{"id":"https://openalex.org/W7154317489","doi":"https://doi.org/10.48550/arxiv.2604.09746","title":"CONSCIENTIA: Can LLM Agents Learn to Strategize? Emergent Deception and Trust in a Multi-Agent NYC Simulation","display_name":"CONSCIENTIA: Can LLM Agents Learn to Strategize? Emergent Deception and Trust in a Multi-Agent NYC Simulation","publication_year":2026,"publication_date":"2026-04-10","ids":{"openalex":"https://openalex.org/W7154317489","doi":"https://doi.org/10.48550/arxiv.2604.09746"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.09746","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09746","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.2604.09746","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109699100","display_name":"Aarush Sinha","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Sinha, Aarush","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122939611","display_name":"Arion Das","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Das, Arion","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079014485","display_name":"Soumyadeep Nag","orcid":"https://orcid.org/0000-0001-7571-4137"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nag, Soumyadeep","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119468804","display_name":"Charan Karnati","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Karnati, Charan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104266908","display_name":"Shravani Nag","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nag, Shravani","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133568366","display_name":"Chandra Vadhan Raj","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Raj, Chandra Vadhan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133552660","display_name":"Aman Chadha","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chadha, Aman","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133579432","display_name":"Vinija Jain","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jain, Vinija","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129784724","display_name":"Suranjana Trivedy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Trivedy, Suranjana","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133578278","display_name":"Amitava Das","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Das, Amitava","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":10,"corresponding_author_ids":["https://openalex.org/A5109699100"],"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.1762000024318695,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.1762000024318695,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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":0.07800000160932541,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.07180000096559525,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"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/deception","display_name":"Deception","score":0.7307000160217285},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.6945000290870667},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6777999997138977},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.629800021648407},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.6025000214576721},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5490000247955322},{"id":"https://openalex.org/keywords/forcing","display_name":"Forcing (mathematics)","score":0.5263000130653381},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.45739999413490295}],"concepts":[{"id":"https://openalex.org/C2779267917","wikidata":"https://www.wikidata.org/wiki/Q170028","display_name":"Deception","level":2,"score":0.7307000160217285},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.6945000290870667},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6777999997138977},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6748999953269958},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.629800021648407},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.6025000214576721},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5490000247955322},{"id":"https://openalex.org/C197115733","wikidata":"https://www.wikidata.org/wiki/Q1003136","display_name":"Forcing (mathematics)","level":2,"score":0.5263000130653381},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.45739999413490295},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.4260999858379364},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.4065000116825104},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.4034000039100647},{"id":"https://openalex.org/C2779436431","wikidata":"https://www.wikidata.org/wiki/Q30672407","display_name":"Policy learning","level":2,"score":0.3686000108718872},{"id":"https://openalex.org/C13687954","wikidata":"https://www.wikidata.org/wiki/Q4826847","display_name":"Autonomous agent","level":2,"score":0.3564999997615814},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3564999997615814},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.3452000021934509},{"id":"https://openalex.org/C74072328","wikidata":"https://www.wikidata.org/wiki/Q1142726","display_name":"Intelligent agent","level":2,"score":0.3357999920845032},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.29910001158714294},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2676999866962433},{"id":"https://openalex.org/C79416737","wikidata":"https://www.wikidata.org/wiki/Q2305519","display_name":"Social learning","level":2,"score":0.267300009727478},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.2669999897480011},{"id":"https://openalex.org/C41065033","wikidata":"https://www.wikidata.org/wiki/Q2825412","display_name":"Adversary","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C140547941","wikidata":"https://www.wikidata.org/wiki/Q7797194","display_name":"Threat model","level":2,"score":0.2615000009536743},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.26080000400543213}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.09746","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09746","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.2604.09746","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09746","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":{"As":[0],"large":[1],"language":[2,87],"models":[3],"(LLMs)":[4],"are":[5,130],"increasingly":[6],"deployed":[7],"as":[8],"autonomous":[9],"agents,":[10],"understanding":[11],"how":[12],"strategic":[13,38,205],"behavior":[14,39],"emerges":[15],"in":[16,36,52],"multi-agent":[17,50],"environments":[18],"has":[19],"become":[20],"an":[21,112],"important":[22],"alignment":[23],"challenge.":[24],"We":[25,46,107],"take":[26],"a":[27,33,48,53,178],"neutral":[28],"empirical":[29],"stance":[30],"and":[31,44,210],"construct":[32],"controlled":[34],"environment":[35],"which":[37],"can":[40,202],"be":[41],"directly":[42],"observed":[43],"measured.":[45],"introduce":[47],"large-scale":[49],"simulation":[51,114],"simplified":[54],"model":[55],"of":[56],"New":[57],"York":[58],"City,":[59],"where":[60],"LLM-driven":[61],"agents":[62,68,77,99,129,142,201],"interact":[63],"under":[64],"opposing":[65],"incentives.":[66],"Blue":[67,128,152],"aim":[69],"to":[70,79,88,100,103,132,144,159,216],"reach":[71],"their":[72],"destinations":[73],"efficiently,":[74],"while":[75,136,173,212],"Red":[76,141],"attempt":[78],"divert":[80],"them":[81],"toward":[82],"billboard-heavy":[83],"routes":[84],"using":[85,124],"persuasive":[86],"maximize":[89,192],"advertising":[90],"revenue.":[91],"Hidden":[92],"identities":[93],"make":[94],"navigation":[95,138],"socially":[96],"mediated,":[97],"forcing":[98],"decide":[101],"when":[102],"trust":[104,209],"or":[105],"deceive.":[106],"study":[108],"policy":[109,153],"learning":[110],"through":[111],"iterative":[113],"pipeline":[115],"that":[116,184,199],"updates":[117],"agent":[118],"policies":[119,168,183],"across":[120],"repeated":[121],"interaction":[122],"rounds":[123],"Kahneman-Tversky":[125],"Optimization":[126],"(KTO).":[127],"optimized":[131],"reduce":[133],"billboard":[134],"exposure":[135],"preserving":[137,174],"efficiency,":[139],"whereas":[140],"adapt":[143],"exploit":[145],"remaining":[146,213],"weaknesses.":[147],"Across":[148],"iterations,":[149],"the":[150],"best":[151],"improves":[154],"task":[155,193],"success":[156],"from":[157],"46.0%":[158],"57.3%,":[160],"although":[161],"susceptibility":[162],"remains":[163],"high":[164],"at":[165],"70.7%.":[166],"Later":[167],"exhibit":[169,203],"stronger":[170],"selective":[171,208],"cooperation":[172],"trajectory":[175],"efficiency.":[176],"However,":[177],"persistent":[179],"safety-helpfulness":[180],"trade-off":[181],"remains:":[182],"better":[185],"resist":[186],"adversarial":[187,217],"steering":[188],"do":[189],"not":[190],"simultaneously":[191],"completion.":[194],"Overall,":[195],"our":[196],"results":[197],"show":[198],"LLM":[200],"limited":[204],"behavior,":[206],"including":[207],"deception,":[211],"highly":[214],"vulnerable":[215],"persuasion.":[218]},"counts_by_year":[],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2026-04-15T00:00:00"}