{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:BAJPWQSRG2XN3SOP4E4GYDYCHR","short_pith_number":"pith:BAJPWQSR","canonical_record":{"source":{"id":"2508.11836","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2025-08-15T23:05:37Z","cross_cats_sorted":[],"title_canon_sha256":"5442d80db96f8219283ee4ab4bb94bca1f5d2cf6d7b9a42be75a1ac8abc3cfa0","abstract_canon_sha256":"92f88308aec1dd75ab2f5e313877dc6c77c9edb8d77b39cc6a4922e60a0d4083"},"schema_version":"1.0"},"canonical_sha256":"0812fb425136aeddc9cfe1386c0f023c4a6587f7c94ff42e544ba66d4daa2baf","source":{"kind":"arxiv","id":"2508.11836","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.11836","created_at":"2026-05-22T01:04:53Z"},{"alias_kind":"arxiv_version","alias_value":"2508.11836v2","created_at":"2026-05-22T01:04:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.11836","created_at":"2026-05-22T01:04:53Z"},{"alias_kind":"pith_short_12","alias_value":"BAJPWQSRG2XN","created_at":"2026-05-22T01:04:53Z"},{"alias_kind":"pith_short_16","alias_value":"BAJPWQSRG2XN3SOP","created_at":"2026-05-22T01:04:53Z"},{"alias_kind":"pith_short_8","alias_value":"BAJPWQSR","created_at":"2026-05-22T01:04:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:BAJPWQSRG2XN3SOP4E4GYDYCHR","target":"record","payload":{"canonical_record":{"source":{"id":"2508.11836","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2025-08-15T23:05:37Z","cross_cats_sorted":[],"title_canon_sha256":"5442d80db96f8219283ee4ab4bb94bca1f5d2cf6d7b9a42be75a1ac8abc3cfa0","abstract_canon_sha256":"92f88308aec1dd75ab2f5e313877dc6c77c9edb8d77b39cc6a4922e60a0d4083"},"schema_version":"1.0"},"canonical_sha256":"0812fb425136aeddc9cfe1386c0f023c4a6587f7c94ff42e544ba66d4daa2baf","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-22T01:04:53.680867Z","signature_b64":"1bb3R1npd6aAl8iH4iYCqykTwfZloh2w2c63vpy3zIXtD983k+vjyeeEI2VGv1OJRlPrE+8Su1cm6URPP3seBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0812fb425136aeddc9cfe1386c0f023c4a6587f7c94ff42e544ba66d4daa2baf","last_reissued_at":"2026-05-22T01:04:53.680251Z","signature_status":"signed_v1","first_computed_at":"2026-05-22T01:04:53.680251Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2508.11836","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":"2026-05-22T01:04:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cyTRTWqsA4QRm6RRKOeAKbwi34+kG8L/LObCeYdPPulHd+tu/tGxwvmuA9dRCLIE2MGXRHYTwDFYJSDgwDviCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T15:57:21.237689Z"},"content_sha256":"8544a7c68a7b912b04b1907eb3d92e48dcdf2bdfd232b5fee72d89c4a6a294f3","schema_version":"1.0","event_id":"sha256:8544a7c68a7b912b04b1907eb3d92e48dcdf2bdfd232b5fee72d89c4a6a294f3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:BAJPWQSRG2XN3SOP4E4GYDYCHR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Finite Automata Extraction: Low-data World Model Learning as Programs from Gameplay Video","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Anurag Sarkar, Dave Goel, Matthew Guzdial","submitted_at":"2025-08-15T23:05:37Z","abstract_excerpt":"World models are defined as a compressed spatial and temporal learned representation of an environment. The learned representation is typically a neural network, making transfer of the learned environment dynamics and explainability a challenge. In this paper, we propose an approach, Finite Automata Extraction (FAE), that learns a neuro-symbolic world model from gameplay video represented as programs in a novel domain-specific language (DSL): Retro Coder. Compared to prior world model approaches, FAE learns a more precise model of the environment and more general code than prior DSL-based appr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.11836","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/2508.11836/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":"2026-05-22T01:04:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rxTtKpqBGpetpTXCISeH1gtuhUPAGDGh07nJyiCZocUPw/n7GAqxD5PtZJzz5nHOUALangdlfsN51E92voUzDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T15:57:21.238408Z"},"content_sha256":"59999ed8ddf7b85cb471268e9f78809cdbaee810a956f681da012fe769e5b773","schema_version":"1.0","event_id":"sha256:59999ed8ddf7b85cb471268e9f78809cdbaee810a956f681da012fe769e5b773"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BAJPWQSRG2XN3SOP4E4GYDYCHR/bundle.json","state_url":"https://pith.science/pith/BAJPWQSRG2XN3SOP4E4GYDYCHR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BAJPWQSRG2XN3SOP4E4GYDYCHR/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":"2026-05-27T15:57:21Z","links":{"resolver":"https://pith.science/pith/BAJPWQSRG2XN3SOP4E4GYDYCHR","bundle":"https://pith.science/pith/BAJPWQSRG2XN3SOP4E4GYDYCHR/bundle.json","state":"https://pith.science/pith/BAJPWQSRG2XN3SOP4E4GYDYCHR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BAJPWQSRG2XN3SOP4E4GYDYCHR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:BAJPWQSRG2XN3SOP4E4GYDYCHR","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":"92f88308aec1dd75ab2f5e313877dc6c77c9edb8d77b39cc6a4922e60a0d4083","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2025-08-15T23:05:37Z","title_canon_sha256":"5442d80db96f8219283ee4ab4bb94bca1f5d2cf6d7b9a42be75a1ac8abc3cfa0"},"schema_version":"1.0","source":{"id":"2508.11836","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.11836","created_at":"2026-05-22T01:04:53Z"},{"alias_kind":"arxiv_version","alias_value":"2508.11836v2","created_at":"2026-05-22T01:04:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.11836","created_at":"2026-05-22T01:04:53Z"},{"alias_kind":"pith_short_12","alias_value":"BAJPWQSRG2XN","created_at":"2026-05-22T01:04:53Z"},{"alias_kind":"pith_short_16","alias_value":"BAJPWQSRG2XN3SOP","created_at":"2026-05-22T01:04:53Z"},{"alias_kind":"pith_short_8","alias_value":"BAJPWQSR","created_at":"2026-05-22T01:04:53Z"}],"graph_snapshots":[{"event_id":"sha256:59999ed8ddf7b85cb471268e9f78809cdbaee810a956f681da012fe769e5b773","target":"graph","created_at":"2026-05-22T01:04:53Z","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/2508.11836/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"World models are defined as a compressed spatial and temporal learned representation of an environment. The learned representation is typically a neural network, making transfer of the learned environment dynamics and explainability a challenge. In this paper, we propose an approach, Finite Automata Extraction (FAE), that learns a neuro-symbolic world model from gameplay video represented as programs in a novel domain-specific language (DSL): Retro Coder. Compared to prior world model approaches, FAE learns a more precise model of the environment and more general code than prior DSL-based appr","authors_text":"Anurag Sarkar, Dave Goel, Matthew Guzdial","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2025-08-15T23:05:37Z","title":"Finite Automata Extraction: Low-data World Model Learning as Programs from Gameplay Video"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.11836","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":"sha256:8544a7c68a7b912b04b1907eb3d92e48dcdf2bdfd232b5fee72d89c4a6a294f3","target":"record","created_at":"2026-05-22T01:04:53Z","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":"92f88308aec1dd75ab2f5e313877dc6c77c9edb8d77b39cc6a4922e60a0d4083","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2025-08-15T23:05:37Z","title_canon_sha256":"5442d80db96f8219283ee4ab4bb94bca1f5d2cf6d7b9a42be75a1ac8abc3cfa0"},"schema_version":"1.0","source":{"id":"2508.11836","kind":"arxiv","version":2}},"canonical_sha256":"0812fb425136aeddc9cfe1386c0f023c4a6587f7c94ff42e544ba66d4daa2baf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0812fb425136aeddc9cfe1386c0f023c4a6587f7c94ff42e544ba66d4daa2baf","first_computed_at":"2026-05-22T01:04:53.680251Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-22T01:04:53.680251Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1bb3R1npd6aAl8iH4iYCqykTwfZloh2w2c63vpy3zIXtD983k+vjyeeEI2VGv1OJRlPrE+8Su1cm6URPP3seBQ==","signature_status":"signed_v1","signed_at":"2026-05-22T01:04:53.680867Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.11836","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8544a7c68a7b912b04b1907eb3d92e48dcdf2bdfd232b5fee72d89c4a6a294f3","sha256:59999ed8ddf7b85cb471268e9f78809cdbaee810a956f681da012fe769e5b773"],"state_sha256":"1ce6b8cbb70753dc543bdcd23b3cdef2fbaf557f09c2e0e211670f00168fcdf2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rz2K4a3eEohljUsocvcjLpz0BKNT/TjK+6ENiefNMbiHK4Xa/kTZuHhMrIzp31OuH94MMH8nAvsxR5h7TzqZBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T15:57:21.242109Z","bundle_sha256":"04372c9d7a7b5b262b81e45ab004bd5a214bf74ceb5977d429f7f4b05c8134f7"}}