{"id":"https://openalex.org/W7225627875","doi":"https://doi.org/10.5281/zenodo.24092865","title":"Depth, Epistemic Gating, or Curriculum Scale?","display_name":"Depth, Epistemic Gating, or Curriculum Scale?","publication_year":2026,"publication_date":"2026-11-09","ids":{"openalex":"https://openalex.org/W7225627875","doi":"https://doi.org/10.5281/zenodo.24092865"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.24092865","doi":"https://doi.org/10.5281/zenodo.24092865","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24092865","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.24092865","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5161330492","display_name":"Antonia Vasile","orcid":"https://orcid.org/0009-0006-7509-0081","observed_orcids":["https://orcid.org/0009-0006-7509-0081"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Vasile, Antonia","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0009-0006-7509-0081","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5161330492"],"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":0.763700008392334,"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":0.763700008392334,"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":0.07440000027418137,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.027499999850988388,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/large-language-models","display_name":"large language models","score":0.5329999923706055},{"id":"https://openalex.org/keywords/model-compression","display_name":"model compression","score":0.3409999907016754},{"id":"https://openalex.org/keywords/contradiction-resolution","display_name":"contradiction resolution","score":0.6620000004768372},{"id":"https://openalex.org/keywords/associative-memory","display_name":"associative memory","score":0.5559999942779541},{"id":"https://openalex.org/keywords/curriculum-learning","display_name":"curriculum learning","score":0.6320000290870667},{"id":"https://openalex.org/keywords/perplexity-reduction","display_name":"perplexity reduction","score":0.47699999809265137},{"id":"https://openalex.org/keywords/inference-latency","display_name":"inference latency","score":0.3330000042915344},{"id":"https://openalex.org/keywords/graph-neural-networks","display_name":"graph neural networks","score":0.43299999833106995}],"concepts":[{"id":"https://openalex.org/C100279451","wikidata":"https://www.wikidata.org/wiki/Q372193","display_name":"Perplexity","level":3,"score":0.9093999862670898},{"id":"https://openalex.org/C2776728590","wikidata":"https://www.wikidata.org/wiki/Q363948","display_name":"Contradiction","level":2,"score":0.7335000038146973},{"id":"https://openalex.org/C2776946740","wikidata":"https://www.wikidata.org/wiki/Q358652","display_name":"Deliberation","level":3,"score":0.5389999747276306},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4837000072002411},{"id":"https://openalex.org/C47177190","wikidata":"https://www.wikidata.org/wiki/Q207137","display_name":"Curriculum","level":2,"score":0.46880000829696655},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.38440001010894775},{"id":"https://openalex.org/C2779899686","wikidata":"https://www.wikidata.org/wiki/Q571227","display_name":"Implicature","level":3,"score":0.3813000023365021},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.3776000142097473},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.3709999918937683},{"id":"https://openalex.org/C98184364","wikidata":"https://www.wikidata.org/wiki/Q1780131","display_name":"Argument (complex analysis)","level":2,"score":0.35659998655319214},{"id":"https://openalex.org/C2185349","wikidata":"https://www.wikidata.org/wiki/Q190558","display_name":"Negation","level":2,"score":0.3262999951839447},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.304500013589859},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28610000014305115},{"id":"https://openalex.org/C100521375","wikidata":"https://www.wikidata.org/wiki/Q2015382","display_name":"Competence (human resources)","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C118147538","wikidata":"https://www.wikidata.org/wiki/Q1126970","display_name":"Metacognition","level":3,"score":0.27900001406669617},{"id":"https://openalex.org/C153180980","wikidata":"https://www.wikidata.org/wiki/Q19776675","display_name":"Commit","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C547764534","wikidata":"https://www.wikidata.org/wiki/Q8236","display_name":"Literacy","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C110099512","wikidata":"https://www.wikidata.org/wiki/Q59115","display_name":"Philosophy of science","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.25060001015663147}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5281/zenodo.24092865","doi":"https://doi.org/10.5281/zenodo.24092865","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24092865","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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"},{"id":"doi:10.5281/zenodo.24092866","doi":"https://doi.org/10.5281/zenodo.24092866","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24092866","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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"}],"best_oa_location":{"id":"doi:10.5281/zenodo.24092865","doi":"https://doi.org/10.5281/zenodo.24092865","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24092865","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7225173246","https://openalex.org/W7166852133","https://openalex.org/W7223018893","https://openalex.org/W7225571499","https://openalex.org/W7140234728","https://openalex.org/W7155400466","https://openalex.org/W7213301781","https://openalex.org/W7166867356","https://openalex.org/W7220909630","https://openalex.org/W7214210444"],"abstract_inverted_index":{"Starting":[0],"from":[1,164,251],"the":[2,44,143,190,207,287,296],"cryptographically":[3,385],"locked":[4],"Mini":[5],"Minx":[6],"V5":[7],"baseline":[8],"(1.812B":[9],"unique":[10],"parameters;":[11],"32-layer":[12],"[GDN,":[13],"GDN,":[14,15],"GQA]x8":[16],"hybrid":[17,80],"backbone":[18],"+":[19,23,28,140],"Titan":[20],"1":[21],"Gatekeeper":[22],"FG2-Titan":[24],"2":[25],"Fast-Weight":[26],"Vault":[27],"TitanGraphDB,":[29],"certified":[30],"at":[31,352],"3.823":[32,192],"GiB":[33,123,193,361],"VRAM":[34,59,124],"across":[35,182],"20,480":[36],"context":[37],"tokens;":[38],"DOI:":[39],"10.17605/OSF.IO/VFJ4G),":[40],"this":[41],"study":[42],"investigates":[43],"physical":[45],"bottlenecks":[46],"governing":[47],"test-time":[48],"associative":[49],"memory,":[50],"live":[51],"contradiction":[52,111,146,162,264],"override,":[53],"and":[54,100,125,185,215,245,291,342,371,384],"contextual":[55,240],"nuance":[56,241],"under":[57,86,266],"consumer":[58,127,362],"limits":[60],"(<":[61],"4":[62],"GiB):":[63],"1.":[64],"Act":[65,131,196,272,301,365],"I":[66],"\u2014":[67,133,198,274],"Adaptive":[68],"Deliberation":[69],"Depth":[70],"(TitanGate,":[71],"32L":[72,139,208],"->":[73,75,98,106,118,224,243],"36L":[74],"40L):":[76],"Appending":[77],"+8":[78],"turn-scoped":[79],"deliberation":[81],"blocks":[82],"(+432.6M":[83],"parameters)":[84],"trained":[85],"a":[87,151],"token-matched":[88],"Matryoshka":[89],"curriculum":[90],"reduces":[91],"foundation":[92],"validation":[93,220],"perplexity":[94,102,221,229],"by":[95,103,113],"-9.20%":[96],"(12.83":[97],"11.65)":[99],"SFT":[101,228],"-6.00%":[104],"(12.01":[105],"11.29),":[107],"but":[108],"lifts":[109],"stale-vault":[110],"override":[112],"only":[114],"+6.25":[115],"pp":[116],"(18.75%":[117],"25.00%)":[119],"while":[120,262],"adding":[121],"+0.825":[122],"+26.4%":[126],"decode":[128],"latency.":[129],"2.":[130],"II":[132],"Zero-Parameter":[134],"Causal":[135,335],"Epistemic":[136],"Rebinding":[137],"(Pure":[138],"Rebind):":[141],"Falsifying":[142],"hypothesis":[144],"that":[145],"resolution":[147],"requires":[148],"deeper":[149],"layers,":[150],"strictly":[152,212],"causal":[153],"User-prefix":[154],"epistemic":[155],"controller":[156],"over":[157],"Layer-3/Layer-15":[158],"telemetry":[159],"boosts":[160],"V5-98M":[161],"accuracy":[163,265],"18.75%":[165],"to":[166,230,254,268],"56.25%":[167],"(+1.48":[168],"nats":[169],"margin)":[170],"with":[171,234],"0":[172,175],"added":[173],"parameters,":[174],"gradient":[176],"steps,":[177],"0%":[178],"valid":[179],"memory":[180,249],"degradation":[181],"72":[183],"episodes,":[184],"-17.6%":[186],"lower":[187],"GFLOPs/token/correct":[188,358],"inside":[189,359],"exact":[191],"footprint.":[194],"3.":[195],"III":[197],"1.1B":[199],"Open-Provenance":[200],"Curriculum":[201],"Saturation":[202],"(PleIAs/common_corpus":[203],"2x2":[204],"Factorial):":[205],"Educating":[206],"spine":[209],"on":[210,258],"999,996,686":[211],"permissive":[213],"English":[214],"code":[216],"tokens":[217],"collapses":[218],"open-world":[219],"16.98x":[222],"(144.32":[223],"8.50),":[225],"drops":[226],"conversational":[227],"7.30":[231],"(8.48":[232],"final)":[233],"100.0%":[235],"zero":[236],"User-turn":[237],"hallucination,":[238],"triples":[239],"(25.0%":[242],"75.0%),":[244],"leaps":[246],"multi-token":[247],"fast-weight":[248],"6.45x":[250],"15.28%":[252],"(11/72)":[253],"98.61%":[255],"(71/72;":[256],"95.83%":[257],"held-out":[259],"unseen":[260],"vocabulary),":[261],"lifting":[263],"Rebind":[267],"68.75%":[269],"(11/16).":[270],"4.":[271],"IV":[273,302,366],"Proprietary":[275],"Sovereign":[276],"Crown":[277,303],"Milestone":[278],"(Benchmark":[279],"Existence":[280],"Proof":[281],"&":[282],"SHA-256":[283,292,388],"Priority":[284],"Lock):":[285],"Records":[286],"hardware-certified":[288],"benchmark":[289],"scorecard":[290],"cryptographic":[293],"proof-of-possession":[294],"for":[295],"proprietary":[297,377],"Wizads":[298,381],"Research":[299,382],"Laboratory":[300,383],"architecture":[304],"(Exp":[305],"2),":[306],"which":[307],"achieves":[308],"100.00%":[309,313,332,338,343],"(72/72)":[310],"In-Distribution":[311],"Memory,":[312],"(24/24,":[314],"NLL":[315],"0.1959)":[316],"Held-Out":[317,346],"Unseen":[318],"Vocabulary,":[319],"87.50%":[320],"(14/16)":[321],"Contradiction":[322],"Override,":[323,331],"83.33%":[324],"(10/12;":[325],"4/4":[326],"canonical":[327],"showcase)":[328],"Exact":[329,350],"Greedy":[330],"(4/4)":[333,339],"4-Way":[334],"Parameter":[336],"Revision,":[337],"Contextual":[340],"Nuance,":[341],"(32/32)":[344],"4-Regime":[345],"Stress":[347],"Accuracy":[348],"(90.62%":[349],"Greedy)":[351],"6.84":[353],"Raw":[354],"/":[355],"7.29":[356],"PMI":[357],"3.824":[360],"VRAM.":[363],"All":[364],"architectural":[367],"blueprints,":[368],"optimization":[369],"schedules,":[370],"calibration":[372],"mechanics":[373],"are":[374],"retained":[375],"as":[376,390],"trade":[378],"secrets":[379],"of":[380,391],"timestamped":[386],"via":[387],"digests":[389],"November":[392],"9,":[393],"2026.":[394]},"counts_by_year":[],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2026-11-10T00:00:00"}
