{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:TQ2FYC5XPNX2AXZKGILO5BMQUD","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":"5996fd267cf2c2e46d4d6f2491fd9bd28f6f0f89f77c0163d64bf340e24fb292","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-27T16:54:12Z","title_canon_sha256":"73d1bd76c7ef74886b6b6c37dbeda4960f27d897629dba41298d11128037bf07"},"schema_version":"1.0","source":{"id":"2406.19320","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.19320","created_at":"2026-07-05T08:37:33Z"},{"alias_kind":"arxiv_version","alias_value":"2406.19320v1","created_at":"2026-07-05T08:37:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.19320","created_at":"2026-07-05T08:37:33Z"},{"alias_kind":"pith_short_12","alias_value":"TQ2FYC5XPNX2","created_at":"2026-07-05T08:37:33Z"},{"alias_kind":"pith_short_16","alias_value":"TQ2FYC5XPNX2AXZK","created_at":"2026-07-05T08:37:33Z"},{"alias_kind":"pith_short_8","alias_value":"TQ2FYC5X","created_at":"2026-07-05T08:37:33Z"}],"graph_snapshots":[{"event_id":"sha256:edd3ffda9cf58b73092e31473cc0b2e246816d9c10982d41789ea9b1a21d3e8f","target":"graph","created_at":"2026-07-05T08:37:33Z","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/2406.19320/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Scaling up deep Reinforcement Learning (RL) methods presents a significant challenge. Following developments in generative modelling, model-based RL positions itself as a strong contender. Recent advances in sequence modelling have led to effective transformer-based world models, albeit at the price of heavy computations due to the long sequences of tokens required to accurately simulate environments. In this work, we propose $\\Delta$-IRIS, a new agent with a world model architecture composed of a discrete autoencoder that encodes stochastic deltas between time steps and an autoregressive tran","authors_text":"Eloi Alonso, Fran\\c{c}ois Fleuret, Vincent Micheli","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-27T16:54:12Z","title":"Efficient World Models with Context-Aware Tokenization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.19320","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":"sha256:08eee5034c7ff88083f52bdd1740ea0a1ebc3818d952ae1281e095f4d0d9caf0","target":"record","created_at":"2026-07-05T08:37:33Z","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":"5996fd267cf2c2e46d4d6f2491fd9bd28f6f0f89f77c0163d64bf340e24fb292","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-27T16:54:12Z","title_canon_sha256":"73d1bd76c7ef74886b6b6c37dbeda4960f27d897629dba41298d11128037bf07"},"schema_version":"1.0","source":{"id":"2406.19320","kind":"arxiv","version":1}},"canonical_sha256":"9c345c0bb77b6fa05f2a3216ee8590a0e416ae9f3a35f76a75f8df0003655c9c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9c345c0bb77b6fa05f2a3216ee8590a0e416ae9f3a35f76a75f8df0003655c9c","first_computed_at":"2026-07-05T08:37:33.080808Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:37:33.080808Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WGUh5GyS/NKouFGsOlAS6O6CwGuyQEiah3HjA+jv5QixLO0qq/hLvIIjcElSiX5v+XBOM1nrSP2BMpx837bLDw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:37:33.081212Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.19320","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:08eee5034c7ff88083f52bdd1740ea0a1ebc3818d952ae1281e095f4d0d9caf0","sha256:edd3ffda9cf58b73092e31473cc0b2e246816d9c10982d41789ea9b1a21d3e8f"],"state_sha256":"2ec65a5286023bbb7b5fd24b8e381ecc51bc3306e540e2237cc40d17aaabf207"}