{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:PJFKTQI5QRDM6CPKISLFPT2B5O","short_pith_number":"pith:PJFKTQI5","schema_version":"1.0","canonical_sha256":"7a4aa9c11d8446cf09ea449657cf41eb9fd71d03680fe12608c418e3eab62b66","source":{"kind":"arxiv","id":"2310.19814","version":1},"attestation_state":"computed","paper":{"title":"Learning Gradient Fields for Scalable and Generalizable Irregular Packing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.LG","authors_text":"Baoquan Chen, Hao Dong, Haoxuan Wang, Lin Lu, Mingdong Wu, Tianyang Xue","submitted_at":"2023-10-18T15:52:55Z","abstract_excerpt":"The packing problem, also known as cutting or nesting, has diverse applications in logistics, manufacturing, layout design, and atlas generation. It involves arranging irregularly shaped pieces to minimize waste while avoiding overlap. Recent advances in machine learning, particularly reinforcement learning, have shown promise in addressing the packing problem. In this work, we delve deeper into a novel machine learning-based approach that formulates the packing problem as conditional generative modeling. To tackle the challenges of irregular packing, including object validity constraints and "},"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":"2310.19814","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-18T15:52:55Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"c2e30dd2e90abe58dd15b0e28e29c786bb9989117204d5c9d0c781807715dda6","abstract_canon_sha256":"f50c249c1ecbb999b361fd263b87d3d0af139ab922eeea4dfdcc42ec39c4b8c2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:07:19.639842Z","signature_b64":"DKqhTO/bQizXzlR/UxrVTZTiLJlEESL+0uZr/lFv6KpSeWMXQ66ko+f4DkxneJ055WbsxrTsy2GOOgIm4ozzDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7a4aa9c11d8446cf09ea449657cf41eb9fd71d03680fe12608c418e3eab62b66","last_reissued_at":"2026-07-05T07:07:19.639336Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:07:19.639336Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Gradient Fields for Scalable and Generalizable Irregular Packing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.LG","authors_text":"Baoquan Chen, Hao Dong, Haoxuan Wang, Lin Lu, Mingdong Wu, Tianyang Xue","submitted_at":"2023-10-18T15:52:55Z","abstract_excerpt":"The packing problem, also known as cutting or nesting, has diverse applications in logistics, manufacturing, layout design, and atlas generation. It involves arranging irregularly shaped pieces to minimize waste while avoiding overlap. Recent advances in machine learning, particularly reinforcement learning, have shown promise in addressing the packing problem. In this work, we delve deeper into a novel machine learning-based approach that formulates the packing problem as conditional generative modeling. To tackle the challenges of irregular packing, including object validity constraints and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.19814","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/2310.19814/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":"2310.19814","created_at":"2026-07-05T07:07:19.639392+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.19814v1","created_at":"2026-07-05T07:07:19.639392+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.19814","created_at":"2026-07-05T07:07:19.639392+00:00"},{"alias_kind":"pith_short_12","alias_value":"PJFKTQI5QRDM","created_at":"2026-07-05T07:07:19.639392+00:00"},{"alias_kind":"pith_short_16","alias_value":"PJFKTQI5QRDM6CPK","created_at":"2026-07-05T07:07:19.639392+00:00"},{"alias_kind":"pith_short_8","alias_value":"PJFKTQI5","created_at":"2026-07-05T07:07:19.639392+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/PJFKTQI5QRDM6CPKISLFPT2B5O","json":"https://pith.science/pith/PJFKTQI5QRDM6CPKISLFPT2B5O.json","graph_json":"https://pith.science/api/pith-number/PJFKTQI5QRDM6CPKISLFPT2B5O/graph.json","events_json":"https://pith.science/api/pith-number/PJFKTQI5QRDM6CPKISLFPT2B5O/events.json","paper":"https://pith.science/paper/PJFKTQI5"},"agent_actions":{"view_html":"https://pith.science/pith/PJFKTQI5QRDM6CPKISLFPT2B5O","download_json":"https://pith.science/pith/PJFKTQI5QRDM6CPKISLFPT2B5O.json","view_paper":"https://pith.science/paper/PJFKTQI5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.19814&json=true","fetch_graph":"https://pith.science/api/pith-number/PJFKTQI5QRDM6CPKISLFPT2B5O/graph.json","fetch_events":"https://pith.science/api/pith-number/PJFKTQI5QRDM6CPKISLFPT2B5O/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PJFKTQI5QRDM6CPKISLFPT2B5O/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PJFKTQI5QRDM6CPKISLFPT2B5O/action/storage_attestation","attest_author":"https://pith.science/pith/PJFKTQI5QRDM6CPKISLFPT2B5O/action/author_attestation","sign_citation":"https://pith.science/pith/PJFKTQI5QRDM6CPKISLFPT2B5O/action/citation_signature","submit_replication":"https://pith.science/pith/PJFKTQI5QRDM6CPKISLFPT2B5O/action/replication_record"}},"created_at":"2026-07-05T07:07:19.639392+00:00","updated_at":"2026-07-05T07:07:19.639392+00:00"}