{"id":"https://openalex.org/W7154381161","doi":"https://doi.org/10.48550/arxiv.2604.10027","title":"SinkTrack: Attention Sink based Context Anchoring for Large Language Models","display_name":"SinkTrack: Attention Sink based Context Anchoring for Large Language Models","publication_year":2026,"publication_date":"2026-04-11","ids":{"openalex":"https://openalex.org/W7154381161","doi":"https://doi.org/10.48550/arxiv.2604.10027"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.10027","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.10027","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"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"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.10027","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133561416","display_name":"Xu Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Xu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133588597","display_name":"Guikun Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Guikun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133603180","display_name":"Wenguan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Wenguan","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.20190000534057617,"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"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.20190000534057617,"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"}},{"id":"https://openalex.org/T14347","display_name":"Big Data and Digital Economy","score":0.08860000222921371,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.0714000016450882,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.626800000667572},{"id":"https://openalex.org/keywords/anchoring","display_name":"Anchoring","score":0.5807999968528748},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.5108000040054321},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.5098000168800354},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5033000111579895},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.45750001072883606},{"id":"https://openalex.org/keywords/context-model","display_name":"Context model","score":0.4528000056743622},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.4334000051021576}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7494000196456909},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.626800000667572},{"id":"https://openalex.org/C18483071","wikidata":"https://www.wikidata.org/wiki/Q168432","display_name":"Anchoring","level":2,"score":0.5807999968528748},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5108000040054321},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.5098000168800354},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5033000111579895},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4586000144481659},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.45750001072883606},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.4528000056743622},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.4334000051021576},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.4235000014305115},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4059999883174896},{"id":"https://openalex.org/C76188268","wikidata":"https://www.wikidata.org/wiki/Q1783165","display_name":"Context effect","level":3,"score":0.383899986743927},{"id":"https://openalex.org/C193221554","wikidata":"https://www.wikidata.org/wiki/Q5153664","display_name":"Commonsense reasoning","level":2,"score":0.36469998955726624},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3458999991416931},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.34290000796318054},{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.33719998598098755},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32749998569488525},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3111000061035156},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.30649998784065247},{"id":"https://openalex.org/C2779458634","wikidata":"https://www.wikidata.org/wiki/Q24963715","display_name":"Debiasing","level":2,"score":0.2980000078678131},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C2779231336","wikidata":"https://www.wikidata.org/wiki/Q7534724","display_name":"Sketch","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C2781020372","wikidata":"https://www.wikidata.org/wiki/Q533093","display_name":"On the fly","level":2,"score":0.2840000092983246},{"id":"https://openalex.org/C188147891","wikidata":"https://www.wikidata.org/wiki/Q147638","display_name":"Cognitive science","level":1,"score":0.26570001244544983},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.26170000433921814}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.10027","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.10027","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.10027","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.10027","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"language":[1],"models":[2],"(LLMs)":[3],"suffer":[4],"from":[5,33,95,175],"hallucination":[6,133],"and":[7,31,86,123,134,146,161,166],"context":[8,76,113,135],"forgetting.":[9],"Prior":[10],"studies":[11],"suggest":[12],"that":[13,130],"attention":[14,51,60],"drift":[15],"is":[16,120,184],"a":[17,45,69],"primary":[18],"cause":[19],"of":[20,44,49,68,178],"these":[21],"problems,":[22],"where":[23],"LLMs'":[24],"focus":[25],"shifts":[26],"towards":[27],"newly":[28],"generated":[29],"tokens":[30],"away":[32],"the":[34,54,62,96,110,115,164,176],"initial":[35,111],"input":[36,97,112],"context.":[37],"To":[38],"counteract":[39],"this,":[40],"we":[41,72],"make":[42],"use":[43],"related,":[46],"intrinsic":[47],"characteristic":[48],"LLMs:":[50],"sink":[52],"--":[53],"tendency":[55],"to":[56,61,109],"consistently":[57],"allocate":[58],"high":[59],"very":[63],"first":[64],"token":[65],"(i.e.,":[66],")":[67],"sequence.":[70],"Concretely,":[71],"propose":[73],"an":[74,83],"advanced":[75],"anchoring":[77],"method,":[78],"SinkTrack,":[79],"which":[80],"treats":[81],"as":[82,92],"information":[84,179],"anchor":[85],"injects":[87],"key":[88],"contextual":[89],"features":[90],"(such":[91],"those":[93],"derived":[94],"image":[98],"or":[99],"instruction)":[100],"into":[101],"its":[102,171],"representation.":[103],"As":[104],"such,":[105],"LLM":[106],"remains":[107],"anchored":[108],"throughout":[114],"entire":[116],"generation":[117],"process.":[118],"SinkTrack":[119,131],"training-free,":[121],"plug-and-play,":[122],"introduces":[124],"negligible":[125],"inference":[126],"overhead.":[127],"Experiments":[128],"demonstrate":[129],"mitigates":[132],"forgetting":[136],"across":[137,158],"both":[138],"textual":[139],"(e.g.,":[140,148],"+21.6%":[141],"on":[142,150],"SQuAD2.0":[143],"with":[144,152],"Llama3.1-8B-Instruct)":[145],"multi-modal":[147],"+22.8%":[149],"M3CoT":[151],"Qwen2.5-VL-7B-Instruct)":[153],"tasks.":[154],"Its":[155],"consistent":[156],"gains":[157],"different":[159],"architectures":[160],"scales":[162],"underscore":[163],"robustness":[165],"generalizability.":[167],"We":[168],"also":[169],"analyze":[170],"underlying":[172],"working":[173],"mechanism":[174],"perspective":[177],"delivery.":[180],"Our":[181],"source":[182],"code":[183],"available":[185],"at":[186],"https://github.com/67L1/SinkTrack.":[187]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-15T00:00:00"}
