{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:3S55DISMD3D2IISR7AMDKCKYKN","short_pith_number":"pith:3S55DISM","canonical_record":{"source":{"id":"2506.13781","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-10T08:09:30Z","cross_cats_sorted":["cs.AI","cs.DM"],"title_canon_sha256":"d9c9b1b9ebe1b66de14cc8cb18197e76f71a42376eb836c88a4d7f604b75ac1a","abstract_canon_sha256":"48dd666171c4d580c41ce828d6ff570b88edcbec3596b6be72beb769f4e65b9a"},"schema_version":"1.0"},"canonical_sha256":"dcbbd1a24c1ec7a42251f8183509585347997853a886d29372df3b255512fc02","source":{"kind":"arxiv","id":"2506.13781","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.13781","created_at":"2026-07-05T11:23:11Z"},{"alias_kind":"arxiv_version","alias_value":"2506.13781v1","created_at":"2026-07-05T11:23:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.13781","created_at":"2026-07-05T11:23:11Z"},{"alias_kind":"pith_short_12","alias_value":"3S55DISMD3D2","created_at":"2026-07-05T11:23:11Z"},{"alias_kind":"pith_short_16","alias_value":"3S55DISMD3D2IISR","created_at":"2026-07-05T11:23:11Z"},{"alias_kind":"pith_short_8","alias_value":"3S55DISM","created_at":"2026-07-05T11:23:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:3S55DISMD3D2IISR7AMDKCKYKN","target":"record","payload":{"canonical_record":{"source":{"id":"2506.13781","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-10T08:09:30Z","cross_cats_sorted":["cs.AI","cs.DM"],"title_canon_sha256":"d9c9b1b9ebe1b66de14cc8cb18197e76f71a42376eb836c88a4d7f604b75ac1a","abstract_canon_sha256":"48dd666171c4d580c41ce828d6ff570b88edcbec3596b6be72beb769f4e65b9a"},"schema_version":"1.0"},"canonical_sha256":"dcbbd1a24c1ec7a42251f8183509585347997853a886d29372df3b255512fc02","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:23:11.153437Z","signature_b64":"AkkQxt4gBW+VIiLqaSTy+v1G2+tRSYQRP0geE0cnM2X+eP5YVj5NGlnqPxmEh2BCKKeEOCzGc9F+Z7ukICW1Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dcbbd1a24c1ec7a42251f8183509585347997853a886d29372df3b255512fc02","last_reissued_at":"2026-07-05T11:23:11.152948Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:23:11.152948Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.13781","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":"2026-07-05T11:23:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NnCAAQMO9mhyzgoQt/Y7xlw/j+Sh5CsmnWQJyKW2aRZ0GVaUnTRHLK9Sm9+ZJwx7oKck1QRr2YGz+hZSP7DJAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T15:32:16.444687Z"},"content_sha256":"b0bb7a927116a482f412af4452b7d2829e1c23ce6ad9abbc2f7fcf1d095c5b77","schema_version":"1.0","event_id":"sha256:b0bb7a927116a482f412af4452b7d2829e1c23ce6ad9abbc2f7fcf1d095c5b77"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:3S55DISMD3D2IISR7AMDKCKYKN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Solving the Job Shop Scheduling Problem with Graph Neural Networks: A Customizable Reinforcement Learning Environment","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.DM"],"primary_cat":"cs.LG","authors_text":"Pablo Ari\\~no Fern\\'andez","submitted_at":"2025-06-10T08:09:30Z","abstract_excerpt":"The job shop scheduling problem is an NP-hard combinatorial optimization problem relevant to manufacturing and timetabling. Traditional approaches use priority dispatching rules based on simple heuristics. Recent work has attempted to replace these with deep learning models, particularly graph neural networks (GNNs), that learn to assign priorities from data. However, training such models requires customizing numerous factors: graph representation, node features, action space, and reward functions. The lack of modular libraries for experimentation makes this research time-consuming. This work "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.13781","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/2506.13781/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":"2026-07-05T11:23:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"j8GZLuSYkeyAVElObsT+JEaZvL5ESdXN2w0dA6oo9fYc4RFozjzt0iqynjebpNWBxk6Efovk8MCU6W4MdSasCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T15:32:16.445199Z"},"content_sha256":"7b53f4154a6897cd776e5bfc56369c65c92cf19442808c39f986043e270101ad","schema_version":"1.0","event_id":"sha256:7b53f4154a6897cd776e5bfc56369c65c92cf19442808c39f986043e270101ad"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3S55DISMD3D2IISR7AMDKCKYKN/bundle.json","state_url":"https://pith.science/pith/3S55DISMD3D2IISR7AMDKCKYKN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3S55DISMD3D2IISR7AMDKCKYKN/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":"2026-08-07T15:32:16Z","links":{"resolver":"https://pith.science/pith/3S55DISMD3D2IISR7AMDKCKYKN","bundle":"https://pith.science/pith/3S55DISMD3D2IISR7AMDKCKYKN/bundle.json","state":"https://pith.science/pith/3S55DISMD3D2IISR7AMDKCKYKN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3S55DISMD3D2IISR7AMDKCKYKN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:3S55DISMD3D2IISR7AMDKCKYKN","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":"48dd666171c4d580c41ce828d6ff570b88edcbec3596b6be72beb769f4e65b9a","cross_cats_sorted":["cs.AI","cs.DM"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-10T08:09:30Z","title_canon_sha256":"d9c9b1b9ebe1b66de14cc8cb18197e76f71a42376eb836c88a4d7f604b75ac1a"},"schema_version":"1.0","source":{"id":"2506.13781","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.13781","created_at":"2026-07-05T11:23:11Z"},{"alias_kind":"arxiv_version","alias_value":"2506.13781v1","created_at":"2026-07-05T11:23:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.13781","created_at":"2026-07-05T11:23:11Z"},{"alias_kind":"pith_short_12","alias_value":"3S55DISMD3D2","created_at":"2026-07-05T11:23:11Z"},{"alias_kind":"pith_short_16","alias_value":"3S55DISMD3D2IISR","created_at":"2026-07-05T11:23:11Z"},{"alias_kind":"pith_short_8","alias_value":"3S55DISM","created_at":"2026-07-05T11:23:11Z"}],"graph_snapshots":[{"event_id":"sha256:7b53f4154a6897cd776e5bfc56369c65c92cf19442808c39f986043e270101ad","target":"graph","created_at":"2026-07-05T11:23:11Z","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/2506.13781/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The job shop scheduling problem is an NP-hard combinatorial optimization problem relevant to manufacturing and timetabling. Traditional approaches use priority dispatching rules based on simple heuristics. Recent work has attempted to replace these with deep learning models, particularly graph neural networks (GNNs), that learn to assign priorities from data. However, training such models requires customizing numerous factors: graph representation, node features, action space, and reward functions. The lack of modular libraries for experimentation makes this research time-consuming. This work ","authors_text":"Pablo Ari\\~no Fern\\'andez","cross_cats":["cs.AI","cs.DM"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-10T08:09:30Z","title":"Solving the Job Shop Scheduling Problem with Graph Neural Networks: A Customizable Reinforcement Learning Environment"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.13781","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:b0bb7a927116a482f412af4452b7d2829e1c23ce6ad9abbc2f7fcf1d095c5b77","target":"record","created_at":"2026-07-05T11:23:11Z","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":"48dd666171c4d580c41ce828d6ff570b88edcbec3596b6be72beb769f4e65b9a","cross_cats_sorted":["cs.AI","cs.DM"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-10T08:09:30Z","title_canon_sha256":"d9c9b1b9ebe1b66de14cc8cb18197e76f71a42376eb836c88a4d7f604b75ac1a"},"schema_version":"1.0","source":{"id":"2506.13781","kind":"arxiv","version":1}},"canonical_sha256":"dcbbd1a24c1ec7a42251f8183509585347997853a886d29372df3b255512fc02","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dcbbd1a24c1ec7a42251f8183509585347997853a886d29372df3b255512fc02","first_computed_at":"2026-07-05T11:23:11.152948Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:23:11.152948Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AkkQxt4gBW+VIiLqaSTy+v1G2+tRSYQRP0geE0cnM2X+eP5YVj5NGlnqPxmEh2BCKKeEOCzGc9F+Z7ukICW1Dw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:23:11.153437Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.13781","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b0bb7a927116a482f412af4452b7d2829e1c23ce6ad9abbc2f7fcf1d095c5b77","sha256:7b53f4154a6897cd776e5bfc56369c65c92cf19442808c39f986043e270101ad"],"state_sha256":"624076bb31e9f4c1b012f8f2458f38d1d60be319ee8a12e833ea383df200542c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6rgJy72GoPbxVhceVVvERnprWZM/CDJi526kPdAJZpp42VIkaWKFJuajGki5OUzkFTfJyAE8IWSQCjUncJ6PCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T15:32:16.450219Z","bundle_sha256":"f6687fb33065aa9d481e1810c2af7a192062dd02568d9241c3f7d73c12666715"}}