{"id":"https://openalex.org/W7160375610","doi":"https://doi.org/10.48550/arxiv.2605.03312","title":"MemFlow: Intent-Driven Memory Orchestration for Small Language Model Agents","display_name":"MemFlow: Intent-Driven Memory Orchestration for Small Language Model Agents","publication_year":2026,"publication_date":"2026-05-05","ids":{"openalex":"https://openalex.org/W7160375610","doi":"https://doi.org/10.48550/arxiv.2605.03312"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.03312","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03312","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.2605.03312","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135530695","display_name":"Jiayi Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Jiayi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135525869","display_name":"Yingcong Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yingcong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135466398","display_name":"Guiling Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Guiling","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":0.301800012588501,"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":0.301800012588501,"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":0.22669999301433563,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.21449999511241913,"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/security-token","display_name":"Security token","score":0.652899980545044},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6324999928474426},{"id":"https://openalex.org/keywords/orchestration","display_name":"Orchestration","score":0.48240000009536743},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.4242999851703644},{"id":"https://openalex.org/keywords/memory-model","display_name":"Memory model","score":0.40630000829696655},{"id":"https://openalex.org/keywords/context-dependent-memory","display_name":"Context-dependent memory","score":0.3393999934196472},{"id":"https://openalex.org/keywords/router","display_name":"Router","score":0.3328999876976013}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8391000032424927},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.652899980545044},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6324999928474426},{"id":"https://openalex.org/C199168358","wikidata":"https://www.wikidata.org/wiki/Q3367000","display_name":"Orchestration","level":3,"score":0.48240000009536743},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4242999851703644},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4122999906539917},{"id":"https://openalex.org/C12186640","wikidata":"https://www.wikidata.org/wiki/Q6815743","display_name":"Memory model","level":3,"score":0.40630000829696655},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.364300012588501},{"id":"https://openalex.org/C76679254","wikidata":"https://www.wikidata.org/wiki/Q5165163","display_name":"Context-dependent memory","level":4,"score":0.3393999934196472},{"id":"https://openalex.org/C2775896111","wikidata":"https://www.wikidata.org/wiki/Q642560","display_name":"Router","level":2,"score":0.3328999876976013},{"id":"https://openalex.org/C176649486","wikidata":"https://www.wikidata.org/wiki/Q2308807","display_name":"Memory management","level":3,"score":0.31520000100135803},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3140000104904175},{"id":"https://openalex.org/C120039088","wikidata":"https://www.wikidata.org/wiki/Q6875176","display_name":"Misattribution of memory","level":3,"score":0.31299999356269836},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.28780001401901245},{"id":"https://openalex.org/C82687282","wikidata":"https://www.wikidata.org/wiki/Q66221","display_name":"Auxiliary memory","level":2,"score":0.2800000011920929},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.2685000002384186},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.2662000060081482},{"id":"https://openalex.org/C53833338","wikidata":"https://www.wikidata.org/wiki/Q1061424","display_name":"Context switch","level":2,"score":0.2612999975681305},{"id":"https://openalex.org/C35292069","wikidata":"https://www.wikidata.org/wiki/Q1575458","display_name":"Validator","level":2,"score":0.2563000023365021}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.03312","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03312","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.2605.03312","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03312","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Modern":[0],"language":[1],"agents":[2,12],"must":[3],"operate":[4],"over":[5,204],"long-horizon,":[6],"multi-turn":[7],"histories,":[8],"yet":[9],"deploying":[10],"such":[11],"with":[13,152],"Small":[14],"Language":[15],"Models":[16],"(SLMs)":[17],"remains":[18],"fundamentally":[19],"difficult.":[20],"Full-context":[21],"prompting":[22],"causes":[23],"context":[24,70,180],"overflow,":[25],"flat":[26],"retrieval":[27,65],"exposes":[28],"the":[29,93,107,126,158,164,178],"model":[30],"to":[31,106],"noisy":[32],"evidence,":[33],"and":[34,69,103,124,146,173,195,215],"open-ended":[35,78],"agentic":[36],"loops":[37,175],"are":[38],"unreliable":[39],"under":[40,129],"limited":[41],"reasoning":[42,174],"capacity.":[43],"We":[44,80],"argue":[45],"that":[46,72,88,211],"a":[47,83,130,140,147,153,184],"substantial":[48],"portion":[49],"of":[50,113],"SLM":[51,206],"memory":[52,57,85,90,155,190],"failure":[53],"arises":[54],"from":[55,92,142],"mismatched":[56],"operations:":[58],"different":[59,64],"query":[60,100],"types":[61],"demand":[62],"categorically":[63],"strategies,":[66],"evidence":[67,128,217],"transformations,":[68],"budgets":[71],"SLMs":[73],"cannot":[74],"reliably":[75],"self-orchestrate":[76],"through":[77],"reasoning.":[79],"introduce":[81],"MemFlow,":[82],"training-free":[84],"orchestration":[86],"framework":[87],"externalizes":[89],"planning":[91],"SLM.":[94],"A":[95],"Router":[96],"Agent":[97,137,149],"classifies":[98],"each":[99],"by":[101,163,201],"intent":[102,213],"dispatches":[104],"it":[105],"Memory":[108],"Agent,":[109],"which":[110],"executes":[111],"one":[112],"three":[114],"specialized":[115],"tiers":[116],"(Profile":[117],"Lookup,":[118],"Targeted":[119],"Retrieval,":[120],"or":[121],"Deep":[122],"Reasoning)":[123],"assembles":[125],"resulting":[127],"dynamic,":[131],"tier-aware":[132],"token":[133],"budget.":[134],"An":[135],"Answer":[136],"then":[138],"generates":[139],"response":[141,159],"this":[143],"compact":[144],"context,":[145],"Validator":[148],"optionally":[150],"retries":[151],"heavier":[154],"tier":[156],"when":[157],"is":[160],"not":[161],"supported":[162],"provided":[165],"evidence.":[166],"This":[167],"route-then-compile":[168],"design":[169],"avoids":[170],"tool-selection":[171],"hallucination":[172],"while":[176],"keeping":[177],"answer":[179],"compact.":[181],"Evaluated":[182],"on":[183],"frozen":[185],"Qwen3-1.7B":[186],"backbone":[187],"across":[188],"long-horizon":[189,228],"benchmarks":[191],"-":[192,197],"LongMemEval,":[193],"LoCoMo,":[194],"LongBench":[196],"MemFlow":[198],"improves":[199],"accuracy":[200],"nearly":[202],"2x":[203],"full-context":[205],"baselines.":[207],"These":[208],"results":[209],"suggest":[210],"structured":[212],"routing":[214],"deterministic":[216],"preparation":[218],"can":[219],"make":[220],"limited-capacity":[221],"models":[222],"substantially":[223],"more":[224],"effective":[225],"in":[226],"resource-constrained":[227],"agents.":[229]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-07T00:00:00"}