{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:RXRKOTKQKKD2PDGDPTANQQE3E2","short_pith_number":"pith:RXRKOTKQ","canonical_record":{"source":{"id":"2111.13545","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-26T15:26:37Z","cross_cats_sorted":["cs.AI","cs.CV","cs.GR"],"title_canon_sha256":"2d6fb1c52eb6df524405c37265382a33fb904bd795a6b5d50d8e57f15e4d4d78","abstract_canon_sha256":"a50f2f2185bebc2443e21f5e434bb4230564b71c0e3b9557bda9d8c6867538e9"},"schema_version":"1.0"},"canonical_sha256":"8de2a74d505287a78cc37cc0d8409b26ba238c39a168a4c092283ee72de4628b","source":{"kind":"arxiv","id":"2111.13545","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.13545","created_at":"2026-07-05T03:35:22Z"},{"alias_kind":"arxiv_version","alias_value":"2111.13545v1","created_at":"2026-07-05T03:35:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.13545","created_at":"2026-07-05T03:35:22Z"},{"alias_kind":"pith_short_12","alias_value":"RXRKOTKQKKD2","created_at":"2026-07-05T03:35:22Z"},{"alias_kind":"pith_short_16","alias_value":"RXRKOTKQKKD2PDGD","created_at":"2026-07-05T03:35:22Z"},{"alias_kind":"pith_short_8","alias_value":"RXRKOTKQ","created_at":"2026-07-05T03:35:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:RXRKOTKQKKD2PDGDPTANQQE3E2","target":"record","payload":{"canonical_record":{"source":{"id":"2111.13545","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-26T15:26:37Z","cross_cats_sorted":["cs.AI","cs.CV","cs.GR"],"title_canon_sha256":"2d6fb1c52eb6df524405c37265382a33fb904bd795a6b5d50d8e57f15e4d4d78","abstract_canon_sha256":"a50f2f2185bebc2443e21f5e434bb4230564b71c0e3b9557bda9d8c6867538e9"},"schema_version":"1.0"},"canonical_sha256":"8de2a74d505287a78cc37cc0d8409b26ba238c39a168a4c092283ee72de4628b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:35:22.351291Z","signature_b64":"Z7ViwtHYrzJJe6mOu4l4x1qdf6O4WjZYQ+8e+zrOiWU1nt7TbjCW0FYX5tA/sKRKipqwzy1UsqsNaKtWGAMpBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8de2a74d505287a78cc37cc0d8409b26ba238c39a168a4c092283ee72de4628b","last_reissued_at":"2026-07-05T03:35:22.350913Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:35:22.350913Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2111.13545","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":"2026-07-05T03:35:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Fh8XpDV4CJSJQFgPPXa2U/SWysFd7zmRiDXuquIb32uMQklPXtu7TjA0rwjp5wGavYETcLl+ypmTE2iU8InDDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T12:28:01.472120Z"},"content_sha256":"0ce491fc392cea7dbf88a8dcb9d297603f0589eb3a0142661a5cb4bca5157e4f","schema_version":"1.0","event_id":"sha256:0ce491fc392cea7dbf88a8dcb9d297603f0589eb3a0142661a5cb4bca5157e4f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:RXRKOTKQKKD2PDGDPTANQQE3E2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"$\\mu$NCA: Texture Generation with Ultra-Compact Neural Cellular Automata","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.GR"],"primary_cat":"cs.LG","authors_text":"Alexander Mordvintsev, Eyvind Niklasson","submitted_at":"2021-11-26T15:26:37Z","abstract_excerpt":"We study the problem of example-based procedural texture synthesis using highly compact models. Given a sample image, we use differentiable programming to train a generative process, parameterised by a recurrent Neural Cellular Automata (NCA) rule. Contrary to the common belief that neural networks should be significantly over-parameterised, we demonstrate that our model architecture and training procedure allows for representing complex texture patterns using just a few hundred learned parameters, making their expressivity comparable to hand-engineered procedural texture generating programs. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.13545","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/2111.13545/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":"2026-07-05T03:35:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/BQKarCGGfrYiGwL14inDdgfo3GxZWezvS6HH1c5mPWSmcMMTwms+cGXCm/9HE5V7qoDcN9x+lXKWDN4bvx8Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T12:28:01.472646Z"},"content_sha256":"7a58de3e0e019a04f72aeeed580cdf015677c5ec41e70fcfe89af0b81b65d73d","schema_version":"1.0","event_id":"sha256:7a58de3e0e019a04f72aeeed580cdf015677c5ec41e70fcfe89af0b81b65d73d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RXRKOTKQKKD2PDGDPTANQQE3E2/bundle.json","state_url":"https://pith.science/pith/RXRKOTKQKKD2PDGDPTANQQE3E2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RXRKOTKQKKD2PDGDPTANQQE3E2/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":"2026-08-12T12:28:01Z","links":{"resolver":"https://pith.science/pith/RXRKOTKQKKD2PDGDPTANQQE3E2","bundle":"https://pith.science/pith/RXRKOTKQKKD2PDGDPTANQQE3E2/bundle.json","state":"https://pith.science/pith/RXRKOTKQKKD2PDGDPTANQQE3E2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RXRKOTKQKKD2PDGDPTANQQE3E2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:RXRKOTKQKKD2PDGDPTANQQE3E2","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":"a50f2f2185bebc2443e21f5e434bb4230564b71c0e3b9557bda9d8c6867538e9","cross_cats_sorted":["cs.AI","cs.CV","cs.GR"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-26T15:26:37Z","title_canon_sha256":"2d6fb1c52eb6df524405c37265382a33fb904bd795a6b5d50d8e57f15e4d4d78"},"schema_version":"1.0","source":{"id":"2111.13545","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.13545","created_at":"2026-07-05T03:35:22Z"},{"alias_kind":"arxiv_version","alias_value":"2111.13545v1","created_at":"2026-07-05T03:35:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.13545","created_at":"2026-07-05T03:35:22Z"},{"alias_kind":"pith_short_12","alias_value":"RXRKOTKQKKD2","created_at":"2026-07-05T03:35:22Z"},{"alias_kind":"pith_short_16","alias_value":"RXRKOTKQKKD2PDGD","created_at":"2026-07-05T03:35:22Z"},{"alias_kind":"pith_short_8","alias_value":"RXRKOTKQ","created_at":"2026-07-05T03:35:22Z"}],"graph_snapshots":[{"event_id":"sha256:7a58de3e0e019a04f72aeeed580cdf015677c5ec41e70fcfe89af0b81b65d73d","target":"graph","created_at":"2026-07-05T03:35:22Z","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/2111.13545/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We study the problem of example-based procedural texture synthesis using highly compact models. Given a sample image, we use differentiable programming to train a generative process, parameterised by a recurrent Neural Cellular Automata (NCA) rule. Contrary to the common belief that neural networks should be significantly over-parameterised, we demonstrate that our model architecture and training procedure allows for representing complex texture patterns using just a few hundred learned parameters, making their expressivity comparable to hand-engineered procedural texture generating programs. ","authors_text":"Alexander Mordvintsev, Eyvind Niklasson","cross_cats":["cs.AI","cs.CV","cs.GR"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-26T15:26:37Z","title":"$\\mu$NCA: Texture Generation with Ultra-Compact Neural Cellular Automata"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.13545","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":"sha256:0ce491fc392cea7dbf88a8dcb9d297603f0589eb3a0142661a5cb4bca5157e4f","target":"record","created_at":"2026-07-05T03:35:22Z","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":"a50f2f2185bebc2443e21f5e434bb4230564b71c0e3b9557bda9d8c6867538e9","cross_cats_sorted":["cs.AI","cs.CV","cs.GR"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-26T15:26:37Z","title_canon_sha256":"2d6fb1c52eb6df524405c37265382a33fb904bd795a6b5d50d8e57f15e4d4d78"},"schema_version":"1.0","source":{"id":"2111.13545","kind":"arxiv","version":1}},"canonical_sha256":"8de2a74d505287a78cc37cc0d8409b26ba238c39a168a4c092283ee72de4628b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8de2a74d505287a78cc37cc0d8409b26ba238c39a168a4c092283ee72de4628b","first_computed_at":"2026-07-05T03:35:22.350913Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:35:22.350913Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Z7ViwtHYrzJJe6mOu4l4x1qdf6O4WjZYQ+8e+zrOiWU1nt7TbjCW0FYX5tA/sKRKipqwzy1UsqsNaKtWGAMpBA==","signature_status":"signed_v1","signed_at":"2026-07-05T03:35:22.351291Z","signed_message":"canonical_sha256_bytes"},"source_id":"2111.13545","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0ce491fc392cea7dbf88a8dcb9d297603f0589eb3a0142661a5cb4bca5157e4f","sha256:7a58de3e0e019a04f72aeeed580cdf015677c5ec41e70fcfe89af0b81b65d73d"],"state_sha256":"5585570788d4469c73a4317c2b1706a8839bb76f212813d7eb4713b3f0d0dcc9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hAw/Anh17eZ37dd0iwZKpmznZzrsEATarRsJXjANRf23j2k9+NmGClEOcof4iW1RyZj3Z1ZBhKAxbytnvU/ODA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T12:28:01.476525Z","bundle_sha256":"c2e77f84900c8860d71f288f2f98c79ebe06fb5696d1312c553a08f98353f44a"}}