{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:VWRDN6PKPFOJR62GK5LGJAE6YZ","short_pith_number":"pith:VWRDN6PK","schema_version":"1.0","canonical_sha256":"ada236f9ea795c98fb46575664809ec67d9ea009ccc5e3158116ced08fe56d86","source":{"kind":"arxiv","id":"2406.15492","version":2},"attestation_state":"computed","paper":{"title":"On the Principles behind Opinion Dynamics in Multi-Agent Systems of Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","physics.soc-ph"],"primary_cat":"cs.MA","authors_text":"Pedro Cisneros-Velarde","submitted_at":"2024-06-18T18:37:23Z","abstract_excerpt":"We study the evolution of opinions inside a population of interacting large language models (LLMs). Every LLM needs to decide how much funding to allocate to an item with three initial possibilities: full, partial, or no funding. We identify biases that drive the exchange of opinions based on the LLM's tendency to find consensus with the other LLM's opinion, display caution when specifying funding, and consider ethical concerns in its opinion. We find these biases are affected by the perceived absence of compelling reasons for opinion change, the perceived willingness to engage in discussion, "},"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":"2406.15492","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MA","submitted_at":"2024-06-18T18:37:23Z","cross_cats_sorted":["cs.LG","physics.soc-ph"],"title_canon_sha256":"bf72e9d264fcd2f609fc2ceca7599a27a16a8aa286ca08b94f8ec6f3509a68f4","abstract_canon_sha256":"83a387f2586636be8324d4f1e43b4def48d7b020884d6f5a5d2b3e2e1f4c2b4b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:11:27.104995Z","signature_b64":"yC2qBblKOpcAquSvx4qfF6muYCbPYBTEpGlpAE8hbOGoaSjRDf8m8zfkolfGcn75FiKvBOPI9ntbDwmMourDBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ada236f9ea795c98fb46575664809ec67d9ea009ccc5e3158116ced08fe56d86","last_reissued_at":"2026-07-05T09:11:27.104468Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:11:27.104468Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On the Principles behind Opinion Dynamics in Multi-Agent Systems of Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","physics.soc-ph"],"primary_cat":"cs.MA","authors_text":"Pedro Cisneros-Velarde","submitted_at":"2024-06-18T18:37:23Z","abstract_excerpt":"We study the evolution of opinions inside a population of interacting large language models (LLMs). Every LLM needs to decide how much funding to allocate to an item with three initial possibilities: full, partial, or no funding. We identify biases that drive the exchange of opinions based on the LLM's tendency to find consensus with the other LLM's opinion, display caution when specifying funding, and consider ethical concerns in its opinion. We find these biases are affected by the perceived absence of compelling reasons for opinion change, the perceived willingness to engage in discussion, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.15492","kind":"arxiv","version":2},"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/2406.15492/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":"2406.15492","created_at":"2026-07-05T09:11:27.104546+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.15492v2","created_at":"2026-07-05T09:11:27.104546+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.15492","created_at":"2026-07-05T09:11:27.104546+00:00"},{"alias_kind":"pith_short_12","alias_value":"VWRDN6PKPFOJ","created_at":"2026-07-05T09:11:27.104546+00:00"},{"alias_kind":"pith_short_16","alias_value":"VWRDN6PKPFOJR62G","created_at":"2026-07-05T09:11:27.104546+00:00"},{"alias_kind":"pith_short_8","alias_value":"VWRDN6PK","created_at":"2026-07-05T09:11:27.104546+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.22203","citing_title":"When Is Emergent Consensus Real? A Measured Coupling Gain and a Validity Diagnostic for LLM Agent Societies","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2606.28456","citing_title":"Is Lying an Emergent Behaviour in LLMs? Evidence from Gaslighting AI agents in a Sustainability Game","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01147","citing_title":"Position: Safety and Fairness in Agentic AI Depend on Interaction Topology, Not on Model Scale or Alignment","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2604.11312","citing_title":"Network Effects and Agreement Drift in LLM Debates","ref_index":60,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VWRDN6PKPFOJR62GK5LGJAE6YZ","json":"https://pith.science/pith/VWRDN6PKPFOJR62GK5LGJAE6YZ.json","graph_json":"https://pith.science/api/pith-number/VWRDN6PKPFOJR62GK5LGJAE6YZ/graph.json","events_json":"https://pith.science/api/pith-number/VWRDN6PKPFOJR62GK5LGJAE6YZ/events.json","paper":"https://pith.science/paper/VWRDN6PK"},"agent_actions":{"view_html":"https://pith.science/pith/VWRDN6PKPFOJR62GK5LGJAE6YZ","download_json":"https://pith.science/pith/VWRDN6PKPFOJR62GK5LGJAE6YZ.json","view_paper":"https://pith.science/paper/VWRDN6PK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.15492&json=true","fetch_graph":"https://pith.science/api/pith-number/VWRDN6PKPFOJR62GK5LGJAE6YZ/graph.json","fetch_events":"https://pith.science/api/pith-number/VWRDN6PKPFOJR62GK5LGJAE6YZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VWRDN6PKPFOJR62GK5LGJAE6YZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VWRDN6PKPFOJR62GK5LGJAE6YZ/action/storage_attestation","attest_author":"https://pith.science/pith/VWRDN6PKPFOJR62GK5LGJAE6YZ/action/author_attestation","sign_citation":"https://pith.science/pith/VWRDN6PKPFOJR62GK5LGJAE6YZ/action/citation_signature","submit_replication":"https://pith.science/pith/VWRDN6PKPFOJR62GK5LGJAE6YZ/action/replication_record"}},"created_at":"2026-07-05T09:11:27.104546+00:00","updated_at":"2026-07-05T09:11:27.104546+00:00"}