{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:O2LDJB7HHTWNWQGQXSLO47TD6W","short_pith_number":"pith:O2LDJB7H","canonical_record":{"source":{"id":"2410.15491","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-20T20:09:47Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"2af78a915ecda92440eaa7af2573894fc0b50524b6b1a68a66bbb64169fd8fbe","abstract_canon_sha256":"597320b5bc63909a3f37b25e124739752324d417c0d065d4aea7570d887fe660"},"schema_version":"1.0"},"canonical_sha256":"76963487e73cecdb40d0bc96ee7e63f59da947173513c637ff3ae109cb57817f","source":{"kind":"arxiv","id":"2410.15491","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.15491","created_at":"2026-07-05T09:23:20Z"},{"alias_kind":"arxiv_version","alias_value":"2410.15491v1","created_at":"2026-07-05T09:23:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.15491","created_at":"2026-07-05T09:23:20Z"},{"alias_kind":"pith_short_12","alias_value":"O2LDJB7HHTWN","created_at":"2026-07-05T09:23:20Z"},{"alias_kind":"pith_short_16","alias_value":"O2LDJB7HHTWNWQGQ","created_at":"2026-07-05T09:23:20Z"},{"alias_kind":"pith_short_8","alias_value":"O2LDJB7H","created_at":"2026-07-05T09:23:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:O2LDJB7HHTWNWQGQXSLO47TD6W","target":"record","payload":{"canonical_record":{"source":{"id":"2410.15491","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-20T20:09:47Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"2af78a915ecda92440eaa7af2573894fc0b50524b6b1a68a66bbb64169fd8fbe","abstract_canon_sha256":"597320b5bc63909a3f37b25e124739752324d417c0d065d4aea7570d887fe660"},"schema_version":"1.0"},"canonical_sha256":"76963487e73cecdb40d0bc96ee7e63f59da947173513c637ff3ae109cb57817f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:23:20.373916Z","signature_b64":"5/VUqtV/tt8W4LtVPN3gurQck3mdml2HXCSEr/OttY7qLZAxtfXquF/bJacPl0bDkYdit00fbuOghu+thRBlBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"76963487e73cecdb40d0bc96ee7e63f59da947173513c637ff3ae109cb57817f","last_reissued_at":"2026-07-05T09:23:20.373502Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:23:20.373502Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.15491","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":"2026-07-05T09:23:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BhoR/Mu0mKHiz6P0lGo4GbMySiG95ge3qzWeyJCp/sJgsY9Vztnp+FNlzExXIIweh7CjvfMZSDn8GkSPICBwCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T01:41:52.810867Z"},"content_sha256":"c86ef9154a2683eeb5ffabf53a3cb1e76c3653d9d8a6913037154dd6f0f0845e","schema_version":"1.0","event_id":"sha256:c86ef9154a2683eeb5ffabf53a3cb1e76c3653d9d8a6913037154dd6f0f0845e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:O2LDJB7HHTWNWQGQXSLO47TD6W","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Structural Causality-based Generalizable Concept Discovery Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"cs.LG","authors_text":"Aidong Zhang, Guangzhi Xiong, Sanchit Sinha","submitted_at":"2024-10-20T20:09:47Z","abstract_excerpt":"The rising need for explainable deep neural network architectures has utilized semantic concepts as explainable units. Several approaches utilizing disentangled representation learning estimate the generative factors and utilize them as concepts for explaining DNNs. However, even though the generative factors for a dataset remain fixed, concepts are not fixed entities and vary based on downstream tasks. In this paper, we propose a disentanglement mechanism utilizing a variational autoencoder (VAE) for learning mutually independent generative factors for a given dataset and subsequently learnin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.15491","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/2410.15491/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":"2026-07-05T09:23:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gydKe+mGyqBliB1xlo6I949O3QNtOzZOkXe1lk9CcAw8ps7rqfOQBWuXD5D72Y+u4Q8YKlVJqPQIaidk9NMsDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T01:41:52.811248Z"},"content_sha256":"94dd1f667a140da78f9c15152baf345877fc4494694c8a737a234f7b41526988","schema_version":"1.0","event_id":"sha256:94dd1f667a140da78f9c15152baf345877fc4494694c8a737a234f7b41526988"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/O2LDJB7HHTWNWQGQXSLO47TD6W/bundle.json","state_url":"https://pith.science/pith/O2LDJB7HHTWNWQGQXSLO47TD6W/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/O2LDJB7HHTWNWQGQXSLO47TD6W/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":"2026-07-09T01:41:52Z","links":{"resolver":"https://pith.science/pith/O2LDJB7HHTWNWQGQXSLO47TD6W","bundle":"https://pith.science/pith/O2LDJB7HHTWNWQGQXSLO47TD6W/bundle.json","state":"https://pith.science/pith/O2LDJB7HHTWNWQGQXSLO47TD6W/state.json","well_known_bundle":"https://pith.science/.well-known/pith/O2LDJB7HHTWNWQGQXSLO47TD6W/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:O2LDJB7HHTWNWQGQXSLO47TD6W","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":"597320b5bc63909a3f37b25e124739752324d417c0d065d4aea7570d887fe660","cross_cats_sorted":["stat.ME"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-20T20:09:47Z","title_canon_sha256":"2af78a915ecda92440eaa7af2573894fc0b50524b6b1a68a66bbb64169fd8fbe"},"schema_version":"1.0","source":{"id":"2410.15491","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.15491","created_at":"2026-07-05T09:23:20Z"},{"alias_kind":"arxiv_version","alias_value":"2410.15491v1","created_at":"2026-07-05T09:23:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.15491","created_at":"2026-07-05T09:23:20Z"},{"alias_kind":"pith_short_12","alias_value":"O2LDJB7HHTWN","created_at":"2026-07-05T09:23:20Z"},{"alias_kind":"pith_short_16","alias_value":"O2LDJB7HHTWNWQGQ","created_at":"2026-07-05T09:23:20Z"},{"alias_kind":"pith_short_8","alias_value":"O2LDJB7H","created_at":"2026-07-05T09:23:20Z"}],"graph_snapshots":[{"event_id":"sha256:94dd1f667a140da78f9c15152baf345877fc4494694c8a737a234f7b41526988","target":"graph","created_at":"2026-07-05T09:23:20Z","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/2410.15491/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The rising need for explainable deep neural network architectures has utilized semantic concepts as explainable units. Several approaches utilizing disentangled representation learning estimate the generative factors and utilize them as concepts for explaining DNNs. However, even though the generative factors for a dataset remain fixed, concepts are not fixed entities and vary based on downstream tasks. In this paper, we propose a disentanglement mechanism utilizing a variational autoencoder (VAE) for learning mutually independent generative factors for a given dataset and subsequently learnin","authors_text":"Aidong Zhang, Guangzhi Xiong, Sanchit Sinha","cross_cats":["stat.ME"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-20T20:09:47Z","title":"Structural Causality-based Generalizable Concept Discovery Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.15491","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:c86ef9154a2683eeb5ffabf53a3cb1e76c3653d9d8a6913037154dd6f0f0845e","target":"record","created_at":"2026-07-05T09:23:20Z","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":"597320b5bc63909a3f37b25e124739752324d417c0d065d4aea7570d887fe660","cross_cats_sorted":["stat.ME"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-20T20:09:47Z","title_canon_sha256":"2af78a915ecda92440eaa7af2573894fc0b50524b6b1a68a66bbb64169fd8fbe"},"schema_version":"1.0","source":{"id":"2410.15491","kind":"arxiv","version":1}},"canonical_sha256":"76963487e73cecdb40d0bc96ee7e63f59da947173513c637ff3ae109cb57817f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"76963487e73cecdb40d0bc96ee7e63f59da947173513c637ff3ae109cb57817f","first_computed_at":"2026-07-05T09:23:20.373502Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:23:20.373502Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5/VUqtV/tt8W4LtVPN3gurQck3mdml2HXCSEr/OttY7qLZAxtfXquF/bJacPl0bDkYdit00fbuOghu+thRBlBA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:23:20.373916Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.15491","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c86ef9154a2683eeb5ffabf53a3cb1e76c3653d9d8a6913037154dd6f0f0845e","sha256:94dd1f667a140da78f9c15152baf345877fc4494694c8a737a234f7b41526988"],"state_sha256":"8d96e1e8072b52c14ac57c7c3c98686f7736d217fd1b347cc16e70ecf1966f4f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9GArtQuIM1pPXgWYAHJPC8QXgC8jcLM2Ih0sBcxNw4B4niv/SCA8zkOnDMjjVBPWp+hXowcTNr90JZ+h27VLDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-09T01:41:52.813677Z","bundle_sha256":"cd55462e22bb0f4fa775e0945e0a38d00eae8d2272f265c232c5c0a26d8b024a"}}