{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:SIPXGVELGZELNXUR2A3NWSFMEI","short_pith_number":"pith:SIPXGVEL","canonical_record":{"source":{"id":"2302.03352","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-02-07T09:50:55Z","cross_cats_sorted":[],"title_canon_sha256":"4fdd135ab7d451e3c3f00bb2a08f5fb6180213f9b0965ef1678ca1ef4f4304d3","abstract_canon_sha256":"114597a10a3df3989a1efcffd61a9b312454afd72c834ca2fe14eb59015d362a"},"schema_version":"1.0"},"canonical_sha256":"921f73548b3648b6de91d036db48ac220d784b008efa13721344c46fc7ef0c65","source":{"kind":"arxiv","id":"2302.03352","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.03352","created_at":"2026-07-05T05:39:36Z"},{"alias_kind":"arxiv_version","alias_value":"2302.03352v1","created_at":"2026-07-05T05:39:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.03352","created_at":"2026-07-05T05:39:36Z"},{"alias_kind":"pith_short_12","alias_value":"SIPXGVELGZEL","created_at":"2026-07-05T05:39:36Z"},{"alias_kind":"pith_short_16","alias_value":"SIPXGVELGZELNXUR","created_at":"2026-07-05T05:39:36Z"},{"alias_kind":"pith_short_8","alias_value":"SIPXGVEL","created_at":"2026-07-05T05:39:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:SIPXGVELGZELNXUR2A3NWSFMEI","target":"record","payload":{"canonical_record":{"source":{"id":"2302.03352","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-02-07T09:50:55Z","cross_cats_sorted":[],"title_canon_sha256":"4fdd135ab7d451e3c3f00bb2a08f5fb6180213f9b0965ef1678ca1ef4f4304d3","abstract_canon_sha256":"114597a10a3df3989a1efcffd61a9b312454afd72c834ca2fe14eb59015d362a"},"schema_version":"1.0"},"canonical_sha256":"921f73548b3648b6de91d036db48ac220d784b008efa13721344c46fc7ef0c65","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:39:36.301452Z","signature_b64":"Mk+Liav613LAHEQsenmettLl7GT1Lud+/LQBN+j8kg51mGOssceFSd810yf+Zbt2h4tjysLt8q3R/uJOiwi2DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"921f73548b3648b6de91d036db48ac220d784b008efa13721344c46fc7ef0c65","last_reissued_at":"2026-07-05T05:39:36.301114Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:39:36.301114Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2302.03352","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":"2026-07-05T05:39:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rpHmW4EgBwzmr3jWhz6K3sab0Dy0eYYl5hzDZC8FPZalbJYCISh+D+Dgh9yTltn4GgvctD1beqM2eLVAhF+oAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T17:20:20.564140Z"},"content_sha256":"948da19acdfdf070baf0f05f006fe19a5986170c6902f4bd70fdbe94a66d63fc","schema_version":"1.0","event_id":"sha256:948da19acdfdf070baf0f05f006fe19a5986170c6902f4bd70fdbe94a66d63fc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:SIPXGVELGZELNXUR2A3NWSFMEI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Towards Understanding the Effects of Evolving the MCTS UCT Selection Policy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Edgar Galvan, Fred Valdez Ameneyro","submitted_at":"2023-02-07T09:50:55Z","abstract_excerpt":"Monte Carlo Tree Search (MCTS) is a sampling best-first method to search for optimal decisions. The success of MCTS depends heavily on how the MCTS statistical tree is built and the selection policy plays a fundamental role in this. A particular selection policy that works particularly well, widely adopted in MCTS, is the Upper Confidence Bounds for Trees, referred to as UCT. Other more sophisticated bounds have been proposed by the community with the goal to improve MCTS performance on particular problems. Thus, it is evident that while the MCTS UCT behaves generally well, some variants might"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.03352","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/2302.03352/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":"2026-07-05T05:39:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8eBXawL9cm/M1piWmA+LZwTOm1w5PhxAHH4LQon35L9/3YybvVFcdopiCi4id7pp5JsfXAIVEka1qdCtHIowBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T17:20:20.564519Z"},"content_sha256":"f6830fd25ae1cd69464d3bf08b491125a3db8098fb1eec126e5d3100ad802967","schema_version":"1.0","event_id":"sha256:f6830fd25ae1cd69464d3bf08b491125a3db8098fb1eec126e5d3100ad802967"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SIPXGVELGZELNXUR2A3NWSFMEI/bundle.json","state_url":"https://pith.science/pith/SIPXGVELGZELNXUR2A3NWSFMEI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SIPXGVELGZELNXUR2A3NWSFMEI/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":"2026-07-08T17:20:20Z","links":{"resolver":"https://pith.science/pith/SIPXGVELGZELNXUR2A3NWSFMEI","bundle":"https://pith.science/pith/SIPXGVELGZELNXUR2A3NWSFMEI/bundle.json","state":"https://pith.science/pith/SIPXGVELGZELNXUR2A3NWSFMEI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SIPXGVELGZELNXUR2A3NWSFMEI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:SIPXGVELGZELNXUR2A3NWSFMEI","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":"114597a10a3df3989a1efcffd61a9b312454afd72c834ca2fe14eb59015d362a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-02-07T09:50:55Z","title_canon_sha256":"4fdd135ab7d451e3c3f00bb2a08f5fb6180213f9b0965ef1678ca1ef4f4304d3"},"schema_version":"1.0","source":{"id":"2302.03352","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.03352","created_at":"2026-07-05T05:39:36Z"},{"alias_kind":"arxiv_version","alias_value":"2302.03352v1","created_at":"2026-07-05T05:39:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.03352","created_at":"2026-07-05T05:39:36Z"},{"alias_kind":"pith_short_12","alias_value":"SIPXGVELGZEL","created_at":"2026-07-05T05:39:36Z"},{"alias_kind":"pith_short_16","alias_value":"SIPXGVELGZELNXUR","created_at":"2026-07-05T05:39:36Z"},{"alias_kind":"pith_short_8","alias_value":"SIPXGVEL","created_at":"2026-07-05T05:39:36Z"}],"graph_snapshots":[{"event_id":"sha256:f6830fd25ae1cd69464d3bf08b491125a3db8098fb1eec126e5d3100ad802967","target":"graph","created_at":"2026-07-05T05:39:36Z","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/2302.03352/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Monte Carlo Tree Search (MCTS) is a sampling best-first method to search for optimal decisions. The success of MCTS depends heavily on how the MCTS statistical tree is built and the selection policy plays a fundamental role in this. A particular selection policy that works particularly well, widely adopted in MCTS, is the Upper Confidence Bounds for Trees, referred to as UCT. Other more sophisticated bounds have been proposed by the community with the goal to improve MCTS performance on particular problems. Thus, it is evident that while the MCTS UCT behaves generally well, some variants might","authors_text":"Edgar Galvan, Fred Valdez Ameneyro","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-02-07T09:50:55Z","title":"Towards Understanding the Effects of Evolving the MCTS UCT Selection Policy"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.03352","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":"sha256:948da19acdfdf070baf0f05f006fe19a5986170c6902f4bd70fdbe94a66d63fc","target":"record","created_at":"2026-07-05T05:39:36Z","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":"114597a10a3df3989a1efcffd61a9b312454afd72c834ca2fe14eb59015d362a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-02-07T09:50:55Z","title_canon_sha256":"4fdd135ab7d451e3c3f00bb2a08f5fb6180213f9b0965ef1678ca1ef4f4304d3"},"schema_version":"1.0","source":{"id":"2302.03352","kind":"arxiv","version":1}},"canonical_sha256":"921f73548b3648b6de91d036db48ac220d784b008efa13721344c46fc7ef0c65","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"921f73548b3648b6de91d036db48ac220d784b008efa13721344c46fc7ef0c65","first_computed_at":"2026-07-05T05:39:36.301114Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:39:36.301114Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Mk+Liav613LAHEQsenmettLl7GT1Lud+/LQBN+j8kg51mGOssceFSd810yf+Zbt2h4tjysLt8q3R/uJOiwi2DQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:39:36.301452Z","signed_message":"canonical_sha256_bytes"},"source_id":"2302.03352","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:948da19acdfdf070baf0f05f006fe19a5986170c6902f4bd70fdbe94a66d63fc","sha256:f6830fd25ae1cd69464d3bf08b491125a3db8098fb1eec126e5d3100ad802967"],"state_sha256":"02da3a600c2b1f641e308c6d911b498df7b3f298e3250a0ad8c277880ba4b794"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zHsnOmuq/AlMWJ/FsKOOewWDmY68nfECMFCelQNZxqKQeZMMDyQnuR2bj5NzTRomR4RqMHBSLjpTcHFuFMKeAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-08T17:20:20.566601Z","bundle_sha256":"edf4dcfbd37af12452514270e0862b93d1b18489c9476b563f0c55fa56226720"}}