{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:23OXFWJV5AU6V74WJWSYEP6FJ5","short_pith_number":"pith:23OXFWJV","canonical_record":{"source":{"id":"2505.24646","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-30T14:31:53Z","cross_cats_sorted":[],"title_canon_sha256":"488982760809ee76a1cdbc6f4057905cd00026af22afe1ed87240a664e096311","abstract_canon_sha256":"8358f33a532e60c7cf5114226591ca15509ebef5761bbdb5dd34282b50709602"},"schema_version":"1.0"},"canonical_sha256":"d6dd72d935e829eaff964da5823fc54f50d5e5a56bdd5555d05d2cf66d9bca2a","source":{"kind":"arxiv","id":"2505.24646","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.24646","created_at":"2026-07-05T11:12:53Z"},{"alias_kind":"arxiv_version","alias_value":"2505.24646v1","created_at":"2026-07-05T11:12:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.24646","created_at":"2026-07-05T11:12:53Z"},{"alias_kind":"pith_short_12","alias_value":"23OXFWJV5AU6","created_at":"2026-07-05T11:12:53Z"},{"alias_kind":"pith_short_16","alias_value":"23OXFWJV5AU6V74W","created_at":"2026-07-05T11:12:53Z"},{"alias_kind":"pith_short_8","alias_value":"23OXFWJV","created_at":"2026-07-05T11:12:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:23OXFWJV5AU6V74WJWSYEP6FJ5","target":"record","payload":{"canonical_record":{"source":{"id":"2505.24646","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-30T14:31:53Z","cross_cats_sorted":[],"title_canon_sha256":"488982760809ee76a1cdbc6f4057905cd00026af22afe1ed87240a664e096311","abstract_canon_sha256":"8358f33a532e60c7cf5114226591ca15509ebef5761bbdb5dd34282b50709602"},"schema_version":"1.0"},"canonical_sha256":"d6dd72d935e829eaff964da5823fc54f50d5e5a56bdd5555d05d2cf66d9bca2a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:12:53.884154Z","signature_b64":"XEaEZNuoKVutLGE5Q0KbTIXLh3ZRlcLXj0nyZyimwoqjBGelidhYJVToQwy5RgzZhCLpp8sz2fUV1Lf05cSkDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d6dd72d935e829eaff964da5823fc54f50d5e5a56bdd5555d05d2cf66d9bca2a","last_reissued_at":"2026-07-05T11:12:53.883669Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:12:53.883669Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.24646","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":"2026-07-05T11:12:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hCaJ7NUudjy4Quuqw8xbjzLLwS4XznbP2lG/YiaWrC0rMYKtcHy0v/A0vVk4U96Ku0/VBeQDPuRrzD/PDVuRCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T15:43:40.775484Z"},"content_sha256":"c798bc518925138a705734233256a938896cc8e3713ef97b5039f95edd9a6dd9","schema_version":"1.0","event_id":"sha256:c798bc518925138a705734233256a938896cc8e3713ef97b5039f95edd9a6dd9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:23OXFWJV5AU6V74WJWSYEP6FJ5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"PRISM: A Framework for Producing Interpretable Political Bias Embeddings with Political-Aware Cross-Encoder","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Anthony K. H. Tung, Jun Yu, Qiang Huang, Yiqun Sun","submitted_at":"2025-05-30T14:31:53Z","abstract_excerpt":"Semantic Text Embedding is a fundamental NLP task that encodes textual content into vector representations, where proximity in the embedding space reflects semantic similarity. While existing embedding models excel at capturing general meaning, they often overlook ideological nuances, limiting their effectiveness in tasks that require an understanding of political bias. To address this gap, we introduce PRISM, the first framework designed to Produce inteRpretable polItical biaS eMbeddings. PRISM operates in two key stages: (1) Controversial Topic Bias Indicator Mining, which systematically ext"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.24646","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/2505.24646/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":"2026-07-05T11:12:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AyJlhH3r/T6HFoEGJ9cxlnpQbJkHESLwSO40XGFrR//KAHXjUKIVo4RRqFLuArmbO94EpVNiOdJMu72FENZSAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T15:43:40.776166Z"},"content_sha256":"035b052e121dc47f42a93f45b6510a414a167e15ef13cc69d4a82f90fcfbb97b","schema_version":"1.0","event_id":"sha256:035b052e121dc47f42a93f45b6510a414a167e15ef13cc69d4a82f90fcfbb97b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/23OXFWJV5AU6V74WJWSYEP6FJ5/bundle.json","state_url":"https://pith.science/pith/23OXFWJV5AU6V74WJWSYEP6FJ5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/23OXFWJV5AU6V74WJWSYEP6FJ5/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":"2026-08-09T15:43:40Z","links":{"resolver":"https://pith.science/pith/23OXFWJV5AU6V74WJWSYEP6FJ5","bundle":"https://pith.science/pith/23OXFWJV5AU6V74WJWSYEP6FJ5/bundle.json","state":"https://pith.science/pith/23OXFWJV5AU6V74WJWSYEP6FJ5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/23OXFWJV5AU6V74WJWSYEP6FJ5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:23OXFWJV5AU6V74WJWSYEP6FJ5","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":"8358f33a532e60c7cf5114226591ca15509ebef5761bbdb5dd34282b50709602","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-30T14:31:53Z","title_canon_sha256":"488982760809ee76a1cdbc6f4057905cd00026af22afe1ed87240a664e096311"},"schema_version":"1.0","source":{"id":"2505.24646","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.24646","created_at":"2026-07-05T11:12:53Z"},{"alias_kind":"arxiv_version","alias_value":"2505.24646v1","created_at":"2026-07-05T11:12:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.24646","created_at":"2026-07-05T11:12:53Z"},{"alias_kind":"pith_short_12","alias_value":"23OXFWJV5AU6","created_at":"2026-07-05T11:12:53Z"},{"alias_kind":"pith_short_16","alias_value":"23OXFWJV5AU6V74W","created_at":"2026-07-05T11:12:53Z"},{"alias_kind":"pith_short_8","alias_value":"23OXFWJV","created_at":"2026-07-05T11:12:53Z"}],"graph_snapshots":[{"event_id":"sha256:035b052e121dc47f42a93f45b6510a414a167e15ef13cc69d4a82f90fcfbb97b","target":"graph","created_at":"2026-07-05T11:12:53Z","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/2505.24646/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Semantic Text Embedding is a fundamental NLP task that encodes textual content into vector representations, where proximity in the embedding space reflects semantic similarity. While existing embedding models excel at capturing general meaning, they often overlook ideological nuances, limiting their effectiveness in tasks that require an understanding of political bias. To address this gap, we introduce PRISM, the first framework designed to Produce inteRpretable polItical biaS eMbeddings. PRISM operates in two key stages: (1) Controversial Topic Bias Indicator Mining, which systematically ext","authors_text":"Anthony K. H. Tung, Jun Yu, Qiang Huang, Yiqun Sun","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-30T14:31:53Z","title":"PRISM: A Framework for Producing Interpretable Political Bias Embeddings with Political-Aware Cross-Encoder"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.24646","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:c798bc518925138a705734233256a938896cc8e3713ef97b5039f95edd9a6dd9","target":"record","created_at":"2026-07-05T11:12:53Z","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":"8358f33a532e60c7cf5114226591ca15509ebef5761bbdb5dd34282b50709602","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-30T14:31:53Z","title_canon_sha256":"488982760809ee76a1cdbc6f4057905cd00026af22afe1ed87240a664e096311"},"schema_version":"1.0","source":{"id":"2505.24646","kind":"arxiv","version":1}},"canonical_sha256":"d6dd72d935e829eaff964da5823fc54f50d5e5a56bdd5555d05d2cf66d9bca2a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d6dd72d935e829eaff964da5823fc54f50d5e5a56bdd5555d05d2cf66d9bca2a","first_computed_at":"2026-07-05T11:12:53.883669Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:12:53.883669Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XEaEZNuoKVutLGE5Q0KbTIXLh3ZRlcLXj0nyZyimwoqjBGelidhYJVToQwy5RgzZhCLpp8sz2fUV1Lf05cSkDg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:12:53.884154Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.24646","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c798bc518925138a705734233256a938896cc8e3713ef97b5039f95edd9a6dd9","sha256:035b052e121dc47f42a93f45b6510a414a167e15ef13cc69d4a82f90fcfbb97b"],"state_sha256":"a82a4da4c5ccdcde5fcae365f57107dc33d76a3ae5159f85e4f10cf3b965aac8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7UjtcOQ9dpgLRmCLSfZR/cWAM/VaTZ3uRl7zTx3/gEsmvjrPxE28HkIlzY3BwG0EFL7O4pQR/DPpIE+lrQbmCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T15:43:40.790648Z","bundle_sha256":"21fd5f6a498738157830e5421b11d56b08cd7e63538986e68d6da5e7848f5c5c"}}