{"paper":{"title":"Belief Engine: Configurable and Inspectable Stance Dynamics in Multi-Agent LLM Deliberation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"The Belief Engine makes stance changes in LLM deliberation auditable by extracting arguments and updating beliefs through a log-odds rule controlled by evidence uptake and prior anchoring.","cross_cats":["cs.LG","cs.MA"],"primary_cat":"cs.AI","authors_text":"Damian Dailisan, Joshua C. Yang, Maurice Flechtner, Michiel A. Bakker","submitted_at":"2026-05-14T19:13:12Z","abstract_excerpt":"LLM-based agents are increasingly used to simulate deliberative interactions such as negotiation, conflict resolution, and multi-turn opinion exchange. Yet generated transcripts often do not reveal why an agent's stance changes: movement may reflect evidence uptake, anchoring, role drift, echoing, or changed prompt and retrieval context. We introduce the Belief Engine (BE), an auditable belief-update layer that treats \"belief\" as an evidential state over a proposition and exposes it as scalar stance. BE extracts arguments into structured memory and updates stance with a log-odds rule controlle"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"BE best reconstructs participants whose final stance follows extracted evidence; stable and evidence-opposed cases instead point to anchoring or factors outside the extracted evidence stream.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the log-odds update rule controlled by evidence uptake u and prior anchoring a, together with the argument extraction step, sufficiently captures the actual mechanisms driving stance change in both LLM agents and human participants on the DEBATE dataset.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Belief Engine is a configurable belief-update mechanism for multi-agent LLM systems that uses structured argument extraction and log-odds stance updates to make evidence-grounded deliberation inspectable and controllable.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"The Belief Engine makes stance changes in LLM deliberation auditable by extracting arguments and updating beliefs through a log-odds rule controlled by evidence uptake and prior anchoring.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"bb2878df1cd7064450a0bd31eadfbb0abe56e818ec0c5dc8eeac5ecb3fcfcef4"},"source":{"id":"2605.15343","kind":"arxiv","version":1},"verdict":{"id":"dd2c0541-f555-46ec-930f-23e2ff25f829","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T15:53:51.226444Z","strongest_claim":"BE best reconstructs participants whose final stance follows extracted evidence; stable and evidence-opposed cases instead point to anchoring or factors outside the extracted evidence stream.","one_line_summary":"Belief Engine is a configurable belief-update mechanism for multi-agent LLM systems that uses structured argument extraction and log-odds stance updates to make evidence-grounded deliberation inspectable and controllable.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the log-odds update rule controlled by evidence uptake u and prior anchoring a, together with the argument extraction step, sufficiently captures the actual mechanisms driving stance change in both LLM agents and human participants on the DEBATE dataset.","pith_extraction_headline":"The Belief Engine makes stance changes in LLM deliberation auditable by extracting arguments and updating beliefs through a log-odds rule controlled by evidence uptake and prior anchoring."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.15343/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_compliance","ran_at":"2026-05-19T16:04:47.699579Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T16:01:18.116954Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T14:21:54.209184Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T13:33:22.754674Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"44968218a411621f21c5828dbeee6dbd1d28a68cd3f74beaa050946807da509c"},"references":{"count":77,"sample":[{"doi":"10.18653/v1/2024.emnlp-main.486","year":2024,"title":"Knowledge Conflicts for LLM s: A Survey","work_id":"9c01124d-3717-4786-be2a-0a015e22089a","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"arXiv preprint arXiv:2504.19622 , year =","work_id":"d44ce627-3319-4eeb-9ccd-815b15cea9fb","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Bayesian teaching enables probabilistic reasoning in large language models","work_id":"fc63e46c-1d8b-4f4b-9977-114097d9cdd2","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.18653/v1/2024.emnlp-main.16","year":2024,"title":"Systematic Biases in LLM Simulations of Debates","work_id":"a02043a4-9f45-412d-a4d8-54af0933d2ce","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"arXiv preprint arXiv:2512.18489 , year =","work_id":"b26bd33d-758c-427c-970b-5969d3933530","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":77,"snapshot_sha256":"34183206a2866a492c6ed0a185a05b11d1cbb3f3cf96c0b029b2b2186c6a67f8","internal_anchors":4},"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"}