{"id":"https://openalex.org/W7225460702","doi":"https://doi.org/10.5281/zenodo.24045042","title":"Roman Urdu Input Costs Small Local ReAct Agents up to 35 Accuracy Points: A Paired Benchmark on Clinic Questions","display_name":"Roman Urdu Input Costs Small Local ReAct Agents up to 35 Accuracy Points: A Paired Benchmark on Clinic Questions","publication_year":2026,"publication_date":"2026-11-07","ids":{"openalex":"https://openalex.org/W7225460702","doi":"https://doi.org/10.5281/zenodo.24045042"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.24045042","doi":"https://doi.org/10.5281/zenodo.24045042","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24045042","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.24045042","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076561462","display_name":"Muzammal Hussain","orcid":"https://orcid.org/0000-0002-7320-4413","observed_orcids":["https://orcid.org/0000-0002-7320-4413"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Hussain, Muzammal","raw_affiliation_strings":["Independent researcher"],"raw_orcid":"https://orcid.org/0009-0001-2570-5686","affiliations":[{"raw_affiliation_string":"Independent researcher","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5076561462"],"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/T10181","display_name":"Natural Language Processing Techniques","score":0.5439000129699707,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.5439000129699707,"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.15950000286102295,"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/T10028","display_name":"Topic Modeling","score":0.1543000042438507,"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/roman-urdu","display_name":"Roman Urdu","score":0.9760000109672546},{"id":"https://openalex.org/keywords/react-agents","display_name":"ReAct agents","score":0.7020000219345093},{"id":"https://openalex.org/keywords/tool-using-agents","display_name":"tool-using agents","score":0.6190000176429749},{"id":"https://openalex.org/keywords/small-language-models","display_name":"small language models","score":0.6679999828338623},{"id":"https://openalex.org/keywords/physiotherapy-clinic","display_name":"physiotherapy clinic","score":0.652999997138977},{"id":"https://openalex.org/keywords/worked-examples","display_name":"worked examples","score":0.4699999988079071},{"id":"https://openalex.org/keywords/question-translation","display_name":"question translation","score":0.6980000138282776},{"id":"https://openalex.org/keywords/local-llm-deployment","display_name":"local LLM deployment","score":0.4410000145435333},{"id":"https://openalex.org/keywords/qwen2-5","display_name":"Qwen2.5","score":0.6769999861717224},{"id":"https://openalex.org/keywords/llama-3-2","display_name":"Llama 3.2","score":0.8700000047683716},{"id":"https://openalex.org/keywords/llama-3-1","display_name":"Llama 3.1","score":0.75}],"concepts":[{"id":"https://openalex.org/C2777350258","wikidata":"https://www.wikidata.org/wiki/Q1617","display_name":"Urdu","level":2,"score":0.9408000111579895},{"id":"https://openalex.org/C2777801307","wikidata":"https://www.wikidata.org/wiki/Q2088390","display_name":"Spelling","level":2,"score":0.8337000012397766},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6097000241279602},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4368000030517578},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4244000017642975},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36739999055862427},{"id":"https://openalex.org/C554936623","wikidata":"https://www.wikidata.org/wiki/Q199657","display_name":"Reading (process)","level":2,"score":0.3646000027656555},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.3625999987125397},{"id":"https://openalex.org/C90329073","wikidata":"https://www.wikidata.org/wiki/Q914232","display_name":"Ask price","level":2,"score":0.3012999892234802},{"id":"https://openalex.org/C109167261","wikidata":"https://www.wikidata.org/wiki/Q5421955","display_name":"Fell","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.28630000352859497},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5281/zenodo.24045042","doi":"https://doi.org/10.5281/zenodo.24045042","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24045042","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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"},{"id":"doi:10.5281/zenodo.24045043","doi":"https://doi.org/10.5281/zenodo.24045043","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24045043","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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"}],"best_oa_location":{"id":"doi:10.5281/zenodo.24045042","doi":"https://doi.org/10.5281/zenodo.24045042","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24045042","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7225009671","https://openalex.org/W4402671194","https://openalex.org/W7140118062","https://openalex.org/W7106562747","https://openalex.org/W7221887050","https://openalex.org/W4417296820","https://openalex.org/W3081372466","https://openalex.org/W4385572278","https://openalex.org/W7036064931","https://openalex.org/W7224248967"],"abstract_inverted_index":{"Tool-using":[0],"agents":[1],"built":[2,64],"on":[3,9,49,172],"small":[4],"language":[5],"models":[6,96],"can":[7],"run":[8],"a":[10,37,45,68,164],"clinic's":[11],"own":[12],"computer,":[13],"but":[14,229],"their":[15],"users":[16],"in":[17,20,23,28,33,76,195,257],"Pakistan":[18],"and":[19,57,82,99,182,216,226,261],"Pakistani":[21],"communities":[22],"the":[24,55,90,186,192,196,204,208,218,239],"UK":[25],"often":[26],"type":[27],"Roman":[29,50,79,125,200,212],"Urdu:":[30],"Urdu":[31,80,126,201,213],"written":[32],"Latin":[34],"script,":[35],"without":[36],"fixed":[38],"spelling.":[39],"We":[40,63,92],"ask":[41],"how":[42],"much":[43],"accuracy":[44,120],"ReAct":[46,119,240],"agent":[47,209],"loses":[48],"Urdu,":[51],"which":[52],"words":[53,174],"cause":[54],"loss,":[56],"whether":[58],"two":[59,187],"cheap":[60],"fixes":[61],"help.":[62],"96":[65],"questions":[66],"over":[67],"fictional":[69],"physiotherapy":[70],"clinic":[71,183],"from":[72,89,122],"12":[73],"templates,":[74],"each":[75],"English,":[77],"common":[78],"spelling":[81,160],"chat":[83,249],"spelling,":[84],"with":[85,191,264],"gold":[86],"answers":[87],"computed":[88],"records.":[91],"ran":[93],"four":[94],"local":[95],"(Qwen2.5":[97],"3B":[98,132,138,163,224,254],"7B,":[100],"Llama":[101,104,136,142,188,222,252],"3.2":[102,137,223,253],"3B,":[103],"3.1":[105,143],"8B)":[106],"at":[107],"temperature":[108],"0,":[109],"for":[110,130,135,141],"3,838":[111],"scored":[112],"calls.":[113],"With":[114],"an":[115],"English":[116,123],"worked":[117,214],"example,":[118],"fell":[121],"to":[124,255],"by:":[127],"34.7":[128],"points":[129,134,140],"Qwen2.5":[131,153,162,231],"31.2":[133],"21.9":[139],"8B":[144],"All":[145],"three":[146],"95%":[147],"bootstrap":[148],"intervals":[149],"are":[150,267],"below":[151],"zero.":[152],"7B":[154],"showed":[155,198,236],"no":[156,199],"loss":[157,205],"(+1.0).":[158],"Chat":[159],"cost":[161],"further":[165],"22.1":[166],"points.":[167],"The":[168],"largest":[169],"losses":[170],"were":[171],"time":[173],"(saal":[175],"baad,":[176],"saal":[177],"pehle),":[178],"mila":[179],"kar":[180],"(combined)":[181],"days.":[184],"For":[185],"models,":[189],"answering":[190],"whole":[193],"database":[194],"prompt":[197],"gap,":[202],"so":[203],"appeared":[206],"inside":[207],"loop.":[210],"A":[211,233],"example":[215],"translating":[217],"question":[219],"first":[220],"helped":[221],"(+16.7":[225],"+20.8":[227],"points)":[228],"not":[230],"3B.":[232],"pilot":[234],"also":[235],"that":[237],"sending":[238],"transcript":[241,266],"as":[242],"one":[243],"user":[244],"message,":[245],"instead":[246],"of":[247],"separate":[248],"turns,":[250],"dropped":[251],"7%":[256],"English.":[258],"Code,":[259],"items":[260],"every":[262],"call":[263],"its":[265],"public.":[268]},"counts_by_year":[],"updated_date":"2026-11-09T13:22:03.289127","created_date":"2026-11-08T00:00:00"}
