{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:I7MXUFSKWVGIQQCL2SZX3ZLT22","short_pith_number":"pith:I7MXUFSK","canonical_record":{"source":{"id":"2506.05391","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-06-03T18:44:54Z","cross_cats_sorted":["cs.CV","cs.LG","stat.AP"],"title_canon_sha256":"3fbdc8df91b8afb553dfbe34e56f7533e49d822ee8caa0a6835e8f5fef35feff","abstract_canon_sha256":"466bf93bcccf9213bd6791f3aab39b5c842e692b3770201f387ec5ba4daad93b"},"schema_version":"1.0"},"canonical_sha256":"47d97a164ab54c88404bd4b37de573d6a037dccd1b3081361fac0cf1941d1719","source":{"kind":"arxiv","id":"2506.05391","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.05391","created_at":"2026-07-05T11:32:30Z"},{"alias_kind":"arxiv_version","alias_value":"2506.05391v2","created_at":"2026-07-05T11:32:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.05391","created_at":"2026-07-05T11:32:30Z"},{"alias_kind":"pith_short_12","alias_value":"I7MXUFSKWVGI","created_at":"2026-07-05T11:32:30Z"},{"alias_kind":"pith_short_16","alias_value":"I7MXUFSKWVGIQQCL","created_at":"2026-07-05T11:32:30Z"},{"alias_kind":"pith_short_8","alias_value":"I7MXUFSK","created_at":"2026-07-05T11:32:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:I7MXUFSKWVGIQQCL2SZX3ZLT22","target":"record","payload":{"canonical_record":{"source":{"id":"2506.05391","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-06-03T18:44:54Z","cross_cats_sorted":["cs.CV","cs.LG","stat.AP"],"title_canon_sha256":"3fbdc8df91b8afb553dfbe34e56f7533e49d822ee8caa0a6835e8f5fef35feff","abstract_canon_sha256":"466bf93bcccf9213bd6791f3aab39b5c842e692b3770201f387ec5ba4daad93b"},"schema_version":"1.0"},"canonical_sha256":"47d97a164ab54c88404bd4b37de573d6a037dccd1b3081361fac0cf1941d1719","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:32:30.243084Z","signature_b64":"q59kRu7nfN5iqjgTAOTiVaUk4Tls7HpD7IJzIuDdBCbNwtTsLZPqoADqS1r4r7x6Hcs5e6Wo1dROula/OsHzBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"47d97a164ab54c88404bd4b37de573d6a037dccd1b3081361fac0cf1941d1719","last_reissued_at":"2026-07-05T11:32:30.242600Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:32:30.242600Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.05391","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":"2026-07-05T11:32:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qK9jnd9cFH7nyJtGKtvR6S/iPneq1ThmSm7JYa0wgSFi48Bk+hpF1ceQwv2UnGL42Ju93GPdgmQPsHZiG6JVAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T22:06:23.156982Z"},"content_sha256":"3367312361e01c4bb431a19dacc464cff423cca205b3ad359b773aa6a24e0d1c","schema_version":"1.0","event_id":"sha256:3367312361e01c4bb431a19dacc464cff423cca205b3ad359b773aa6a24e0d1c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:I7MXUFSKWVGIQQCL2SZX3ZLT22","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Enhancing Neural Autoregressive Distribution Estimators for Image Reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG","stat.AP"],"primary_cat":"eess.IV","authors_text":"Ambrose Emmett-Iwaniw, Nathan Kirk","submitted_at":"2025-06-03T18:44:54Z","abstract_excerpt":"Autoregressive models are often employed to learn distributions of image data by decomposing the $D$-dimensional density function into a product of one-dimensional conditional distributions. Each conditional depends on preceding variables (pixels, in the case of image data), making the order in which variables are processed fundamental to the model performance. In this paper, we study the problem of observing a small subset of image pixels (referred to as a pixel patch) to predict the unobserved parts of the image. As our prediction mechanism, we propose a generalized version of the convolutio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.05391","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/2506.05391/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":"2026-07-05T11:32:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zjTvPP+fe0Sh7SkyMPyEFePJnX2oy3Nusr5shKNS/aroLv8bplrAHhXdO3Cjbs3ZhzKOcX7sDvxqKRDk9ihHBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T22:06:23.157486Z"},"content_sha256":"68ecfec5ff1fb23689af9d70b0017ecae8fef0f40bec4a61c789ee6e78a92928","schema_version":"1.0","event_id":"sha256:68ecfec5ff1fb23689af9d70b0017ecae8fef0f40bec4a61c789ee6e78a92928"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/I7MXUFSKWVGIQQCL2SZX3ZLT22/bundle.json","state_url":"https://pith.science/pith/I7MXUFSKWVGIQQCL2SZX3ZLT22/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/I7MXUFSKWVGIQQCL2SZX3ZLT22/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":"2026-08-07T22:06:23Z","links":{"resolver":"https://pith.science/pith/I7MXUFSKWVGIQQCL2SZX3ZLT22","bundle":"https://pith.science/pith/I7MXUFSKWVGIQQCL2SZX3ZLT22/bundle.json","state":"https://pith.science/pith/I7MXUFSKWVGIQQCL2SZX3ZLT22/state.json","well_known_bundle":"https://pith.science/.well-known/pith/I7MXUFSKWVGIQQCL2SZX3ZLT22/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:I7MXUFSKWVGIQQCL2SZX3ZLT22","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":"466bf93bcccf9213bd6791f3aab39b5c842e692b3770201f387ec5ba4daad93b","cross_cats_sorted":["cs.CV","cs.LG","stat.AP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-06-03T18:44:54Z","title_canon_sha256":"3fbdc8df91b8afb553dfbe34e56f7533e49d822ee8caa0a6835e8f5fef35feff"},"schema_version":"1.0","source":{"id":"2506.05391","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.05391","created_at":"2026-07-05T11:32:30Z"},{"alias_kind":"arxiv_version","alias_value":"2506.05391v2","created_at":"2026-07-05T11:32:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.05391","created_at":"2026-07-05T11:32:30Z"},{"alias_kind":"pith_short_12","alias_value":"I7MXUFSKWVGI","created_at":"2026-07-05T11:32:30Z"},{"alias_kind":"pith_short_16","alias_value":"I7MXUFSKWVGIQQCL","created_at":"2026-07-05T11:32:30Z"},{"alias_kind":"pith_short_8","alias_value":"I7MXUFSK","created_at":"2026-07-05T11:32:30Z"}],"graph_snapshots":[{"event_id":"sha256:68ecfec5ff1fb23689af9d70b0017ecae8fef0f40bec4a61c789ee6e78a92928","target":"graph","created_at":"2026-07-05T11:32:30Z","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/2506.05391/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Autoregressive models are often employed to learn distributions of image data by decomposing the $D$-dimensional density function into a product of one-dimensional conditional distributions. Each conditional depends on preceding variables (pixels, in the case of image data), making the order in which variables are processed fundamental to the model performance. In this paper, we study the problem of observing a small subset of image pixels (referred to as a pixel patch) to predict the unobserved parts of the image. As our prediction mechanism, we propose a generalized version of the convolutio","authors_text":"Ambrose Emmett-Iwaniw, Nathan Kirk","cross_cats":["cs.CV","cs.LG","stat.AP"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-06-03T18:44:54Z","title":"Enhancing Neural Autoregressive Distribution Estimators for Image Reconstruction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.05391","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":"sha256:3367312361e01c4bb431a19dacc464cff423cca205b3ad359b773aa6a24e0d1c","target":"record","created_at":"2026-07-05T11:32:30Z","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":"466bf93bcccf9213bd6791f3aab39b5c842e692b3770201f387ec5ba4daad93b","cross_cats_sorted":["cs.CV","cs.LG","stat.AP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-06-03T18:44:54Z","title_canon_sha256":"3fbdc8df91b8afb553dfbe34e56f7533e49d822ee8caa0a6835e8f5fef35feff"},"schema_version":"1.0","source":{"id":"2506.05391","kind":"arxiv","version":2}},"canonical_sha256":"47d97a164ab54c88404bd4b37de573d6a037dccd1b3081361fac0cf1941d1719","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"47d97a164ab54c88404bd4b37de573d6a037dccd1b3081361fac0cf1941d1719","first_computed_at":"2026-07-05T11:32:30.242600Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:32:30.242600Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"q59kRu7nfN5iqjgTAOTiVaUk4Tls7HpD7IJzIuDdBCbNwtTsLZPqoADqS1r4r7x6Hcs5e6Wo1dROula/OsHzBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:32:30.243084Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.05391","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3367312361e01c4bb431a19dacc464cff423cca205b3ad359b773aa6a24e0d1c","sha256:68ecfec5ff1fb23689af9d70b0017ecae8fef0f40bec4a61c789ee6e78a92928"],"state_sha256":"a7acb9f34bf712110fd8c657732da8f056a1fd459e39dca427851a80d1675793"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"52w/r3hmgU4d5zBFAQBfwN2YhjVTq81ZuUHc3zW1tJ7vFgl4r5zdRmQGP8jeAjVZABRMKFkyO2mfaYX7rmjZCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T22:06:23.161618Z","bundle_sha256":"f5f040213599cb9d316e8b9aa62f466511e88c016f4edd322f72a5f1494dc7e3"}}