{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:3EUIXIOWB3PAMVRSTOKOAQ6DMC","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":"f4ff4ad5bfe0f313ea23b2f6eeba11ef92ed0a253012d15ca8e3c93a7da63e73","cross_cats_sorted":["cs.LG","cs.SE"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.PL","submitted_at":"2021-05-10T12:21:42Z","title_canon_sha256":"74cc6861a3c6cccce6a01223136c168a7f369ed6ec0d2ed0dff43812f7247af3"},"schema_version":"1.0","source":{"id":"2105.04297","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2105.04297","created_at":"2026-07-05T03:34:43Z"},{"alias_kind":"arxiv_version","alias_value":"2105.04297v2","created_at":"2026-07-05T03:34:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.04297","created_at":"2026-07-05T03:34:43Z"},{"alias_kind":"pith_short_12","alias_value":"3EUIXIOWB3PA","created_at":"2026-07-05T03:34:43Z"},{"alias_kind":"pith_short_16","alias_value":"3EUIXIOWB3PAMVRS","created_at":"2026-07-05T03:34:43Z"},{"alias_kind":"pith_short_8","alias_value":"3EUIXIOW","created_at":"2026-07-05T03:34:43Z"}],"graph_snapshots":[{"event_id":"sha256:89ba1bae050abed6c84acba40c4df7a6aeabe26f3462f2772b860e465dbccbed","target":"graph","created_at":"2026-07-05T03:34:43Z","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/2105.04297/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Semantic understanding of programs is a fundamental problem for programming language processing (PLP). Recent works that learn representations of code based on pre-training techniques in NLP have pushed the frontiers in this direction. However, the semantics of PL and NL have essential differences. These being ignored, we believe it is difficult to build a model to better understand programs, by either directly applying off-the-shelf NLP pre-training techniques to the source code, or adding features to the model by the heuristic. In fact, the semantics of a program can be rigorously defined by","authors_text":"Di He, Dinglan Peng, Guolin Ke, Shuxin Zheng, Tie-Yan Liu, Yatao Li","cross_cats":["cs.LG","cs.SE"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.PL","submitted_at":"2021-05-10T12:21:42Z","title":"How could Neural Networks understand Programs?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.04297","kind":"arxiv","version":2},"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":"sha256:82d8b800c057af3f5f8d6831282a6e6e63ff36b5bd1848f8acbfcda1f499c97f","target":"record","created_at":"2026-07-05T03:34:43Z","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":"f4ff4ad5bfe0f313ea23b2f6eeba11ef92ed0a253012d15ca8e3c93a7da63e73","cross_cats_sorted":["cs.LG","cs.SE"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.PL","submitted_at":"2021-05-10T12:21:42Z","title_canon_sha256":"74cc6861a3c6cccce6a01223136c168a7f369ed6ec0d2ed0dff43812f7247af3"},"schema_version":"1.0","source":{"id":"2105.04297","kind":"arxiv","version":2}},"canonical_sha256":"d9288ba1d60ede0656329b94e043c360bf9eab7fc94203ae48d690bdf9e73d6b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d9288ba1d60ede0656329b94e043c360bf9eab7fc94203ae48d690bdf9e73d6b","first_computed_at":"2026-07-05T03:34:43.821333Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:34:43.821333Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jBEu3ybbvXga2T+VLImiAtuIjt/2wrHlY2bnNxWc7lmLGIBpDyXP2ZcegLztsJ7w6Ouri4v6DBpUgmL49kEMDg==","signature_status":"signed_v1","signed_at":"2026-07-05T03:34:43.821699Z","signed_message":"canonical_sha256_bytes"},"source_id":"2105.04297","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:82d8b800c057af3f5f8d6831282a6e6e63ff36b5bd1848f8acbfcda1f499c97f","sha256:89ba1bae050abed6c84acba40c4df7a6aeabe26f3462f2772b860e465dbccbed"],"state_sha256":"5a2acf6de9692318552b003b5bdf8670f46408e91acfb7f6903b7e58e6f80fd1"}