{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:EQU73ZMKHTS3C7KAPFPUILX725","short_pith_number":"pith:EQU73ZMK","schema_version":"1.0","canonical_sha256":"2429fde58a3ce5b17d40795f442effd744085dc67d1d02bc799a83ccc5d2a92b","source":{"kind":"arxiv","id":"2605.18803","version":1},"attestation_state":"computed","paper":{"title":"PROWL: Prioritized Regret-Driven Optimization for World Model Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Ahmet H. G\\\"uzel, Benjamin Graham, Ilija Bogunovic, Jack Parker-Holder, Jeffrey Hawke, Jenny Seidenschwarz, Jonathan Sadeghi","submitted_at":"2026-05-11T14:24:19Z","abstract_excerpt":"Modern action-conditioned video world models achieve strong short-horizon visual realism, yet remain unreliable on rare, interaction-critical transitions that dominate downstream planning and policy performance. Because passive demonstration data systematically under-samples these high-impact regimes, improving robustness requires actively eliciting model failures rather than relying on their natural occurrence. We introduce a KL-constrained adversarial curriculum in which a policy is trained to expose high-error trajectories of a diffusion-based world model while remaining close to the behavi"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.18803","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-11T14:24:19Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ecb53f26f389e65c34ab6661b1439a2d64f9550a120e9a14ecbf02ce7c803060","abstract_canon_sha256":"53384821af8b867519c73eeb46941c2ac33b2d5761f67e13e00246a84889115a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:06:23.313528Z","signature_b64":"560tLytyieoBHYiOyFGi0DxSiBaaF2Pb1bCGIGN176ARvxn0h0meVQSx0dzl0Fxx0JuYG/s2lX7qX5y5EmV6Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2429fde58a3ce5b17d40795f442effd744085dc67d1d02bc799a83ccc5d2a92b","last_reissued_at":"2026-05-20T00:06:23.312783Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:06:23.312783Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PROWL: Prioritized Regret-Driven Optimization for World Model Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Ahmet H. G\\\"uzel, Benjamin Graham, Ilija Bogunovic, Jack Parker-Holder, Jeffrey Hawke, Jenny Seidenschwarz, Jonathan Sadeghi","submitted_at":"2026-05-11T14:24:19Z","abstract_excerpt":"Modern action-conditioned video world models achieve strong short-horizon visual realism, yet remain unreliable on rare, interaction-critical transitions that dominate downstream planning and policy performance. Because passive demonstration data systematically under-samples these high-impact regimes, improving robustness requires actively eliciting model failures rather than relying on their natural occurrence. We introduce a KL-constrained adversarial curriculum in which a policy is trained to expose high-error trajectories of a diffusion-based world model while remaining close to the behavi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.18803","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/2605.18803/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2605.18803","created_at":"2026-05-20T00:06:23.312889+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.18803v1","created_at":"2026-05-20T00:06:23.312889+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.18803","created_at":"2026-05-20T00:06:23.312889+00:00"},{"alias_kind":"pith_short_12","alias_value":"EQU73ZMKHTS3","created_at":"2026-05-20T00:06:23.312889+00:00"},{"alias_kind":"pith_short_16","alias_value":"EQU73ZMKHTS3C7KA","created_at":"2026-05-20T00:06:23.312889+00:00"},{"alias_kind":"pith_short_8","alias_value":"EQU73ZMK","created_at":"2026-05-20T00:06:23.312889+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EQU73ZMKHTS3C7KAPFPUILX725","json":"https://pith.science/pith/EQU73ZMKHTS3C7KAPFPUILX725.json","graph_json":"https://pith.science/api/pith-number/EQU73ZMKHTS3C7KAPFPUILX725/graph.json","events_json":"https://pith.science/api/pith-number/EQU73ZMKHTS3C7KAPFPUILX725/events.json","paper":"https://pith.science/paper/EQU73ZMK"},"agent_actions":{"view_html":"https://pith.science/pith/EQU73ZMKHTS3C7KAPFPUILX725","download_json":"https://pith.science/pith/EQU73ZMKHTS3C7KAPFPUILX725.json","view_paper":"https://pith.science/paper/EQU73ZMK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.18803&json=true","fetch_graph":"https://pith.science/api/pith-number/EQU73ZMKHTS3C7KAPFPUILX725/graph.json","fetch_events":"https://pith.science/api/pith-number/EQU73ZMKHTS3C7KAPFPUILX725/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EQU73ZMKHTS3C7KAPFPUILX725/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EQU73ZMKHTS3C7KAPFPUILX725/action/storage_attestation","attest_author":"https://pith.science/pith/EQU73ZMKHTS3C7KAPFPUILX725/action/author_attestation","sign_citation":"https://pith.science/pith/EQU73ZMKHTS3C7KAPFPUILX725/action/citation_signature","submit_replication":"https://pith.science/pith/EQU73ZMKHTS3C7KAPFPUILX725/action/replication_record"}},"created_at":"2026-05-20T00:06:23.312889+00:00","updated_at":"2026-05-20T00:06:23.312889+00:00"}