{"id":"https://openalex.org/W7119133833","doi":"https://doi.org/10.48550/arxiv.2601.00938","title":"Rate-Distortion Analysis of Compressed Query Delegation with Low-Rank Riemannian Updates","display_name":"Rate-Distortion Analysis of Compressed Query Delegation with Low-Rank Riemannian Updates","publication_year":2026,"publication_date":"2026-01-02","ids":{"openalex":"https://openalex.org/W7119133833","doi":"https://doi.org/10.48550/arxiv.2601.00938"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.00938","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.00938","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2601.00938","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5117457278","display_name":"Faruk Alpay","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Alpay, Faruk","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5117932337","display_name":"Bu\u011fra K\u0131l\u0131\u00e7ta\u015f","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kilictas, Bugra","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5117457278"],"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/T13553","display_name":"Age of Information Optimization","score":0.3666999936103821,"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/T13553","display_name":"Age of Information Optimization","score":0.3666999936103821,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.13089999556541443,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.05469999834895134,"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/oracle","display_name":"Oracle","score":0.5688999891281128},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5073999762535095},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.48500001430511475},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.4422000050544739},{"id":"https://openalex.org/keywords/smoothness","display_name":"Smoothness","score":0.4390999972820282},{"id":"https://openalex.org/keywords/random-oracle","display_name":"Random oracle","score":0.4388999938964844},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.4171000123023987},{"id":"https://openalex.org/keywords/delegate","display_name":"Delegate","score":0.4108000099658966},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.40450000762939453},{"id":"https://openalex.org/keywords/delegation","display_name":"Delegation","score":0.4043000042438507}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6122000217437744},{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.5688999891281128},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5073999762535095},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.48500001430511475},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4584999978542328},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.4422000050544739},{"id":"https://openalex.org/C102634674","wikidata":"https://www.wikidata.org/wiki/Q868473","display_name":"Smoothness","level":2,"score":0.4390999972820282},{"id":"https://openalex.org/C94284585","wikidata":"https://www.wikidata.org/wiki/Q228184","display_name":"Random oracle","level":4,"score":0.4388999938964844},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.4171000123023987},{"id":"https://openalex.org/C143273055","wikidata":"https://www.wikidata.org/wiki/Q2382794","display_name":"Delegate","level":2,"score":0.4108000099658966},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.40450000762939453},{"id":"https://openalex.org/C86532276","wikidata":"https://www.wikidata.org/wiki/Q1184065","display_name":"Delegation","level":2,"score":0.4043000042438507},{"id":"https://openalex.org/C2776029896","wikidata":"https://www.wikidata.org/wiki/Q3935810","display_name":"Relaxation (psychology)","level":2,"score":0.38440001010894775},{"id":"https://openalex.org/C157692150","wikidata":"https://www.wikidata.org/wiki/Q2919848","display_name":"Query optimization","level":2,"score":0.3840999901294708},{"id":"https://openalex.org/C208081375","wikidata":"https://www.wikidata.org/wiki/Q274502","display_name":"Degrees of freedom (physics and chemistry)","level":2,"score":0.3840999901294708},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.3691999912261963},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.36230000853538513},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.36079999804496765},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3594000041484833},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.35690000653266907},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.3495999872684479},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34940001368522644},{"id":"https://openalex.org/C2778029271","wikidata":"https://www.wikidata.org/wiki/Q5421931","display_name":"Extension (predicate logic)","level":2,"score":0.3391999900341034},{"id":"https://openalex.org/C60008888","wikidata":"https://www.wikidata.org/wiki/Q6031013","display_name":"Information bottleneck method","level":3,"score":0.33889999985694885},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.33059999346733093},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.326200008392334},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.3156999945640564},{"id":"https://openalex.org/C2779593128","wikidata":"https://www.wikidata.org/wiki/Q632814","display_name":"Riemannian manifold","level":2,"score":0.3138999938964844},{"id":"https://openalex.org/C84392682","wikidata":"https://www.wikidata.org/wiki/Q1952404","display_name":"Multilinear map","level":2,"score":0.3012999892234802},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2937000095844269},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.27390000224113464},{"id":"https://openalex.org/C168773769","wikidata":"https://www.wikidata.org/wiki/Q1350299","display_name":"Satisfiability","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.25920000672340393},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.258899986743927}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.00938","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.00938","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2601.00938","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.00938","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":"article"},"sustainable_development_goals":[{"score":0.44931361079216003,"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Bounded-context":[0],"agents":[1],"fail":[2],"when":[3],"intermediate":[4],"reasoning":[5,22,119],"exceeds":[6],"an":[7,35,66],"effective":[8],"working-memory":[9],"budget.":[10],"We":[11,50,73],"study":[12],"compressed":[13],"query":[14,33],"delegation":[15],"(CQD):":[16],"(i)":[17],"compress":[18],"a":[19,25,52,57,62,70,90,116,139],"high-dimensional":[20],"latent":[21,42],"state":[23,43],"into":[24],"low-rank":[26],"tensor":[27],"query,":[28],"(ii)":[29],"delegate":[30],"the":[31,41],"minimal":[32],"to":[34,76],"external":[36],"oracle,":[37],"and":[38,65,79,96,109,135,137,148],"(iii)":[39],"update":[40],"via":[44],"Riemannian":[45,102],"optimization":[46],"on":[47],"fixed-rank":[48],"manifolds.":[49],"give":[51],"math-first":[53],"formulation:":[54],"CQD":[55,75,128],"is":[56,87],"constrained":[58,92],"stochastic":[59,103],"program":[60],"with":[61],"query-budget":[63],"functional":[64],"oracle":[67,107],"modeled":[68],"as":[69],"noisy":[71],"operator.":[72],"connect":[74],"classical":[77],"rate-distortion":[78],"information":[80],"bottleneck":[81],"principles,":[82],"showing":[83],"that":[84],"spectral":[85],"hard-thresholding":[86],"optimal":[88],"for":[89,101],"natural":[91],"quadratic":[93],"distortion":[94],"problem,":[95],"we":[97,113],"derive":[98],"convergence":[99],"guarantees":[100],"approximation":[104],"under":[105,132],"bounded":[106],"noise":[108],"smoothness":[110],"assumptions.":[111],"Empirically,":[112],"report":[114],"(A)":[115],"2,500-item":[117],"bounded-context":[118],"suite":[120],"(BBH-derived":[121],"tasks":[122],"plus":[123],"curated":[124],"paradox":[125],"instances)":[126],"comparing":[127],"against":[129],"chain-of-thought":[130],"baselines":[131],"fixed":[133],"compute":[134],"context;":[136],"(B)":[138],"human":[140],"\"cognitive":[141],"mirror\"":[142],"benchmark":[143],"(N=200)":[144],"measuring":[145],"epistemic":[146],"gain":[147],"semantic":[149],"drift":[150],"across":[151],"modern":[152],"oracles.":[153]},"counts_by_year":[],"updated_date":"2026-01-08T20:10:11.968330","created_date":"2026-01-08T00:00:00"}
