{"id":"https://openalex.org/W7133354896","doi":"https://doi.org/10.48550/arxiv.2603.01326","title":"Truth as a Trajectory: What Internal Representations Reveal About Large Language Model Reasoning","display_name":"Truth as a Trajectory: What Internal Representations Reveal About Large Language Model Reasoning","publication_year":2026,"publication_date":"2026-03-01","ids":{"openalex":"https://openalex.org/W7133354896","doi":"https://doi.org/10.48550/arxiv.2603.01326"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.01326","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01326","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.2603.01326","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5127990180","display_name":"Hamed Damirchi","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Damirchi, Hamed","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127915922","display_name":"Ignacio Meza De la Jara","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"De la Jara, Ignacio Meza","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047006945","display_name":"Ehsan M Abbasnejad","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abbasnejad, Ehsan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004778253","display_name":"Afshar Shamsi","orcid":"https://orcid.org/0000-0002-1122-2149"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shamsi, Afshar","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127934060","display_name":"Zhen Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zhen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Shi, Javen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Javen","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5127990180"],"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.30329999327659607,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.30329999327659607,"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":0.18780000507831573,"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/T13910","display_name":"Computational and Text Analysis Methods","score":0.09870000183582306,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"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/spurious-relationship","display_name":"Spurious relationship","score":0.6790000200271606},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6644999980926514},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.5799000263214111},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5371999740600586},{"id":"https://openalex.org/keywords/commonsense-reasoning","display_name":"Commonsense reasoning","score":0.4855000078678131},{"id":"https://openalex.org/keywords/visual-reasoning","display_name":"Visual reasoning","score":0.45719999074935913},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.42669999599456787},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.41679999232292175}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6887999773025513},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.6790000200271606},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6644999980926514},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5835000276565552},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.5799000263214111},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5371999740600586},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5135999917984009},{"id":"https://openalex.org/C193221554","wikidata":"https://www.wikidata.org/wiki/Q5153664","display_name":"Commonsense reasoning","level":2,"score":0.4855000078678131},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.45719999074935913},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.42669999599456787},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.41679999232292175},{"id":"https://openalex.org/C131598440","wikidata":"https://www.wikidata.org/wiki/Q107342","display_name":"Syllogism","level":2,"score":0.302700012922287},{"id":"https://openalex.org/C107551265","wikidata":"https://www.wikidata.org/wiki/Q1458245","display_name":"Displacement (psychology)","level":2,"score":0.29660001397132874},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.29490000009536743},{"id":"https://openalex.org/C2983448237","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Language understanding","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.2685999870300293},{"id":"https://openalex.org/C2779982483","wikidata":"https://www.wikidata.org/wiki/Q6094420","display_name":"Iterative refinement","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26179999113082886},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.25769999623298645},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.25589999556541443},{"id":"https://openalex.org/C45493050","wikidata":"https://www.wikidata.org/wiki/Q7884934","display_name":"Unified Model","level":2,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.01326","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01326","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.2603.01326","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01326","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Existing":[0],"explainability":[1],"methods":[2],"for":[3],"Large":[4],"Language":[5],"Models":[6],"(LLMs)":[7],"typically":[8],"treat":[9],"hidden":[10],"states":[11],"as":[12,57,66,151],"static":[13,76,141],"points":[14],"in":[15,129],"activation":[16],"space,":[17],"assuming":[18],"that":[19,93,135],"correct":[20],"and":[21,105,116,125,147],"incorrect":[22],"inferences":[23],"can":[24],"be":[25],"separated":[26],"using":[27,126],"representations":[28,86],"from":[29,75,97],"an":[30,67],"individual":[31],"layer.":[32],"However,":[33],"these":[34],"activations":[35,77,123,130],"are":[36],"saturated":[37],"with":[38],"polysemantic":[39],"features,":[40],"leading":[41],"to":[42,78,121],"linear":[43],"probes":[44],"learning":[45],"surface-level":[46],"lexical":[47,142],"patterns":[48],"rather":[49],"than":[50],"underlying":[51],"reasoning":[52,96],"structures.":[53],"We":[54,100],"introduce":[55],"Truth":[56],"a":[58,152],"Trajectory":[59],"(TaT),":[60],"which":[61],"models":[62],"the":[63,122],"transformer":[64],"inference":[65],"unfolded":[68],"trajectory":[69,149],"of":[70,85],"iterative":[71],"refinements,":[72],"shifting":[73],"analysis":[74,150],"layer-wise":[79],"geometric":[80,91],"displacement.":[81],"By":[82],"analyzing":[83],"displacement":[84],"across":[87,103,131],"layers,":[88,132],"TaT":[89,102,136],"uncovers":[90],"invariants":[92],"distinguish":[94],"valid":[95],"spurious":[98],"behavior.":[99],"evaluate":[101],"dense":[104],"Mixture-of-Experts":[106],"(MoE)":[107],"architectures":[108],"on":[109,140,155],"benchmarks":[110],"spanning":[111],"commonsense":[112],"reasoning,":[113],"question":[114],"answering,":[115],"toxicity":[117],"detection.":[118],"Without":[119],"access":[120],"themselves":[124],"only":[127],"changes":[128],"we":[133],"show":[134],"effectively":[137],"mitigates":[138],"reliance":[139],"confounds,":[143],"outperforming":[144],"conventional":[145],"probing,":[146],"establishes":[148],"complementary":[153],"perspective":[154],"LLM":[156],"explainability.":[157]},"counts_by_year":[],"updated_date":"2026-03-25T23:56:10.502304","created_date":"2026-03-04T00:00:00"}