{"id":"https://openalex.org/W4415190843","doi":"https://doi.org/10.18653/v1/2026.findings-acl.1756","title":"Can Large Language Models Adequately Perform Symbolic Reasoning Over Time Series?","display_name":"Can Large Language Models Adequately Perform Symbolic Reasoning Over Time Series?","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W4415190843","doi":"https://doi.org/10.18653/v1/2026.findings-acl.1756"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2026.findings-acl.1756","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.1756","pdf_url":"https://aclanthology.org/2026.findings-acl.1756.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2026.findings-acl.1756.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101809661","display_name":"Zewen Liu","orcid":"https://orcid.org/0000-0003-2582-8042"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zewen Liu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101769931","display_name":"Juntong Ni","orcid":"https://orcid.org/0009-0006-7070-8137"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Juntong Ni","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070663881","display_name":"Xianfeng Tang","orcid":"https://orcid.org/0000-0002-7955-3104"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xianfeng Tang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052515775","display_name":"Max S. Y. Lau","orcid":"https://orcid.org/0000-0001-6590-0294"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Max SY Lau","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Qi He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qi He","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038386528","display_name":"Wenpeng Yin","orcid":"https://orcid.org/0000-0002-3154-1139"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenpeng Yin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100758371","display_name":"Wei Jin","orcid":"https://orcid.org/0000-0002-5054-954X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei Jin","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":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.013627,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"35201","last_page":"35226"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9850999712944031,"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":0.9850999712944031,"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":0.9760000109672546,"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/context","display_name":"Context (archaeology)","score":0.487199991941452},{"id":"https://openalex.org/keywords/genetic-programming","display_name":"Genetic programming","score":0.4747999906539917},{"id":"https://openalex.org/keywords/causal-reasoning","display_name":"Causal reasoning","score":0.4462999999523163},{"id":"https://openalex.org/keywords/automated-reasoning","display_name":"Automated reasoning","score":0.4422999918460846},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.43059998750686646},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4302999973297119},{"id":"https://openalex.org/keywords/the-symbolic","display_name":"The Symbolic","score":0.38830000162124634},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.38100001215934753}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6593999862670898},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5019999742507935},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.487199991941452},{"id":"https://openalex.org/C110332635","wikidata":"https://www.wikidata.org/wiki/Q629498","display_name":"Genetic programming","level":2,"score":0.4747999906539917},{"id":"https://openalex.org/C115086926","wikidata":"https://www.wikidata.org/wiki/Q17004651","display_name":"Causal reasoning","level":3,"score":0.4462999999523163},{"id":"https://openalex.org/C195344581","wikidata":"https://www.wikidata.org/wiki/Q2555318","display_name":"Automated reasoning","level":2,"score":0.4422999918460846},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.43059998750686646},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4302999973297119},{"id":"https://openalex.org/C2776095079","wikidata":"https://www.wikidata.org/wiki/Q489538","display_name":"The Symbolic","level":2,"score":0.38830000162124634},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.38100001215934753},{"id":"https://openalex.org/C37335422","wikidata":"https://www.wikidata.org/wiki/Q6888134","display_name":"Model-based reasoning","level":3,"score":0.3472000062465668},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.32670000195503235},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.32100000977516174},{"id":"https://openalex.org/C65620979","wikidata":"https://www.wikidata.org/wiki/Q7661176","display_name":"Symbolic data analysis","level":2,"score":0.31709998846054077},{"id":"https://openalex.org/C89288958","wikidata":"https://www.wikidata.org/wiki/Q7301504","display_name":"Reasoning system","level":2,"score":0.30820000171661377},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.3077999949455261},{"id":"https://openalex.org/C23123167","wikidata":"https://www.wikidata.org/wiki/Q7661193","display_name":"Symbolic trajectory evaluation","level":3,"score":0.30309998989105225},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2897000014781952},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2800999879837036},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.26980000734329224},{"id":"https://openalex.org/C166088908","wikidata":"https://www.wikidata.org/wiki/Q308495","display_name":"Abductive reasoning","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C47884741","wikidata":"https://www.wikidata.org/wiki/Q1166618","display_name":"Mathematical logic","level":2,"score":0.25769999623298645},{"id":"https://openalex.org/C97364631","wikidata":"https://www.wikidata.org/wiki/Q484284","display_name":"Deductive reasoning","level":2,"score":0.257099986076355}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.18653/v1/2026.findings-acl.1756","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.1756","pdf_url":"https://aclanthology.org/2026.findings-acl.1756.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2508.03963","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2508.03963","pdf_url":"https://arxiv.org/pdf/2508.03963","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2508.03963","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2508.03963","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:10.18653/v1/2026.findings-acl.1756","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.1756","pdf_url":"https://aclanthology.org/2026.findings-acl.1756.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5859890489","display_name":"Collaborative Research: III: Medium: Empowering Graph Neural Networks from a Data Perspective","funder_award_id":"2504088","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337355","display_name":"National Institute of Allergy and Infectious Diseases","ror":"https://ror.org/043z4tv69"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4415190843.pdf","grobid_xml":"https://content.openalex.org/works/W4415190843.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Uncovering":[0],"hidden":[1],"symbolic":[2,44,66,76,97,117],"laws":[3],"from":[4,46],"time":[5,47,70],"series":[6,48,71],"data,":[7],"as":[8],"an":[9],"aspiration":[10],"dating":[11],"back":[12],"to":[13,40,64,87,113,142],"Kepler's":[14],"discovery":[15,25],"of":[16,96,127,133],"planetary":[17],"motion,":[18],"remains":[19],"a":[20,60,93,104,115],"core":[21],"challenge":[22],"in":[23,34,145],"scientific":[24,147],"and":[26,81,125,139],"artificial":[27],"intelligence.While":[28],"Large":[29],"Language":[30],"Models":[31],"show":[32],"promise":[33],"structured":[35],"reasoning":[36,67,118,140],"tasks,":[37],"their":[38],"ability":[39],"infer":[41],"interpretable,":[42],"context-aligned":[43],"structures":[45],"data":[49],"is":[50],"still":[51],"underexplored.To":[52],"systematically":[53],"evaluate":[54],"this":[55],"capability,":[56],"we":[57],"introduce":[58],"SymbolBench,":[59],"comprehensive":[61],"benchmark":[62],"designed":[63],"assess":[65],"over":[68],"real-world":[69],"across":[72],"three":[73],"tasks:":[74],"multivariate":[75],"regression,":[77],"Boolean":[78],"network":[79],"inference,":[80],"causal":[82],"discovery.Unlike":[83],"prior":[84],"efforts":[85],"limited":[86],"simple":[88],"algebraic":[89],"equations,":[90],"SymbolBench":[91],"spans":[92],"diverse":[94],"set":[95],"forms":[98],"with":[99,110],"varying":[100],"complexity.We":[101],"further":[102],"propose":[103],"unified":[105],"framework":[106],"that":[107],"integrates":[108],"LLMs":[109,144],"genetic":[111],"programming":[112],"form":[114],"closedloop":[116],"system.Our":[119],"empirical":[120],"results":[121],"reveal":[122],"key":[123],"strengths":[124],"limitations":[126],"current":[128],"models,":[129],"highlighting":[130],"the":[131],"importance":[132],"combining":[134],"domain":[135],"knowledge,":[136],"context":[137],"alignment,":[138],"structure":[141],"improve":[143],"automated":[146],"discovery.":[148]},"counts_by_year":[],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2025-10-15T00:00:00"}