{"id":"https://openalex.org/W7164844137","doi":"https://doi.org/10.48550/arxiv.2606.14386","title":"Discovery under Hypothesis Redundancy: A Geometric Theory of Discovery Bottlenecks","display_name":"Discovery under Hypothesis Redundancy: A Geometric Theory of Discovery Bottlenecks","publication_year":2026,"publication_date":"2026-06-12","ids":{"openalex":"https://openalex.org/W7164844137","doi":"https://doi.org/10.48550/arxiv.2606.14386"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.14386","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.14386","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.2606.14386","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125204482","display_name":"Li Xia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xia, Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5047681321","display_name":"Baoxun Wang","orcid":"https://orcid.org/0000-0001-8995-7543"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Baoxun","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/T12536","display_name":"Topological and Geometric Data Analysis","score":0.13510000705718994,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T12536","display_name":"Topological and Geometric Data Analysis","score":0.13510000705718994,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T10799","display_name":"Data Visualization and Analytics","score":0.13019999861717224,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.06909999996423721,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/novelty","display_name":"Novelty","score":0.741100013256073},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.43130001425743103},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.36410000920295715},{"id":"https://openalex.org/keywords/compression","display_name":"Compression (physics)","score":0.362199991941452},{"id":"https://openalex.org/keywords/statistical-hypothesis-testing","display_name":"Statistical hypothesis testing","score":0.353300005197525},{"id":"https://openalex.org/keywords/false-discovery-rate","display_name":"False discovery rate","score":0.337799996137619},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.3305000066757202}],"concepts":[{"id":"https://openalex.org/C2778738651","wikidata":"https://www.wikidata.org/wiki/Q16546687","display_name":"Novelty","level":2,"score":0.741100013256073},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5328999757766724},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44440001249313354},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.43130001425743103},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.414000004529953},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.40049999952316284},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3919999897480011},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.36410000920295715},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.362199991941452},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3580999970436096},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.353300005197525},{"id":"https://openalex.org/C193244246","wikidata":"https://www.wikidata.org/wiki/Q5432696","display_name":"False discovery rate","level":3,"score":0.337799996137619},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.3305000066757202},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.31839999556541443},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3052999973297119},{"id":"https://openalex.org/C2781039887","wikidata":"https://www.wikidata.org/wiki/Q1391724","display_name":"Factor (programming language)","level":2,"score":0.3019999861717224},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.296999990940094},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.2953999936580658},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2833999991416931},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.27379998564720154},{"id":"https://openalex.org/C2781089630","wikidata":"https://www.wikidata.org/wiki/Q21856745","display_name":"Realization (probability)","level":2,"score":0.2639999985694885}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.14386","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.14386","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.2606.14386","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.14386","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Scientific":[0],"discovery":[1,22,153],"saturates":[2],"when":[3,43,164],"new":[4],"hypotheses":[5],"cease":[6],"to":[7],"provide":[8],"independent":[9],"information,":[10],"even":[11],"if":[12],"the":[13,35,53,61,75,95,143],"nominal":[14],"hypothesis":[15,144],"space":[16,145],"remains":[17],"large.":[18],"We":[19,63],"study":[20],"hybrid":[21,71,131],"systems":[23],"that":[24,104],"combine":[25],"structured":[26],"local":[27],"search":[28,157],"with":[29,60],"LLM-generated":[30],"non-local":[31,39,166],"proposals":[32],"and":[33,56,73,85,100,128,140],"pose":[34],"Search":[36],"Compression":[37],"Hypothesis:":[38],"exploration":[40,167],"helps":[41],"only":[42],"three":[44],"geometric":[45],"conditions":[46,69],"co-occur:":[47],"spectral":[48],"compression,":[49],"orthogonal":[50,110],"escape":[51],"from":[52,154],"explored":[54],"span,":[55],"residual":[57],"signal":[58],"alignment":[59],"target.":[62],"formalize":[64],"these":[65],"conditions,":[66],"derive":[67],"necessary":[68],"for":[70,162],"advantage,":[72],"test":[74],"mechanism":[76],"in":[77,134],"controlled":[78],"synthetic":[79],"environments,":[80],"large-scale":[81],"A-share":[82],"factor":[83,126],"discovery,":[84],"symbolic-regression":[86],"benchmarks;":[87],"a":[88,159],"public":[89],"tabular":[90],"operational":[91],"sanity":[92],"check":[93],"tests":[94],"associated":[96],"budget-allocation":[97],"implication.":[98],"Signal-planting":[99],"directed-versus-random":[101],"experiments":[102],"show":[103],"novelty":[105,156],"alone":[106],"is":[107,168],"insufficient:":[108],"random":[109],"jumps":[111],"expand":[112],"coverage":[113],"but":[114,137],"do":[115],"not":[116],"improve":[117],"yield":[118],"without":[119],"predictive":[120],"alignment.":[121],"Across":[122],"compression":[123],"sweeps,":[124],"real":[125],"archives,":[127],"LLM-SRBench":[129],"tasks,":[130],"gains":[132],"concentrate":[133],"weakly":[135],"represented":[136],"target-bearing":[138],"directions":[139],"vanish":[141],"as":[142],"approaches":[146],"full":[147],"rank.":[148],"The":[149],"framework":[150],"turns":[151],"LLM-guided":[152],"generic":[155],"into":[158],"diagnostic":[160],"procedure":[161],"deciding":[163],"directed":[165],"warranted.":[169]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-16T00:00:00"}
