{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:TIIS3D6NK22IW7TUJLPQHMIVAC","short_pith_number":"pith:TIIS3D6N","canonical_record":{"source":{"id":"2107.14593","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-07-20T20:55:02Z","cross_cats_sorted":["cs.AI","cs.LG","cs.RO"],"title_canon_sha256":"7ee596da7a3c16ed429c170e6da73c8429ba2a1dcd5ce03d860a998469f721c4","abstract_canon_sha256":"1d2f21ba3039db4a4248c88068790ce3295670add4b1977b748eaf719be6dacd"},"schema_version":"1.0"},"canonical_sha256":"9a112d8fcd56b48b7e744adf03b11500af80b2aea40518fa05a8372f6ee96229","source":{"kind":"arxiv","id":"2107.14593","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.14593","created_at":"2026-07-05T03:02:02Z"},{"alias_kind":"arxiv_version","alias_value":"2107.14593v1","created_at":"2026-07-05T03:02:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.14593","created_at":"2026-07-05T03:02:02Z"},{"alias_kind":"pith_short_12","alias_value":"TIIS3D6NK22I","created_at":"2026-07-05T03:02:02Z"},{"alias_kind":"pith_short_16","alias_value":"TIIS3D6NK22IW7TU","created_at":"2026-07-05T03:02:02Z"},{"alias_kind":"pith_short_8","alias_value":"TIIS3D6N","created_at":"2026-07-05T03:02:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:TIIS3D6NK22IW7TUJLPQHMIVAC","target":"record","payload":{"canonical_record":{"source":{"id":"2107.14593","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-07-20T20:55:02Z","cross_cats_sorted":["cs.AI","cs.LG","cs.RO"],"title_canon_sha256":"7ee596da7a3c16ed429c170e6da73c8429ba2a1dcd5ce03d860a998469f721c4","abstract_canon_sha256":"1d2f21ba3039db4a4248c88068790ce3295670add4b1977b748eaf719be6dacd"},"schema_version":"1.0"},"canonical_sha256":"9a112d8fcd56b48b7e744adf03b11500af80b2aea40518fa05a8372f6ee96229","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:02:02.418326Z","signature_b64":"xH5BK/yuMDGTZB8Z6wLDI+3Catn/M34RYbhOxK6O3MIIRJrcwSTfm/YKCSGsZtWPCvYHtCJY/w6V8SNtU7CVDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9a112d8fcd56b48b7e744adf03b11500af80b2aea40518fa05a8372f6ee96229","last_reissued_at":"2026-07-05T03:02:02.417884Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:02:02.417884Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2107.14593","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":"2026-07-05T03:02:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"c624nvdunMF0HIOpJyzTZ3ZbyRsuSg08rPG8hzf0CmIulAX64q8EZg2IUBn+Wnl3bUk5gGRyb66+f4UqJFekAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T19:31:40.403585Z"},"content_sha256":"813d71d12d3e792177aef86f922aada40fee8115c0e3909c6246aa829480dc03","schema_version":"1.0","event_id":"sha256:813d71d12d3e792177aef86f922aada40fee8115c0e3909c6246aa829480dc03"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:TIIS3D6NK22IW7TUJLPQHMIVAC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Neural Variational Learning for Grounded Language Acquisition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.RO"],"primary_cat":"cs.CL","authors_text":"Cynthia Matuszek, Francis Ferraro, Nisha Pillai","submitted_at":"2021-07-20T20:55:02Z","abstract_excerpt":"We propose a learning system in which language is grounded in visual percepts without specific pre-defined categories of terms. We present a unified generative method to acquire a shared semantic/visual embedding that enables the learning of language about a wide range of real-world objects. We evaluate the efficacy of this learning by predicting the semantics of objects and comparing the performance with neural and non-neural inputs. We show that this generative approach exhibits promising results in language grounding without pre-specifying visual categories under low resource settings. Our "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.14593","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/2107.14593/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":"2026-07-05T03:02:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l+3/Wt8Z2g50BaBAIc+ttJbGJkY9qDXwAXIRwq4q84uEeAD8hl9jT+Le1AbOs6eBEvGqx+0phS3u1J7jGkU/Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T19:31:40.403969Z"},"content_sha256":"c5960ed6831835affcc559061c7e33574a33d89ceec8b8e957497d6f2c50a96b","schema_version":"1.0","event_id":"sha256:c5960ed6831835affcc559061c7e33574a33d89ceec8b8e957497d6f2c50a96b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TIIS3D6NK22IW7TUJLPQHMIVAC/bundle.json","state_url":"https://pith.science/pith/TIIS3D6NK22IW7TUJLPQHMIVAC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TIIS3D6NK22IW7TUJLPQHMIVAC/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":"2026-07-06T19:31:40Z","links":{"resolver":"https://pith.science/pith/TIIS3D6NK22IW7TUJLPQHMIVAC","bundle":"https://pith.science/pith/TIIS3D6NK22IW7TUJLPQHMIVAC/bundle.json","state":"https://pith.science/pith/TIIS3D6NK22IW7TUJLPQHMIVAC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TIIS3D6NK22IW7TUJLPQHMIVAC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:TIIS3D6NK22IW7TUJLPQHMIVAC","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":"1d2f21ba3039db4a4248c88068790ce3295670add4b1977b748eaf719be6dacd","cross_cats_sorted":["cs.AI","cs.LG","cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-07-20T20:55:02Z","title_canon_sha256":"7ee596da7a3c16ed429c170e6da73c8429ba2a1dcd5ce03d860a998469f721c4"},"schema_version":"1.0","source":{"id":"2107.14593","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.14593","created_at":"2026-07-05T03:02:02Z"},{"alias_kind":"arxiv_version","alias_value":"2107.14593v1","created_at":"2026-07-05T03:02:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.14593","created_at":"2026-07-05T03:02:02Z"},{"alias_kind":"pith_short_12","alias_value":"TIIS3D6NK22I","created_at":"2026-07-05T03:02:02Z"},{"alias_kind":"pith_short_16","alias_value":"TIIS3D6NK22IW7TU","created_at":"2026-07-05T03:02:02Z"},{"alias_kind":"pith_short_8","alias_value":"TIIS3D6N","created_at":"2026-07-05T03:02:02Z"}],"graph_snapshots":[{"event_id":"sha256:c5960ed6831835affcc559061c7e33574a33d89ceec8b8e957497d6f2c50a96b","target":"graph","created_at":"2026-07-05T03:02:02Z","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/2107.14593/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose a learning system in which language is grounded in visual percepts without specific pre-defined categories of terms. We present a unified generative method to acquire a shared semantic/visual embedding that enables the learning of language about a wide range of real-world objects. We evaluate the efficacy of this learning by predicting the semantics of objects and comparing the performance with neural and non-neural inputs. We show that this generative approach exhibits promising results in language grounding without pre-specifying visual categories under low resource settings. Our ","authors_text":"Cynthia Matuszek, Francis Ferraro, Nisha Pillai","cross_cats":["cs.AI","cs.LG","cs.RO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-07-20T20:55:02Z","title":"Neural Variational Learning for Grounded Language Acquisition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.14593","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":"sha256:813d71d12d3e792177aef86f922aada40fee8115c0e3909c6246aa829480dc03","target":"record","created_at":"2026-07-05T03:02:02Z","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":"1d2f21ba3039db4a4248c88068790ce3295670add4b1977b748eaf719be6dacd","cross_cats_sorted":["cs.AI","cs.LG","cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-07-20T20:55:02Z","title_canon_sha256":"7ee596da7a3c16ed429c170e6da73c8429ba2a1dcd5ce03d860a998469f721c4"},"schema_version":"1.0","source":{"id":"2107.14593","kind":"arxiv","version":1}},"canonical_sha256":"9a112d8fcd56b48b7e744adf03b11500af80b2aea40518fa05a8372f6ee96229","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9a112d8fcd56b48b7e744adf03b11500af80b2aea40518fa05a8372f6ee96229","first_computed_at":"2026-07-05T03:02:02.417884Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:02:02.417884Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xH5BK/yuMDGTZB8Z6wLDI+3Catn/M34RYbhOxK6O3MIIRJrcwSTfm/YKCSGsZtWPCvYHtCJY/w6V8SNtU7CVDw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:02:02.418326Z","signed_message":"canonical_sha256_bytes"},"source_id":"2107.14593","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:813d71d12d3e792177aef86f922aada40fee8115c0e3909c6246aa829480dc03","sha256:c5960ed6831835affcc559061c7e33574a33d89ceec8b8e957497d6f2c50a96b"],"state_sha256":"1d0721c2fee039e50cdab68b910a2de309af8e247d042ce31c9a3b600b92fad3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wuE8THYAbn3/CEFhSmii/+w1iW8z/L/4BivajOJAk33ovTmOvCNijiF+J5XOco8o1HiZbFrcQN9PNdIIgijRCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T19:31:40.406159Z","bundle_sha256":"fb411a07dcf69c032244aa640377a078b25483d92662b760929bbb48b33f896d"}}