{"id":"https://openalex.org/W7154300043","doi":"https://doi.org/10.48550/arxiv.2604.11322","title":"Do LLMs Know Tool Irrelevance? Demystifying Structural Alignment Bias in Tool Invocations","display_name":"Do LLMs Know Tool Irrelevance? Demystifying Structural Alignment Bias in Tool Invocations","publication_year":2026,"publication_date":"2026-04-13","ids":{"openalex":"https://openalex.org/W7154300043","doi":"https://doi.org/10.48550/arxiv.2604.11322"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.11322","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11322","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":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.2604.11322","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133563703","display_name":"Yilong Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yilong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101641900","display_name":"Xixun Lin","orcid":"https://orcid.org/0009-0004-6645-0597"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Xixun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133626355","display_name":"Pengfei Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Pengfei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133558795","display_name":"Ge Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Ge","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133616228","display_name":"Fang Fang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Fang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133568548","display_name":"Yanan Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Yanan","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/T10028","display_name":"Topic Modeling","score":0.2054000049829483,"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/T10028","display_name":"Topic Modeling","score":0.2054000049829483,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.13269999623298645,"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.07410000264644623,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5099999904632568},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.40209999680519104},{"id":"https://openalex.org/keywords/invocation","display_name":"Invocation","score":0.3138999938964844},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.2906000018119812}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6784999966621399},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5099999904632568},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.40209999680519104},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3199999928474426},{"id":"https://openalex.org/C2776527387","wikidata":"https://www.wikidata.org/wiki/Q1671839","display_name":"Invocation","level":2,"score":0.3138999938964844},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2906000018119812},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.27559998631477356},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.2721000015735626},{"id":"https://openalex.org/C4668613","wikidata":"https://www.wikidata.org/wiki/Q4116110","display_name":"Structural alignment","level":5,"score":0.24799999594688416},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2434999942779541}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.11322","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11322","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":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.2604.11322","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11322","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":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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"language":[1],"models":[2],"(LLMs)":[3],"have":[4],"demonstrated":[5,176],"impressive":[6],"capabilities":[7],"in":[8,28,50,114,121],"utilizing":[9],"external":[10],"tools.":[11],"In":[12,39],"practice,":[13],"however,":[14],"LLMs":[15,69],"are":[16,22],"often":[17],"exposed":[18],"to":[19,24,35,64,72,82],"tools":[20],"that":[21,96,106,169],"irrelevant":[23],"the":[25,31,66,126],"user's":[26,67],"query,":[27],"which":[29,53,137],"case":[30],"desired":[32],"behavior":[33],"is":[34],"refrain":[36],"from":[37,100],"invocations.":[38],"this":[40,88,130],"work,":[41],"we":[42,54,90,132,163],"identify":[43],"a":[44,61,93,166],"widespread":[45],"yet":[46,116],"overlooked":[47],"mechanistic":[48],"flaw":[49],"tool":[51,62,83,156],"refusal,":[52],"term":[55],"structural":[56,98,107,146,172],"alignment":[57,99,108,173],"bias:":[58],"Even":[59],"when":[60],"fails":[63],"serve":[65],"goal,":[68],"still":[70],"tend":[71],"invoke":[73],"it":[74],"whenever":[75],"query":[76],"attributes":[77],"can":[78],"be":[79],"validly":[80],"assigned":[81],"parameters.":[84],"To":[85,124],"systematically":[86],"study":[87],"bias,":[89,131,174],"introduce":[91,165],"SABEval,":[92],"new":[94],"dataset":[95],"decouples":[97],"semantic":[101,143],"relevance.":[102],"Our":[103],"analysis":[104],"shows":[105],"bias":[109],"induces":[110],"severe":[111],"tool-invocation":[112],"errors":[113],"LLMs,":[115],"remains":[117],"largely":[118],"unaccounted":[119],"for":[120,142],"existing":[122],"evaluations.":[123],"investigate":[125],"internal":[127],"mechanisms":[128],"underlying":[129],"propose":[133],"Contrastive":[134],"Attention":[135],"Attribution,":[136],"reveals":[138],"two":[139],"competing":[140],"pathways":[141,153],"checking":[144],"and":[145],"matching.":[147],"The":[148],"relative":[149],"strength":[150],"of":[151],"these":[152,161],"drives":[154],"LLMs'":[155],"invocation":[157],"decisions.":[158],"Based":[159],"on":[160],"findings,":[162],"further":[164],"rebalancing":[167],"strategy":[168],"effectively":[170],"mitigates":[171],"as":[175],"by":[177],"extensive":[178],"experiments,":[179],"without":[180],"degrading":[181],"general":[182],"tool-use":[183],"capabilities.":[184]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-15T00:00:00"}
