{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:5YVBUYYT4LAHWETX33A4IDIIDF","short_pith_number":"pith:5YVBUYYT","canonical_record":{"source":{"id":"2502.12704","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SI","submitted_at":"2025-02-18T10:13:06Z","cross_cats_sorted":[],"title_canon_sha256":"6f6a57511e1d48c23836354d9c1a2270a4f779c76f22acf8f0814e3e3194b5ab","abstract_canon_sha256":"502f12a6434cac6d9c52e2dab40d52781cb939c90c653a8149347e62784d01e0"},"schema_version":"1.0"},"canonical_sha256":"ee2a1a6313e2c07b1277dec1c40d08196c257004d5307490226aa0f58999847c","source":{"kind":"arxiv","id":"2502.12704","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.12704","created_at":"2026-07-05T10:16:22Z"},{"alias_kind":"arxiv_version","alias_value":"2502.12704v1","created_at":"2026-07-05T10:16:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.12704","created_at":"2026-07-05T10:16:22Z"},{"alias_kind":"pith_short_12","alias_value":"5YVBUYYT4LAH","created_at":"2026-07-05T10:16:22Z"},{"alias_kind":"pith_short_16","alias_value":"5YVBUYYT4LAHWETX","created_at":"2026-07-05T10:16:22Z"},{"alias_kind":"pith_short_8","alias_value":"5YVBUYYT","created_at":"2026-07-05T10:16:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:5YVBUYYT4LAHWETX33A4IDIIDF","target":"record","payload":{"canonical_record":{"source":{"id":"2502.12704","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SI","submitted_at":"2025-02-18T10:13:06Z","cross_cats_sorted":[],"title_canon_sha256":"6f6a57511e1d48c23836354d9c1a2270a4f779c76f22acf8f0814e3e3194b5ab","abstract_canon_sha256":"502f12a6434cac6d9c52e2dab40d52781cb939c90c653a8149347e62784d01e0"},"schema_version":"1.0"},"canonical_sha256":"ee2a1a6313e2c07b1277dec1c40d08196c257004d5307490226aa0f58999847c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:16:22.861659Z","signature_b64":"/xt/KcXgdwDIGkoUoimIWL9abEY9AxuFtvxJS1sI1EGUNOqSC1YSvwNfnFrh1HxyNyyt4uXgCmgnAc9u+AQBCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ee2a1a6313e2c07b1277dec1c40d08196c257004d5307490226aa0f58999847c","last_reissued_at":"2026-07-05T10:16:22.861136Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:16:22.861136Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.12704","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":"2026-07-05T10:16:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2Mqjh8miHjCrzMfoONaTMOx7w2m6asw/K10kjFVIAhDz8WhqCLEHPCBDD1mPGpImpb4vItI8gTXliS1upTpvCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T08:41:17.252849Z"},"content_sha256":"16eb66138288e1d1232f128e70dac5994b36897dcf1bdc778333f14b1b94e347","schema_version":"1.0","event_id":"sha256:16eb66138288e1d1232f128e70dac5994b36897dcf1bdc778333f14b1b94e347"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:5YVBUYYT4LAHWETX33A4IDIIDF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Maximizing Truth Learning in a Social Network is NP-hard","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SI","authors_text":"Amanda Wang, Filip \\'Uradn\\'ik, Jie Gao","submitted_at":"2025-02-18T10:13:06Z","abstract_excerpt":"Sequential learning models situations where agents predict a ground truth in sequence, by using their private, noisy measurements, and the predictions of agents who came earlier in the sequence. We study sequential learning in a social network, where agents only see the actions of the previous agents in their own neighborhood. The fraction of agents who predict the ground truth correctly depends heavily on both the network topology and the ordering in which the predictions are made. A natural question is to find an ordering, with a given network, to maximize the (expected) number of agents who"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.12704","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/2502.12704/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":"2026-07-05T10:16:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SGuLBa3+guYqq92K/TiItBVTS6icDjQ7CWBzqOJzsuJCpr//M1REKUScRTN2yHu9AtUlZ18+iIQvN4hzZFqMAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T08:41:17.253219Z"},"content_sha256":"a3638747a807209139bc6e03a64764c45a4612d37a146829fffa081138118c39","schema_version":"1.0","event_id":"sha256:a3638747a807209139bc6e03a64764c45a4612d37a146829fffa081138118c39"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5YVBUYYT4LAHWETX33A4IDIIDF/bundle.json","state_url":"https://pith.science/pith/5YVBUYYT4LAHWETX33A4IDIIDF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5YVBUYYT4LAHWETX33A4IDIIDF/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":"2026-07-06T08:41:17Z","links":{"resolver":"https://pith.science/pith/5YVBUYYT4LAHWETX33A4IDIIDF","bundle":"https://pith.science/pith/5YVBUYYT4LAHWETX33A4IDIIDF/bundle.json","state":"https://pith.science/pith/5YVBUYYT4LAHWETX33A4IDIIDF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5YVBUYYT4LAHWETX33A4IDIIDF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:5YVBUYYT4LAHWETX33A4IDIIDF","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":"502f12a6434cac6d9c52e2dab40d52781cb939c90c653a8149347e62784d01e0","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SI","submitted_at":"2025-02-18T10:13:06Z","title_canon_sha256":"6f6a57511e1d48c23836354d9c1a2270a4f779c76f22acf8f0814e3e3194b5ab"},"schema_version":"1.0","source":{"id":"2502.12704","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.12704","created_at":"2026-07-05T10:16:22Z"},{"alias_kind":"arxiv_version","alias_value":"2502.12704v1","created_at":"2026-07-05T10:16:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.12704","created_at":"2026-07-05T10:16:22Z"},{"alias_kind":"pith_short_12","alias_value":"5YVBUYYT4LAH","created_at":"2026-07-05T10:16:22Z"},{"alias_kind":"pith_short_16","alias_value":"5YVBUYYT4LAHWETX","created_at":"2026-07-05T10:16:22Z"},{"alias_kind":"pith_short_8","alias_value":"5YVBUYYT","created_at":"2026-07-05T10:16:22Z"}],"graph_snapshots":[{"event_id":"sha256:a3638747a807209139bc6e03a64764c45a4612d37a146829fffa081138118c39","target":"graph","created_at":"2026-07-05T10:16:22Z","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/2502.12704/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Sequential learning models situations where agents predict a ground truth in sequence, by using their private, noisy measurements, and the predictions of agents who came earlier in the sequence. We study sequential learning in a social network, where agents only see the actions of the previous agents in their own neighborhood. The fraction of agents who predict the ground truth correctly depends heavily on both the network topology and the ordering in which the predictions are made. A natural question is to find an ordering, with a given network, to maximize the (expected) number of agents who","authors_text":"Amanda Wang, Filip \\'Uradn\\'ik, Jie Gao","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SI","submitted_at":"2025-02-18T10:13:06Z","title":"Maximizing Truth Learning in a Social Network is NP-hard"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.12704","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":"sha256:16eb66138288e1d1232f128e70dac5994b36897dcf1bdc778333f14b1b94e347","target":"record","created_at":"2026-07-05T10:16:22Z","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":"502f12a6434cac6d9c52e2dab40d52781cb939c90c653a8149347e62784d01e0","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SI","submitted_at":"2025-02-18T10:13:06Z","title_canon_sha256":"6f6a57511e1d48c23836354d9c1a2270a4f779c76f22acf8f0814e3e3194b5ab"},"schema_version":"1.0","source":{"id":"2502.12704","kind":"arxiv","version":1}},"canonical_sha256":"ee2a1a6313e2c07b1277dec1c40d08196c257004d5307490226aa0f58999847c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ee2a1a6313e2c07b1277dec1c40d08196c257004d5307490226aa0f58999847c","first_computed_at":"2026-07-05T10:16:22.861136Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:16:22.861136Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/xt/KcXgdwDIGkoUoimIWL9abEY9AxuFtvxJS1sI1EGUNOqSC1YSvwNfnFrh1HxyNyyt4uXgCmgnAc9u+AQBCw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:16:22.861659Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.12704","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:16eb66138288e1d1232f128e70dac5994b36897dcf1bdc778333f14b1b94e347","sha256:a3638747a807209139bc6e03a64764c45a4612d37a146829fffa081138118c39"],"state_sha256":"e033d4287f47490129dd9dd6383175bdc03e91732ea4e13d72eb8b0275d45abb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3sV9t9m/zbdVfDzNhmzYzbYeSjmcJSG+6OkMI+XDdICADge+wjv0HwjI/RutQqM50tTNPscxx4Bc3R6hjVY4Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T08:41:17.255475Z","bundle_sha256":"f0736d6616158620fd8b715b36e462ec103cbe3c72061135d5607ae2a6e1a52b"}}