{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:27PXWOTD5QFNBYNROIKNZDABUK","short_pith_number":"pith:27PXWOTD","canonical_record":{"source":{"id":"2402.02030","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-02-03T05:01:04Z","cross_cats_sorted":[],"title_canon_sha256":"fba7d1055cc6a2b66a17088d63cb208bd242c2c37eb233cf110ba9dce21b10bd","abstract_canon_sha256":"3f5aaa873e8d3d7c680257b99925f65f4e17e2e86754f490912488c8cc11a225"},"schema_version":"1.0"},"canonical_sha256":"d7df7b3a63ec0ad0e1b17214dc8c01a29d656065f0a564340375f314573fed5b","source":{"kind":"arxiv","id":"2402.02030","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.02030","created_at":"2026-07-05T08:22:22Z"},{"alias_kind":"arxiv_version","alias_value":"2402.02030v2","created_at":"2026-07-05T08:22:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.02030","created_at":"2026-07-05T08:22:22Z"},{"alias_kind":"pith_short_12","alias_value":"27PXWOTD5QFN","created_at":"2026-07-05T08:22:22Z"},{"alias_kind":"pith_short_16","alias_value":"27PXWOTD5QFNBYNR","created_at":"2026-07-05T08:22:22Z"},{"alias_kind":"pith_short_8","alias_value":"27PXWOTD","created_at":"2026-07-05T08:22:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:27PXWOTD5QFNBYNROIKNZDABUK","target":"record","payload":{"canonical_record":{"source":{"id":"2402.02030","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-02-03T05:01:04Z","cross_cats_sorted":[],"title_canon_sha256":"fba7d1055cc6a2b66a17088d63cb208bd242c2c37eb233cf110ba9dce21b10bd","abstract_canon_sha256":"3f5aaa873e8d3d7c680257b99925f65f4e17e2e86754f490912488c8cc11a225"},"schema_version":"1.0"},"canonical_sha256":"d7df7b3a63ec0ad0e1b17214dc8c01a29d656065f0a564340375f314573fed5b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:22:22.310244Z","signature_b64":"1lxt/nB7smO81KmZrWzXpg3XjSEggcWqIXENIKCSHGbQLZJ0f2+PIsn0HBdMuu1yFc3CZC7TF/DeCeE1AwxlCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d7df7b3a63ec0ad0e1b17214dc8c01a29d656065f0a564340375f314573fed5b","last_reissued_at":"2026-07-05T08:22:22.309677Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:22:22.309677Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.02030","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:22:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WeFtlf3mHGubl6WWrHjHwxpP8VWebUtV2GHk9Rv4iLYdLtDGd0i+OtswPygllFGEC6fpV0l1fqj3SpaT2DZ5BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T09:39:20.513396Z"},"content_sha256":"01e6fee34acbd7c3126b98be813e9815c7a524fb4896e04a1d35521d9a475da4","schema_version":"1.0","event_id":"sha256:01e6fee34acbd7c3126b98be813e9815c7a524fb4896e04a1d35521d9a475da4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:27PXWOTD5QFNBYNROIKNZDABUK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Panacea: Pareto Alignment via Preference Adaptation for LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chengdong Ma, Haojun Chen, Qingfu Zhang, Siyuan Qi, Xiaoyuan Zhang, Yaodong Yang, Yifan Zhong, Ziran Yang","submitted_at":"2024-02-03T05:01:04Z","abstract_excerpt":"Current methods for large language model alignment typically use scalar human preference labels. However, this convention tends to oversimplify the multi-dimensional and heterogeneous nature of human preferences, leading to reduced expressivity and even misalignment. This paper presents Panacea, an innovative approach that reframes alignment as a multi-dimensional preference optimization problem. Panacea trains a single model capable of adapting online and Pareto-optimally to diverse sets of preferences without the need for further tuning. A major challenge here is using a low-dimensional pref"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.02030","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/2402.02030/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":"2026-07-05T08:22:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"I04aklc+IU+4WM/rUT3DFWadQK2ZJZQk/15FVj2YPooRS/D91qClrcW6GMxa2kALTcy7PMTMSaTZbKSps1vnCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T09:39:20.513878Z"},"content_sha256":"299a2a496bd8ad23a2fd895f609bc5353824cd11a4f1f79d51eb974f7e7dc376","schema_version":"1.0","event_id":"sha256:299a2a496bd8ad23a2fd895f609bc5353824cd11a4f1f79d51eb974f7e7dc376"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/27PXWOTD5QFNBYNROIKNZDABUK/bundle.json","state_url":"https://pith.science/pith/27PXWOTD5QFNBYNROIKNZDABUK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/27PXWOTD5QFNBYNROIKNZDABUK/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":"2026-08-08T09:39:20Z","links":{"resolver":"https://pith.science/pith/27PXWOTD5QFNBYNROIKNZDABUK","bundle":"https://pith.science/pith/27PXWOTD5QFNBYNROIKNZDABUK/bundle.json","state":"https://pith.science/pith/27PXWOTD5QFNBYNROIKNZDABUK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/27PXWOTD5QFNBYNROIKNZDABUK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:27PXWOTD5QFNBYNROIKNZDABUK","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":"3f5aaa873e8d3d7c680257b99925f65f4e17e2e86754f490912488c8cc11a225","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-02-03T05:01:04Z","title_canon_sha256":"fba7d1055cc6a2b66a17088d63cb208bd242c2c37eb233cf110ba9dce21b10bd"},"schema_version":"1.0","source":{"id":"2402.02030","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.02030","created_at":"2026-07-05T08:22:22Z"},{"alias_kind":"arxiv_version","alias_value":"2402.02030v2","created_at":"2026-07-05T08:22:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.02030","created_at":"2026-07-05T08:22:22Z"},{"alias_kind":"pith_short_12","alias_value":"27PXWOTD5QFN","created_at":"2026-07-05T08:22:22Z"},{"alias_kind":"pith_short_16","alias_value":"27PXWOTD5QFNBYNR","created_at":"2026-07-05T08:22:22Z"},{"alias_kind":"pith_short_8","alias_value":"27PXWOTD","created_at":"2026-07-05T08:22:22Z"}],"graph_snapshots":[{"event_id":"sha256:299a2a496bd8ad23a2fd895f609bc5353824cd11a4f1f79d51eb974f7e7dc376","target":"graph","created_at":"2026-07-05T08:22:22Z","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/2402.02030/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Current methods for large language model alignment typically use scalar human preference labels. However, this convention tends to oversimplify the multi-dimensional and heterogeneous nature of human preferences, leading to reduced expressivity and even misalignment. This paper presents Panacea, an innovative approach that reframes alignment as a multi-dimensional preference optimization problem. Panacea trains a single model capable of adapting online and Pareto-optimally to diverse sets of preferences without the need for further tuning. A major challenge here is using a low-dimensional pref","authors_text":"Chengdong Ma, Haojun Chen, Qingfu Zhang, Siyuan Qi, Xiaoyuan Zhang, Yaodong Yang, Yifan Zhong, Ziran Yang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-02-03T05:01:04Z","title":"Panacea: Pareto Alignment via Preference Adaptation for LLMs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.02030","kind":"arxiv","version":2},"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":"sha256:01e6fee34acbd7c3126b98be813e9815c7a524fb4896e04a1d35521d9a475da4","target":"record","created_at":"2026-07-05T08:22:22Z","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":"3f5aaa873e8d3d7c680257b99925f65f4e17e2e86754f490912488c8cc11a225","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-02-03T05:01:04Z","title_canon_sha256":"fba7d1055cc6a2b66a17088d63cb208bd242c2c37eb233cf110ba9dce21b10bd"},"schema_version":"1.0","source":{"id":"2402.02030","kind":"arxiv","version":2}},"canonical_sha256":"d7df7b3a63ec0ad0e1b17214dc8c01a29d656065f0a564340375f314573fed5b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d7df7b3a63ec0ad0e1b17214dc8c01a29d656065f0a564340375f314573fed5b","first_computed_at":"2026-07-05T08:22:22.309677Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:22:22.309677Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1lxt/nB7smO81KmZrWzXpg3XjSEggcWqIXENIKCSHGbQLZJ0f2+PIsn0HBdMuu1yFc3CZC7TF/DeCeE1AwxlCw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:22:22.310244Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.02030","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:01e6fee34acbd7c3126b98be813e9815c7a524fb4896e04a1d35521d9a475da4","sha256:299a2a496bd8ad23a2fd895f609bc5353824cd11a4f1f79d51eb974f7e7dc376"],"state_sha256":"39abf26c7aa9f0fa8870b292e4fab4d03b4b31f2e0b8c889ca5a85e6d0ab0091"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QjHDgQGNb2P7mzUBYmpB53iPHW0OOXrcTRNgNEcRInfsQQya4AZ6RugEK0D+rcsnnb95kKYSGXoue4bGSOegCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T09:39:20.516962Z","bundle_sha256":"3f627ab06ff95960dfae4774e07c775d2b089dc5ecbed92a2534db9d67010c50"}}