{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:DJ54WUDOUTKF3J3RNDUDBKZ7BV","short_pith_number":"pith:DJ54WUDO","canonical_record":{"source":{"id":"2504.14416","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-19T22:47:59Z","cross_cats_sorted":[],"title_canon_sha256":"d56c51ed555aeb9a2ab8b8345869f85e32eaa4d00c63538f15c8fca43f7f77e0","abstract_canon_sha256":"676ac5e8d622098ebbd2f2072e39bc165b567554ca82dada7e42489fbe981613"},"schema_version":"1.0"},"canonical_sha256":"1a7bcb506ea4d45da77168e830ab3f0d5dba60c5e23134c5839f3fc3708314e9","source":{"kind":"arxiv","id":"2504.14416","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.14416","created_at":"2026-07-05T10:51:42Z"},{"alias_kind":"arxiv_version","alias_value":"2504.14416v1","created_at":"2026-07-05T10:51:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.14416","created_at":"2026-07-05T10:51:42Z"},{"alias_kind":"pith_short_12","alias_value":"DJ54WUDOUTKF","created_at":"2026-07-05T10:51:42Z"},{"alias_kind":"pith_short_16","alias_value":"DJ54WUDOUTKF3J3R","created_at":"2026-07-05T10:51:42Z"},{"alias_kind":"pith_short_8","alias_value":"DJ54WUDO","created_at":"2026-07-05T10:51:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:DJ54WUDOUTKF3J3RNDUDBKZ7BV","target":"record","payload":{"canonical_record":{"source":{"id":"2504.14416","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-19T22:47:59Z","cross_cats_sorted":[],"title_canon_sha256":"d56c51ed555aeb9a2ab8b8345869f85e32eaa4d00c63538f15c8fca43f7f77e0","abstract_canon_sha256":"676ac5e8d622098ebbd2f2072e39bc165b567554ca82dada7e42489fbe981613"},"schema_version":"1.0"},"canonical_sha256":"1a7bcb506ea4d45da77168e830ab3f0d5dba60c5e23134c5839f3fc3708314e9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:51:42.855428Z","signature_b64":"MqXXaz9KADoqM62U3WH4WDZ7jVmRJkoiy5qmjocC7VXtSd1hCrH9Y3oKcWh0esKp4V78sDmQ4r+uTG2023DIDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1a7bcb506ea4d45da77168e830ab3f0d5dba60c5e23134c5839f3fc3708314e9","last_reissued_at":"2026-07-05T10:51:42.854853Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:51:42.854853Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.14416","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":"2026-07-05T10:51:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"I1c/3Xc5VDoP2ogmaPyvurg+CRN8anYLu6k3VlMZaDwUd0VJ0Db6qAab/4+5vjE22bxKyh98jAlIESdC/SXQAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T12:08:27.010473Z"},"content_sha256":"020ba0b4e543e89486603d290c813d6186ddb2786ba4f9442a80293af34bd684","schema_version":"1.0","event_id":"sha256:020ba0b4e543e89486603d290c813d6186ddb2786ba4f9442a80293af34bd684"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:DJ54WUDOUTKF3J3RNDUDBKZ7BV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Exploring Pseudo-Token Approaches in Transformer Neural Processes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Fengzhe Zhang, Jose Lara-Rangel, Nanze Chen","submitted_at":"2025-04-19T22:47:59Z","abstract_excerpt":"Neural Processes (NPs) have gained attention in meta-learning for their ability to quantify uncertainty, together with their rapid prediction and adaptability. However, traditional NPs are prone to underfitting. Transformer Neural Processes (TNPs) significantly outperform existing NPs, yet their applicability in real-world scenarios is hindered by their quadratic computational complexity relative to both context and target data points. To address this, pseudo-token-based TNPs (PT-TNPs) have emerged as a novel NPs subset that condense context data into latent vectors or pseudo-tokens, reducing "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.14416","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/2504.14416/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":"2026-07-05T10:51:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OXfY4AQ3JmWYzL2Hanjm3ZiII2cNbh6ft1irDFnUaUH3tW3id4swgqvnPH+kGGfErZa12w6d8F4BSRKjhQPQBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T12:08:27.010812Z"},"content_sha256":"256f11cdd8765e1b035d0bb28dda3c3dd256d1e762fafad5190030e4b5b4e1d1","schema_version":"1.0","event_id":"sha256:256f11cdd8765e1b035d0bb28dda3c3dd256d1e762fafad5190030e4b5b4e1d1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DJ54WUDOUTKF3J3RNDUDBKZ7BV/bundle.json","state_url":"https://pith.science/pith/DJ54WUDOUTKF3J3RNDUDBKZ7BV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DJ54WUDOUTKF3J3RNDUDBKZ7BV/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":"2026-08-20T12:08:27Z","links":{"resolver":"https://pith.science/pith/DJ54WUDOUTKF3J3RNDUDBKZ7BV","bundle":"https://pith.science/pith/DJ54WUDOUTKF3J3RNDUDBKZ7BV/bundle.json","state":"https://pith.science/pith/DJ54WUDOUTKF3J3RNDUDBKZ7BV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DJ54WUDOUTKF3J3RNDUDBKZ7BV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:DJ54WUDOUTKF3J3RNDUDBKZ7BV","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":"676ac5e8d622098ebbd2f2072e39bc165b567554ca82dada7e42489fbe981613","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-19T22:47:59Z","title_canon_sha256":"d56c51ed555aeb9a2ab8b8345869f85e32eaa4d00c63538f15c8fca43f7f77e0"},"schema_version":"1.0","source":{"id":"2504.14416","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.14416","created_at":"2026-07-05T10:51:42Z"},{"alias_kind":"arxiv_version","alias_value":"2504.14416v1","created_at":"2026-07-05T10:51:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.14416","created_at":"2026-07-05T10:51:42Z"},{"alias_kind":"pith_short_12","alias_value":"DJ54WUDOUTKF","created_at":"2026-07-05T10:51:42Z"},{"alias_kind":"pith_short_16","alias_value":"DJ54WUDOUTKF3J3R","created_at":"2026-07-05T10:51:42Z"},{"alias_kind":"pith_short_8","alias_value":"DJ54WUDO","created_at":"2026-07-05T10:51:42Z"}],"graph_snapshots":[{"event_id":"sha256:256f11cdd8765e1b035d0bb28dda3c3dd256d1e762fafad5190030e4b5b4e1d1","target":"graph","created_at":"2026-07-05T10:51:42Z","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/2504.14416/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Neural Processes (NPs) have gained attention in meta-learning for their ability to quantify uncertainty, together with their rapid prediction and adaptability. However, traditional NPs are prone to underfitting. Transformer Neural Processes (TNPs) significantly outperform existing NPs, yet their applicability in real-world scenarios is hindered by their quadratic computational complexity relative to both context and target data points. To address this, pseudo-token-based TNPs (PT-TNPs) have emerged as a novel NPs subset that condense context data into latent vectors or pseudo-tokens, reducing ","authors_text":"Fengzhe Zhang, Jose Lara-Rangel, Nanze Chen","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-19T22:47:59Z","title":"Exploring Pseudo-Token Approaches in Transformer Neural Processes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.14416","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":"sha256:020ba0b4e543e89486603d290c813d6186ddb2786ba4f9442a80293af34bd684","target":"record","created_at":"2026-07-05T10:51:42Z","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":"676ac5e8d622098ebbd2f2072e39bc165b567554ca82dada7e42489fbe981613","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-19T22:47:59Z","title_canon_sha256":"d56c51ed555aeb9a2ab8b8345869f85e32eaa4d00c63538f15c8fca43f7f77e0"},"schema_version":"1.0","source":{"id":"2504.14416","kind":"arxiv","version":1}},"canonical_sha256":"1a7bcb506ea4d45da77168e830ab3f0d5dba60c5e23134c5839f3fc3708314e9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1a7bcb506ea4d45da77168e830ab3f0d5dba60c5e23134c5839f3fc3708314e9","first_computed_at":"2026-07-05T10:51:42.854853Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:51:42.854853Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MqXXaz9KADoqM62U3WH4WDZ7jVmRJkoiy5qmjocC7VXtSd1hCrH9Y3oKcWh0esKp4V78sDmQ4r+uTG2023DIDA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:51:42.855428Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.14416","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:020ba0b4e543e89486603d290c813d6186ddb2786ba4f9442a80293af34bd684","sha256:256f11cdd8765e1b035d0bb28dda3c3dd256d1e762fafad5190030e4b5b4e1d1"],"state_sha256":"fa330f509e000aca81efa8a7faaaee8b27689c60811119ef96d000ac5adf0e54"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uGS80joJP4sl6l+ZhiWChDpVGA13LXE3L/W+qwOLiGcebaDa8GRcljZU2kF4OwitEdQvvDFOckWoL7DIsBeqBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T12:08:27.017942Z","bundle_sha256":"4b46352dc77ffbcec8bd696ce88a5fa81e51e4e3389713d87df96aa91c3b6275"}}