{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:TVP54AZ6X5JPCYKNR43QZ4SLNX","short_pith_number":"pith:TVP54AZ6","canonical_record":{"source":{"id":"2505.00610","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-05-01T15:40:58Z","cross_cats_sorted":[],"title_canon_sha256":"99d3a40efa22f530feefc23f0db2f8a8398487562c420a5e70e93394c8b64f13","abstract_canon_sha256":"60cbc5c7fe5a7ecde7361616f61c16a834dc27db0a37a0aeb8772075954ea2da"},"schema_version":"1.0"},"canonical_sha256":"9d5fde033ebf52f1614d8f370cf24b6df745f61bed028c8bc4609f3d1d8c051e","source":{"kind":"arxiv","id":"2505.00610","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.00610","created_at":"2026-07-05T10:57:22Z"},{"alias_kind":"arxiv_version","alias_value":"2505.00610v1","created_at":"2026-07-05T10:57:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.00610","created_at":"2026-07-05T10:57:22Z"},{"alias_kind":"pith_short_12","alias_value":"TVP54AZ6X5JP","created_at":"2026-07-05T10:57:22Z"},{"alias_kind":"pith_short_16","alias_value":"TVP54AZ6X5JPCYKN","created_at":"2026-07-05T10:57:22Z"},{"alias_kind":"pith_short_8","alias_value":"TVP54AZ6","created_at":"2026-07-05T10:57:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:TVP54AZ6X5JPCYKNR43QZ4SLNX","target":"record","payload":{"canonical_record":{"source":{"id":"2505.00610","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-05-01T15:40:58Z","cross_cats_sorted":[],"title_canon_sha256":"99d3a40efa22f530feefc23f0db2f8a8398487562c420a5e70e93394c8b64f13","abstract_canon_sha256":"60cbc5c7fe5a7ecde7361616f61c16a834dc27db0a37a0aeb8772075954ea2da"},"schema_version":"1.0"},"canonical_sha256":"9d5fde033ebf52f1614d8f370cf24b6df745f61bed028c8bc4609f3d1d8c051e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:57:22.943688Z","signature_b64":"63sh3fR9VYGoc29G+LIFQ2d3VQm1Jfi9hurzCEAFeUhAU9oJDCF46CiAOWb2n5USoPSlZo4/cAWBrD0fmCQuDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9d5fde033ebf52f1614d8f370cf24b6df745f61bed028c8bc4609f3d1d8c051e","last_reissued_at":"2026-07-05T10:57:22.943264Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:57:22.943264Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.00610","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":"2026-07-05T10:57:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IVVYK+XjKvYEkyHYOsxe14fiBTUGr0MTFIfuP/5Xn+NXLL36bSoII83T2rx9pfZwnp3VkhgxUcOXnE7LEJ72Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T00:32:59.358272Z"},"content_sha256":"3d825a9e23aad6a5941bae9e0c816f308fcd9e195cd1646c1f278887d8867786","schema_version":"1.0","event_id":"sha256:3d825a9e23aad6a5941bae9e0c816f308fcd9e195cd1646c1f278887d8867786"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:TVP54AZ6X5JPCYKNR43QZ4SLNX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Combining LLMs with Logic-Based Framework to Explain MCTS","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Abhishek Dubey, Ayan Mukhopadhyay, Hendrik Baier, Jonathan Sprinkle, Meiyi Ma, Taylor T. Johnson, Xia Wang, Zirong Chen, Ziyan An","submitted_at":"2025-05-01T15:40:58Z","abstract_excerpt":"In response to the lack of trust in Artificial Intelligence (AI) for sequential planning, we design a Computational Tree Logic-guided large language model (LLM)-based natural language explanation framework designed for the Monte Carlo Tree Search (MCTS) algorithm. MCTS is often considered challenging to interpret due to the complexity of its search trees, but our framework is flexible enough to handle a wide range of free-form post-hoc queries and knowledge-based inquiries centered around MCTS and the Markov Decision Process (MDP) of the application domain. By transforming user queries into lo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.00610","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/2505.00610/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":"2026-07-05T10:57:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DYiPihYFx52quJysPehT9ptnMFhnL+r9r7wN+kNObl2RdvDPtX6VsZJk+OOM70wvV5l7Z3Pb8r44CKek7pTiCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T00:32:59.359194Z"},"content_sha256":"5113a3cfb89980ffd04e61e7973c8cc078e0c0d6383a86ee8d41ae5b3aab05c0","schema_version":"1.0","event_id":"sha256:5113a3cfb89980ffd04e61e7973c8cc078e0c0d6383a86ee8d41ae5b3aab05c0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TVP54AZ6X5JPCYKNR43QZ4SLNX/bundle.json","state_url":"https://pith.science/pith/TVP54AZ6X5JPCYKNR43QZ4SLNX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TVP54AZ6X5JPCYKNR43QZ4SLNX/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":"2026-08-10T00:32:59Z","links":{"resolver":"https://pith.science/pith/TVP54AZ6X5JPCYKNR43QZ4SLNX","bundle":"https://pith.science/pith/TVP54AZ6X5JPCYKNR43QZ4SLNX/bundle.json","state":"https://pith.science/pith/TVP54AZ6X5JPCYKNR43QZ4SLNX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TVP54AZ6X5JPCYKNR43QZ4SLNX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:TVP54AZ6X5JPCYKNR43QZ4SLNX","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":"60cbc5c7fe5a7ecde7361616f61c16a834dc27db0a37a0aeb8772075954ea2da","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-05-01T15:40:58Z","title_canon_sha256":"99d3a40efa22f530feefc23f0db2f8a8398487562c420a5e70e93394c8b64f13"},"schema_version":"1.0","source":{"id":"2505.00610","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.00610","created_at":"2026-07-05T10:57:22Z"},{"alias_kind":"arxiv_version","alias_value":"2505.00610v1","created_at":"2026-07-05T10:57:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.00610","created_at":"2026-07-05T10:57:22Z"},{"alias_kind":"pith_short_12","alias_value":"TVP54AZ6X5JP","created_at":"2026-07-05T10:57:22Z"},{"alias_kind":"pith_short_16","alias_value":"TVP54AZ6X5JPCYKN","created_at":"2026-07-05T10:57:22Z"},{"alias_kind":"pith_short_8","alias_value":"TVP54AZ6","created_at":"2026-07-05T10:57:22Z"}],"graph_snapshots":[{"event_id":"sha256:5113a3cfb89980ffd04e61e7973c8cc078e0c0d6383a86ee8d41ae5b3aab05c0","target":"graph","created_at":"2026-07-05T10:57:22Z","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/2505.00610/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In response to the lack of trust in Artificial Intelligence (AI) for sequential planning, we design a Computational Tree Logic-guided large language model (LLM)-based natural language explanation framework designed for the Monte Carlo Tree Search (MCTS) algorithm. MCTS is often considered challenging to interpret due to the complexity of its search trees, but our framework is flexible enough to handle a wide range of free-form post-hoc queries and knowledge-based inquiries centered around MCTS and the Markov Decision Process (MDP) of the application domain. By transforming user queries into lo","authors_text":"Abhishek Dubey, Ayan Mukhopadhyay, Hendrik Baier, Jonathan Sprinkle, Meiyi Ma, Taylor T. Johnson, Xia Wang, Zirong Chen, Ziyan An","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-05-01T15:40:58Z","title":"Combining LLMs with Logic-Based Framework to Explain MCTS"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.00610","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":"sha256:3d825a9e23aad6a5941bae9e0c816f308fcd9e195cd1646c1f278887d8867786","target":"record","created_at":"2026-07-05T10:57:22Z","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":"60cbc5c7fe5a7ecde7361616f61c16a834dc27db0a37a0aeb8772075954ea2da","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-05-01T15:40:58Z","title_canon_sha256":"99d3a40efa22f530feefc23f0db2f8a8398487562c420a5e70e93394c8b64f13"},"schema_version":"1.0","source":{"id":"2505.00610","kind":"arxiv","version":1}},"canonical_sha256":"9d5fde033ebf52f1614d8f370cf24b6df745f61bed028c8bc4609f3d1d8c051e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9d5fde033ebf52f1614d8f370cf24b6df745f61bed028c8bc4609f3d1d8c051e","first_computed_at":"2026-07-05T10:57:22.943264Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:57:22.943264Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"63sh3fR9VYGoc29G+LIFQ2d3VQm1Jfi9hurzCEAFeUhAU9oJDCF46CiAOWb2n5USoPSlZo4/cAWBrD0fmCQuDA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:57:22.943688Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.00610","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3d825a9e23aad6a5941bae9e0c816f308fcd9e195cd1646c1f278887d8867786","sha256:5113a3cfb89980ffd04e61e7973c8cc078e0c0d6383a86ee8d41ae5b3aab05c0"],"state_sha256":"d2eedc702aee0324b46e70552cb9065122aaa64fb8515022f13e0f08474305f2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ODu3wikgXy0XGDCpN5HLA2JiJ+cmEpVivXUB21L8neCQg0Hn80jSBKuDsY7JUc7rVEQW34SU0rf6DYZKQm7BBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T00:32:59.366352Z","bundle_sha256":"9dea256536001f29a50f2ab3841fdb66bfa81710b06077d8d55ae216e0c7734d"}}