{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:T5QCGFMN3DISHDS5XZWK5Y2KYH","short_pith_number":"pith:T5QCGFMN","canonical_record":{"source":{"id":"2410.22133","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-29T15:31:03Z","cross_cats_sorted":[],"title_canon_sha256":"28f1060a4ebfbf37c7cee2e1961b934475ae1b5c9bc219dad28aba3aa348bd64","abstract_canon_sha256":"272994522554b5695852c6a671b563ea5d16c6eba04dd8ba450c981765c1d316"},"schema_version":"1.0"},"canonical_sha256":"9f6023158dd8d1238e5dbe6caee34ac1e8f59fbaf12d72b7a3e62b75f3fc975d","source":{"kind":"arxiv","id":"2410.22133","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.22133","created_at":"2026-07-05T09:28:57Z"},{"alias_kind":"arxiv_version","alias_value":"2410.22133v2","created_at":"2026-07-05T09:28:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.22133","created_at":"2026-07-05T09:28:57Z"},{"alias_kind":"pith_short_12","alias_value":"T5QCGFMN3DIS","created_at":"2026-07-05T09:28:57Z"},{"alias_kind":"pith_short_16","alias_value":"T5QCGFMN3DISHDS5","created_at":"2026-07-05T09:28:57Z"},{"alias_kind":"pith_short_8","alias_value":"T5QCGFMN","created_at":"2026-07-05T09:28:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:T5QCGFMN3DISHDS5XZWK5Y2KYH","target":"record","payload":{"canonical_record":{"source":{"id":"2410.22133","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-29T15:31:03Z","cross_cats_sorted":[],"title_canon_sha256":"28f1060a4ebfbf37c7cee2e1961b934475ae1b5c9bc219dad28aba3aa348bd64","abstract_canon_sha256":"272994522554b5695852c6a671b563ea5d16c6eba04dd8ba450c981765c1d316"},"schema_version":"1.0"},"canonical_sha256":"9f6023158dd8d1238e5dbe6caee34ac1e8f59fbaf12d72b7a3e62b75f3fc975d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:28:57.450752Z","signature_b64":"3rJADdYgqShi78477j/Vpr482xajud9pGZ91IHusstXidhmYpf/XYcQ5v5ydCIcCbGEfPG9OBcKaFV0KQMzsAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9f6023158dd8d1238e5dbe6caee34ac1e8f59fbaf12d72b7a3e62b75f3fc975d","last_reissued_at":"2026-07-05T09:28:57.450219Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:28:57.450219Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.22133","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":"2026-07-05T09:28:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"22OSEW+b3x/e9+kbwZV2yicsOO09VhcZ+bXewOXAbg6XY5o4RxYJ5gH4ZimYyGS85/t+2/q4wu7aCUaYhD8PDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T02:24:22.291136Z"},"content_sha256":"90621d83501cabeafbfee1f966831cc597ea88a5449ea553f0b2ef62957e6be5","schema_version":"1.0","event_id":"sha256:90621d83501cabeafbfee1f966831cc597ea88a5449ea553f0b2ef62957e6be5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:T5QCGFMN3DISHDS5XZWK5Y2KYH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Successor Features the Simple Way","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Arna Ghosh, Blake A. Richards, Christos Kaplanis, Doina Precup, Raymond Chua","submitted_at":"2024-10-29T15:31:03Z","abstract_excerpt":"In Deep Reinforcement Learning (RL), it is a challenge to learn representations that do not exhibit catastrophic forgetting or interference in non-stationary environments. Successor Features (SFs) offer a potential solution to this challenge. However, canonical techniques for learning SFs from pixel-level observations often lead to representation collapse, wherein representations degenerate and fail to capture meaningful variations in the data. More recent methods for learning SFs can avoid representation collapse, but they often involve complex losses and multiple learning phases, reducing th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.22133","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/2410.22133/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":"2026-07-05T09:28:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YesZaoL61yJffGqLgKMkfEnT5Xfq8MFG/GJyNPoqby1BDpDV0yr+AcRVAwOVdAgxVbN9hnqdWhmdHtcYQIdYBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T02:24:22.291635Z"},"content_sha256":"c83f59052779982cf72ecd9ff8051380a5967557699360be386b236186e108a1","schema_version":"1.0","event_id":"sha256:c83f59052779982cf72ecd9ff8051380a5967557699360be386b236186e108a1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/T5QCGFMN3DISHDS5XZWK5Y2KYH/bundle.json","state_url":"https://pith.science/pith/T5QCGFMN3DISHDS5XZWK5Y2KYH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/T5QCGFMN3DISHDS5XZWK5Y2KYH/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":"2026-08-04T02:24:22Z","links":{"resolver":"https://pith.science/pith/T5QCGFMN3DISHDS5XZWK5Y2KYH","bundle":"https://pith.science/pith/T5QCGFMN3DISHDS5XZWK5Y2KYH/bundle.json","state":"https://pith.science/pith/T5QCGFMN3DISHDS5XZWK5Y2KYH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/T5QCGFMN3DISHDS5XZWK5Y2KYH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:T5QCGFMN3DISHDS5XZWK5Y2KYH","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":"272994522554b5695852c6a671b563ea5d16c6eba04dd8ba450c981765c1d316","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-29T15:31:03Z","title_canon_sha256":"28f1060a4ebfbf37c7cee2e1961b934475ae1b5c9bc219dad28aba3aa348bd64"},"schema_version":"1.0","source":{"id":"2410.22133","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.22133","created_at":"2026-07-05T09:28:57Z"},{"alias_kind":"arxiv_version","alias_value":"2410.22133v2","created_at":"2026-07-05T09:28:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.22133","created_at":"2026-07-05T09:28:57Z"},{"alias_kind":"pith_short_12","alias_value":"T5QCGFMN3DIS","created_at":"2026-07-05T09:28:57Z"},{"alias_kind":"pith_short_16","alias_value":"T5QCGFMN3DISHDS5","created_at":"2026-07-05T09:28:57Z"},{"alias_kind":"pith_short_8","alias_value":"T5QCGFMN","created_at":"2026-07-05T09:28:57Z"}],"graph_snapshots":[{"event_id":"sha256:c83f59052779982cf72ecd9ff8051380a5967557699360be386b236186e108a1","target":"graph","created_at":"2026-07-05T09:28:57Z","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/2410.22133/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In Deep Reinforcement Learning (RL), it is a challenge to learn representations that do not exhibit catastrophic forgetting or interference in non-stationary environments. Successor Features (SFs) offer a potential solution to this challenge. However, canonical techniques for learning SFs from pixel-level observations often lead to representation collapse, wherein representations degenerate and fail to capture meaningful variations in the data. More recent methods for learning SFs can avoid representation collapse, but they often involve complex losses and multiple learning phases, reducing th","authors_text":"Arna Ghosh, Blake A. Richards, Christos Kaplanis, Doina Precup, Raymond Chua","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-29T15:31:03Z","title":"Learning Successor Features the Simple Way"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.22133","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":"sha256:90621d83501cabeafbfee1f966831cc597ea88a5449ea553f0b2ef62957e6be5","target":"record","created_at":"2026-07-05T09:28:57Z","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":"272994522554b5695852c6a671b563ea5d16c6eba04dd8ba450c981765c1d316","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-29T15:31:03Z","title_canon_sha256":"28f1060a4ebfbf37c7cee2e1961b934475ae1b5c9bc219dad28aba3aa348bd64"},"schema_version":"1.0","source":{"id":"2410.22133","kind":"arxiv","version":2}},"canonical_sha256":"9f6023158dd8d1238e5dbe6caee34ac1e8f59fbaf12d72b7a3e62b75f3fc975d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9f6023158dd8d1238e5dbe6caee34ac1e8f59fbaf12d72b7a3e62b75f3fc975d","first_computed_at":"2026-07-05T09:28:57.450219Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:28:57.450219Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3rJADdYgqShi78477j/Vpr482xajud9pGZ91IHusstXidhmYpf/XYcQ5v5ydCIcCbGEfPG9OBcKaFV0KQMzsAg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:28:57.450752Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.22133","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:90621d83501cabeafbfee1f966831cc597ea88a5449ea553f0b2ef62957e6be5","sha256:c83f59052779982cf72ecd9ff8051380a5967557699360be386b236186e108a1"],"state_sha256":"2348d7a09256a1b1fe78e0a33619d0ca2d804fd8b2586aa952f5f00dea5b2051"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wfM3XkfS3PnE6imjvImxMTUOiGZtnSX+kMLpDkUWLYPH+ZqIzGue2fn+HxpQDIkc7X9ICpDg3fHPUtAxb5t5AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T02:24:22.295123Z","bundle_sha256":"77a2e65685ff47f949fee24e2caad6bf3f8a64693d06ddd78eddc46a68512e29"}}