{"id":"https://openalex.org/W7161012925","doi":"https://doi.org/10.48550/arxiv.2605.12213","title":"Goal-Oriented Reasoning for RAG-based Memory in Conversational Agentic LLM Systems","display_name":"Goal-Oriented Reasoning for RAG-based Memory in Conversational Agentic LLM Systems","publication_year":2026,"publication_date":"2026-05-12","ids":{"openalex":"https://openalex.org/W7161012925","doi":"https://doi.org/10.48550/arxiv.2605.12213"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.12213","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.12213","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.2605.12213","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5104197870","display_name":"Jiazhou Liang","orcid":"https://orcid.org/0009-0001-4278-8321"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Jiazhou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012568207","display_name":"Armin Toroghi","orcid":"https://orcid.org/0009-0007-8058-2088"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Toroghi, Armin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127900191","display_name":"Yifan Simon Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yifan Simon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044960802","display_name":"Faeze Moradi Kalarde","orcid":"https://orcid.org/0009-0001-2201-5604"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kalarde, Faeze Moradi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128033547","display_name":"Liam Gallagher","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gallagher, Liam","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136001815","display_name":"Scott Sanner","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sanner, Scott","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":0.6636999845504761,"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":0.6636999845504761,"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":0.09179999679327011,"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/T12031","display_name":"Speech and dialogue systems","score":0.08259999752044678,"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/question-answering","display_name":"Question answering","score":0.5893999934196472},{"id":"https://openalex.org/keywords/utterance","display_name":"Utterance","score":0.5181000232696533},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4796000123023987},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.4722000062465668},{"id":"https://openalex.org/keywords/backward-chaining","display_name":"Backward chaining","score":0.4708000123500824},{"id":"https://openalex.org/keywords/natural-language-understanding","display_name":"Natural language understanding","score":0.4564000070095062},{"id":"https://openalex.org/keywords/reasoning-system","display_name":"Reasoning system","score":0.4431999921798706},{"id":"https://openalex.org/keywords/forward-chaining","display_name":"Forward chaining","score":0.4323999881744385},{"id":"https://openalex.org/keywords/chaining","display_name":"Chaining","score":0.42480000853538513}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7595999836921692},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.5893999934196472},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5242999792098999},{"id":"https://openalex.org/C2775852435","wikidata":"https://www.wikidata.org/wiki/Q258403","display_name":"Utterance","level":2,"score":0.5181000232696533},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4796000123023987},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.4722000062465668},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4722000062465668},{"id":"https://openalex.org/C129916263","wikidata":"https://www.wikidata.org/wiki/Q1141183","display_name":"Backward chaining","level":4,"score":0.4708000123500824},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.4564000070095062},{"id":"https://openalex.org/C89288958","wikidata":"https://www.wikidata.org/wiki/Q7301504","display_name":"Reasoning system","level":2,"score":0.4431999921798706},{"id":"https://openalex.org/C142614401","wikidata":"https://www.wikidata.org/wiki/Q777433","display_name":"Forward chaining","level":3,"score":0.4323999881744385},{"id":"https://openalex.org/C49020025","wikidata":"https://www.wikidata.org/wiki/Q1059099","display_name":"Chaining","level":2,"score":0.42480000853538513},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.40070000290870667},{"id":"https://openalex.org/C2776608160","wikidata":"https://www.wikidata.org/wiki/Q4785462","display_name":"Natural (archaeology)","level":2,"score":0.3977000117301941},{"id":"https://openalex.org/C12186640","wikidata":"https://www.wikidata.org/wiki/Q6815743","display_name":"Memory model","level":3,"score":0.3831000030040741},{"id":"https://openalex.org/C197914299","wikidata":"https://www.wikidata.org/wiki/Q18650","display_name":"Semantic memory","level":3,"score":0.36970001459121704},{"id":"https://openalex.org/C97364631","wikidata":"https://www.wikidata.org/wiki/Q484284","display_name":"Deductive reasoning","level":2,"score":0.3675999939441681},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.36410000920295715},{"id":"https://openalex.org/C159032336","wikidata":"https://www.wikidata.org/wiki/Q2488768","display_name":"Non-monotonic logic","level":2,"score":0.3499999940395355},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.3386000096797943},{"id":"https://openalex.org/C2780876879","wikidata":"https://www.wikidata.org/wiki/Q3054749","display_name":"Meaning (existential)","level":2,"score":0.2976999878883362},{"id":"https://openalex.org/C188147891","wikidata":"https://www.wikidata.org/wiki/Q147638","display_name":"Cognitive science","level":1,"score":0.2973000109195709},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.28790000081062317},{"id":"https://openalex.org/C88576662","wikidata":"https://www.wikidata.org/wiki/Q18646","display_name":"Episodic memory","level":3,"score":0.2775000035762787},{"id":"https://openalex.org/C134752490","wikidata":"https://www.wikidata.org/wiki/Q374182","display_name":"Logical consequence","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C43971567","wikidata":"https://www.wikidata.org/wiki/Q3142865","display_name":"Logical reasoning","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C82687282","wikidata":"https://www.wikidata.org/wiki/Q66221","display_name":"Auxiliary memory","level":2,"score":0.2644999921321869},{"id":"https://openalex.org/C20162079","wikidata":"https://www.wikidata.org/wiki/Q1151406","display_name":"Case-based reasoning","level":2,"score":0.25540000200271606}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.12213","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.12213","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.2605.12213","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.12213","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":[{"display_name":"Quality Education","score":0.6041544079780579,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"LLM-based":[0],"conversational":[1],"AI":[2],"agents":[3],"struggle":[4],"to":[5,13,22,53,69,138,192],"maintain":[6],"coherent":[7],"behavior":[8],"over":[9,55],"long":[10],"horizons":[11],"due":[12],"limited":[14],"context.":[15],"While":[16],"RAG-based":[17,105],"approaches":[18],"are":[19],"increasingly":[20],"adopted":[21],"overcome":[23],"this":[24,95,160],"limitation":[25],"by":[26,176],"storing":[27],"interactions":[28],"in":[29,40,162],"external":[30],"memory":[31,64,107,136,148,195],"modules":[32],"and":[33,82,142,190,210],"performing":[34],"retrieval":[35,137],"from":[36,113,124,147],"them,":[37],"their":[38],"effectiveness":[39],"answering":[41],"challenging":[42],"questions":[43],"(e.g.,":[44],"multi-hop,":[45],"commonsense)":[46],"ultimately":[47],"depends":[48],"on":[49,66,187,205],"the":[50,56,70,114,171,179],"agent's":[51],"ability":[52],"reason":[54],"retrieved":[57,125,151],"information.":[58],"However,":[59],"existing":[60],"methods":[61],"typically":[62],"retrieve":[63],"based":[65],"semantic":[67],"similarity":[68],"raw":[71],"user":[72],"utterance,":[73],"which":[74],"lacks":[75],"explicit":[76,110],"reasoning":[77,102,174,209],"about":[78],"missing":[79],"intermediate":[80,153],"facts":[81],"often":[83],"returns":[84],"evidence":[85],"that":[86,108,169,199],"is":[87],"irrelevant":[88],"or":[89],"insufficient":[90],"for":[91,104],"grounded":[92],"reasoning.":[93],"In":[94],"work,":[96],"we":[97,197],"introduce":[98],"Goal-Mem,":[99],"a":[100,118,166],"goal-oriented":[101],"framework":[103],"agentic":[106],"performs":[109,134],"backward":[111],"chaining":[112],"user's":[115],"utterance":[116],"as":[117],"goal.":[119],"Rather":[120],"than":[121],"progressively":[122],"expanding":[123],"context,":[126],"Goal-Mem":[127,200],"decomposes":[128],"each":[129,140],"goal":[130],"into":[131],"atomic":[132],"subgoals,":[133],"targeted":[135],"satisfy":[139],"subgoal,":[141],"iteratively":[143],"identifies":[144],"what":[145],"information":[146],"should":[149],"be":[150,156],"when":[152],"goals":[154],"cannot":[155],"resolved.":[157],"We":[158],"formalize":[159],"process":[161],"Natural":[163],"Language":[164],"Logic,":[165],"logical":[167],"system":[168],"combines":[170],"verifiability":[172],"of":[173,181],"provided":[175],"FOL":[177],"with":[178],"expressivity":[180],"natural":[182],"language.":[183],"Through":[184],"extensive":[185],"experiments":[186],"two":[188],"datasets":[189],"comparing":[191],"nine":[193],"strong":[194],"baselines,":[196],"show":[198],"consistently":[201],"improves":[202],"performance,":[203],"particularly":[204],"tasks":[206],"requiring":[207],"multi-hop":[208],"implicit":[211],"inference.":[212]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-14T00:00:00"}
