{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:A54OIRCQ6CUQCVSRMVNTPQD32V","short_pith_number":"pith:A54OIRCQ","canonical_record":{"source":{"id":"2209.15240","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-09-30T05:19:27Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e3fa81d9375bba3f13e20ddcdcd6665a4c6633300c5345438618205a5c26a301","abstract_canon_sha256":"bbdb7c04fddbd928cd6e2be11e411e47a8b4e14fab81f955857e6ae6b8efcf94"},"schema_version":"1.0"},"canonical_sha256":"0778e44450f0a9015651655b37c07bd56ed6c427d0e253805e8c6a0b3295d96c","source":{"kind":"arxiv","id":"2209.15240","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2209.15240","created_at":"2026-07-05T08:06:22Z"},{"alias_kind":"arxiv_version","alias_value":"2209.15240v5","created_at":"2026-07-05T08:06:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.15240","created_at":"2026-07-05T08:06:22Z"},{"alias_kind":"pith_short_12","alias_value":"A54OIRCQ6CUQ","created_at":"2026-07-05T08:06:22Z"},{"alias_kind":"pith_short_16","alias_value":"A54OIRCQ6CUQCVSR","created_at":"2026-07-05T08:06:22Z"},{"alias_kind":"pith_short_8","alias_value":"A54OIRCQ","created_at":"2026-07-05T08:06:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:A54OIRCQ6CUQCVSRMVNTPQD32V","target":"record","payload":{"canonical_record":{"source":{"id":"2209.15240","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-09-30T05:19:27Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e3fa81d9375bba3f13e20ddcdcd6665a4c6633300c5345438618205a5c26a301","abstract_canon_sha256":"bbdb7c04fddbd928cd6e2be11e411e47a8b4e14fab81f955857e6ae6b8efcf94"},"schema_version":"1.0"},"canonical_sha256":"0778e44450f0a9015651655b37c07bd56ed6c427d0e253805e8c6a0b3295d96c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:06:22.470763Z","signature_b64":"xg4VwXIwsj3Tj9S9rru6TboYiMfm8mZgJJ+up2k+l9GO+xLpaFYoDuS884e1iY7V2ahxz5EFVpwSGagb2TXyBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0778e44450f0a9015651655b37c07bd56ed6c427d0e253805e8c6a0b3295d96c","last_reissued_at":"2026-07-05T08:06:22.470233Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:06:22.470233Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2209.15240","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:06:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5uIP6AJbOaKesQ41F9BBaScFvv0Xkvgvkaz4uwXuknBMmKZafOxFEN1f52oHKSN/wlwCDc5P1+hmuTfTcKzhAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T10:34:05.535975Z"},"content_sha256":"14e256ac67a6f75fd00cb4ab71f43127f6c2aea4dd0165da4ba8a0d2d759ff74","schema_version":"1.0","event_id":"sha256:14e256ac67a6f75fd00cb4ab71f43127f6c2aea4dd0165da4ba8a0d2d759ff74"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:A54OIRCQ6CUQCVSRMVNTPQD32V","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Universal Prompt Tuning for Graph Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Chunping Wang, Lei Chen, Taoran Fang, Yang Yang, Yunchao Zhang","submitted_at":"2022-09-30T05:19:27Z","abstract_excerpt":"In recent years, prompt tuning has sparked a research surge in adapting pre-trained models. Unlike the unified pre-training strategy employed in the language field, the graph field exhibits diverse pre-training strategies, posing challenges in designing appropriate prompt-based tuning methods for graph neural networks. While some pioneering work has devised specialized prompting functions for models that employ edge prediction as their pre-training tasks, these methods are limited to specific pre-trained GNN models and lack broader applicability. In this paper, we introduce a universal prompt-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.15240","kind":"arxiv","version":5},"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/2209.15240/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":"2026-07-05T08:06:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6T7llefmTP1PNfGmJIMMjvQCOEQbcYok+y/FAkFbqZarxfKx53leSUWQIs2D8132Qj0IGBH4vg+ZXEU1O2pOCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T10:34:05.536480Z"},"content_sha256":"168bd5bb0c837cb327fac32ded5d1dbe6815bacfd849896bd568f66161af6820","schema_version":"1.0","event_id":"sha256:168bd5bb0c837cb327fac32ded5d1dbe6815bacfd849896bd568f66161af6820"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/A54OIRCQ6CUQCVSRMVNTPQD32V/bundle.json","state_url":"https://pith.science/pith/A54OIRCQ6CUQCVSRMVNTPQD32V/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/A54OIRCQ6CUQCVSRMVNTPQD32V/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":"2026-08-05T10:34:05Z","links":{"resolver":"https://pith.science/pith/A54OIRCQ6CUQCVSRMVNTPQD32V","bundle":"https://pith.science/pith/A54OIRCQ6CUQCVSRMVNTPQD32V/bundle.json","state":"https://pith.science/pith/A54OIRCQ6CUQCVSRMVNTPQD32V/state.json","well_known_bundle":"https://pith.science/.well-known/pith/A54OIRCQ6CUQCVSRMVNTPQD32V/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:A54OIRCQ6CUQCVSRMVNTPQD32V","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":"bbdb7c04fddbd928cd6e2be11e411e47a8b4e14fab81f955857e6ae6b8efcf94","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-09-30T05:19:27Z","title_canon_sha256":"e3fa81d9375bba3f13e20ddcdcd6665a4c6633300c5345438618205a5c26a301"},"schema_version":"1.0","source":{"id":"2209.15240","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2209.15240","created_at":"2026-07-05T08:06:22Z"},{"alias_kind":"arxiv_version","alias_value":"2209.15240v5","created_at":"2026-07-05T08:06:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.15240","created_at":"2026-07-05T08:06:22Z"},{"alias_kind":"pith_short_12","alias_value":"A54OIRCQ6CUQ","created_at":"2026-07-05T08:06:22Z"},{"alias_kind":"pith_short_16","alias_value":"A54OIRCQ6CUQCVSR","created_at":"2026-07-05T08:06:22Z"},{"alias_kind":"pith_short_8","alias_value":"A54OIRCQ","created_at":"2026-07-05T08:06:22Z"}],"graph_snapshots":[{"event_id":"sha256:168bd5bb0c837cb327fac32ded5d1dbe6815bacfd849896bd568f66161af6820","target":"graph","created_at":"2026-07-05T08:06:22Z","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/2209.15240/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In recent years, prompt tuning has sparked a research surge in adapting pre-trained models. Unlike the unified pre-training strategy employed in the language field, the graph field exhibits diverse pre-training strategies, posing challenges in designing appropriate prompt-based tuning methods for graph neural networks. While some pioneering work has devised specialized prompting functions for models that employ edge prediction as their pre-training tasks, these methods are limited to specific pre-trained GNN models and lack broader applicability. In this paper, we introduce a universal prompt-","authors_text":"Chunping Wang, Lei Chen, Taoran Fang, Yang Yang, Yunchao Zhang","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-09-30T05:19:27Z","title":"Universal Prompt Tuning for Graph Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.15240","kind":"arxiv","version":5},"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":"sha256:14e256ac67a6f75fd00cb4ab71f43127f6c2aea4dd0165da4ba8a0d2d759ff74","target":"record","created_at":"2026-07-05T08:06:22Z","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":"bbdb7c04fddbd928cd6e2be11e411e47a8b4e14fab81f955857e6ae6b8efcf94","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-09-30T05:19:27Z","title_canon_sha256":"e3fa81d9375bba3f13e20ddcdcd6665a4c6633300c5345438618205a5c26a301"},"schema_version":"1.0","source":{"id":"2209.15240","kind":"arxiv","version":5}},"canonical_sha256":"0778e44450f0a9015651655b37c07bd56ed6c427d0e253805e8c6a0b3295d96c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0778e44450f0a9015651655b37c07bd56ed6c427d0e253805e8c6a0b3295d96c","first_computed_at":"2026-07-05T08:06:22.470233Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:06:22.470233Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xg4VwXIwsj3Tj9S9rru6TboYiMfm8mZgJJ+up2k+l9GO+xLpaFYoDuS884e1iY7V2ahxz5EFVpwSGagb2TXyBw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:06:22.470763Z","signed_message":"canonical_sha256_bytes"},"source_id":"2209.15240","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:14e256ac67a6f75fd00cb4ab71f43127f6c2aea4dd0165da4ba8a0d2d759ff74","sha256:168bd5bb0c837cb327fac32ded5d1dbe6815bacfd849896bd568f66161af6820"],"state_sha256":"f312ea246fc2f72df148ce248ffdcddca5b3417a8be7d0594ea508f7445f7910"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9U1XHnvg9nT2tcA77+g6jFDK/mLmcDSrxWaZO1YJIAPcXskIT5P6x4NDKjtDq30qEYZYuRaBbCsOuRAVchJfCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T10:34:05.539469Z","bundle_sha256":"8f4fff73f69d8a0d37959a11e0527df422cdb21d9dba9c07c488dd91224ea10e"}}