{"id":"https://openalex.org/W4415269707","doi":"https://doi.org/10.18653/v1/2026.findings-acl.1338","title":"Towards Inference-time Scaling for Continuous Space Reasoning","display_name":"Towards Inference-time Scaling for Continuous Space Reasoning","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W4415269707","doi":"https://doi.org/10.18653/v1/2026.findings-acl.1338"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2026.findings-acl.1338","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.1338","pdf_url":"https://aclanthology.org/2026.findings-acl.1338.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.1338.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5033151808","display_name":"Minghan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Minghan Wang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062855474","display_name":"Thuy-Trang Vu","orcid":"https://orcid.org/0009-0005-6296-4184"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thuy-Trang Vu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086032589","display_name":"Ehsan Shareghi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ehsan Shareghi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5081525024","display_name":"Gholamreza Haffari","orcid":"https://orcid.org/0000-0001-7326-8380"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gholamreza Haffari","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":0.01032205,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"26842","last_page":"26856"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9156000018119812,"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"}},"topics":[{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9156000018119812,"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/continuous-variable","display_name":"Continuous variable","score":0.5060999989509583},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.454800009727478},{"id":"https://openalex.org/keywords/inductive-reasoning","display_name":"Inductive reasoning","score":0.4309000074863434},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4262999892234802},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.38940000534057617},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.3734000027179718}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6414999961853027},{"id":"https://openalex.org/C2983394010","wikidata":"https://www.wikidata.org/wiki/Q19885330","display_name":"Continuous variable","level":2,"score":0.5060999989509583},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45570001006126404},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.454800009727478},{"id":"https://openalex.org/C21563000","wikidata":"https://www.wikidata.org/wiki/Q484511","display_name":"Inductive reasoning","level":2,"score":0.4309000074863434},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4262999892234802},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.38940000534057617},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.3734000027179718},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.34529998898506165},{"id":"https://openalex.org/C83725634","wikidata":"https://www.wikidata.org/wiki/Q7268699","display_name":"Qualitative reasoning","level":2,"score":0.33719998598098755},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3361999988555908},{"id":"https://openalex.org/C89288958","wikidata":"https://www.wikidata.org/wiki/Q7301504","display_name":"Reasoning system","level":2,"score":0.32679998874664307},{"id":"https://openalex.org/C10184394","wikidata":"https://www.wikidata.org/wiki/Q5165491","display_name":"Continuous modelling","level":2,"score":0.30399999022483826},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.28839999437332153},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.2680000066757202},{"id":"https://openalex.org/C197352929","wikidata":"https://www.wikidata.org/wiki/Q1074074","display_name":"Inductive bias","level":4,"score":0.26510000228881836}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.18653/v1/2026.findings-acl.1338","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.1338","pdf_url":"https://aclanthology.org/2026.findings-acl.1338.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:2510.12167","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.12167","pdf_url":"https://arxiv.org/pdf/2510.12167","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.2510.12167","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.12167","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.1338","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.1338","pdf_url":"https://aclanthology.org/2026.findings-acl.1338.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":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4415269707.pdf","grobid_xml":"https://content.openalex.org/works/W4415269707.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Inference-time":[0],"scaling":[1],"through":[2,63],"multiple":[3],"sample":[4],"generation":[5,110],"in":[6,22,38,81,87,100,117,125,173],"combination":[7],"with":[8],"Process-or":[9],"Outcome-Reward":[10],"Model":[11],"(PRM":[12],"or":[13],"ORM)":[14],"reranking":[15],"has":[16],"proven":[17],"effective":[18,145],"for":[19,96,108,153],"text-based":[20],"reasoning":[21,37,50,61,151],"large":[23],"language":[24],"models.This":[25],"paper":[26],"investigates":[27],"whether":[28],"such":[29],"established":[30],"techniques":[31],"can":[32],"be":[33],"successfully":[34],"adapted":[35],"to":[36,84],"the":[39,53,56,69,73,88,101,118,126,140,154,167],"continuous":[40,48,102,127,174],"space,":[41],"using":[42],"COCONUT":[43],"(Hao":[44],"et":[45],"al.,":[46],"2024)":[47],"space":[49,120],"LM":[51],"as":[52],"backbone.We":[54],"demonstrate":[55],"feasibility":[57],"of":[58,156,169],"generating":[59],"diverse":[60],"paths":[62],"dropout-based":[64],"sampling.Our":[65],"Pass@N":[66],"analysis":[67],"on":[68],"generated":[70],"samples":[71],"reveals":[72],"potential":[74],"that":[75,143,162],"could":[76],"enable":[77],"a":[78],"significant":[79],"gain":[80,99],"performance":[82],"akin":[83],"gains":[85],"observed":[86],"discrete":[89,119],"space.However,":[90],"we":[91,138],"highlight":[92],"unique":[93],"challenges":[94],"faced":[95],"materializing":[97],"this":[98],"thought":[103,175],"space.In":[104],"particular,":[105],"working":[106],"recipes":[107],"data":[109],"and":[111,114,135,149,158],"training":[112],"PRM":[113,157],"ORM":[115],"models":[116],"unlocks":[121],"only":[122],"marginal":[123],"improvements":[124],"space.Through":[128],"probing":[129],"various":[130],"aspects":[131],"including":[132],"geometric":[133],"properties":[134],"trajectory":[136],"dynamics,":[137],"identify":[139],"underlying":[141],"reasons":[142],"prevent":[144],"discrimination":[146],"between":[147],"correct":[148],"incorrect":[150],"(essential":[152],"functioning":[155],"ORM).Our":[159],"findings":[160],"reveal":[161],"current":[163],"limitations":[164],"stem":[165],"from":[166],"absence":[168],"key":[170],"inductive":[171],"biases":[172],"representations.":[176],"1":[177]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-17T00:00:00"}