","display_name":"Intervention (counseling)","level":2,"score":0.4578999876976013},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.39590001106262207},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.3903999924659729},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.33559998869895935},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3319999873638153},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32339999079704285},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.31049999594688416},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2964000105857849},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.27469998598098755},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.2578999996185303},{"id":"https://openalex.org/C39628806","wikidata":"https://www.wikidata.org/wiki/Q916150","display_name":"Prospective memory","level":3,"score":0.25290000438690186}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.08716","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.08716","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.2607.08716","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.08716","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":0.8224018812179565,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"long-horizon":[1],"tasks,":[2],"decision-relevant":[3],"state":[4,57],"is":[5,103],"often":[6],"scattered":[7],"across":[8],"an":[9,63,77,157],"expanding":[10],"trajectory,":[11],"while":[12],"the":[13,38,87],"action":[14,79,107,126],"agent":[15,74,111],"must":[16],"surface":[17],"it":[18,118],"and":[19,31,90,109,116,124,135,153,171,176],"act.":[20],"As":[21,156],"trajectories":[22],"grow,":[23],"task":[24],"requirements,":[25],"environment":[26],"facts,":[27],"prior":[28],"attempts,":[29],"diagnoses,":[30],"open":[32],"subgoals":[33],"can":[34],"be":[35],"buried":[36],"in":[37],"context":[39],"window":[40],"or":[41,98],"pushed":[42],"beyond":[43],"it,":[44],"failing":[45],"to":[46,93,180],"influence":[47],"decisions":[48],"when":[49],"needed.":[50],"We":[51,59],"call":[52],"this":[53],"failure":[54],"mode":[55],"\"behavioral":[56],"decay\".":[58],"study":[60],"memory":[61,73,84,162],"as":[62],"active":[64],"intervention":[65,144],"mechanism":[66],"rather":[67],"than":[68],"passive":[69,146],"retrieval.":[70,155],"A":[71],"separate":[72],"runs":[75],"alongside":[76],"unmodified":[78],"agent,":[80],"updating":[81],"a":[82,95],"structured":[83],"bank":[85,147],"from":[86],"recent":[88],"trajectory":[89],"deciding":[91],"whether":[92],"inject":[94],"memory-grounded":[96],"reminder":[97],"remain":[99],"silent.":[100],"The":[101],"module":[102],"plug-and-play":[104],"with":[105,128],"frontier":[106],"agents":[108],"existing":[110],"harnesses.":[112],"Across":[113],"Terminal-Bench":[114,134],"2.0":[115],"$\u03c4^2$-Bench,":[117],"improves":[119],"pass@1":[120],"for":[121],"both":[122],"weaker":[123],"stronger":[125],"agents,":[127],"gains":[129],"of":[130],"+8.3":[131],"pp":[132,137],"on":[133,138,167],"+6.8":[136],"$\u03c4^2$-Bench.":[139],"Ablations":[140],"show":[141],"that":[142],"selective":[143],"outperforms":[145],"exposure,":[148],"always-on":[149],"injection,":[150],"advisor-only":[151],"guidance,":[152],"general":[154],"early":[158],"step":[159],"toward":[160],"open-weight":[161],"policies,":[163],"we":[164],"train":[165],"Qwen3.5-27B":[166],"SETA":[168],"using":[169],"SFT":[170],"GRPO,":[172],"improving":[173],"validation":[174],"reward":[175],"achieving":[177],"partial":[178],"transfer":[179],"Terminal-Bench.":[181]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-11T00:00:00"}