{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:TLM3A3CP6YRT7QHE6HE5YATKGA","short_pith_number":"pith:TLM3A3CP","schema_version":"1.0","canonical_sha256":"9ad9b06c4ff6233fc0e4f1c9dc026a3038adbabae54befb767734408d406b573","source":{"kind":"arxiv","id":"2504.08640","version":1},"attestation_state":"computed","paper":{"title":"Do LLMs trust AI regulation? Emerging behaviour of game-theoretic LLM agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CY","cs.GT","nlin.CD"],"primary_cat":"cs.AI","authors_text":"Adeela Bashir, Alessandro Di Stefano, Alessio Buscemi, Antonio M. Fernandes, Daniele Proverbio, Elias Fernandez Domingos, Fernando P. Santos, Henrique Correia da Fonseca, Manh Hong Duong, Marcus Krellner, Nataliya Balabanova, Ndidi Bianca Ogbo, Paolo Bova, Simon T. Powers, The Anh Han, Theodor Cimpeanu, Zhao Song, Zia Ush Shamszaman","submitted_at":"2025-04-11T15:41:21Z","abstract_excerpt":"There is general agreement that fostering trust and cooperation within the AI development ecosystem is essential to promote the adoption of trustworthy AI systems. By embedding Large Language Model (LLM) agents within an evolutionary game-theoretic framework, this paper investigates the complex interplay between AI developers, regulators and users, modelling their strategic choices under different regulatory scenarios. Evolutionary game theory (EGT) is used to quantitatively model the dilemmas faced by each actor, and LLMs provide additional degrees of complexity and nuances and enable repeate"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2504.08640","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-04-11T15:41:21Z","cross_cats_sorted":["cs.CY","cs.GT","nlin.CD"],"title_canon_sha256":"0d9af69894d27f1bf13895c294d4d85594718d993f9a974be96afe475838fd40","abstract_canon_sha256":"822c12f4e785d543f599cbc52dd1425838153b806af3028bb1a53161f24b9bca"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:47:52.008818Z","signature_b64":"VNjAsVP5IMkLT1QBahTIgvnecvEWl9J6aOU1vFvGx9cw46tfQwWNKXyO7vE991A1gZegLWP6QcbNpslTc8yGBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9ad9b06c4ff6233fc0e4f1c9dc026a3038adbabae54befb767734408d406b573","last_reissued_at":"2026-07-05T10:47:52.008262Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:47:52.008262Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Do LLMs trust AI regulation? Emerging behaviour of game-theoretic LLM agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CY","cs.GT","nlin.CD"],"primary_cat":"cs.AI","authors_text":"Adeela Bashir, Alessandro Di Stefano, Alessio Buscemi, Antonio M. Fernandes, Daniele Proverbio, Elias Fernandez Domingos, Fernando P. Santos, Henrique Correia da Fonseca, Manh Hong Duong, Marcus Krellner, Nataliya Balabanova, Ndidi Bianca Ogbo, Paolo Bova, Simon T. Powers, The Anh Han, Theodor Cimpeanu, Zhao Song, Zia Ush Shamszaman","submitted_at":"2025-04-11T15:41:21Z","abstract_excerpt":"There is general agreement that fostering trust and cooperation within the AI development ecosystem is essential to promote the adoption of trustworthy AI systems. By embedding Large Language Model (LLM) agents within an evolutionary game-theoretic framework, this paper investigates the complex interplay between AI developers, regulators and users, modelling their strategic choices under different regulatory scenarios. Evolutionary game theory (EGT) is used to quantitatively model the dilemmas faced by each actor, and LLMs provide additional degrees of complexity and nuances and enable repeate"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.08640","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2504.08640/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2504.08640","created_at":"2026-07-05T10:47:52.008344+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.08640v1","created_at":"2026-07-05T10:47:52.008344+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.08640","created_at":"2026-07-05T10:47:52.008344+00:00"},{"alias_kind":"pith_short_12","alias_value":"TLM3A3CP6YRT","created_at":"2026-07-05T10:47:52.008344+00:00"},{"alias_kind":"pith_short_16","alias_value":"TLM3A3CP6YRT7QHE","created_at":"2026-07-05T10:47:52.008344+00:00"},{"alias_kind":"pith_short_8","alias_value":"TLM3A3CP","created_at":"2026-07-05T10:47:52.008344+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.28710","citing_title":"The Two Genie Game: Adoption and Welfare in Audit-Grounded AI Governance","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TLM3A3CP6YRT7QHE6HE5YATKGA","json":"https://pith.science/pith/TLM3A3CP6YRT7QHE6HE5YATKGA.json","graph_json":"https://pith.science/api/pith-number/TLM3A3CP6YRT7QHE6HE5YATKGA/graph.json","events_json":"https://pith.science/api/pith-number/TLM3A3CP6YRT7QHE6HE5YATKGA/events.json","paper":"https://pith.science/paper/TLM3A3CP"},"agent_actions":{"view_html":"https://pith.science/pith/TLM3A3CP6YRT7QHE6HE5YATKGA","download_json":"https://pith.science/pith/TLM3A3CP6YRT7QHE6HE5YATKGA.json","view_paper":"https://pith.science/paper/TLM3A3CP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.08640&json=true","fetch_graph":"https://pith.science/api/pith-number/TLM3A3CP6YRT7QHE6HE5YATKGA/graph.json","fetch_events":"https://pith.science/api/pith-number/TLM3A3CP6YRT7QHE6HE5YATKGA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TLM3A3CP6YRT7QHE6HE5YATKGA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TLM3A3CP6YRT7QHE6HE5YATKGA/action/storage_attestation","attest_author":"https://pith.science/pith/TLM3A3CP6YRT7QHE6HE5YATKGA/action/author_attestation","sign_citation":"https://pith.science/pith/TLM3A3CP6YRT7QHE6HE5YATKGA/action/citation_signature","submit_replication":"https://pith.science/pith/TLM3A3CP6YRT7QHE6HE5YATKGA/action/replication_record"}},"created_at":"2026-07-05T10:47:52.008344+00:00","updated_at":"2026-07-05T10:47:52.008344+00:00"}