{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:FH7ELPHAL4WQJCZ7NTLZFENGLA","short_pith_number":"pith:FH7ELPHA","canonical_record":{"source":{"id":"2501.08603","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-01-15T06:00:50Z","cross_cats_sorted":[],"title_canon_sha256":"12d9b1a7dccaa67ef66841f65a8d95750bd7450ea62fb83f86cfa0860eb96658","abstract_canon_sha256":"66d2705601bde244489894df2fd57d1994c0423c3d650c6961b64ea339b2f432"},"schema_version":"1.0"},"canonical_sha256":"29fe45bce05f2d048b3f6cd79291a6583bcc5f2783e550452f96e68dad62d2f7","source":{"kind":"arxiv","id":"2501.08603","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.08603","created_at":"2026-07-05T10:07:47Z"},{"alias_kind":"arxiv_version","alias_value":"2501.08603v3","created_at":"2026-07-05T10:07:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.08603","created_at":"2026-07-05T10:07:47Z"},{"alias_kind":"pith_short_12","alias_value":"FH7ELPHAL4WQ","created_at":"2026-07-05T10:07:47Z"},{"alias_kind":"pith_short_16","alias_value":"FH7ELPHAL4WQJCZ7","created_at":"2026-07-05T10:07:47Z"},{"alias_kind":"pith_short_8","alias_value":"FH7ELPHA","created_at":"2026-07-05T10:07:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:FH7ELPHAL4WQJCZ7NTLZFENGLA","target":"record","payload":{"canonical_record":{"source":{"id":"2501.08603","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-01-15T06:00:50Z","cross_cats_sorted":[],"title_canon_sha256":"12d9b1a7dccaa67ef66841f65a8d95750bd7450ea62fb83f86cfa0860eb96658","abstract_canon_sha256":"66d2705601bde244489894df2fd57d1994c0423c3d650c6961b64ea339b2f432"},"schema_version":"1.0"},"canonical_sha256":"29fe45bce05f2d048b3f6cd79291a6583bcc5f2783e550452f96e68dad62d2f7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:07:47.681207Z","signature_b64":"DZrU24Vw1TA+FYdeNRSKrz1you+dddzs8UBTpgBk0Q/qSruL/ZzAfHzhkrPQFms9+3chVZ+n1vq6kJxLBtDmAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"29fe45bce05f2d048b3f6cd79291a6583bcc5f2783e550452f96e68dad62d2f7","last_reissued_at":"2026-07-05T10:07:47.680748Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:07:47.680748Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.08603","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:07:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bYdpdljB65SVKZGOL+QI6WbrxeijrbdnhOnRSqAVBNvpA6M8THLoP6NZk7nx44NP2jBYqLQCrnlvUdxemGqPBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T00:42:38.647670Z"},"content_sha256":"5efa9920842adc96ca8485e5b0f00cb97ead4602d9330a0d335d7d5b89138413","schema_version":"1.0","event_id":"sha256:5efa9920842adc96ca8485e5b0f00cb97ead4602d9330a0d335d7d5b89138413"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:FH7ELPHAL4WQJCZ7NTLZFENGLA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Monte Carlo Tree Search for Comprehensive Exploration in LLM-Based Automatic Heuristic Design","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Bryan Hooi, Zhenkun Wang, Zhi Zheng, Zhuoliang Xie","submitted_at":"2025-01-15T06:00:50Z","abstract_excerpt":"Handcrafting heuristics for solving complex optimization tasks (e.g., route planning and task allocation) is a common practice but requires extensive domain knowledge. Recently, Large Language Model (LLM)-based automatic heuristic design (AHD) methods have shown promise in generating high-quality heuristics without manual interventions. Existing LLM-based AHD methods employ a population to maintain a fixed number of top-performing LLM-generated heuristics and introduce evolutionary computation (EC) to iteratively enhance the population. However, these population-based procedures cannot fully d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.08603","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/2501.08603/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":"2026-07-05T10:07:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"viyhe7M1sE9JMsp449yGbLAawiKlS8C8bpboeX4FDjkl5l0yEFs0L0J0pxQn2clnxmGk6FiVsbGXetJ2TC1xAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T00:42:38.648032Z"},"content_sha256":"3d86b7e19b4412c14d1f4fc70376207c7838c2ad8c7b93fc647197391dbed590","schema_version":"1.0","event_id":"sha256:3d86b7e19b4412c14d1f4fc70376207c7838c2ad8c7b93fc647197391dbed590"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FH7ELPHAL4WQJCZ7NTLZFENGLA/bundle.json","state_url":"https://pith.science/pith/FH7ELPHAL4WQJCZ7NTLZFENGLA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FH7ELPHAL4WQJCZ7NTLZFENGLA/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":"2026-08-08T00:42:38Z","links":{"resolver":"https://pith.science/pith/FH7ELPHAL4WQJCZ7NTLZFENGLA","bundle":"https://pith.science/pith/FH7ELPHAL4WQJCZ7NTLZFENGLA/bundle.json","state":"https://pith.science/pith/FH7ELPHAL4WQJCZ7NTLZFENGLA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FH7ELPHAL4WQJCZ7NTLZFENGLA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:FH7ELPHAL4WQJCZ7NTLZFENGLA","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":"66d2705601bde244489894df2fd57d1994c0423c3d650c6961b64ea339b2f432","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-01-15T06:00:50Z","title_canon_sha256":"12d9b1a7dccaa67ef66841f65a8d95750bd7450ea62fb83f86cfa0860eb96658"},"schema_version":"1.0","source":{"id":"2501.08603","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.08603","created_at":"2026-07-05T10:07:47Z"},{"alias_kind":"arxiv_version","alias_value":"2501.08603v3","created_at":"2026-07-05T10:07:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.08603","created_at":"2026-07-05T10:07:47Z"},{"alias_kind":"pith_short_12","alias_value":"FH7ELPHAL4WQ","created_at":"2026-07-05T10:07:47Z"},{"alias_kind":"pith_short_16","alias_value":"FH7ELPHAL4WQJCZ7","created_at":"2026-07-05T10:07:47Z"},{"alias_kind":"pith_short_8","alias_value":"FH7ELPHA","created_at":"2026-07-05T10:07:47Z"}],"graph_snapshots":[{"event_id":"sha256:3d86b7e19b4412c14d1f4fc70376207c7838c2ad8c7b93fc647197391dbed590","target":"graph","created_at":"2026-07-05T10:07:47Z","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/2501.08603/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Handcrafting heuristics for solving complex optimization tasks (e.g., route planning and task allocation) is a common practice but requires extensive domain knowledge. Recently, Large Language Model (LLM)-based automatic heuristic design (AHD) methods have shown promise in generating high-quality heuristics without manual interventions. Existing LLM-based AHD methods employ a population to maintain a fixed number of top-performing LLM-generated heuristics and introduce evolutionary computation (EC) to iteratively enhance the population. However, these population-based procedures cannot fully d","authors_text":"Bryan Hooi, Zhenkun Wang, Zhi Zheng, Zhuoliang Xie","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-01-15T06:00:50Z","title":"Monte Carlo Tree Search for Comprehensive Exploration in LLM-Based Automatic Heuristic Design"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.08603","kind":"arxiv","version":3},"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":"sha256:5efa9920842adc96ca8485e5b0f00cb97ead4602d9330a0d335d7d5b89138413","target":"record","created_at":"2026-07-05T10:07:47Z","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":"66d2705601bde244489894df2fd57d1994c0423c3d650c6961b64ea339b2f432","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-01-15T06:00:50Z","title_canon_sha256":"12d9b1a7dccaa67ef66841f65a8d95750bd7450ea62fb83f86cfa0860eb96658"},"schema_version":"1.0","source":{"id":"2501.08603","kind":"arxiv","version":3}},"canonical_sha256":"29fe45bce05f2d048b3f6cd79291a6583bcc5f2783e550452f96e68dad62d2f7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"29fe45bce05f2d048b3f6cd79291a6583bcc5f2783e550452f96e68dad62d2f7","first_computed_at":"2026-07-05T10:07:47.680748Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:07:47.680748Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DZrU24Vw1TA+FYdeNRSKrz1you+dddzs8UBTpgBk0Q/qSruL/ZzAfHzhkrPQFms9+3chVZ+n1vq6kJxLBtDmAg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:07:47.681207Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.08603","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5efa9920842adc96ca8485e5b0f00cb97ead4602d9330a0d335d7d5b89138413","sha256:3d86b7e19b4412c14d1f4fc70376207c7838c2ad8c7b93fc647197391dbed590"],"state_sha256":"809119c1a7698bf1e3cb0dd3a24317a90293a78f2f610401c0bba851a18a778b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TS0mE6lCRisixn9BCcjQH2xy7kOGqAniFIqm/FbZo6dFqolnXX1TBoQaVZjskqRrLyg+EThGGqHDZZLvG5A0Dw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T00:42:38.650442Z","bundle_sha256":"edaf2e1077c76388a449d12e7712e3075fe9221bb87315203b6f95ec3761916a"}}