{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:KZXSPLDDTW4337DDJPKD24SJ46","short_pith_number":"pith:KZXSPLDD","canonical_record":{"source":{"id":"1608.04428","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-08-15T22:34:50Z","cross_cats_sorted":["cs.AI","cs.NE"],"title_canon_sha256":"1676f6d6742748fbff36f0dd26a99d31c3ba1c49deddf8490209448ea3f25633","abstract_canon_sha256":"7fafd3eb7ba244770837b7cda9120846eeedab07ee251567aa54662108bdb224"},"schema_version":"1.0"},"canonical_sha256":"566f27ac639db9bdfc634bd43d7249e7af190d171c332aa109c016aa7e4a2f2e","source":{"kind":"arxiv","id":"1608.04428","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.04428","created_at":"2026-05-18T01:08:45Z"},{"alias_kind":"arxiv_version","alias_value":"1608.04428v1","created_at":"2026-05-18T01:08:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.04428","created_at":"2026-05-18T01:08:45Z"},{"alias_kind":"pith_short_12","alias_value":"KZXSPLDDTW43","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_16","alias_value":"KZXSPLDDTW4337DD","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_8","alias_value":"KZXSPLDD","created_at":"2026-05-18T12:30:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:KZXSPLDDTW4337DDJPKD24SJ46","target":"record","payload":{"canonical_record":{"source":{"id":"1608.04428","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-08-15T22:34:50Z","cross_cats_sorted":["cs.AI","cs.NE"],"title_canon_sha256":"1676f6d6742748fbff36f0dd26a99d31c3ba1c49deddf8490209448ea3f25633","abstract_canon_sha256":"7fafd3eb7ba244770837b7cda9120846eeedab07ee251567aa54662108bdb224"},"schema_version":"1.0"},"canonical_sha256":"566f27ac639db9bdfc634bd43d7249e7af190d171c332aa109c016aa7e4a2f2e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:08:45.179710Z","signature_b64":"z2T57J3Q7owdQW/l8xd+JqIO0I6AajsxNu7NBuMaO0uM+BZrwlHFNLP6Tb8numZ/coHaSJ5ozG3yW2k6ej/mAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"566f27ac639db9bdfc634bd43d7249e7af190d171c332aa109c016aa7e4a2f2e","last_reissued_at":"2026-05-18T01:08:45.179306Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:08:45.179306Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1608.04428","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":"2026-05-18T01:08:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Hbc8Vj9W+vBST69ODEsEmQQGCXUJKD5W4mwDU/zCPz/BqOrTmsPU2G3v3up7YXXAy7Hj3IdeESirUeWujj2RDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T17:40:17.065199Z"},"content_sha256":"e11709a1c6420c03002e9d78e7e1618fa05195bfc74ec54a9aae71ccdc64eb52","schema_version":"1.0","event_id":"sha256:e11709a1c6420c03002e9d78e7e1618fa05195bfc74ec54a9aae71ccdc64eb52"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:KZXSPLDDTW4337DDJPKD24SJ46","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"TerpreT: A Probabilistic Programming Language for Program Induction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.NE"],"primary_cat":"cs.LG","authors_text":"Alexander L. Gaunt, Daniel Tarlow, Jonathan Taylor, Marc Brockschmidt, Nate Kushman, Pushmeet Kohli, Rishabh Singh","submitted_at":"2016-08-15T22:34:50Z","abstract_excerpt":"We study machine learning formulations of inductive program synthesis; given input-output examples, we try to synthesize source code that maps inputs to corresponding outputs. Our aims are to develop new machine learning approaches based on neural networks and graphical models, and to understand the capabilities of machine learning techniques relative to traditional alternatives, such as those based on constraint solving from the programming languages community.\n  Our key contribution is the proposal of TerpreT, a domain-specific language for expressing program synthesis problems. TerpreT is s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.04428","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":""},"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":"2026-05-18T01:08:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zFJAAiBSk2r7UFJDQ+fVchBY4sr8cMrxsM+cOX8dvR2HYdgC+AKC5XV7WM2Obc6tNsUiDGOfnZN7zBuQdN3KBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T17:40:17.065601Z"},"content_sha256":"becd9782f4f3388088dc8f0cb95f46bcacd59cf0846f3ca7a5d4fad897fdd59a","schema_version":"1.0","event_id":"sha256:becd9782f4f3388088dc8f0cb95f46bcacd59cf0846f3ca7a5d4fad897fdd59a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KZXSPLDDTW4337DDJPKD24SJ46/bundle.json","state_url":"https://pith.science/pith/KZXSPLDDTW4337DDJPKD24SJ46/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KZXSPLDDTW4337DDJPKD24SJ46/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":"2026-05-25T17:40:17Z","links":{"resolver":"https://pith.science/pith/KZXSPLDDTW4337DDJPKD24SJ46","bundle":"https://pith.science/pith/KZXSPLDDTW4337DDJPKD24SJ46/bundle.json","state":"https://pith.science/pith/KZXSPLDDTW4337DDJPKD24SJ46/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KZXSPLDDTW4337DDJPKD24SJ46/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:KZXSPLDDTW4337DDJPKD24SJ46","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":"7fafd3eb7ba244770837b7cda9120846eeedab07ee251567aa54662108bdb224","cross_cats_sorted":["cs.AI","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-08-15T22:34:50Z","title_canon_sha256":"1676f6d6742748fbff36f0dd26a99d31c3ba1c49deddf8490209448ea3f25633"},"schema_version":"1.0","source":{"id":"1608.04428","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.04428","created_at":"2026-05-18T01:08:45Z"},{"alias_kind":"arxiv_version","alias_value":"1608.04428v1","created_at":"2026-05-18T01:08:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.04428","created_at":"2026-05-18T01:08:45Z"},{"alias_kind":"pith_short_12","alias_value":"KZXSPLDDTW43","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_16","alias_value":"KZXSPLDDTW4337DD","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_8","alias_value":"KZXSPLDD","created_at":"2026-05-18T12:30:29Z"}],"graph_snapshots":[{"event_id":"sha256:becd9782f4f3388088dc8f0cb95f46bcacd59cf0846f3ca7a5d4fad897fdd59a","target":"graph","created_at":"2026-05-18T01:08:45Z","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"},"paper":{"abstract_excerpt":"We study machine learning formulations of inductive program synthesis; given input-output examples, we try to synthesize source code that maps inputs to corresponding outputs. Our aims are to develop new machine learning approaches based on neural networks and graphical models, and to understand the capabilities of machine learning techniques relative to traditional alternatives, such as those based on constraint solving from the programming languages community.\n  Our key contribution is the proposal of TerpreT, a domain-specific language for expressing program synthesis problems. TerpreT is s","authors_text":"Alexander L. Gaunt, Daniel Tarlow, Jonathan Taylor, Marc Brockschmidt, Nate Kushman, Pushmeet Kohli, Rishabh Singh","cross_cats":["cs.AI","cs.NE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-08-15T22:34:50Z","title":"TerpreT: A Probabilistic Programming Language for Program Induction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.04428","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:e11709a1c6420c03002e9d78e7e1618fa05195bfc74ec54a9aae71ccdc64eb52","target":"record","created_at":"2026-05-18T01:08:45Z","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":"7fafd3eb7ba244770837b7cda9120846eeedab07ee251567aa54662108bdb224","cross_cats_sorted":["cs.AI","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-08-15T22:34:50Z","title_canon_sha256":"1676f6d6742748fbff36f0dd26a99d31c3ba1c49deddf8490209448ea3f25633"},"schema_version":"1.0","source":{"id":"1608.04428","kind":"arxiv","version":1}},"canonical_sha256":"566f27ac639db9bdfc634bd43d7249e7af190d171c332aa109c016aa7e4a2f2e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"566f27ac639db9bdfc634bd43d7249e7af190d171c332aa109c016aa7e4a2f2e","first_computed_at":"2026-05-18T01:08:45.179306Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:08:45.179306Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"z2T57J3Q7owdQW/l8xd+JqIO0I6AajsxNu7NBuMaO0uM+BZrwlHFNLP6Tb8numZ/coHaSJ5ozG3yW2k6ej/mAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:08:45.179710Z","signed_message":"canonical_sha256_bytes"},"source_id":"1608.04428","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e11709a1c6420c03002e9d78e7e1618fa05195bfc74ec54a9aae71ccdc64eb52","sha256:becd9782f4f3388088dc8f0cb95f46bcacd59cf0846f3ca7a5d4fad897fdd59a"],"state_sha256":"7a3b28903674b62b773dad0f18dc73c673ed956a3310f5df7143a268d31a94dc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lPW42YyolE9HHGnW/T5nJnkMLiT5opWltS0AeAZgljgDt9rqEouueUQNbTo0hhqVd0ODm4lYUJDFDZ3MwMTnCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T17:40:17.068351Z","bundle_sha256":"ca968b7658a887e53125d6a78077056ff5348542fbcc4b05a52db9e20d4e9dc3"}}