{"id":"https://openalex.org/W7130599345","doi":"https://doi.org/10.48550/arxiv.2602.15895","title":"Understand Then Memory: A Cognitive Gist-Driven RAG Framework with Global Semantic Diffusion","display_name":"Understand Then Memory: A Cognitive Gist-Driven RAG Framework with Global Semantic Diffusion","publication_year":2026,"publication_date":"2026-02-11","ids":{"openalex":"https://openalex.org/W7130599345","doi":"https://doi.org/10.48550/arxiv.2602.15895"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.15895","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.15895","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2602.15895","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126408515","display_name":"Pengcheng Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zhou, Pengcheng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126428537","display_name":"Haochen Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Haochen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126378200","display_name":"Zhiqiang Nie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nie, Zhiqiang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126372898","display_name":"JiaLe Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, JiaLe","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126369737","display_name":"Qing Gong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gong, Qing","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019283199","display_name":"Weizhen Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Weizhen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5110638385","display_name":"Chun Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Chun","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5126408515"],"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":0.4489000141620636,"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":0.4489000141620636,"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.27900001406669617,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.08429999649524689,"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/semantic-memory","display_name":"Semantic memory","score":0.5774000287055969},{"id":"https://openalex.org/keywords/wordnet","display_name":"WordNet","score":0.48890000581741333},{"id":"https://openalex.org/keywords/associative-property","display_name":"Associative property","score":0.45840001106262207},{"id":"https://openalex.org/keywords/cognition","display_name":"Cognition","score":0.44760000705718994},{"id":"https://openalex.org/keywords/search-engine-indexing","display_name":"Search engine indexing","score":0.4345000088214874},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.43389999866485596},{"id":"https://openalex.org/keywords/semantic-network","display_name":"Semantic network","score":0.4002000093460083},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.3856000006198883},{"id":"https://openalex.org/keywords/episodic-memory","display_name":"Episodic memory","score":0.38269999623298645}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8295000195503235},{"id":"https://openalex.org/C197914299","wikidata":"https://www.wikidata.org/wiki/Q18650","display_name":"Semantic memory","level":3,"score":0.5774000287055969},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5221999883651733},{"id":"https://openalex.org/C157659113","wikidata":"https://www.wikidata.org/wiki/Q533822","display_name":"WordNet","level":2,"score":0.48890000581741333},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4724999964237213},{"id":"https://openalex.org/C159423971","wikidata":"https://www.wikidata.org/wiki/Q177251","display_name":"Associative property","level":2,"score":0.45840001106262207},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.44760000705718994},{"id":"https://openalex.org/C75165309","wikidata":"https://www.wikidata.org/wiki/Q2258979","display_name":"Search engine indexing","level":2,"score":0.4345000088214874},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.43389999866485596},{"id":"https://openalex.org/C85407183","wikidata":"https://www.wikidata.org/wiki/Q1045785","display_name":"Semantic network","level":2,"score":0.4002000093460083},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3856000006198883},{"id":"https://openalex.org/C88576662","wikidata":"https://www.wikidata.org/wiki/Q18646","display_name":"Episodic memory","level":3,"score":0.38269999623298645},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.37130001187324524},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.3589000105857849},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3546000123023987},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.3402999937534332},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.33959999680519104},{"id":"https://openalex.org/C161407221","wikidata":"https://www.wikidata.org/wiki/Q4382939","display_name":"Cognitive model","level":3,"score":0.3312999904155731},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.3249000012874603},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3084999918937683},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.3019999861717224},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.29440000653266907},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26499998569488525},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.2628999948501587},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.259799987077713},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C121375916","wikidata":"https://www.wikidata.org/wiki/Q936559","display_name":"Principle of compositionality","level":2,"score":0.25380000472068787}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.15895","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.15895","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2602.15895","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.15895","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"},"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":{"Retrieval-Augmented":[0],"Generation":[1],"(RAG)":[2],"effectively":[3],"mitigates":[4],"hallucinations":[5],"in":[6,19,24,59,172,204],"LLMs":[7],"by":[8,35,138,157],"incorporating":[9],"external":[10],"knowledge.":[11],"However,":[12],"the":[13,36,60,65,69,99,103,119,135,149,170],"inherent":[14],"discrete":[15],"representation":[16],"of":[17,27,55,64],"text":[18],"existing":[20],"frameworks":[21],"often":[22],"results":[23,182],"a":[25,44,87,173],"loss":[26],"semantic":[28,162],"integrity,":[29],"leading":[30],"to":[31,169],"retrieval":[32,101,133],"deviations.":[33],"Inspired":[34],"human":[37,49],"episodic":[38],"memory":[39,51,80,96],"mechanism,":[40],"we":[41,147],"propose":[42,148],"CogitoRAG,":[43],"RAG":[45,199],"framework":[46,57,104],"that":[47,112,194],"simulates":[48],"cognitive":[50,120],"processes.":[52],"The":[53,164],"core":[54],"this":[56],"lies":[58],"extraction":[61],"and":[62,95,141,188,208],"evolution":[63],"Semantic":[66],"Gist.":[67],"During":[68],"offline":[70],"indexing":[71],"stage,":[72,102],"CogitoRAG":[73,195],"first":[74],"deduces":[75],"unstructured":[76],"corpora":[77],"into":[78,86,115],"gist":[79],"corpora,":[81],"which":[82,152],"are":[83],"then":[84],"transformed":[85],"multi-dimensional":[88],"knowledge":[89,206],"graph":[90],"integrating":[91],"entities,":[92],"relational":[93],"facts,":[94],"nodes.":[97],"In":[98],"online":[100],"handles":[105],"complex":[106,125,205],"queries":[107],"via":[108],"Query":[109],"Decomposition":[110],"Module":[111,130],"breaks":[113],"them":[114],"comprehensive":[116],"sub-queries,":[117],"mimicking":[118],"decomposition":[121],"humans":[122],"employ":[123],"for":[124],"information.":[126],"Subsequently,":[127],"Entity":[128],"Diffusion":[129],"performs":[131],"associative":[132],"across":[134,183],"graph,":[136],"guided":[137],"structural":[139],"relevance":[140],"an":[142],"entity-frequency":[143],"reward":[144],"mechanism.":[145],"Furthermore,":[146],"CogniRank":[150],"algorithm,":[151],"precisely":[153],"reranks":[154],"candidate":[155],"passages":[156],"fusing":[158],"diffusion-derived":[159],"scores":[160],"with":[161],"similarity.":[163],"final":[165],"evidence":[166],"is":[167],"delivered":[168],"generator":[171],"passage-memory":[174],"pairing":[175],"format,":[176],"providing":[177],"high-density":[178],"information":[179],"support.":[180],"Experimental":[181],"five":[184],"mainstream":[185],"QA":[186],"benchmarks":[187],"multi-task":[189],"generation":[190],"on":[191],"GraphBench":[192],"demonstrate":[193],"significantly":[196],"outperforms":[197],"state-of-the-art":[198],"methods,":[200],"showcasing":[201],"superior":[202],"capabilities":[203],"integration":[207],"reasoning.":[209]},"counts_by_year":[],"updated_date":"2026-02-20T06:18:38.638704","created_date":"2026-02-20T00:00:00"}
