{"id":"https://openalex.org/W7154205423","doi":"https://doi.org/10.48550/arxiv.2604.11365","title":"Learning from Contrasts: Synthesizing Reasoning Paths from Diverse Search Trajectories","display_name":"Learning from Contrasts: Synthesizing Reasoning Paths from Diverse Search Trajectories","publication_year":2026,"publication_date":"2026-04-13","ids":{"openalex":"https://openalex.org/W7154205423","doi":"https://doi.org/10.48550/arxiv.2604.11365"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.11365","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11365","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2604.11365","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133572360","display_name":"Peiyang Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Peiyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133563031","display_name":"Zhirui Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Zhirui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133595657","display_name":"Xi Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133561897","display_name":"Di Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Di","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024633033","display_name":"Youru Li","orcid":"https://orcid.org/0000-0002-9326-9863"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Youru","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133616935","display_name":"Zhi Cai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cai, Zhi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133625571","display_name":"Wei Ye","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye, Wei","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/T10906","display_name":"AI-based Problem Solving and Planning","score":0.3547999858856201,"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/T10906","display_name":"AI-based Problem Solving and Planning","score":0.3547999858856201,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.12139999866485596,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.09749999642372131,"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/generalization","display_name":"Generalization","score":0.680899977684021},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.595300018787384},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.5335999727249146},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.5275999903678894},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.44929999113082886},{"id":"https://openalex.org/keywords/structured-prediction","display_name":"Structured prediction","score":0.4458000063896179},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.37139999866485596},{"id":"https://openalex.org/keywords/monte-carlo-tree-search","display_name":"Monte Carlo tree search","score":0.35260000824928284}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7221999764442444},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.680899977684021},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6098999977111816},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.595300018787384},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.5335999727249146},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5275999903678894},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5205000042915344},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.44929999113082886},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.4458000063896179},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.37139999866485596},{"id":"https://openalex.org/C46149586","wikidata":"https://www.wikidata.org/wiki/Q11785332","display_name":"Monte Carlo tree search","level":3,"score":0.35260000824928284},{"id":"https://openalex.org/C2780735816","wikidata":"https://www.wikidata.org/wiki/Q28324931","display_name":"Incremental learning","level":2,"score":0.3257000148296356},{"id":"https://openalex.org/C59656382","wikidata":"https://www.wikidata.org/wiki/Q191536","display_name":"Conjunction (astronomy)","level":2,"score":0.3172000050544739},{"id":"https://openalex.org/C163797641","wikidata":"https://www.wikidata.org/wiki/Q2067937","display_name":"Tree structure","level":3,"score":0.31360000371932983},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.298799991607666},{"id":"https://openalex.org/C207024777","wikidata":"https://www.wikidata.org/wiki/Q621673","display_name":"Search tree","level":3,"score":0.2732999920845032},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.2662000060081482},{"id":"https://openalex.org/C20162079","wikidata":"https://www.wikidata.org/wiki/Q1151406","display_name":"Case-based reasoning","level":2,"score":0.2513999938964844},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.11365","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11365","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2604.11365","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11365","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":{"Monte":[0],"Carlo":[1],"Tree":[2],"Search":[3],"(MCTS)":[4],"has":[5],"been":[6],"widely":[7],"used":[8],"for":[9],"automated":[10],"reasoning":[11,93,156],"data":[12],"exploration,":[13],"but":[14],"current":[15],"supervision":[16,51],"extraction":[17,52],"methods":[18],"remain":[19],"inefficient.":[20],"Standard":[21],"approaches":[22],"retain":[23],"only":[24],"the":[25,30,35,69,90,117,147],"single":[26],"highest-reward":[27],"trajectory,":[28],"discarding":[29],"comparative":[31],"signals":[32],"present":[33],"in":[34,133],"many":[36],"explored":[37],"paths.":[38],"Here":[39],"we":[40],"introduce":[41],"\\textbf{Contrastive":[42],"Reasoning":[43],"Path":[44],"Synthesis":[45],"(CRPS)},":[46],"a":[47,54,58,63,130],"framework":[48],"that":[49,95,106,144],"transforms":[50],"from":[53,126,146,160],"filtering":[55],"process":[56,66],"into":[57],"synthesis":[59,91],"procedure.":[60],"CRPS":[61,137],"uses":[62],"structured":[64],"reflective":[65],"to":[67],"analyze":[68],"differences":[70],"between":[71,149],"high-":[72],"and":[73,83,151],"low-quality":[74],"search":[75],"trajectories,":[76],"extracting":[77],"explicit":[78],"information":[79],"about":[80],"strategic":[81],"pivots":[82],"local":[84],"failure":[85,152],"modes.":[86],"These":[87],"insights":[88],"guide":[89],"of":[92,119],"chains":[94],"incorporate":[96],"success":[97,150,161],"patterns":[98],"while":[99],"avoiding":[100],"identified":[101],"pitfalls.":[102],"We":[103],"show":[104],"empirically":[105],"models":[107],"fine-tuned":[108],"on":[109,122,140],"just":[110],"60K":[111],"CRPS-synthesized":[112],"examples":[113,124],"match":[114],"or":[115],"exceed":[116],"performance":[118],"baselines":[120],"trained":[121],"590K":[123],"derived":[125],"standard":[127],"rejection":[128],"sampling,":[129],"20$\\times$":[131],"reduction":[132],"dataset":[134],"size.":[135],"Furthermore,":[136],"improves":[138],"generalization":[139],"out-of-domain":[141],"benchmarks,":[142],"demonstrating":[143],"learning":[145,159],"contrast":[148],"produces":[153],"more":[154],"transferable":[155],"capabilities":[157],"than":[158],"alone.":[162]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-15T00:00:00"}
