{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:C4XZURRJUM4I36XIFBWESV2ZSR","short_pith_number":"pith:C4XZURRJ","canonical_record":{"source":{"id":"2505.11526","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"math.OC","submitted_at":"2025-05-11T10:41:44Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"0e63d0af0e73ae7326b3d5d2cc54e93d942c2599aa1f6ec32951eb19c05f9135","abstract_canon_sha256":"d3a3b155b156720e25c093743ff430c46e8269683b59d67f1f8204c3a8ce99ca"},"schema_version":"1.0"},"canonical_sha256":"172f9a4629a3388dfae8286c4957599475f47fc5d3f32a5310bbd9603825722a","source":{"kind":"arxiv","id":"2505.11526","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.11526","created_at":"2026-07-05T11:04:23Z"},{"alias_kind":"arxiv_version","alias_value":"2505.11526v1","created_at":"2026-07-05T11:04:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.11526","created_at":"2026-07-05T11:04:23Z"},{"alias_kind":"pith_short_12","alias_value":"C4XZURRJUM4I","created_at":"2026-07-05T11:04:23Z"},{"alias_kind":"pith_short_16","alias_value":"C4XZURRJUM4I36XI","created_at":"2026-07-05T11:04:23Z"},{"alias_kind":"pith_short_8","alias_value":"C4XZURRJ","created_at":"2026-07-05T11:04:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:C4XZURRJUM4I36XIFBWESV2ZSR","target":"record","payload":{"canonical_record":{"source":{"id":"2505.11526","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"math.OC","submitted_at":"2025-05-11T10:41:44Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"0e63d0af0e73ae7326b3d5d2cc54e93d942c2599aa1f6ec32951eb19c05f9135","abstract_canon_sha256":"d3a3b155b156720e25c093743ff430c46e8269683b59d67f1f8204c3a8ce99ca"},"schema_version":"1.0"},"canonical_sha256":"172f9a4629a3388dfae8286c4957599475f47fc5d3f32a5310bbd9603825722a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:04:23.185740Z","signature_b64":"WWJFdR5Cbd0XK0PdQ35zD7L12Jmm2H46aqiNQcur7ZghkhZeaLs0WlujVPdLEEY2nn4j6sLAPlXv8/iepCvhDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"172f9a4629a3388dfae8286c4957599475f47fc5d3f32a5310bbd9603825722a","last_reissued_at":"2026-07-05T11:04:23.185222Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:04:23.185222Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.11526","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":"2026-07-05T11:04:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"91xar0xOMwkJ6eq9qbHu/P+6miPxbcLtBNA6nwFsuszsGUeGLYpovgSE7e2Tng71ZCHlhLmURa9TWxP2UD2TCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T04:36:50.534799Z"},"content_sha256":"6b71e0d9d2b02c35b570776197114dc7c2c9f45f3b3e0396149a94fb62ea54ae","schema_version":"1.0","event_id":"sha256:6b71e0d9d2b02c35b570776197114dc7c2c9f45f3b3e0396149a94fb62ea54ae"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:C4XZURRJUM4I36XIFBWESV2ZSR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Code Retrieval for MILP Instance Generation","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"math.OC","authors_text":"Hua Xu, Huigen Ye, Tianxing Yang","submitted_at":"2025-05-11T10:41:44Z","abstract_excerpt":"Mixed-Integer Linear Programming (MILP) is widely used in fields such as scheduling, logistics, and planning. Enhancing the performance of MILP solvers, particularly learning-based solvers, requires substantial amounts of high-quality data. However, existing methods for MILP instance generation typically necessitate training a separate model for each problem class and are computationally intensive when generating new instances. To address these limitations, we reformulate the MILP Instance Generation task as MILP Code Generation task, enabling efficient, flexible, and interpretable instance ge"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.11526","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/2505.11526/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":"2026-07-05T11:04:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cvMPwH6T/aNlHO1RK9KiLGPAwbDcCe4QBHyp+zBOHSLYdoheExsQ1Nb14i8Y2unmD9GtHDdBZx5G6F71ZYXBDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T04:36:50.535490Z"},"content_sha256":"fbd56f44a5d5059dfac06c870f618d66147e0925ae08bc0fb3b3f83eec1de211","schema_version":"1.0","event_id":"sha256:fbd56f44a5d5059dfac06c870f618d66147e0925ae08bc0fb3b3f83eec1de211"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/C4XZURRJUM4I36XIFBWESV2ZSR/bundle.json","state_url":"https://pith.science/pith/C4XZURRJUM4I36XIFBWESV2ZSR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/C4XZURRJUM4I36XIFBWESV2ZSR/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":"2026-08-18T04:36:50Z","links":{"resolver":"https://pith.science/pith/C4XZURRJUM4I36XIFBWESV2ZSR","bundle":"https://pith.science/pith/C4XZURRJUM4I36XIFBWESV2ZSR/bundle.json","state":"https://pith.science/pith/C4XZURRJUM4I36XIFBWESV2ZSR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/C4XZURRJUM4I36XIFBWESV2ZSR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:C4XZURRJUM4I36XIFBWESV2ZSR","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":"d3a3b155b156720e25c093743ff430c46e8269683b59d67f1f8204c3a8ce99ca","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"math.OC","submitted_at":"2025-05-11T10:41:44Z","title_canon_sha256":"0e63d0af0e73ae7326b3d5d2cc54e93d942c2599aa1f6ec32951eb19c05f9135"},"schema_version":"1.0","source":{"id":"2505.11526","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.11526","created_at":"2026-07-05T11:04:23Z"},{"alias_kind":"arxiv_version","alias_value":"2505.11526v1","created_at":"2026-07-05T11:04:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.11526","created_at":"2026-07-05T11:04:23Z"},{"alias_kind":"pith_short_12","alias_value":"C4XZURRJUM4I","created_at":"2026-07-05T11:04:23Z"},{"alias_kind":"pith_short_16","alias_value":"C4XZURRJUM4I36XI","created_at":"2026-07-05T11:04:23Z"},{"alias_kind":"pith_short_8","alias_value":"C4XZURRJ","created_at":"2026-07-05T11:04:23Z"}],"graph_snapshots":[{"event_id":"sha256:fbd56f44a5d5059dfac06c870f618d66147e0925ae08bc0fb3b3f83eec1de211","target":"graph","created_at":"2026-07-05T11:04:23Z","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/2505.11526/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Mixed-Integer Linear Programming (MILP) is widely used in fields such as scheduling, logistics, and planning. Enhancing the performance of MILP solvers, particularly learning-based solvers, requires substantial amounts of high-quality data. However, existing methods for MILP instance generation typically necessitate training a separate model for each problem class and are computationally intensive when generating new instances. To address these limitations, we reformulate the MILP Instance Generation task as MILP Code Generation task, enabling efficient, flexible, and interpretable instance ge","authors_text":"Hua Xu, Huigen Ye, Tianxing Yang","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"math.OC","submitted_at":"2025-05-11T10:41:44Z","title":"Code Retrieval for MILP Instance Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.11526","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":"sha256:6b71e0d9d2b02c35b570776197114dc7c2c9f45f3b3e0396149a94fb62ea54ae","target":"record","created_at":"2026-07-05T11:04:23Z","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":"d3a3b155b156720e25c093743ff430c46e8269683b59d67f1f8204c3a8ce99ca","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"math.OC","submitted_at":"2025-05-11T10:41:44Z","title_canon_sha256":"0e63d0af0e73ae7326b3d5d2cc54e93d942c2599aa1f6ec32951eb19c05f9135"},"schema_version":"1.0","source":{"id":"2505.11526","kind":"arxiv","version":1}},"canonical_sha256":"172f9a4629a3388dfae8286c4957599475f47fc5d3f32a5310bbd9603825722a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"172f9a4629a3388dfae8286c4957599475f47fc5d3f32a5310bbd9603825722a","first_computed_at":"2026-07-05T11:04:23.185222Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:04:23.185222Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WWJFdR5Cbd0XK0PdQ35zD7L12Jmm2H46aqiNQcur7ZghkhZeaLs0WlujVPdLEEY2nn4j6sLAPlXv8/iepCvhDA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:04:23.185740Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.11526","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6b71e0d9d2b02c35b570776197114dc7c2c9f45f3b3e0396149a94fb62ea54ae","sha256:fbd56f44a5d5059dfac06c870f618d66147e0925ae08bc0fb3b3f83eec1de211"],"state_sha256":"40eb4c461a248ee1ea23fa074ca308b47d0143899f48eee9c27c999bf12a7498"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JZdcqdY9oV/vZJj1kKmftl1y12m0xZSBLRJ8zcW0x/DBSmLzBbGCcu+uVls01DsZbKgpxz3F/LwjatpNj0DEBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T04:36:50.543873Z","bundle_sha256":"9611d35e2e6cb9360d4fbd1bcc3533284d591cf730d5bccd3da722d2db3e6b1b"}}