{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:QT4KCTXHANQ2O4DGZHDYSAXCG7","short_pith_number":"pith:QT4KCTXH","canonical_record":{"source":{"id":"2605.15543","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.GT","submitted_at":"2026-05-15T02:30:36Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6292d94238f2ff7daefe18a9097c2bc48872d2393be4364f67478eed6665778f","abstract_canon_sha256":"67a998ff300182a8d85cd021d183663951c4a29c08f39164b4c918ac02173caa"},"schema_version":"1.0"},"canonical_sha256":"84f8a14ee70361a77066c9c78902e237c885a76e6df8b26900d4a12d45f197f6","source":{"kind":"arxiv","id":"2605.15543","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15543","created_at":"2026-05-20T00:01:04Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15543v1","created_at":"2026-05-20T00:01:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15543","created_at":"2026-05-20T00:01:04Z"},{"alias_kind":"pith_short_12","alias_value":"QT4KCTXHANQ2","created_at":"2026-05-20T00:01:04Z"},{"alias_kind":"pith_short_16","alias_value":"QT4KCTXHANQ2O4DG","created_at":"2026-05-20T00:01:04Z"},{"alias_kind":"pith_short_8","alias_value":"QT4KCTXH","created_at":"2026-05-20T00:01:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:QT4KCTXHANQ2O4DGZHDYSAXCG7","target":"record","payload":{"canonical_record":{"source":{"id":"2605.15543","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.GT","submitted_at":"2026-05-15T02:30:36Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6292d94238f2ff7daefe18a9097c2bc48872d2393be4364f67478eed6665778f","abstract_canon_sha256":"67a998ff300182a8d85cd021d183663951c4a29c08f39164b4c918ac02173caa"},"schema_version":"1.0"},"canonical_sha256":"84f8a14ee70361a77066c9c78902e237c885a76e6df8b26900d4a12d45f197f6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:01:04.435018Z","signature_b64":"wJTEevNMjOW7GTC67p5DleNaCzfNUVPcIhIZUJSV/SlWINJRU8LoiHgFjNHlWWozMLNr+CAYc9x8gGkrzTaGDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"84f8a14ee70361a77066c9c78902e237c885a76e6df8b26900d4a12d45f197f6","last_reissued_at":"2026-05-20T00:01:04.434171Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:01:04.434171Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.15543","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:01:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Kjwi8i/fYYBFABcIdvIuUItq/aw4mk2mbhM3uPLkzF+blJXX0573CtDEGGXGc0GN7QSpcslZ+ntRu8Ep+okEDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T00:40:36.964004Z"},"content_sha256":"71b1507d5b285a105f1e42a9704c03bede78e0418942b0ccf6bd8fd523eac955","schema_version":"1.0","event_id":"sha256:71b1507d5b285a105f1e42a9704c03bede78e0418942b0ccf6bd8fd523eac955"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:QT4KCTXHANQ2O4DGZHDYSAXCG7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Domain-Independent Game Abstraction using Word Embedding Techniques","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Word embeddings trained on gameplay data can cluster actions to create effective abstractions for large games without domain-specific knowledge.","cross_cats":["cs.AI"],"primary_cat":"cs.GT","authors_text":"Juho Kim, Tuomas Sandholm","submitted_at":"2026-05-15T02:30:36Z","abstract_excerpt":"Many games of interest in the real world are often intractably large, thereby necessitating the use of game abstraction to shrink them in size, typically by many magnitudes. Over the last two decades, there have been significant advances in game abstraction; however, the domain-specific nature (usually poker) of much of the prior work prevents those techniques from being easily generalized to other settings without extensively analyzing the game at hand. In this paper, we propose a domain-independent approach to game abstraction, which applies word embedding techniques from the field of natura"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Experimental results demonstrate that our proposed game abstraction technique is effective, although it does not outperform specialized algorithms tailored to specific games.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That vector similarities learned from raw action sequences in gameplay data will group actions in a way that preserves strategic value for the purpose of abstraction, without additional game-specific features or labels.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Uses word embeddings on action sequences from gameplay data to produce domain-independent game abstractions via vector clustering, with experiments showing effectiveness but no outperformance over specialized methods.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Word embeddings trained on gameplay data can cluster actions to create effective abstractions for large games without domain-specific knowledge.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"f479da8fbab50dd8e7392b6291e9f6c2677d2ef2300c1928fc1f5dff9e6e2a97"},"source":{"id":"2605.15543","kind":"arxiv","version":1},"verdict":{"id":"f5d955b4-183d-4851-923e-4cbe35cd8fd4","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T14:16:01.354666Z","strongest_claim":"Experimental results demonstrate that our proposed game abstraction technique is effective, although it does not outperform specialized algorithms tailored to specific games.","one_line_summary":"Uses word embeddings on action sequences from gameplay data to produce domain-independent game abstractions via vector clustering, with experiments showing effectiveness but no outperformance over specialized methods.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That vector similarities learned from raw action sequences in gameplay data will group actions in a way that preserves strategic value for the purpose of abstraction, without additional game-specific features or labels.","pith_extraction_headline":"Word embeddings trained on gameplay data can cluster actions to create effective abstractions for large games without domain-specific knowledge."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.15543/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T14:31:17.425474Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T14:27:26.717720Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"cited_work_retraction","ran_at":"2026-05-19T14:22:00.879014Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T14:21:54.024288Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"shingle_duplication","ran_at":"2026-05-19T13:49:41.825000Z","status":"skipped","version":"0.1.0","findings_count":0},{"name":"citation_quote_validity","ran_at":"2026-05-19T13:49:41.362550Z","status":"skipped","version":"0.1.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T13:33:22.610107Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"a5fa78e658e35d53e8146fea1769e1f79d01dcd5c86ace3e7b7b3ab56892f61a"},"references":{"count":36,"sample":[{"doi":"","year":2003,"title":"D. Billings, N. Burch, A. Davidson, R. Holte, J. Schaeffer, T. Schauenberg, and D. Szafron. Approximating game-theoretic optimal strategies for full-scale poker. InProceedings of the International Joi","work_id":"dce26b7b-5b1a-4563-9246-ce8bff829f19","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2018,"title":"N. Brown and T. Sandholm. Superhuman AI for heads-up no-limit poker: Libratus beats top professionals.Science, 359(6374):418–424, 2018","work_id":"ef7b56a3-56e6-44b7-91f5-2599930721da","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2019,"title":"N. Brown and T. Sandholm. Superhuman AI for multiplayer poker.Science, 365(6456):885–890, 2019","work_id":"deee810e-0e04-44e2-a5ee-100c7117dc2d","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2015,"title":"N. Brown, S. Ganzfried, and T. Sandholm. Hierarchical abstraction, distributed equilibrium computation, and post-processing, with application to a champion no-limit Texas hold’em agent. InProceedings ","work_id":"742de4bd-ada7-4fdc-8061-193680c2604e","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"R. Carlson, J. Bauer, and C. D. Manning. A new pair of GloVes, 2025","work_id":"b6fd0574-051a-47c0-92f9-a4a1271b8264","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":36,"snapshot_sha256":"9a3b1afa581eeb5be8e501ceb3e3dcc087377d930367a5b938c9ac8de844d70d","internal_anchors":0},"formal_canon":{"evidence_count":1,"snapshot_sha256":"47a594a2965b8424b89af92b4caed15deaad5e50869eb20be320f94564e3da03"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"f5d955b4-183d-4851-923e-4cbe35cd8fd4"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:01:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"h6G2XoCWtUQZ63NDznKHFSR3FzWU9ZROyCgxTlo9NDZUm52LX9NvnGG5sPPuwCLPPFQ8GIlkYNv+KVjwWWJUCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T00:40:36.965486Z"},"content_sha256":"ead2724a279a46033cf98ac454861ce1b77099392e4fe403991644069bf26a2e","schema_version":"1.0","event_id":"sha256:ead2724a279a46033cf98ac454861ce1b77099392e4fe403991644069bf26a2e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QT4KCTXHANQ2O4DGZHDYSAXCG7/bundle.json","state_url":"https://pith.science/pith/QT4KCTXHANQ2O4DGZHDYSAXCG7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QT4KCTXHANQ2O4DGZHDYSAXCG7/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":"2026-05-26T00:40:36Z","links":{"resolver":"https://pith.science/pith/QT4KCTXHANQ2O4DGZHDYSAXCG7","bundle":"https://pith.science/pith/QT4KCTXHANQ2O4DGZHDYSAXCG7/bundle.json","state":"https://pith.science/pith/QT4KCTXHANQ2O4DGZHDYSAXCG7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QT4KCTXHANQ2O4DGZHDYSAXCG7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:QT4KCTXHANQ2O4DGZHDYSAXCG7","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":"67a998ff300182a8d85cd021d183663951c4a29c08f39164b4c918ac02173caa","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.GT","submitted_at":"2026-05-15T02:30:36Z","title_canon_sha256":"6292d94238f2ff7daefe18a9097c2bc48872d2393be4364f67478eed6665778f"},"schema_version":"1.0","source":{"id":"2605.15543","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15543","created_at":"2026-05-20T00:01:04Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15543v1","created_at":"2026-05-20T00:01:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15543","created_at":"2026-05-20T00:01:04Z"},{"alias_kind":"pith_short_12","alias_value":"QT4KCTXHANQ2","created_at":"2026-05-20T00:01:04Z"},{"alias_kind":"pith_short_16","alias_value":"QT4KCTXHANQ2O4DG","created_at":"2026-05-20T00:01:04Z"},{"alias_kind":"pith_short_8","alias_value":"QT4KCTXH","created_at":"2026-05-20T00:01:04Z"}],"graph_snapshots":[{"event_id":"sha256:ead2724a279a46033cf98ac454861ce1b77099392e4fe403991644069bf26a2e","target":"graph","created_at":"2026-05-20T00:01:04Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Experimental results demonstrate that our proposed game abstraction technique is effective, although it does not outperform specialized algorithms tailored to specific games."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That vector similarities learned from raw action sequences in gameplay data will group actions in a way that preserves strategic value for the purpose of abstraction, without additional game-specific features or labels."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Uses word embeddings on action sequences from gameplay data to produce domain-independent game abstractions via vector clustering, with experiments showing effectiveness but no outperformance over specialized methods."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Word embeddings trained on gameplay data can cluster actions to create effective abstractions for large games without domain-specific knowledge."}],"snapshot_sha256":"f479da8fbab50dd8e7392b6291e9f6c2677d2ef2300c1928fc1f5dff9e6e2a97"},"formal_canon":{"evidence_count":1,"snapshot_sha256":"47a594a2965b8424b89af92b4caed15deaad5e50869eb20be320f94564e3da03"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T14:31:17.425474Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T14:27:26.717720Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"cited_work_retraction","ran_at":"2026-05-19T14:22:00.879014Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T14:21:54.024288Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"shingle_duplication","ran_at":"2026-05-19T13:49:41.825000Z","status":"skipped","version":"0.1.0"},{"findings_count":0,"name":"citation_quote_validity","ran_at":"2026-05-19T13:49:41.362550Z","status":"skipped","version":"0.1.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T13:33:22.610107Z","status":"skipped","version":"1.0.0"}],"endpoint":"/pith/2605.15543/integrity.json","findings":[],"snapshot_sha256":"a5fa78e658e35d53e8146fea1769e1f79d01dcd5c86ace3e7b7b3ab56892f61a","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Many games of interest in the real world are often intractably large, thereby necessitating the use of game abstraction to shrink them in size, typically by many magnitudes. Over the last two decades, there have been significant advances in game abstraction; however, the domain-specific nature (usually poker) of much of the prior work prevents those techniques from being easily generalized to other settings without extensively analyzing the game at hand. In this paper, we propose a domain-independent approach to game abstraction, which applies word embedding techniques from the field of natura","authors_text":"Juho Kim, Tuomas Sandholm","cross_cats":["cs.AI"],"headline":"Word embeddings trained on gameplay data can cluster actions to create effective abstractions for large games without domain-specific knowledge.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.GT","submitted_at":"2026-05-15T02:30:36Z","title":"Domain-Independent Game Abstraction using Word Embedding Techniques"},"references":{"count":36,"internal_anchors":0,"resolved_work":36,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"D. Billings, N. Burch, A. Davidson, R. Holte, J. Schaeffer, T. Schauenberg, and D. Szafron. Approximating game-theoretic optimal strategies for full-scale poker. InProceedings of the International Joi","work_id":"dce26b7b-5b1a-4563-9246-ce8bff829f19","year":2003},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"N. Brown and T. Sandholm. Superhuman AI for heads-up no-limit poker: Libratus beats top professionals.Science, 359(6374):418–424, 2018","work_id":"ef7b56a3-56e6-44b7-91f5-2599930721da","year":2018},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"N. Brown and T. Sandholm. Superhuman AI for multiplayer poker.Science, 365(6456):885–890, 2019","work_id":"deee810e-0e04-44e2-a5ee-100c7117dc2d","year":2019},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"N. Brown, S. Ganzfried, and T. Sandholm. Hierarchical abstraction, distributed equilibrium computation, and post-processing, with application to a champion no-limit Texas hold’em agent. InProceedings ","work_id":"742de4bd-ada7-4fdc-8061-193680c2604e","year":2015},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"R. Carlson, J. Bauer, and C. D. Manning. A new pair of GloVes, 2025","work_id":"b6fd0574-051a-47c0-92f9-a4a1271b8264","year":2025}],"snapshot_sha256":"9a3b1afa581eeb5be8e501ceb3e3dcc087377d930367a5b938c9ac8de844d70d"},"source":{"id":"2605.15543","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T14:16:01.354666Z","id":"f5d955b4-183d-4851-923e-4cbe35cd8fd4","model_set":{"reader":"grok-4.3"},"one_line_summary":"Uses word embeddings on action sequences from gameplay data to produce domain-independent game abstractions via vector clustering, with experiments showing effectiveness but no outperformance over specialized methods.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Word embeddings trained on gameplay data can cluster actions to create effective abstractions for large games without domain-specific knowledge.","strongest_claim":"Experimental results demonstrate that our proposed game abstraction technique is effective, although it does not outperform specialized algorithms tailored to specific games.","weakest_assumption":"That vector similarities learned from raw action sequences in gameplay data will group actions in a way that preserves strategic value for the purpose of abstraction, without additional game-specific features or labels."}},"verdict_id":"f5d955b4-183d-4851-923e-4cbe35cd8fd4"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:71b1507d5b285a105f1e42a9704c03bede78e0418942b0ccf6bd8fd523eac955","target":"record","created_at":"2026-05-20T00:01:04Z","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":"67a998ff300182a8d85cd021d183663951c4a29c08f39164b4c918ac02173caa","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.GT","submitted_at":"2026-05-15T02:30:36Z","title_canon_sha256":"6292d94238f2ff7daefe18a9097c2bc48872d2393be4364f67478eed6665778f"},"schema_version":"1.0","source":{"id":"2605.15543","kind":"arxiv","version":1}},"canonical_sha256":"84f8a14ee70361a77066c9c78902e237c885a76e6df8b26900d4a12d45f197f6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"84f8a14ee70361a77066c9c78902e237c885a76e6df8b26900d4a12d45f197f6","first_computed_at":"2026-05-20T00:01:04.434171Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:01:04.434171Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wJTEevNMjOW7GTC67p5DleNaCzfNUVPcIhIZUJSV/SlWINJRU8LoiHgFjNHlWWozMLNr+CAYc9x8gGkrzTaGDA==","signature_status":"signed_v1","signed_at":"2026-05-20T00:01:04.435018Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.15543","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:71b1507d5b285a105f1e42a9704c03bede78e0418942b0ccf6bd8fd523eac955","sha256:ead2724a279a46033cf98ac454861ce1b77099392e4fe403991644069bf26a2e"],"state_sha256":"892a89634d9ce8c89e65aa805a032da353f2fb84f3f27d341cef6ee6f3e5b39f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Em+yUPCsPnMhBiyxpr89zC6TXuNk4bOJqOazeT5sNtWa2hc+MS9hYKwM2rCpGe6+CKXwWp4F4FHcjOg+r/yHDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T00:40:36.971521Z","bundle_sha256":"4bc3f74befdd2200176db9db74b45ffd73d965f7c9fc8381f82c457687ec56b1"}}