{"id":"https://openalex.org/W7163395202","doi":"https://doi.org/10.48550/arxiv.2606.03727","title":"When Does Latent Reasoning Help? MeRa: Metric-Space Bias for Spatial Prediction","display_name":"When Does Latent Reasoning Help? MeRa: Metric-Space Bias for Spatial Prediction","publication_year":2026,"publication_date":"2026-06-02","ids":{"openalex":"https://openalex.org/W7163395202","doi":"https://doi.org/10.48550/arxiv.2606.03727"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.03727","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.03727","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":null,"license_id":null,"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.2606.03727","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137716848","display_name":"Zhenyu Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Zhenyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137794567","display_name":"Shuigeng Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Shuigeng","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/T11596","display_name":"Constraint Satisfaction and Optimization","score":0.579800009727478,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11596","display_name":"Constraint Satisfaction and Optimization","score":0.579800009727478,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.211899995803833,"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/T11106","display_name":"Data Management and Algorithms","score":0.026200000196695328,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/pairwise-comparison","display_name":"Pairwise comparison","score":0.696399986743927},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5877000093460083},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.548799991607666},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.5178999900817871},{"id":"https://openalex.org/keywords/euclidean-geometry","display_name":"Euclidean geometry","score":0.5066999793052673},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4343000054359436},{"id":"https://openalex.org/keywords/spatial-intelligence","display_name":"Spatial intelligence","score":0.42890000343322754},{"id":"https://openalex.org/keywords/visual-reasoning","display_name":"Visual reasoning","score":0.4156999886035919},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.40959998965263367}],"concepts":[{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.696399986743927},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5877000093460083},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5810999870300293},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.548799991607666},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.5178999900817871},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5169000029563904},{"id":"https://openalex.org/C129782007","wikidata":"https://www.wikidata.org/wiki/Q162886","display_name":"Euclidean geometry","level":2,"score":0.5066999793052673},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4343000054359436},{"id":"https://openalex.org/C155911833","wikidata":"https://www.wikidata.org/wiki/Q3817354","display_name":"Spatial intelligence","level":2,"score":0.42890000343322754},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.4156999886035919},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.40959998965263367},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37380000948905945},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3582000136375427},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.35420000553131104},{"id":"https://openalex.org/C83725634","wikidata":"https://www.wikidata.org/wiki/Q7268699","display_name":"Qualitative reasoning","level":2,"score":0.3472999930381775},{"id":"https://openalex.org/C115086926","wikidata":"https://www.wikidata.org/wiki/Q17004651","display_name":"Causal reasoning","level":3,"score":0.3393999934196472},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.322299987077713},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.3197000026702881},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.31310001015663147},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.30970001220703125},{"id":"https://openalex.org/C20162079","wikidata":"https://www.wikidata.org/wiki/Q1151406","display_name":"Case-based reasoning","level":2,"score":0.2978000044822693},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.2939000129699707},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.29100000858306885},{"id":"https://openalex.org/C89288958","wikidata":"https://www.wikidata.org/wiki/Q7301504","display_name":"Reasoning system","level":2,"score":0.2879999876022339},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28299999237060547},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.26409998536109924},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25519999861717224}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.03727","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.03727","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.03727","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.03727","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Latent":[0],"reasoning":[1,26,38,89,125,135],"has":[2],"improved":[3],"sequential":[4],"recommendation":[5],"by":[6],"iteratively":[7],"refining":[8],"representations":[9],"before":[10],"prediction,":[11],"but":[12],"does":[13],"it":[14],"help":[15],"spatial":[16,40,106],"prediction?":[17],"We":[18,58,121],"find":[19],"that":[20,70,123,133,152],"the":[21,30,43,83,86,100,110,153,164],"answer":[22],"depends":[23],"on":[24,103,146],"whether":[25],"is":[27,136,161],"grounded":[28],"in":[29,163],"underlying":[31],"metric":[32],"space.":[33],"Without":[34],"such":[35,116],"grounding,":[36],"latent":[37],"degrades":[39],"prediction":[41,80,107],"below":[42],"unmodified":[44],"baseline,":[45],"while":[46],"a":[47,66,128],"learned":[48],"metric-space":[49,93],"bias":[50,94],"derived":[51],"from":[52],"pairwise":[53],"distances":[54],"produces":[55],"consistent":[56],"gains.":[57],"formalize":[59],"this":[60],"finding":[61,154],"through":[62],"MeRa":[63,98],"(Metric-space":[64],"Reasoning),":[65],"lightweight":[67],"backbone-agnostic":[68],"module":[69],"can":[71],"be":[72],"inserted":[73],"between":[74,88],"any":[75],"sequence":[76],"encoder":[77],"and":[78,91,119,132],"its":[79],"heads.":[81],"On":[82],"GETNext":[84],"backbone,":[85],"gap":[87],"without":[90],"with":[92,148],"reaches":[95],"4.5%":[96],"NDCG@10.":[97],"achieves":[99],"best":[101],"NDCG@10":[102],"all":[104],"three":[105],"benchmarks":[108],"among":[109],"compared":[111],"methods,":[112],"surpassing":[113],"recent":[114],"approaches":[115],"as":[117],"GeoMamba":[118],"HMST.":[120],"prove":[122],"metric-space-constrained":[124],"converges":[126],"to":[127],"unique":[129],"fixed":[130],"point":[131],"N-step":[134],"strictly":[137],"more":[138],"expressive":[139],"than":[140],"(N-1)-step":[141],"reasoning.":[142],"A":[143],"controlled":[144],"experiment":[145],"CLEVR":[147],"Euclidean":[149],"distance":[150],"confirms":[151],"generalizes":[155],"beyond":[156],"geographic":[157],"coordinates.":[158],"The":[159],"code":[160],"included":[162],"supplementary":[165],"material.":[166]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-04T00:00:00"}
