{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:W6HPV73L2KN4KHGRCT5KAQUF6V","short_pith_number":"pith:W6HPV73L","canonical_record":{"source":{"id":"2412.14581","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-12-19T07:01:25Z","cross_cats_sorted":[],"title_canon_sha256":"36d53f1a72599256723d7b8fe0a30a7ca39794adb8155841652c2ecb673747cd","abstract_canon_sha256":"142bed152362e3e9cbae11729f8abb4da203f78afa94a869d2d1ac5b3ccdeb22"},"schema_version":"1.0"},"canonical_sha256":"b78efaff6bd29bc51cd114faa04285f5767d2528b7c13fc6fa21a8a57a835647","source":{"kind":"arxiv","id":"2412.14581","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.14581","created_at":"2026-07-05T09:51:49Z"},{"alias_kind":"arxiv_version","alias_value":"2412.14581v1","created_at":"2026-07-05T09:51:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.14581","created_at":"2026-07-05T09:51:49Z"},{"alias_kind":"pith_short_12","alias_value":"W6HPV73L2KN4","created_at":"2026-07-05T09:51:49Z"},{"alias_kind":"pith_short_16","alias_value":"W6HPV73L2KN4KHGR","created_at":"2026-07-05T09:51:49Z"},{"alias_kind":"pith_short_8","alias_value":"W6HPV73L","created_at":"2026-07-05T09:51:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:W6HPV73L2KN4KHGRCT5KAQUF6V","target":"record","payload":{"canonical_record":{"source":{"id":"2412.14581","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-12-19T07:01:25Z","cross_cats_sorted":[],"title_canon_sha256":"36d53f1a72599256723d7b8fe0a30a7ca39794adb8155841652c2ecb673747cd","abstract_canon_sha256":"142bed152362e3e9cbae11729f8abb4da203f78afa94a869d2d1ac5b3ccdeb22"},"schema_version":"1.0"},"canonical_sha256":"b78efaff6bd29bc51cd114faa04285f5767d2528b7c13fc6fa21a8a57a835647","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:51:49.673966Z","signature_b64":"nq76h/TeucixL5XAPOpIbtSd3lz8EgJMEdJlYsWpnsMqn7Qqh6/fVQUYBuGpD4uJls3wgok5q3H2oEEjgltJBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b78efaff6bd29bc51cd114faa04285f5767d2528b7c13fc6fa21a8a57a835647","last_reissued_at":"2026-07-05T09:51:49.673493Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:51:49.673493Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.14581","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:51:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Vc6cg5/c9Z5bfQO8IbWp71alUCvpEMPIclJgW8s1p1WzlWjMINy6fa+LjuhQ9cLyxdZgLO0nqjGgoN55DnTCCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T02:31:01.480049Z"},"content_sha256":"d4a1ec4334d43141e0bc5864d11687486d8a9e4342bbabd9a745b9c49511e014","schema_version":"1.0","event_id":"sha256:d4a1ec4334d43141e0bc5864d11687486d8a9e4342bbabd9a745b9c49511e014"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:W6HPV73L2KN4KHGRCT5KAQUF6V","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CORD: Balancing COnsistency and Rank Distillation for Robust Retrieval-Augmented Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Daniel Campos, Filip Grali\\'nski, Seung-won Hwang, Youngwon Lee, Yuxiong He, Zhewei Yao","submitted_at":"2024-12-19T07:01:25Z","abstract_excerpt":"With the adoption of retrieval-augmented generation (RAG), large language models (LLMs) are expected to ground their generation to the retrieved contexts. Yet, this is hindered by position bias of LLMs, failing to evenly attend to all contexts. Previous work has addressed this by synthesizing contexts with perturbed positions of gold segment, creating a position-diversified train set. We extend this intuition to propose consistency regularization with augmentation and distillation. First, we augment each training instance with its position perturbation to encourage consistent predictions, rega"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.14581","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/2412.14581/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:51:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MeiivPAfLyYTA9eNW2QyfZWIwdLhcOKMx6bc88sBPTbElgQXXpO572GY4bePGHil5FSqTY3YyB1RS6K5o0+NBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T02:31:01.480429Z"},"content_sha256":"6d8966a7210d8ec690e402d8fbf16cd433e3c94a2862f1f19b05a7657a4cd197","schema_version":"1.0","event_id":"sha256:6d8966a7210d8ec690e402d8fbf16cd433e3c94a2862f1f19b05a7657a4cd197"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/W6HPV73L2KN4KHGRCT5KAQUF6V/bundle.json","state_url":"https://pith.science/pith/W6HPV73L2KN4KHGRCT5KAQUF6V/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/W6HPV73L2KN4KHGRCT5KAQUF6V/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-13T02:31:01Z","links":{"resolver":"https://pith.science/pith/W6HPV73L2KN4KHGRCT5KAQUF6V","bundle":"https://pith.science/pith/W6HPV73L2KN4KHGRCT5KAQUF6V/bundle.json","state":"https://pith.science/pith/W6HPV73L2KN4KHGRCT5KAQUF6V/state.json","well_known_bundle":"https://pith.science/.well-known/pith/W6HPV73L2KN4KHGRCT5KAQUF6V/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:W6HPV73L2KN4KHGRCT5KAQUF6V","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"142bed152362e3e9cbae11729f8abb4da203f78afa94a869d2d1ac5b3ccdeb22","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-12-19T07:01:25Z","title_canon_sha256":"36d53f1a72599256723d7b8fe0a30a7ca39794adb8155841652c2ecb673747cd"},"schema_version":"1.0","source":{"id":"2412.14581","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.14581","created_at":"2026-07-05T09:51:49Z"},{"alias_kind":"arxiv_version","alias_value":"2412.14581v1","created_at":"2026-07-05T09:51:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.14581","created_at":"2026-07-05T09:51:49Z"},{"alias_kind":"pith_short_12","alias_value":"W6HPV73L2KN4","created_at":"2026-07-05T09:51:49Z"},{"alias_kind":"pith_short_16","alias_value":"W6HPV73L2KN4KHGR","created_at":"2026-07-05T09:51:49Z"},{"alias_kind":"pith_short_8","alias_value":"W6HPV73L","created_at":"2026-07-05T09:51:49Z"}],"graph_snapshots":[{"event_id":"sha256:6d8966a7210d8ec690e402d8fbf16cd433e3c94a2862f1f19b05a7657a4cd197","target":"graph","created_at":"2026-07-05T09:51:49Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2412.14581/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"With the adoption of retrieval-augmented generation (RAG), large language models (LLMs) are expected to ground their generation to the retrieved contexts. Yet, this is hindered by position bias of LLMs, failing to evenly attend to all contexts. Previous work has addressed this by synthesizing contexts with perturbed positions of gold segment, creating a position-diversified train set. We extend this intuition to propose consistency regularization with augmentation and distillation. First, we augment each training instance with its position perturbation to encourage consistent predictions, rega","authors_text":"Daniel Campos, Filip Grali\\'nski, Seung-won Hwang, Youngwon Lee, Yuxiong He, Zhewei Yao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-12-19T07:01:25Z","title":"CORD: Balancing COnsistency and Rank Distillation for Robust Retrieval-Augmented Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.14581","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:d4a1ec4334d43141e0bc5864d11687486d8a9e4342bbabd9a745b9c49511e014","target":"record","created_at":"2026-07-05T09:51:49Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"142bed152362e3e9cbae11729f8abb4da203f78afa94a869d2d1ac5b3ccdeb22","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-12-19T07:01:25Z","title_canon_sha256":"36d53f1a72599256723d7b8fe0a30a7ca39794adb8155841652c2ecb673747cd"},"schema_version":"1.0","source":{"id":"2412.14581","kind":"arxiv","version":1}},"canonical_sha256":"b78efaff6bd29bc51cd114faa04285f5767d2528b7c13fc6fa21a8a57a835647","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b78efaff6bd29bc51cd114faa04285f5767d2528b7c13fc6fa21a8a57a835647","first_computed_at":"2026-07-05T09:51:49.673493Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:51:49.673493Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nq76h/TeucixL5XAPOpIbtSd3lz8EgJMEdJlYsWpnsMqn7Qqh6/fVQUYBuGpD4uJls3wgok5q3H2oEEjgltJBw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:51:49.673966Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.14581","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d4a1ec4334d43141e0bc5864d11687486d8a9e4342bbabd9a745b9c49511e014","sha256:6d8966a7210d8ec690e402d8fbf16cd433e3c94a2862f1f19b05a7657a4cd197"],"state_sha256":"ae9aa09d91beccfb1b563f83d97c7da95ebff684fb773119c20cf61f0a4fdf74"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AyhsZ4YxAIo0FJPeTd6nITdvTYnphMeT67bCTvag1Q9VCZgMIMB5huY5Jxpid9eBahCPN8plwr6KLVOQhEjRDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T02:31:01.482915Z","bundle_sha256":"459811d7790c1b9c7469e1b0e5f79f9c1db225917a4442091921162b36b4aef9"}}