{"id":"https://openalex.org/W7162513886","doi":"https://doi.org/10.48550/arxiv.2605.27033","title":"Tracing Computation Density in LLMs","display_name":"Tracing Computation Density in LLMs","publication_year":2026,"publication_date":"2026-05-26","ids":{"openalex":"https://openalex.org/W7162513886","doi":"https://doi.org/10.48550/arxiv.2605.27033"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.27033","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27033","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.27033","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064783559","display_name":"Corentin Kervadec","orcid":"https://orcid.org/0000-0003-1284-1037"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kervadec, Corentin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119684050","display_name":"Iuliia Lysova","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lysova, Iuliia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047049400","display_name":"Iuri Macocco","orcid":"https://orcid.org/0000-0001-5701-7725"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Macocco, Iuri","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137191056","display_name":"Marco Baroni","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Baroni, Marco","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5026675870","display_name":"Gemma Boleda","orcid":"https://orcid.org/0000-0001-6140-7080"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Boleda, Gemma","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.499099999666214,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.499099999666214,"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/T10028","display_name":"Topic Modeling","score":0.13019999861717224,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.04270000010728836,"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/computation","display_name":"Computation","score":0.8597999811172485},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6331999897956848},{"id":"https://openalex.org/keywords/modular-design","display_name":"Modular design","score":0.6204000115394592},{"id":"https://openalex.org/keywords/tracing","display_name":"Tracing","score":0.52920001745224},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.526199996471405},{"id":"https://openalex.org/keywords/core","display_name":"Core (optical fiber)","score":0.4343999922275543}],"concepts":[{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.8597999811172485},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6331999897956848},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.6204000115394592},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6141999959945679},{"id":"https://openalex.org/C138673069","wikidata":"https://www.wikidata.org/wiki/Q322229","display_name":"Tracing","level":2,"score":0.52920001745224},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.526199996471405},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5192999839782715},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.4343999922275543},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.42579999566078186},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4009999930858612},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30720001459121704},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2526000142097473}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.27033","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27033","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.27033","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27033","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"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":{"Transformer-based":[0],"large":[1],"language":[2],"models":[3],"(LLMs)":[4],"are":[5],"comprised":[6],"of":[7,9,42,62,76,83,100,119],"billions":[8],"parameters":[10],"arranged":[11],"in":[12,59,67,94,107,147,167],"deep":[13],"and":[14,97,128],"wide":[15],"computational":[16],"graphs,":[17],"but":[18],"it":[19],"is":[20,161],"not":[21],"clear":[22],"that":[23,45,116,129,160],"they":[24],"exploit":[25],"their":[26],"full":[27,49,85,110],"capacity":[28],"for":[29],"all":[30],"inputs.":[31],"We":[32,113],"introduce":[33],"the":[34,40,57,81,84,109,117],"s-Trace":[35],"method":[36],"to":[37,64,104],"efficiently":[38],"estimate":[39],"subgraph":[41,73],"size":[43],"s":[44],"best":[46],"approximates":[47],"a":[48,60,143,152,157],"model":[50,86,126],"output.":[51],"With":[52],"this":[53],"method,":[54],"we":[55],"find":[56,114],"computation":[58,121],"variety":[61],"LLMs":[63],"be":[65],"organized":[66],"two":[68],"distinct":[69],"phases.":[70],"A":[71],"small":[72],"mostly":[74,92],"composed":[75],"early-layer":[77,154],"nodes":[78],"can":[79],"reconstruct":[80],"head":[82],"output":[87,111],"distribution.":[88,112],"Adding":[89],"further":[90,162],"nodes,":[91],"located":[93],"later":[95,168],"layers":[96],"increasingly":[98],"consisting":[99],"attention":[101],"heads,":[102],"leads":[103],"incremental":[105],"refinements":[106],"approximating":[108],"moreover":[115],"amount":[118],"necessary":[120],"per":[122],"input":[123],"correlates":[124],"with":[125,151],"uncertainty,":[127],"sparser":[130],"subgraphs":[131],"encode":[132],"shallow":[133],"statistics,":[134],"such":[135],"as":[136],"unigram":[137],"frequency.":[138],"Overall,":[139],"our":[140],"results":[141],"suggest":[142],"consistent":[144],"modular":[145],"organization":[146],"effective":[148],"LLM":[149],"computation,":[150],"sparse":[153],"core":[155],"providing":[156],"rough":[158],"prediction":[159],"refined":[163],"through":[164],"denser":[165],"computations":[166],"layers.":[169]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-28T00:00:00"}