{"id":"https://openalex.org/W7224856014","doi":"https://doi.org/10.48550/arxiv.2610.40350","title":"Don't Lose the Thread: Empowering Long-Horizon LLM Agents with Cognitive Resource Self-Allocation","display_name":"Don't Lose the Thread: Empowering Long-Horizon LLM Agents with Cognitive Resource Self-Allocation","publication_year":2026,"publication_date":"2026-10-29","ids":{"openalex":"https://openalex.org/W7224856014","doi":"https://doi.org/10.48550/arxiv.2610.40350"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2610.40350","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.40350","pdf_url":"https://arxiv.org/pdf/2610.40350","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2610.40350","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5160156558","display_name":"Zheng Zhu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Zhu, Zheng","raw_affiliation_strings":["The Chinese University of Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5160212770","display_name":"Xiaozhuan Liang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang, Xiaozhuan","raw_affiliation_strings":["Tencent"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5160231351","display_name":"Xi Chen","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen, Xi","raw_affiliation_strings":["Tencent"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5160169102","display_name":"Bei Yu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Yu, Bei","raw_affiliation_strings":["The Chinese University of Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5158734755","display_name":"Jiaya Jia","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Jia, Jiaya","raw_affiliation_strings":["The Hong Kong University of Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Hong Kong University of Science and Technology","institution_ids":["https://openalex.org/I200769079"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T10906","display_name":"AI-based Problem Solving and Planning","score":0.7842000126838684,"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/T10906","display_name":"AI-based Problem Solving and Planning","score":0.7842000126838684,"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/T10028","display_name":"Topic Modeling","score":0.0794999971985817,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.039400000125169754,"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/llm-based-agents","display_name":"LLM agents","score":0.7639999985694885},{"id":"https://openalex.org/keywords/long-horizon-reasoning","display_name":"long-horizon reasoning","score":0.8759999871253967},{"id":"https://openalex.org/keywords/inference-cost-reduction","display_name":"inference cost reduction","score":0.6299999952316284},{"id":"https://openalex.org/keywords/react-agents","display_name":"ReAct agents","score":0.49900001287460327}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6777999997138977},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.6381999850273132},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.635200023651123},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6190999746322632},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5565000176429749},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.5113000273704529},{"id":"https://openalex.org/C21963081","wikidata":"https://www.wikidata.org/wiki/Q11337567","display_name":"Working memory","level":3,"score":0.4629000127315521},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.42309999465942383},{"id":"https://openalex.org/C161407221","wikidata":"https://www.wikidata.org/wiki/Q4382939","display_name":"Cognitive model","level":3,"score":0.38760000467300415},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.367000013589859},{"id":"https://openalex.org/C29202148","wikidata":"https://www.wikidata.org/wiki/Q287260","display_name":"Resource allocation","level":2,"score":0.3626999855041504},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35670000314712524},{"id":"https://openalex.org/C150420422","wikidata":"https://www.wikidata.org/wiki/Q5141238","display_name":"Cognitive resource theory","level":3,"score":0.34279999136924744},{"id":"https://openalex.org/C2780609101","wikidata":"https://www.wikidata.org/wiki/Q17156588","display_name":"Resource management (computing)","level":2,"score":0.32499998807907104},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.32199999690055847},{"id":"https://openalex.org/C20854674","wikidata":"https://www.wikidata.org/wiki/Q4386060","display_name":"Cognitive architecture","level":3,"score":0.3160000145435333},{"id":"https://openalex.org/C61641136","wikidata":"https://www.wikidata.org/wiki/Q1107019","display_name":"Cognitive load","level":3,"score":0.2955000102519989},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.2944999933242798},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.29249998927116394},{"id":"https://openalex.org/C195094911","wikidata":"https://www.wikidata.org/wiki/Q14167904","display_name":"Process management","level":1,"score":0.2906999886035919},{"id":"https://openalex.org/C3017813396","wikidata":"https://www.wikidata.org/wiki/Q17078173","display_name":"Resource constraints","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C188147891","wikidata":"https://www.wikidata.org/wiki/Q147638","display_name":"Cognitive science","level":1,"score":0.25699999928474426},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.25189998745918274},{"id":"https://openalex.org/C170494330","wikidata":"https://www.wikidata.org/wiki/Q1778434","display_name":"Cognitive map","level":3,"score":0.25110000371932983}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2610.40350","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.40350","pdf_url":"https://arxiv.org/pdf/2610.40350","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2610.40350","doi":"https://doi.org/10.48550/arxiv.2610.40350","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2610.40350","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,"listed_in":[],"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2610.40350","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.40350","pdf_url":"https://arxiv.org/pdf/2610.40350","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7224856014.pdf","grobid_xml":"https://content.openalex.org/works/W7224856014.grobid-xml"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W3115947671","https://openalex.org/W4304195432","https://openalex.org/W4391620956","https://openalex.org/W4391631327","https://openalex.org/W4414281281","https://openalex.org/W4414691912","https://openalex.org/W4415067413","https://openalex.org/W4415189231","https://openalex.org/W4415276218","https://openalex.org/W4416036398","https://openalex.org/W4416036988","https://openalex.org/W4416037139","https://openalex.org/W4416048747","https://openalex.org/W4416453547","https://openalex.org/W4416854066","https://openalex.org/W4417069093","https://openalex.org/W4417089504","https://openalex.org/W4417093501","https://openalex.org/W4417095241","https://openalex.org/W4417136850","https://openalex.org/W4417150317","https://openalex.org/W4417422257","https://openalex.org/W4417440832","https://openalex.org/W4417485409","https://openalex.org/W4417511763","https://openalex.org/W4417525709","https://openalex.org/W6891936057","https://openalex.org/W6910528035","https://openalex.org/W6947947872","https://openalex.org/W6967397561","https://openalex.org/W7196953391"],"related_works":["https://openalex.org/W7154426196","https://openalex.org/W7203718731","https://openalex.org/W7164182171","https://openalex.org/W7164370897","https://openalex.org/W4417116171","https://openalex.org/W7170173822","https://openalex.org/W7118560772","https://openalex.org/W7124169916","https://openalex.org/W7166798138","https://openalex.org/W4417095241"],"abstract_inverted_index":{"Agents":[0],"powered":[1],"by":[2,98,201],"large":[3],"language":[4],"models":[5],"(LLMs)":[6],"have":[7],"demonstrated":[8],"remarkable":[9],"progress":[10,87],"in":[11],"solving":[12],"complex":[13],"reasoning":[14,61,112],"tasks.":[15],"However,":[16],"LLM":[17],"agents":[18,65],"often":[19],"falter":[20],"on":[21,149],"long-horizon":[22,153],"tasks":[23,154],"due":[24],"to":[25,66,83,110,183,189],"cognitive":[26],"overload,":[27],"as":[28,72],"their":[29,41,69],"working":[30,75],"memory":[31,76],"becomes":[32],"cluttered":[33,100],"with":[34,137,145],"expanding":[35],"and":[36,43,47,88,91,157,188],"irrelevant":[37],"information,":[38,107],"which":[39],"dilutes":[40],"attention":[42],"hinders":[44],"effective":[45],"planning":[46],"reasoning.":[48],"To":[49],"mitigate":[50],"this":[51],"challenge,":[52],"we":[53],"introduce":[54],"Cognitive":[55],"Resource":[56],"Self-Allocation":[57],"(CORAL),":[58],"a":[59,94,114,125,165,177,197],"novel":[60],"paradigm":[62],"that":[63],"empowers":[64],"proactively":[67],"optimize":[68],"context.":[70],"Implemented":[71],"an":[73,81,138],"agent-callable":[74],"management":[77],"toolset,":[78],"CORAL":[79,136,170],"allows":[80],"agent":[82,200],"checkpoint":[84],"its":[85],"task":[86],"verified":[89],"facts,":[90],"adaptively":[92],"initiate":[93],"new":[95],"problem-solving":[96],"episode":[97],"resetting":[99],"context":[101],"while":[102],"preserving":[103],"only":[104],"the":[105,120,133,150,190],"checkpointed":[106],"enabling":[108],"it":[109,195],"resume":[111],"from":[113],"clean":[115],"state.":[116],"We":[117],"further":[118],"enhance":[119],"agent's":[121],"checkpointing":[122],"capabilities":[123],"using":[124],"Multi-episode":[126],"Agentic":[127],"Reinforced":[128],"Policy":[129],"Optimization":[130],"algorithm.":[131],"On":[132],"GAIA":[134],"benchmark,":[135],"8B":[139],"model":[140],"achieves":[141],"42.8%":[142],"average":[143],"accuracy,":[144],"particularly":[146],"strong":[147,198],"gains":[148,162],"most":[151],"challenging":[152],"(Level":[155],"2":[156],"Level":[158],"3).":[159],"Crucially,":[160],"these":[161],"come":[163],"at":[164],"substantially":[166],"lower":[167],"inference":[168],"cost:":[169],"consumes":[171],"6-7$\\times$":[172],"fewer":[173],"input":[174],"tokens":[175],"than":[176],"ReAct":[178],"agent.":[179],"It":[180],"also":[181],"generalizes":[182],"multi-hop":[184],"Knowledge":[185],"QA":[186],"benchmarks":[187],"considerably":[191],"harder":[192],"BrowseComp,":[193],"where":[194],"improves":[196],"proprietary":[199],"8.0":[202],"points.":[203]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-10-31T00:00:00"}