{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:QBOTEC6SKWTPTVU5MP6OVGIKHF","short_pith_number":"pith:QBOTEC6S","schema_version":"1.0","canonical_sha256":"805d320bd255a6f9d69d63fcea990a39401a6b3e47e175995814577a2d6b9402","source":{"kind":"arxiv","id":"2606.08397","version":1},"attestation_state":"computed","paper":{"title":"TrustMargin: Training-Free Arbitration between Parametric Memory and Retrieved Evidence in Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CL","authors_text":"Hong Shi, Jingyan Xu, Ningyuan Li, Penghui Liu, Xueyang Liu, Yi Shan, Yunhao Bai","submitted_at":"2026-06-07T01:25:09Z","abstract_excerpt":"Large language models answer knowledge-intensive questions using both parametric memory and retrieved evidence, but neither source is uniformly reliable. Retrieval can fill knowledge gaps, yet distracting passages may override correct closed-book answers. We study this post-generation conflict as answer-level source arbitration: given Direct and RAG answers from the same frozen model, decide which source to trust. We propose TRUSTMARGIN, a training-free, plug-and-play arbitration layer that scores the two existing candidates with the model's own likelihoods. It combines a parametric-prior marg"},"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":"2606.08397","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-06-07T01:25:09Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"dc953494aa2d3b6352b7d3cef81193ace2fa9550d8f40d79195f8fed88cf7d8a","abstract_canon_sha256":"8e1d445f0a220efa7c49becd5b8667e8ef3be06dce04d7828af1fb1ee4e8e7c5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T01:05:35.599348Z","signature_b64":"kLTNkumjDXb+gPIhQMhHOaEBxxuymjM79xc9dFjMCQ6+iWhtSMUca/oYsMKKUkK0Dc00UkfXJRwUY6lq4YopDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"805d320bd255a6f9d69d63fcea990a39401a6b3e47e175995814577a2d6b9402","last_reissued_at":"2026-06-09T01:05:35.598919Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T01:05:35.598919Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TrustMargin: Training-Free Arbitration between Parametric Memory and Retrieved Evidence in Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CL","authors_text":"Hong Shi, Jingyan Xu, Ningyuan Li, Penghui Liu, Xueyang Liu, Yi Shan, Yunhao Bai","submitted_at":"2026-06-07T01:25:09Z","abstract_excerpt":"Large language models answer knowledge-intensive questions using both parametric memory and retrieved evidence, but neither source is uniformly reliable. Retrieval can fill knowledge gaps, yet distracting passages may override correct closed-book answers. We study this post-generation conflict as answer-level source arbitration: given Direct and RAG answers from the same frozen model, decide which source to trust. We propose TRUSTMARGIN, a training-free, plug-and-play arbitration layer that scores the two existing candidates with the model's own likelihoods. It combines a parametric-prior marg"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.08397","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/2606.08397/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":"2606.08397","created_at":"2026-06-09T01:05:35.598986+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.08397v1","created_at":"2026-06-09T01:05:35.598986+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.08397","created_at":"2026-06-09T01:05:35.598986+00:00"},{"alias_kind":"pith_short_12","alias_value":"QBOTEC6SKWTP","created_at":"2026-06-09T01:05:35.598986+00:00"},{"alias_kind":"pith_short_16","alias_value":"QBOTEC6SKWTPTVU5","created_at":"2026-06-09T01:05:35.598986+00:00"},{"alias_kind":"pith_short_8","alias_value":"QBOTEC6S","created_at":"2026-06-09T01:05:35.598986+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QBOTEC6SKWTPTVU5MP6OVGIKHF","json":"https://pith.science/pith/QBOTEC6SKWTPTVU5MP6OVGIKHF.json","graph_json":"https://pith.science/api/pith-number/QBOTEC6SKWTPTVU5MP6OVGIKHF/graph.json","events_json":"https://pith.science/api/pith-number/QBOTEC6SKWTPTVU5MP6OVGIKHF/events.json","paper":"https://pith.science/paper/QBOTEC6S"},"agent_actions":{"view_html":"https://pith.science/pith/QBOTEC6SKWTPTVU5MP6OVGIKHF","download_json":"https://pith.science/pith/QBOTEC6SKWTPTVU5MP6OVGIKHF.json","view_paper":"https://pith.science/paper/QBOTEC6S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.08397&json=true","fetch_graph":"https://pith.science/api/pith-number/QBOTEC6SKWTPTVU5MP6OVGIKHF/graph.json","fetch_events":"https://pith.science/api/pith-number/QBOTEC6SKWTPTVU5MP6OVGIKHF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QBOTEC6SKWTPTVU5MP6OVGIKHF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QBOTEC6SKWTPTVU5MP6OVGIKHF/action/storage_attestation","attest_author":"https://pith.science/pith/QBOTEC6SKWTPTVU5MP6OVGIKHF/action/author_attestation","sign_citation":"https://pith.science/pith/QBOTEC6SKWTPTVU5MP6OVGIKHF/action/citation_signature","submit_replication":"https://pith.science/pith/QBOTEC6SKWTPTVU5MP6OVGIKHF/action/replication_record"}},"created_at":"2026-06-09T01:05:35.598986+00:00","updated_at":"2026-06-09T01:05:35.598986+00:00"}