{"id":"https://openalex.org/W7225246734","doi":"https://doi.org/10.5281/zenodo.23942796","title":"EvoNova Scientist: A Neuro-Symbolic Multi-Agent Framework for Physical Constraint-Preserving Autonomous Scientific Discovery","display_name":"EvoNova Scientist: A Neuro-Symbolic Multi-Agent Framework for Physical Constraint-Preserving Autonomous Scientific Discovery","publication_year":2026,"publication_date":"2026-11-04","ids":{"openalex":"https://openalex.org/W7225246734","doi":"https://doi.org/10.5281/zenodo.23942796"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.23942796","doi":"https://doi.org/10.5281/zenodo.23942796","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.23942796","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","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.23942796","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070017214","display_name":"Roman Pokushko","orcid":"https://orcid.org/0000-0003-1073-6655","observed_orcids":["https://orcid.org/0000-0003-1073-6655"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pokushko, Roman V.","raw_affiliation_strings":["EvoNova Labs"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EvoNova Labs","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5160659196","display_name":"Maria V. Pokushko","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pokushko, Maria V.","raw_affiliation_strings":["EvoNova Labs"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EvoNova Labs","institution_ids":[]}]}],"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/T11948","display_name":"Machine Learning in Materials Science","score":0.30300000309944153,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.30300000309944153,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.298799991607666,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13904","display_name":"Artificial Intelligence Applications","score":0.23180000483989716,"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":[],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6690000295639038},{"id":"https://openalex.org/C2984917352","wikidata":"https://www.wikidata.org/wiki/Q12772819","display_name":"Scientific discovery","level":2,"score":0.4408999979496002},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41130000352859497},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.39730000495910645},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.38519999384880066},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.35339999198913574},{"id":"https://openalex.org/C99692599","wikidata":"https://www.wikidata.org/wiki/Q596577","display_name":"Dissipative system","level":2,"score":0.3253999948501587},{"id":"https://openalex.org/C167729594","wikidata":"https://www.wikidata.org/wiki/Q17736","display_name":"Axiom","level":2,"score":0.32280001044273376},{"id":"https://openalex.org/C2778770139","wikidata":"https://www.wikidata.org/wiki/Q1966904","display_name":"Solver","level":2,"score":0.32269999384880066},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3197000026702881},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.30160000920295715},{"id":"https://openalex.org/C116672817","wikidata":"https://www.wikidata.org/wiki/Q1454986","display_name":"Physical system","level":2,"score":0.2937000095844269},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.2904999852180481},{"id":"https://openalex.org/C60640748","wikidata":"https://www.wikidata.org/wiki/Q2337858","display_name":"Lyapunov function","level":3,"score":0.28850001096725464},{"id":"https://openalex.org/C194583477","wikidata":"https://www.wikidata.org/wiki/Q408891","display_name":"Physical law","level":2,"score":0.2863999903202057},{"id":"https://openalex.org/C13687954","wikidata":"https://www.wikidata.org/wiki/Q4826847","display_name":"Autonomous agent","level":2,"score":0.28380000591278076},{"id":"https://openalex.org/C138379479","wikidata":"https://www.wikidata.org/wiki/Q1116876","display_name":"Scientific modelling","level":2,"score":0.27570000290870667},{"id":"https://openalex.org/C79379906","wikidata":"https://www.wikidata.org/wiki/Q3174497","display_name":"Dynamical systems theory","level":2,"score":0.26109999418258667},{"id":"https://openalex.org/C26713055","wikidata":"https://www.wikidata.org/wiki/Q245962","display_name":"Implementation","level":2,"score":0.26089999079704285},{"id":"https://openalex.org/C96146094","wikidata":"https://www.wikidata.org/wiki/Q609057","display_name":"Unification","level":2,"score":0.25589999556541443},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2529999911785126}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5281/zenodo.23942796","doi":"https://doi.org/10.5281/zenodo.23942796","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.23942796","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.23942797","doi":"https://doi.org/10.5281/zenodo.23942797","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.23942797","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.23942796","doi":"https://doi.org/10.5281/zenodo.23942796","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.23942796","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":[],"abstract_inverted_index":{"Abstract":[0],"\u2014":[1],"The":[2,57],"acceleration":[3],"of":[4,19,75,87],"scientific":[5,16,88,193,214],"discovery":[6,31,215],"requires":[7],"transitioning":[8],"from":[9,115],"specialized":[10],"predictive":[11],"models":[12],"to":[13,47,73],"fully":[14],"autonomous":[15],"agents":[17,55],"capable":[18],"continuous":[20],"hypothesis":[21],"generation,":[22],"symbolic":[23],"modeling,":[24],"and":[25,68,91,112,128,160,173,201,233],"experimental":[26],"verification.":[27],"However,":[28],"first-generation":[29],"AI":[30,58],"systems":[32],"face":[33],"a":[34,84,95,134,207],"fundamental":[35],"dichotomy:":[36],"neural":[37],"structure":[38],"predictors":[39],"(e.g.,":[40,56],"AlphaFold)":[41],"act":[42],"as":[43],"static":[44],"oracles":[45],"unable":[46],"synthesize":[48],"dynamic":[49],"governing":[50],"equations,":[51],"whereas":[52],"unconstrained":[53],"generative":[54,106],"Scientist)":[59],"exhibit":[60],"severe":[61],"physical":[62,117],"hallucinations,":[63],"violating":[64],"basic":[65],"conservation":[66],"laws":[67],"dimensional":[69,123],"homogeneity":[70],"in":[71,243],"up":[72],"41.4%":[74,156],"candidate":[76],"outputs.":[77],"To":[78,229],"resolve":[79],"this":[80],"crisis,":[81],"we":[82],"propose":[83],"five-level":[85],"taxonomy":[86],"autonomy":[89],"(L1\u2013L5)":[90],"introduce":[92],"EvoNova":[93,101,103,148],"Scientist,":[94],"dual-system":[96],"neuro-symbolic":[97],"framework":[98],"developed":[99],"at":[100],"Labs.":[102],"decouples":[104],"stochastic":[105],"search":[107],"(System":[108,119],"1:":[109],"foundation":[110,210],"LLMs":[111,159],"graph":[113],"mutators)":[114],"deterministic":[116],"validation":[118],"2:":[120],"Buckingham":[121],"\u03a0-theorem":[122],"checks,":[124],"thermodynamic":[125],"equilibrium":[126],"constraints,":[127],"Lyapunov":[129],"dissipative":[130],"bounds)":[131],"governed":[132],"by":[133,171],"five-dimensional":[135],"Pareto":[136],"consensus":[137],"arbitration":[138],"mechanism":[139],"(Rospatent":[140],"Certificate":[141],"\u2116":[142],"2026667533).":[143],"In":[144],"cross-system":[145],"empirical":[146],"benchmarking,":[147],"completely":[149],"eliminates":[150],"unphysical":[151],"defect":[152],"generation":[153],"(0.0%":[154],"vs.":[155],"for":[157,162,211],"raw":[158],"14.5%":[161],"tool-assisted":[163],"ReAct":[164],"agents),":[165],"reduces":[166],"wasted":[167],"numerical":[168],"solver":[169],"compute":[170],"3.8-fold,":[172],"achieves":[174],"state-of-the-art":[175],"non-linear":[176],"dynamical":[177],"model":[178],"identification":[179],"accuracy":[180],"(median":[181],"R\u00b2":[182],"=":[183],"0.96)":[184],"with":[185],"100%":[186],"verified":[187],"citation":[188],"fidelity":[189],"across":[190],"ten":[191],"heterogeneous":[192],"domains":[194],"(catalysis,":[195],"metallurgy,":[196],"fluid":[197],"mechanics,":[198],"computational":[199],"biology,":[200],"environmental":[202],"engineering).":[203],"This":[204],"architecture":[205],"establishes":[206],"rigorous,":[208],"energy-efficient":[209],"trustworthy,":[212],"self-governing":[213],"engines.":[216],"Correspondence":[217],"&":[218,226],"Academic":[219],"Inquiries:":[220],"Roman":[221],"V.":[222],"Pokushko":[223],"(pokushko@mail.ru).":[224],"Anti-spam":[225],"verification":[227],"notice:":[228],"ensure":[230],"priority":[231],"delivery":[232],"bypass":[234],"automated":[235],"spam/fraud":[236],"filters,":[237],"please":[238],"include":[239],"the":[240],"tag":[241],"[EvoNova-Scientist]":[242],"your":[244],"email":[245],"subject":[246],"line.":[247]},"counts_by_year":[],"updated_date":"2026-11-05T12:25:35.769452","created_date":"2026-11-05T00:00:00"}
