{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:MUYG35ARQUXMSYT5DO44PJ2B5G","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":"c0f76486dec77320b19e4616097517310cef7a114f2e85aa68695d9046779727","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-11-01T15:43:29Z","title_canon_sha256":"31bd3a1df2bc209f7fc3af9d5f2a8b6344fba3a6fea0cc31a0d555c784cab932"},"schema_version":"1.0","source":{"id":"2211.00539","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.00539","created_at":"2026-07-05T07:15:49Z"},{"alias_kind":"arxiv_version","alias_value":"2211.00539v3","created_at":"2026-07-05T07:15:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.00539","created_at":"2026-07-05T07:15:49Z"},{"alias_kind":"pith_short_12","alias_value":"MUYG35ARQUXM","created_at":"2026-07-05T07:15:49Z"},{"alias_kind":"pith_short_16","alias_value":"MUYG35ARQUXMSYT5","created_at":"2026-07-05T07:15:49Z"},{"alias_kind":"pith_short_8","alias_value":"MUYG35AR","created_at":"2026-07-05T07:15:49Z"}],"graph_snapshots":[{"event_id":"sha256:b2520826c634a8873cfd9f3eff44aac6b9d81298fe9f37418cb5e29b48ab2391","target":"graph","created_at":"2026-07-05T07:15:49Z","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/2211.00539/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent breakthroughs in the development of agents to solve challenging sequential decision making problems such as Go, StarCraft, or DOTA, have relied on both simulated environments and large-scale datasets. However, progress on this research has been hindered by the scarcity of open-sourced datasets and the prohibitive computational cost to work with them. Here we present the NetHack Learning Dataset (NLD), a large and highly-scalable dataset of trajectories from the popular game of NetHack, which is both extremely challenging for current methods and very fast to run. NLD consists of three pa","authors_text":"Danielle Rothermel, Eric Hambro, Heinrich K\\\"uttler, Naila Murray, Roberta Raileanu, Tim Rockt\\\"aschel, Vegard Mella","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-11-01T15:43:29Z","title":"Dungeons and Data: A Large-Scale NetHack Dataset"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.00539","kind":"arxiv","version":3},"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":"sha256:28726778eb1568ec3f4dc3e5bb1fe733a3956f6eb5acf976129865c077da5270","target":"record","created_at":"2026-07-05T07:15:49Z","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":"c0f76486dec77320b19e4616097517310cef7a114f2e85aa68695d9046779727","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-11-01T15:43:29Z","title_canon_sha256":"31bd3a1df2bc209f7fc3af9d5f2a8b6344fba3a6fea0cc31a0d555c784cab932"},"schema_version":"1.0","source":{"id":"2211.00539","kind":"arxiv","version":3}},"canonical_sha256":"65306df411852ec9627d1bb9c7a741e998e8f620b00ea7ab3ed56407baaf8389","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"65306df411852ec9627d1bb9c7a741e998e8f620b00ea7ab3ed56407baaf8389","first_computed_at":"2026-07-05T07:15:49.320216Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:15:49.320216Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NyffTV9QAgJdRQSG5Wq5XXoR1LYRXw7foaTOHxwvHbUC69ztjl4P+Fa4MVL0pnhKfJCDX35ixDUli0++5TQYCw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:15:49.320662Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.00539","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:28726778eb1568ec3f4dc3e5bb1fe733a3956f6eb5acf976129865c077da5270","sha256:b2520826c634a8873cfd9f3eff44aac6b9d81298fe9f37418cb5e29b48ab2391"],"state_sha256":"3051bb40af81ae49d857cb8524fb51a2a328dff62c5314f219993a3053ee2381"}