{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:3S245FYT4IFB73MXXBY2WC3H6G","short_pith_number":"pith:3S245FYT","canonical_record":{"source":{"id":"1905.02925","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-05-08T06:01:33Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"74ab7acb71efb1fa531dd96d5f480fdcebd4baf103cc368d8e03e44e35da7571","abstract_canon_sha256":"923d623e585cd6623a38128b28956fd010c926f338e70db063eb0e687c982e0b"},"schema_version":"1.0"},"canonical_sha256":"dcb5ce9713e20a1fed97b871ab0b67f194af60194f55dc43af303e551e653349","source":{"kind":"arxiv","id":"1905.02925","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.02925","created_at":"2026-05-17T23:46:45Z"},{"alias_kind":"arxiv_version","alias_value":"1905.02925v1","created_at":"2026-05-17T23:46:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.02925","created_at":"2026-05-17T23:46:45Z"},{"alias_kind":"pith_short_12","alias_value":"3S245FYT4IFB","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"3S245FYT4IFB73MX","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"3S245FYT","created_at":"2026-05-18T12:33:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:3S245FYT4IFB73MXXBY2WC3H6G","target":"record","payload":{"canonical_record":{"source":{"id":"1905.02925","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-05-08T06:01:33Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"74ab7acb71efb1fa531dd96d5f480fdcebd4baf103cc368d8e03e44e35da7571","abstract_canon_sha256":"923d623e585cd6623a38128b28956fd010c926f338e70db063eb0e687c982e0b"},"schema_version":"1.0"},"canonical_sha256":"dcb5ce9713e20a1fed97b871ab0b67f194af60194f55dc43af303e551e653349","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:45.012554Z","signature_b64":"kqnhWuTkSr3qr4qQ1BGJ66D/W2xWicXe+ZU7TvA3PaSSsrUeoZgxP/BJ18mYbO7IIs/cShag/MBG+WzoAy13Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dcb5ce9713e20a1fed97b871ab0b67f194af60194f55dc43af303e551e653349","last_reissued_at":"2026-05-17T23:46:45.012063Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:45.012063Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.02925","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":"2026-05-17T23:46:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WVrSOQ99dIrOT12CdCdv+ws9xuCibjx+56Gxn1E87RABoA9hgyq64D+cz4j5i2viiu5uzB7yZKsKodjOL4rHCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T07:52:20.139068Z"},"content_sha256":"a2f8587fbac10579d4442303181dc8b46d7086fd830e15bf07559965b7e83e4c","schema_version":"1.0","event_id":"sha256:a2f8587fbac10579d4442303181dc8b46d7086fd830e15bf07559965b7e83e4c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:3S245FYT4IFB73MXXBY2WC3H6G","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ShapeGlot: Learning Language for Shape Differentiation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.CL","authors_text":"Judy Fan, Leonidas J. Guibas, Noah D. Goodman, Panos Achlioptas, Robert X.D. Hawkins","submitted_at":"2019-05-08T06:01:33Z","abstract_excerpt":"In this work we explore how fine-grained differences between the shapes of common objects are expressed in language, grounded on images and 3D models of the objects. We first build a large scale, carefully controlled dataset of human utterances that each refers to a 2D rendering of a 3D CAD model so as to distinguish it from a set of shape-wise similar alternatives. Using this dataset, we develop neural language understanding (listening) and production (speaking) models that vary in their grounding (pure 3D forms via point-clouds vs. rendered 2D images), the degree of pragmatic reasoning captu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.02925","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":"2026-05-17T23:46:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cpSdOUA81qr/ZLSbI/yM4ns+pYgHgjLif69IRyvh7srnGifVm9gCqrEnO0aFSF01mXqD3UpXLrL/M8kIBDXgBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T07:52:20.139700Z"},"content_sha256":"035d9e77ca8c1081bcb3152e1e828c0ed857a346e3e7088708a0a128e0925a2a","schema_version":"1.0","event_id":"sha256:035d9e77ca8c1081bcb3152e1e828c0ed857a346e3e7088708a0a128e0925a2a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3S245FYT4IFB73MXXBY2WC3H6G/bundle.json","state_url":"https://pith.science/pith/3S245FYT4IFB73MXXBY2WC3H6G/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3S245FYT4IFB73MXXBY2WC3H6G/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":"2026-05-30T07:52:20Z","links":{"resolver":"https://pith.science/pith/3S245FYT4IFB73MXXBY2WC3H6G","bundle":"https://pith.science/pith/3S245FYT4IFB73MXXBY2WC3H6G/bundle.json","state":"https://pith.science/pith/3S245FYT4IFB73MXXBY2WC3H6G/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3S245FYT4IFB73MXXBY2WC3H6G/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:3S245FYT4IFB73MXXBY2WC3H6G","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":"923d623e585cd6623a38128b28956fd010c926f338e70db063eb0e687c982e0b","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-05-08T06:01:33Z","title_canon_sha256":"74ab7acb71efb1fa531dd96d5f480fdcebd4baf103cc368d8e03e44e35da7571"},"schema_version":"1.0","source":{"id":"1905.02925","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.02925","created_at":"2026-05-17T23:46:45Z"},{"alias_kind":"arxiv_version","alias_value":"1905.02925v1","created_at":"2026-05-17T23:46:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.02925","created_at":"2026-05-17T23:46:45Z"},{"alias_kind":"pith_short_12","alias_value":"3S245FYT4IFB","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"3S245FYT4IFB73MX","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"3S245FYT","created_at":"2026-05-18T12:33:10Z"}],"graph_snapshots":[{"event_id":"sha256:035d9e77ca8c1081bcb3152e1e828c0ed857a346e3e7088708a0a128e0925a2a","target":"graph","created_at":"2026-05-17T23:46:45Z","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":"In this work we explore how fine-grained differences between the shapes of common objects are expressed in language, grounded on images and 3D models of the objects. We first build a large scale, carefully controlled dataset of human utterances that each refers to a 2D rendering of a 3D CAD model so as to distinguish it from a set of shape-wise similar alternatives. Using this dataset, we develop neural language understanding (listening) and production (speaking) models that vary in their grounding (pure 3D forms via point-clouds vs. rendered 2D images), the degree of pragmatic reasoning captu","authors_text":"Judy Fan, Leonidas J. Guibas, Noah D. Goodman, Panos Achlioptas, Robert X.D. Hawkins","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-05-08T06:01:33Z","title":"ShapeGlot: Learning Language for Shape Differentiation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.02925","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:a2f8587fbac10579d4442303181dc8b46d7086fd830e15bf07559965b7e83e4c","target":"record","created_at":"2026-05-17T23:46:45Z","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":"923d623e585cd6623a38128b28956fd010c926f338e70db063eb0e687c982e0b","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-05-08T06:01:33Z","title_canon_sha256":"74ab7acb71efb1fa531dd96d5f480fdcebd4baf103cc368d8e03e44e35da7571"},"schema_version":"1.0","source":{"id":"1905.02925","kind":"arxiv","version":1}},"canonical_sha256":"dcb5ce9713e20a1fed97b871ab0b67f194af60194f55dc43af303e551e653349","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dcb5ce9713e20a1fed97b871ab0b67f194af60194f55dc43af303e551e653349","first_computed_at":"2026-05-17T23:46:45.012063Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:46:45.012063Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kqnhWuTkSr3qr4qQ1BGJ66D/W2xWicXe+ZU7TvA3PaSSsrUeoZgxP/BJ18mYbO7IIs/cShag/MBG+WzoAy13Cg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:46:45.012554Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.02925","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a2f8587fbac10579d4442303181dc8b46d7086fd830e15bf07559965b7e83e4c","sha256:035d9e77ca8c1081bcb3152e1e828c0ed857a346e3e7088708a0a128e0925a2a"],"state_sha256":"66bd049d60c6c77006a34eb14b283a14591a325f92c41784b970cca304fa64ec"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ruFRe89kZAF6AZDEEGoRfgMmRfrRaPZB8iWoBOHH+U5h7zQqiqV8jPST21j61b8vJOW1WORJe/j9DYVAj2UfCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T07:52:20.142289Z","bundle_sha256":"0b323b36b2bd6ba7314c69ad38c786e14916e1128d158415cb2d57faec41e52a"}}