{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:CMD7LBTF2LKYDXH5VXQ3MPZMAK","short_pith_number":"pith:CMD7LBTF","canonical_record":{"source":{"id":"2410.08893","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-11T15:10:40Z","cross_cats_sorted":["cs.AI","cs.RO"],"title_canon_sha256":"2ffa24d1323eb9be52b2633bef5ec8deccfe720f4355e634ceb859fbca847945","abstract_canon_sha256":"69c2eb0d71a04660454d70c0ec89cb6349bde10afc847057aa0da6b749ddeeee"},"schema_version":"1.0"},"canonical_sha256":"1307f58665d2d581dcfdade1b63f2c02be4aa4e1a5893b94592b914ba68f8957","source":{"kind":"arxiv","id":"2410.08893","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.08893","created_at":"2026-07-05T11:03:52Z"},{"alias_kind":"arxiv_version","alias_value":"2410.08893v4","created_at":"2026-07-05T11:03:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.08893","created_at":"2026-07-05T11:03:52Z"},{"alias_kind":"pith_short_12","alias_value":"CMD7LBTF2LKY","created_at":"2026-07-05T11:03:52Z"},{"alias_kind":"pith_short_16","alias_value":"CMD7LBTF2LKYDXH5","created_at":"2026-07-05T11:03:52Z"},{"alias_kind":"pith_short_8","alias_value":"CMD7LBTF","created_at":"2026-07-05T11:03:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:CMD7LBTF2LKYDXH5VXQ3MPZMAK","target":"record","payload":{"canonical_record":{"source":{"id":"2410.08893","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-11T15:10:40Z","cross_cats_sorted":["cs.AI","cs.RO"],"title_canon_sha256":"2ffa24d1323eb9be52b2633bef5ec8deccfe720f4355e634ceb859fbca847945","abstract_canon_sha256":"69c2eb0d71a04660454d70c0ec89cb6349bde10afc847057aa0da6b749ddeeee"},"schema_version":"1.0"},"canonical_sha256":"1307f58665d2d581dcfdade1b63f2c02be4aa4e1a5893b94592b914ba68f8957","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:03:52.471557Z","signature_b64":"paRZ7AtXLDz/8hOdEgu3pvYnEVAkszigxx2B4dTG5O1zBMKFlTseGMRnCrvxQyeAzGUOF3d6pUWai49osYMLCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1307f58665d2d581dcfdade1b63f2c02be4aa4e1a5893b94592b914ba68f8957","last_reissued_at":"2026-07-05T11:03:52.471056Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:03:52.471056Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.08893","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:03:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FhQzjzLdKuucnX8EmN/LQBfygW5mGBSVraWZIKTVNSBOLYjsl7rU8UnSuH1fOuPqndJqf4eJQ0l17Ver/Hz5Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T16:18:04.549951Z"},"content_sha256":"72f5e8e4434c35d82ac1af76986af7f84ab407541cf28e2f3b9f12d2a12f44a9","schema_version":"1.0","event_id":"sha256:72f5e8e4434c35d82ac1af76986af7f84ab407541cf28e2f3b9f12d2a12f44a9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:CMD7LBTF2LKYDXH5VXQ3MPZMAK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Drama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter Efficient","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.RO"],"primary_cat":"cs.LG","authors_text":"Ivana Dusparic, Ke Zhang, Vinny Cahill, Wenlong Wang, Yucheng Shi","submitted_at":"2024-10-11T15:10:40Z","abstract_excerpt":"Model-based reinforcement learning (RL) offers a solution to the data inefficiency that plagues most model-free RL algorithms. However, learning a robust world model often requires complex and deep architectures, which are computationally expensive and challenging to train. Within the world model, sequence models play a critical role in accurate predictions, and various architectures have been explored, each with its own challenges. Currently, recurrent neural network (RNN)-based world models struggle with vanishing gradients and capturing long-term dependencies. Transformers, on the other han"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.08893","kind":"arxiv","version":4},"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/2410.08893/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":"2026-07-05T11:03:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/uijIGXjDVQhZnApIBiPIv0xtg/ImNto8S5vlPchcR88SmCmHiprVcrcZklB9WPfQMqNoniGoqbUVYV/6wQKAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T16:18:04.550430Z"},"content_sha256":"15e166c8726a10d1c2a4f1be3d3b9d838448ee57d0e2378fcb264c143c4135a3","schema_version":"1.0","event_id":"sha256:15e166c8726a10d1c2a4f1be3d3b9d838448ee57d0e2378fcb264c143c4135a3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CMD7LBTF2LKYDXH5VXQ3MPZMAK/bundle.json","state_url":"https://pith.science/pith/CMD7LBTF2LKYDXH5VXQ3MPZMAK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CMD7LBTF2LKYDXH5VXQ3MPZMAK/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":"2026-08-10T16:18:04Z","links":{"resolver":"https://pith.science/pith/CMD7LBTF2LKYDXH5VXQ3MPZMAK","bundle":"https://pith.science/pith/CMD7LBTF2LKYDXH5VXQ3MPZMAK/bundle.json","state":"https://pith.science/pith/CMD7LBTF2LKYDXH5VXQ3MPZMAK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CMD7LBTF2LKYDXH5VXQ3MPZMAK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:CMD7LBTF2LKYDXH5VXQ3MPZMAK","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":"69c2eb0d71a04660454d70c0ec89cb6349bde10afc847057aa0da6b749ddeeee","cross_cats_sorted":["cs.AI","cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-11T15:10:40Z","title_canon_sha256":"2ffa24d1323eb9be52b2633bef5ec8deccfe720f4355e634ceb859fbca847945"},"schema_version":"1.0","source":{"id":"2410.08893","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.08893","created_at":"2026-07-05T11:03:52Z"},{"alias_kind":"arxiv_version","alias_value":"2410.08893v4","created_at":"2026-07-05T11:03:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.08893","created_at":"2026-07-05T11:03:52Z"},{"alias_kind":"pith_short_12","alias_value":"CMD7LBTF2LKY","created_at":"2026-07-05T11:03:52Z"},{"alias_kind":"pith_short_16","alias_value":"CMD7LBTF2LKYDXH5","created_at":"2026-07-05T11:03:52Z"},{"alias_kind":"pith_short_8","alias_value":"CMD7LBTF","created_at":"2026-07-05T11:03:52Z"}],"graph_snapshots":[{"event_id":"sha256:15e166c8726a10d1c2a4f1be3d3b9d838448ee57d0e2378fcb264c143c4135a3","target":"graph","created_at":"2026-07-05T11:03:52Z","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/2410.08893/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Model-based reinforcement learning (RL) offers a solution to the data inefficiency that plagues most model-free RL algorithms. However, learning a robust world model often requires complex and deep architectures, which are computationally expensive and challenging to train. Within the world model, sequence models play a critical role in accurate predictions, and various architectures have been explored, each with its own challenges. Currently, recurrent neural network (RNN)-based world models struggle with vanishing gradients and capturing long-term dependencies. Transformers, on the other han","authors_text":"Ivana Dusparic, Ke Zhang, Vinny Cahill, Wenlong Wang, Yucheng Shi","cross_cats":["cs.AI","cs.RO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-11T15:10:40Z","title":"Drama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter Efficient"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.08893","kind":"arxiv","version":4},"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":"sha256:72f5e8e4434c35d82ac1af76986af7f84ab407541cf28e2f3b9f12d2a12f44a9","target":"record","created_at":"2026-07-05T11:03:52Z","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":"69c2eb0d71a04660454d70c0ec89cb6349bde10afc847057aa0da6b749ddeeee","cross_cats_sorted":["cs.AI","cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-11T15:10:40Z","title_canon_sha256":"2ffa24d1323eb9be52b2633bef5ec8deccfe720f4355e634ceb859fbca847945"},"schema_version":"1.0","source":{"id":"2410.08893","kind":"arxiv","version":4}},"canonical_sha256":"1307f58665d2d581dcfdade1b63f2c02be4aa4e1a5893b94592b914ba68f8957","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1307f58665d2d581dcfdade1b63f2c02be4aa4e1a5893b94592b914ba68f8957","first_computed_at":"2026-07-05T11:03:52.471056Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:03:52.471056Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"paRZ7AtXLDz/8hOdEgu3pvYnEVAkszigxx2B4dTG5O1zBMKFlTseGMRnCrvxQyeAzGUOF3d6pUWai49osYMLCw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:03:52.471557Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.08893","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:72f5e8e4434c35d82ac1af76986af7f84ab407541cf28e2f3b9f12d2a12f44a9","sha256:15e166c8726a10d1c2a4f1be3d3b9d838448ee57d0e2378fcb264c143c4135a3"],"state_sha256":"546e5049aef274f9be8c61f134b3ffb8ae50b103dc2ccd480902e408b4605105"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VBAnkRhzPZg7qeYUF/q0vX5Hdo1vNZ/bAZjeAEUIZy+oJ/Vjx7OBe6uPKeYBCEAkRJyFU41v+QA03imOzcgbAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T16:18:04.553708Z","bundle_sha256":"d45f590e3883873bcd0d702959e0863d8adcef936f8534692b3ad3c7e18a8284"}}