{"id":"https://openalex.org/W7162650567","doi":"https://doi.org/10.1145/3774905.3795087","title":"Real-Time Procedural Learning From Experience for AI Agents","display_name":"Real-Time Procedural Learning From Experience for AI Agents","publication_year":2026,"publication_date":"2026-05-28","ids":{"openalex":"https://openalex.org/W7162650567","doi":"https://doi.org/10.1145/3774905.3795087"},"language":null,"primary_location":{"id":"doi:10.1145/3774905.3795087","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774905.3795087","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":"Companion Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3774905.3795087","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5083732362","display_name":"Dasheng Bi","orcid":"https://orcid.org/0009-0002-9631-2211"},"institutions":[{"id":"https://openalex.org/I1315810332","display_name":"Altair Engineering (United States)","ror":"https://ror.org/05939ef94","country_code":"US","type":"company","lineage":["https://openalex.org/I1315810332"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dasheng Bi","raw_affiliation_strings":["Altrina, Menlo Park, CA, USA"],"raw_orcid":"https://orcid.org/0009-0002-9631-2211","affiliations":[{"raw_affiliation_string":"Altrina, Menlo Park, CA, USA","institution_ids":["https://openalex.org/I1315810332"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137301740","display_name":"Yubin Hu","orcid":"https://orcid.org/0009-0000-8444-3121"},"institutions":[{"id":"https://openalex.org/I1315810332","display_name":"Altair Engineering (United States)","ror":"https://ror.org/05939ef94","country_code":"US","type":"company","lineage":["https://openalex.org/I1315810332"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yubin Hu","raw_affiliation_strings":["Altrina, Menlo Park, CA, USA"],"raw_orcid":"https://orcid.org/0009-0000-8444-3121","affiliations":[{"raw_affiliation_string":"Altrina, Menlo Park, CA, USA","institution_ids":["https://openalex.org/I1315810332"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5137298268","display_name":"Mohammed N. Nasir","orcid":"https://orcid.org/0009-0005-9210-2016"},"institutions":[{"id":"https://openalex.org/I1315810332","display_name":"Altair Engineering (United States)","ror":"https://ror.org/05939ef94","country_code":"US","type":"company","lineage":["https://openalex.org/I1315810332"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mohammed N. Nasir","raw_affiliation_strings":["Altrina, Menlo Park, CA, USA"],"raw_orcid":"https://orcid.org/0009-0005-9210-2016","affiliations":[{"raw_affiliation_string":"Altrina, Menlo Park, CA, USA","institution_ids":["https://openalex.org/I1315810332"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1315810332"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.77278349,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"505","last_page":"508"},"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":0.33959999680519104,"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":0.33959999680519104,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.20389999449253082,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.03790000081062317,"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/procedural-knowledge","display_name":"Procedural knowledge","score":0.3490999937057495},{"id":"https://openalex.org/keywords/procedural-justice","display_name":"Procedural justice","score":0.27970001101493835},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.25540000200271606},{"id":"https://openalex.org/keywords/experiential-learning","display_name":"Experiential learning","score":0.25060001015663147}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.43220001459121704},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.38190001249313354},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36899998784065247},{"id":"https://openalex.org/C124469403","wikidata":"https://www.wikidata.org/wiki/Q1813993","display_name":"Procedural knowledge","level":3,"score":0.3490999937057495},{"id":"https://openalex.org/C2779149496","wikidata":"https://www.wikidata.org/wiki/Q442100","display_name":"Procedural justice","level":3,"score":0.27970001101493835},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.27129998803138733},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.25690001249313354},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.25540000200271606},{"id":"https://openalex.org/C37228920","wikidata":"https://www.wikidata.org/wiki/Q1307600","display_name":"Experiential learning","level":2,"score":0.25060001015663147},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.24160000681877136}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3774905.3795087","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774905.3795087","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":"Companion Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2511.22074","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2511.22074","pdf_url":"https://arxiv.org/pdf/2511.22074","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3774905.3795087","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774905.3795087","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":"Companion Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2021625970","https://openalex.org/W2963888186","https://openalex.org/W3027879771","https://openalex.org/W4294351883","https://openalex.org/W4353112996","https://openalex.org/W4362508231","https://openalex.org/W4377163995","https://openalex.org/W4380374951","https://openalex.org/W4380715434","https://openalex.org/W4385327683","https://openalex.org/W4386081135","https://openalex.org/W4387724681","https://openalex.org/W4391272423","https://openalex.org/W4403622204","https://openalex.org/W4407759090","https://openalex.org/W4416540657","https://openalex.org/W4416895257"],"related_works":[],"abstract_inverted_index":{"Learning":[0],"how":[1],"to":[2,24,66,111],"do":[3],"things":[4],"from":[5],"trial":[6],"and":[7,53,60,99,107],"error":[8],"in":[9,82,114,129],"real":[10,83],"time":[11],"is":[12],"a":[13,42],"hallmark":[14],"of":[15,51,63,126],"biological":[16],"intelligence,":[17],"yet":[18],"most":[19],"LLM-based":[20],"agents":[21,128],"lack":[22],"mechanisms":[23],"acquire":[25],"procedural":[26],"knowledge":[27],"after":[28],"deployment.":[29],"We":[30],"propose":[31],"Procedural":[32],"Recall":[33],"for":[34],"Agents":[35],"with":[36,75],"eXperiences":[37],"Indexed":[38],"by":[39,56,133],"State":[40],"(PRAXIS),":[41],"lightweight":[43],"post-training":[44],"learning":[45],"mechanism":[46],"that":[47,79,120],"stores":[48],"the":[49,67,88,123],"consequences":[50],"actions":[52],"retrieves":[54],"them":[55,135],"jointly":[57],"matching":[58],"environmental":[59],"internal":[61],"states":[62],"past":[64],"episodes":[65],"current":[68],"state.":[69],"PRAXIS":[70,93,121],"augments":[71],"agentic":[72],"action":[73],"selection":[74],"retrieved":[76],"state-action-result":[77],"exemplars":[78],"are":[80],"generated":[81],"time.":[84],"When":[85],"evaluated":[86],"on":[87],"REAL":[89],"web":[90],"browsing":[91],"benchmark,":[92],"improves":[94],"task":[95],"completion":[96],"accuracy,":[97],"reliability,":[98],"cost":[100],"efficiency":[101],"across":[102],"different":[103],"foundation":[104],"model":[105],"backbones,":[106],"shows":[108],"preliminary":[109],"generalization":[110],"unseen":[112],"tasks":[113],"similar":[115],"environments.":[116],"These":[117],"results":[118],"demonstrate":[119],"enables":[122],"practical":[124],"adoption":[125],"AI":[127],"fast-evolving":[130],"stateful":[131],"environments":[132],"helping":[134],"learn":[136],"new":[137],"procedures":[138],"effectively.":[139]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2026-05-29T00:00:00"}