{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:CK5RHDAD4VBYZOKV4EHKLOUQJA","short_pith_number":"pith:CK5RHDAD","schema_version":"1.0","canonical_sha256":"12bb138c03e5438cb955e10ea5ba90483312379452f89ad80238ee9762f8a29a","source":{"kind":"arxiv","id":"2507.03711","version":3},"attestation_state":"computed","paper":{"title":"Can LLMs Play \\^O \\u{A}n Quan Game? A Study of Multi-Step Planning and Decision Making","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Duy-Dinh Le, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen, Sang Quang Nguyen, Thanh Duc Ngo, Vinh-Tiep Nguyen","submitted_at":"2025-07-04T16:50:40Z","abstract_excerpt":"In this paper, we explore the ability of large language models (LLMs) to plan and make decisions through the lens of the traditional Vietnamese board game, \\^O \\u{A}n Quan. This game, which involves a series of strategic token movements and captures, offers a unique environment for evaluating the decision-making and strategic capabilities of LLMs. Specifically, we develop various agent personas, ranging from aggressive to defensive, and employ the \\^O \\u{A}n Quan game as a testbed for assessing LLM performance across different strategies. Through experimentation with models like Llama-3.2-3B-I"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2507.03711","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-07-04T16:50:40Z","cross_cats_sorted":[],"title_canon_sha256":"5b434622ded9f2b64b94cc66113ddb27a4f3159f5ba28d59301078b0e5cb3e5d","abstract_canon_sha256":"70db8be6bafe9c56a051b536d6b7c51da9519a920fdcd65388a5b81bdf845749"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:34:11.371088Z","signature_b64":"HHEPpnUGy2FmlgvsbEzOJ4/P18pwaWOkinCHawhu8pZ0lzvn4TCYddAD4S3FqetbrKXB2+5qmkgQNfEY6iTBCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"12bb138c03e5438cb955e10ea5ba90483312379452f89ad80238ee9762f8a29a","last_reissued_at":"2026-07-05T11:34:11.370650Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:34:11.370650Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Can LLMs Play \\^O \\u{A}n Quan Game? A Study of Multi-Step Planning and Decision Making","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Duy-Dinh Le, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen, Sang Quang Nguyen, Thanh Duc Ngo, Vinh-Tiep Nguyen","submitted_at":"2025-07-04T16:50:40Z","abstract_excerpt":"In this paper, we explore the ability of large language models (LLMs) to plan and make decisions through the lens of the traditional Vietnamese board game, \\^O \\u{A}n Quan. This game, which involves a series of strategic token movements and captures, offers a unique environment for evaluating the decision-making and strategic capabilities of LLMs. Specifically, we develop various agent personas, ranging from aggressive to defensive, and employ the \\^O \\u{A}n Quan game as a testbed for assessing LLM performance across different strategies. Through experimentation with models like Llama-3.2-3B-I"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.03711","kind":"arxiv","version":3},"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/2507.03711/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2507.03711","created_at":"2026-07-05T11:34:11.370710+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.03711v3","created_at":"2026-07-05T11:34:11.370710+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.03711","created_at":"2026-07-05T11:34:11.370710+00:00"},{"alias_kind":"pith_short_12","alias_value":"CK5RHDAD4VBY","created_at":"2026-07-05T11:34:11.370710+00:00"},{"alias_kind":"pith_short_16","alias_value":"CK5RHDAD4VBYZOKV","created_at":"2026-07-05T11:34:11.370710+00:00"},{"alias_kind":"pith_short_8","alias_value":"CK5RHDAD","created_at":"2026-07-05T11:34:11.370710+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CK5RHDAD4VBYZOKV4EHKLOUQJA","json":"https://pith.science/pith/CK5RHDAD4VBYZOKV4EHKLOUQJA.json","graph_json":"https://pith.science/api/pith-number/CK5RHDAD4VBYZOKV4EHKLOUQJA/graph.json","events_json":"https://pith.science/api/pith-number/CK5RHDAD4VBYZOKV4EHKLOUQJA/events.json","paper":"https://pith.science/paper/CK5RHDAD"},"agent_actions":{"view_html":"https://pith.science/pith/CK5RHDAD4VBYZOKV4EHKLOUQJA","download_json":"https://pith.science/pith/CK5RHDAD4VBYZOKV4EHKLOUQJA.json","view_paper":"https://pith.science/paper/CK5RHDAD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.03711&json=true","fetch_graph":"https://pith.science/api/pith-number/CK5RHDAD4VBYZOKV4EHKLOUQJA/graph.json","fetch_events":"https://pith.science/api/pith-number/CK5RHDAD4VBYZOKV4EHKLOUQJA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CK5RHDAD4VBYZOKV4EHKLOUQJA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CK5RHDAD4VBYZOKV4EHKLOUQJA/action/storage_attestation","attest_author":"https://pith.science/pith/CK5RHDAD4VBYZOKV4EHKLOUQJA/action/author_attestation","sign_citation":"https://pith.science/pith/CK5RHDAD4VBYZOKV4EHKLOUQJA/action/citation_signature","submit_replication":"https://pith.science/pith/CK5RHDAD4VBYZOKV4EHKLOUQJA/action/replication_record"}},"created_at":"2026-07-05T11:34:11.370710+00:00","updated_at":"2026-07-05T11:34:11.370710+00:00"}