{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:ZXUYMY3LTCLLRKGYCUC7RPLWGY","short_pith_number":"pith:ZXUYMY3L","schema_version":"1.0","canonical_sha256":"cde986636b9896b8a8d81505f8bd7636191df21e28b73f5c50f1b91bcd3cc1c1","source":{"kind":"arxiv","id":"2601.10896","version":2},"attestation_state":"computed","paper":{"title":"DialDefer: A Framework for Detecting and Mitigating LLM Dialogic Deference","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Aishee Mondal, Dilek Hakkani-T\\\"ur, Harshita Ketharaman, Nimet Beyza Bozdag, Parisa Rabbani, Priyam Sahoo, Ruben Mathew","submitted_at":"2026-01-15T22:50:46Z","abstract_excerpt":"LLMs are increasingly used as third-party judges, yet their reliability when evaluating speakers in dialogue remains poorly understood. We show that LLMs judge identical claims differently depending on framing: the same content receives different verdicts when presented as a statement to verify (\"Is this statement correct?\") versus attributed to a speaker (\"Is this speaker correct?\"). We call this dialogic deference and introduce DialDefer, a framework for detecting and mitigating these framing-induced judgment shifts. Our Dialogic Deference Score (DDS) captures directional shifts that aggrega"},"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":"2601.10896","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-01-15T22:50:46Z","cross_cats_sorted":[],"title_canon_sha256":"4ea60d40d8618179da8fb37976bc407a1747b1612494104464e203aeb42bb01c","abstract_canon_sha256":"9d0a9b7a6874c0a22c466ae7cf3e70b3cb654a98e301686b00314f0fb87fec41"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-08T01:03:55.560079Z","signature_b64":"Ics+I1lkv3iEYDxZz3EUOdJ8CrZwyS0j5fokweBoKnh9dIwkbEdD7qNPF+iodrBRxU0rTqHPC0Be5kIO35w9Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cde986636b9896b8a8d81505f8bd7636191df21e28b73f5c50f1b91bcd3cc1c1","last_reissued_at":"2026-06-08T01:03:55.558956Z","signature_status":"signed_v1","first_computed_at":"2026-06-08T01:03:55.558956Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DialDefer: A Framework for Detecting and Mitigating LLM Dialogic Deference","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Aishee Mondal, Dilek Hakkani-T\\\"ur, Harshita Ketharaman, Nimet Beyza Bozdag, Parisa Rabbani, Priyam Sahoo, Ruben Mathew","submitted_at":"2026-01-15T22:50:46Z","abstract_excerpt":"LLMs are increasingly used as third-party judges, yet their reliability when evaluating speakers in dialogue remains poorly understood. We show that LLMs judge identical claims differently depending on framing: the same content receives different verdicts when presented as a statement to verify (\"Is this statement correct?\") versus attributed to a speaker (\"Is this speaker correct?\"). We call this dialogic deference and introduce DialDefer, a framework for detecting and mitigating these framing-induced judgment shifts. Our Dialogic Deference Score (DDS) captures directional shifts that aggrega"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.10896","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/2601.10896/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":"2601.10896","created_at":"2026-06-08T01:03:55.559097+00:00"},{"alias_kind":"arxiv_version","alias_value":"2601.10896v2","created_at":"2026-06-08T01:03:55.559097+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.10896","created_at":"2026-06-08T01:03:55.559097+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZXUYMY3LTCLL","created_at":"2026-06-08T01:03:55.559097+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZXUYMY3LTCLLRKGY","created_at":"2026-06-08T01:03:55.559097+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZXUYMY3L","created_at":"2026-06-08T01:03:55.559097+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2605.12991","citing_title":"Not Just RLHF: Why Alignment Alone Won't Fix Multi-Agent Sycophancy","ref_index":28,"is_internal_anchor":true},{"citing_arxiv_id":"2605.12991","citing_title":"Not Just RLHF: Why Alignment Alone Won't Fix Multi-Agent Sycophancy","ref_index":28,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZXUYMY3LTCLLRKGYCUC7RPLWGY","json":"https://pith.science/pith/ZXUYMY3LTCLLRKGYCUC7RPLWGY.json","graph_json":"https://pith.science/api/pith-number/ZXUYMY3LTCLLRKGYCUC7RPLWGY/graph.json","events_json":"https://pith.science/api/pith-number/ZXUYMY3LTCLLRKGYCUC7RPLWGY/events.json","paper":"https://pith.science/paper/ZXUYMY3L"},"agent_actions":{"view_html":"https://pith.science/pith/ZXUYMY3LTCLLRKGYCUC7RPLWGY","download_json":"https://pith.science/pith/ZXUYMY3LTCLLRKGYCUC7RPLWGY.json","view_paper":"https://pith.science/paper/ZXUYMY3L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2601.10896&json=true","fetch_graph":"https://pith.science/api/pith-number/ZXUYMY3LTCLLRKGYCUC7RPLWGY/graph.json","fetch_events":"https://pith.science/api/pith-number/ZXUYMY3LTCLLRKGYCUC7RPLWGY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZXUYMY3LTCLLRKGYCUC7RPLWGY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZXUYMY3LTCLLRKGYCUC7RPLWGY/action/storage_attestation","attest_author":"https://pith.science/pith/ZXUYMY3LTCLLRKGYCUC7RPLWGY/action/author_attestation","sign_citation":"https://pith.science/pith/ZXUYMY3LTCLLRKGYCUC7RPLWGY/action/citation_signature","submit_replication":"https://pith.science/pith/ZXUYMY3LTCLLRKGYCUC7RPLWGY/action/replication_record"}},"created_at":"2026-06-08T01:03:55.559097+00:00","updated_at":"2026-06-08T01:03:55.559097+00:00"}