{"id":"https://openalex.org/W7164839839","doi":"https://doi.org/10.1145/3805622.3810632","title":"Lookahead-R: Budget-Aware Tool Retrieval via Execution-Centric Planning","display_name":"Lookahead-R: Budget-Aware Tool Retrieval via Execution-Centric Planning","publication_year":2026,"publication_date":"2026-06-15","ids":{"openalex":"https://openalex.org/W7164839839","doi":"https://doi.org/10.1145/3805622.3810632"},"language":null,"primary_location":{"id":"doi:10.1145/3805622.3810632","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805622.3810632","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3805622.3810632","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138645027","display_name":"Zongze Wu","orcid":"https://orcid.org/0009-0009-4674-9707"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zongze Wu","raw_affiliation_strings":["Beijing University Of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0009-4674-9707","affiliations":[{"raw_affiliation_string":"Beijing University Of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134403332","display_name":"Yani Guo","orcid":"https://orcid.org/0009-0007-5563-662X"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yani Guo","raw_affiliation_strings":["Beiing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0007-5563-662X","affiliations":[{"raw_affiliation_string":"Beiing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109735217","display_name":"Runnan Li","orcid":"https://orcid.org/0009-0007-2220-7626"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Runnan Li","raw_affiliation_strings":["Beiing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0007-2220-7626","affiliations":[{"raw_affiliation_string":"Beiing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.96503542,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1662","last_page":"1671"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10260","display_name":"Software Engineering Research","score":0.31220000982284546,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10260","display_name":"Software Engineering Research","score":0.31220000982284546,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T12127","display_name":"Software System Performance and Reliability","score":0.16990000009536743,"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/T10639","display_name":"Advanced Software Engineering Methodologies","score":0.11110000312328339,"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/bottleneck","display_name":"Bottleneck","score":0.7487999796867371},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.4862000048160553},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.48570001125335693},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4325999915599823},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.35370001196861267},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.3337000012397766},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.2996000051498413}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8367000222206116},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.7487999796867371},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.4862000048160553},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.48570001125335693},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4462999999523163},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4325999915599823},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42730000615119934},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40869998931884766},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.35370001196861267},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.3337000012397766},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2996000051498413},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.28459998965263367},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.2797999978065491},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C46149586","wikidata":"https://www.wikidata.org/wiki/Q11785332","display_name":"Monte Carlo tree search","level":3,"score":0.27489998936653137},{"id":"https://openalex.org/C551230270","wikidata":"https://www.wikidata.org/wiki/Q4368942","display_name":"Data retrieval","level":2,"score":0.273499995470047},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.26109999418258667},{"id":"https://openalex.org/C75165309","wikidata":"https://www.wikidata.org/wiki/Q2258979","display_name":"Search engine indexing","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3805622.3810632","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805622.3810632","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3805622.3810632","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805622.3810632","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.5008237957954407},{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.4755842387676239}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2069870183","https://openalex.org/W3099700870","https://openalex.org/W3129831491","https://openalex.org/W4385572634","https://openalex.org/W4389520747","https://openalex.org/W4404781376","https://openalex.org/W4411119636"],"related_works":[],"abstract_inverted_index":{"Tool":[0],"retrieval":[1,51],"is":[2,145],"a":[3,45,53,63,87,111],"critical":[4],"bottleneck":[5],"for":[6,150],"LLM-based":[7],"agents":[8],"operating":[9],"over":[10],"large,":[11],"heterogeneous":[12],"API":[13],"ecosystems.":[14],"Existing":[15],"approaches":[16],"face":[17],"an":[18,127],"inherent":[19],"trade-off:":[20],"semantic":[21,78],"retrievers":[22],"are":[23],"fast":[24],"but":[25],"suffer":[26],"from":[27],"the":[28,37,96,105,120,132,146],"semantic-functional":[29],"gap,":[30],"while":[31],"execution-based":[32],"validation":[33],"improves":[34],"precision":[35],"at":[36],"cost":[38],"of":[39,129],"prohibitive":[40],"latency.":[41],"We":[42],"propose":[43],"Lookahead-R,":[44],"planning-based":[46],"framework":[47],"that":[48,69,94,141],"reformulates":[49],"tool":[50,72,97],"as":[52],"resource-constrained":[54],"sequential":[55],"decision-making":[56],"problem.":[57],"At":[58],"its":[59],"core,":[60],"Lookahead-R":[61,109],"introduces":[62],"lightweight":[64],"execution-aware":[65],"surrogate":[66],"world":[67,84],"model":[68,85],"jointly":[70],"predicts":[71],"execution":[73],"success,":[74],"latency":[75,143],"cost,":[76],"and":[77],"utility\u2014without":[79],"invoking":[80],"real":[81],"APIs.":[82],"This":[83],"drives":[86],"cost-sensitive,":[88],"uncertainty-guided":[89],"Monte":[90],"Carlo":[91],"Tree":[92],"Search":[93],"navigates":[95],"space":[98],"under":[99,154],"strict":[100],"budget":[101],"constraints.":[102,156],"Evaluated":[103],"on":[104],"large-scale":[106],"ToolBench":[107],"benchmark,":[108],"achieves":[110],"superior":[112],"accuracy-efficiency":[113],"trade-off":[114],"across":[115],"all":[116],"test":[117],"scenarios.":[118],"On":[119],"most":[121],"challenging":[122],"I3":[123],"split,":[124],"it":[125],"attains":[126],"NDCG@5":[128],"91.40%,":[130],"outperforming":[131],"state-of-the-art":[133],"ToolGen":[134],"(90.16%)":[135],"by":[136],"1.24%.":[137],"Ablation":[138],"studies":[139],"confirm":[140],"explicit":[142],"modeling":[144],"key":[147],"discriminative":[148],"signal":[149],"identifying":[151],"high-quality":[152],"tools":[153],"resource":[155]},"counts_by_year":[],"updated_date":"2026-06-16T07:37:23.134862","created_date":"2026-06-16T00:00:00"}
