{"id":"https://openalex.org/W7166803104","doi":"https://doi.org/10.18653/v1/2026.findings-acl.973","title":"MemTR: Enhancing Tool-Calling Reliability via Uncertainty-Triggered FFN-Space Retracing","display_name":"MemTR: Enhancing Tool-Calling Reliability via Uncertainty-Triggered FFN-Space Retracing","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7166803104","doi":"https://doi.org/10.18653/v1/2026.findings-acl.973"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2026.findings-acl.973","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.973","pdf_url":"https://aclanthology.org/2026.findings-acl.973.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2026.findings-acl.973.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139783361","display_name":"Hongtao Duan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hongtao Duan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139748964","display_name":"Lu Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu Jiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139761564","display_name":"Minying Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Minying Zhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139766519","display_name":"Xiaobing Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiaobing Zhu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139763914","display_name":"Tianpeng Bu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tianpeng Bu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139766881","display_name":"Hao Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hao Jiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139824383","display_name":"Xinyu Wei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xinyu Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139844688","display_name":"Lulu hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lulu hu","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":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.79242473,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"19476","last_page":"19493"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.13459999859333038,"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":0.13459999859333038,"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":0.1298999935388565,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.03700000047683716,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/reliability","display_name":"Reliability (semiconductor)","score":0.5570999979972839},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.3027999997138977},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.27079999446868896},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.24879999458789825},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.23919999599456787}],"concepts":[{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5570999979972839},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5537999868392944},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.4237000048160553},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3027999997138977},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.27129998803138733},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.24879999458789825},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.23919999599456787},{"id":"https://openalex.org/C17500928","wikidata":"https://www.wikidata.org/wiki/Q959968","display_name":"Control system","level":2,"score":0.23849999904632568},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.23839999735355377}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2026.findings-acl.973","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.973","pdf_url":"https://aclanthology.org/2026.findings-acl.973.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2026.findings-acl.973","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.973","pdf_url":"https://aclanthology.org/2026.findings-acl.973.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7166803104.pdf","grobid_xml":"https://content.openalex.org/works/W7166803104.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Tool":[0,108],"calling":[1,48,157],"requires":[2],"Large":[3],"Language":[4],"Models":[5],"(LLMs)":[6],"to":[7],"generate":[8],"structured":[9],"decisions":[10],"including":[11],"tool":[12,38,47,118,122,156],"names":[13,71],"and":[14,40,72,81,98,124,147,149],"schema-constrained":[15],"arguments,":[16],"where":[17],"small":[18],"decoding":[19],"mistakes":[20],"can":[21],"cause":[22],"hard":[23],"failures.Existing":[24],"methods":[25],"either":[26],"rely":[27],"on":[28,67,79,141],"costly":[29],"tool-use":[30,171],"training":[31,172],"data":[32],"or":[33,101,169],"only":[34,162],"constrain":[35],"syntax,":[36],"leaving":[37],"selection":[39],"argument":[41,73],"value":[42],"errors":[43],"largely":[44],"unsolved.We":[45],"analyze":[46],"failures":[49,55,158],"through":[50],"a":[51,111],"Where-When":[52],"lens:":[53],"(Where)":[54],"correlate":[56],"with":[57,161],"persistent":[58],"uncertainty":[59,65],"in":[60],"late":[61],"transformer":[62,86],"layers,":[63],"(When)":[64],"concentrates":[66],"content-bearing":[68],"tokens":[69],"(tool":[70],"values)":[74],"rather":[75],"than":[76],"schema":[77],"tokens.Based":[78],"this,":[80],"motivated":[82],"by":[83,159],"evidence":[84,119],"that":[85,96,115],"Feed":[87],"Forward":[88],"Networks":[89],"(FFNs)":[90],"act":[91],"as":[92,136],"key-value":[93,137],"style":[94],"memories":[95],"store":[97],"retrieve":[99],"factual":[100],"associative":[102],"mappings,":[103],"we":[104],"propose":[105],"Memory":[106],"Space":[107],"Retracing":[109],"(MemTR),":[110],"weight-free":[112],"decoding-time":[113],"method":[114],"retrieves":[116],"relevant":[117],"from":[120],"the":[121,128,131],"library":[123],"mixes":[125],"it":[126],"into":[127],"FFN-output":[129],"at":[130],"uncertain":[132],"layer,":[133],"treating":[134],"FFNs":[135],"memories.Through":[138],"extensive":[139],"experiments":[140],"various":[142],"model":[143],"families":[144],"(Qwen,":[145],"Llama,":[146],"xLAM)":[148],"benchmarks":[150],"(BFCL,":[151],"ACEBench,":[152],"APIBank),":[153],"MemTR":[154],"reduces":[155],"2%-9%":[160],"1%-2%":[163],"runtime":[164],"overhead,":[165],"without":[166],"any":[167],"fine-tuning":[168],"additional":[170],"data.":[173]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-07-02T00:00:00"}