{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:4WNZHJUEHNNYMIJJSAZ74DSMWN","short_pith_number":"pith:4WNZHJUE","canonical_record":{"source":{"id":"1810.06543","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-15T17:45:02Z","cross_cats_sorted":["cs.AI","cs.RO"],"title_canon_sha256":"4ac8ef565d4499140a1a7264836966555029bc50a5a1af3b708df04f2ef298bc","abstract_canon_sha256":"00c32565e8823fe67b3f60f7b0950a8cb0a91bb45ebdc75c49cdea8ef3712dc9"},"schema_version":"1.0"},"canonical_sha256":"e59b93a6843b5b8621299033fe0e4cb34d069e9bc3b22556eb1fe3fa43d296e0","source":{"kind":"arxiv","id":"1810.06543","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.06543","created_at":"2026-05-18T00:03:20Z"},{"alias_kind":"arxiv_version","alias_value":"1810.06543v1","created_at":"2026-05-18T00:03:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.06543","created_at":"2026-05-18T00:03:20Z"},{"alias_kind":"pith_short_12","alias_value":"4WNZHJUEHNNY","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_16","alias_value":"4WNZHJUEHNNYMIJJ","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_8","alias_value":"4WNZHJUE","created_at":"2026-05-18T12:32:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:4WNZHJUEHNNYMIJJSAZ74DSMWN","target":"record","payload":{"canonical_record":{"source":{"id":"1810.06543","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-15T17:45:02Z","cross_cats_sorted":["cs.AI","cs.RO"],"title_canon_sha256":"4ac8ef565d4499140a1a7264836966555029bc50a5a1af3b708df04f2ef298bc","abstract_canon_sha256":"00c32565e8823fe67b3f60f7b0950a8cb0a91bb45ebdc75c49cdea8ef3712dc9"},"schema_version":"1.0"},"canonical_sha256":"e59b93a6843b5b8621299033fe0e4cb34d069e9bc3b22556eb1fe3fa43d296e0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:03:20.306188Z","signature_b64":"l9t8eO1LKUSB9c4dDu5kfKuhSpzSdCI6gq2AUNbiylgkgQMwU1re1JO3r7Br5xM+5TSKFdeROK6MPPvh4U+XCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e59b93a6843b5b8621299033fe0e4cb34d069e9bc3b22556eb1fe3fa43d296e0","last_reissued_at":"2026-05-18T00:03:20.305557Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:03:20.305557Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.06543","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":"2026-05-18T00:03:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"45OWedVIA3XvsEaPMH7MXOrKm92sf9BdCFrCfOSuqwiXfgKJ+YLnPAmBoU4sh64sPx0rGqXUTuvTyTge6mHfCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T21:08:13.601561Z"},"content_sha256":"32fedbbc01fedc011520f43f7748a9ff2c00d9bb6f93cdf20571c21bd22e1384","schema_version":"1.0","event_id":"sha256:32fedbbc01fedc011520f43f7748a9ff2c00d9bb6f93cdf20571c21bd22e1384"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:4WNZHJUEHNNYMIJJSAZ74DSMWN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Visual Semantic Navigation using Scene Priors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.RO"],"primary_cat":"cs.CV","authors_text":"Abhinav Gupta, Ali Farhadi, Roozbeh Mottaghi, Wei Yang, Xiaolong Wang","submitted_at":"2018-10-15T17:45:02Z","abstract_excerpt":"How do humans navigate to target objects in novel scenes? Do we use the semantic/functional priors we have built over years to efficiently search and navigate? For example, to search for mugs, we search cabinets near the coffee machine and for fruits we try the fridge. In this work, we focus on incorporating semantic priors in the task of semantic navigation. We propose to use Graph Convolutional Networks for incorporating the prior knowledge into a deep reinforcement learning framework. The agent uses the features from the knowledge graph to predict the actions. For evaluation, we use the AI2"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.06543","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":"2026-05-18T00:03:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FwApabimQwbcjdWbsiJX5JE6ZnxtNaK3hfO0MZUP5GgJTeiQ6+NJo4wr56BQY2GmkZap3gCML091GmO2IPzDDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T21:08:13.602266Z"},"content_sha256":"99fc7f6946a2f547a5844d2d42698662e95d5547d643024f78fd9d5b743c258f","schema_version":"1.0","event_id":"sha256:99fc7f6946a2f547a5844d2d42698662e95d5547d643024f78fd9d5b743c258f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4WNZHJUEHNNYMIJJSAZ74DSMWN/bundle.json","state_url":"https://pith.science/pith/4WNZHJUEHNNYMIJJSAZ74DSMWN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4WNZHJUEHNNYMIJJSAZ74DSMWN/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":"2026-05-31T21:08:13Z","links":{"resolver":"https://pith.science/pith/4WNZHJUEHNNYMIJJSAZ74DSMWN","bundle":"https://pith.science/pith/4WNZHJUEHNNYMIJJSAZ74DSMWN/bundle.json","state":"https://pith.science/pith/4WNZHJUEHNNYMIJJSAZ74DSMWN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4WNZHJUEHNNYMIJJSAZ74DSMWN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:4WNZHJUEHNNYMIJJSAZ74DSMWN","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":"00c32565e8823fe67b3f60f7b0950a8cb0a91bb45ebdc75c49cdea8ef3712dc9","cross_cats_sorted":["cs.AI","cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-15T17:45:02Z","title_canon_sha256":"4ac8ef565d4499140a1a7264836966555029bc50a5a1af3b708df04f2ef298bc"},"schema_version":"1.0","source":{"id":"1810.06543","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.06543","created_at":"2026-05-18T00:03:20Z"},{"alias_kind":"arxiv_version","alias_value":"1810.06543v1","created_at":"2026-05-18T00:03:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.06543","created_at":"2026-05-18T00:03:20Z"},{"alias_kind":"pith_short_12","alias_value":"4WNZHJUEHNNY","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_16","alias_value":"4WNZHJUEHNNYMIJJ","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_8","alias_value":"4WNZHJUE","created_at":"2026-05-18T12:32:05Z"}],"graph_snapshots":[{"event_id":"sha256:99fc7f6946a2f547a5844d2d42698662e95d5547d643024f78fd9d5b743c258f","target":"graph","created_at":"2026-05-18T00:03:20Z","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":"How do humans navigate to target objects in novel scenes? Do we use the semantic/functional priors we have built over years to efficiently search and navigate? For example, to search for mugs, we search cabinets near the coffee machine and for fruits we try the fridge. In this work, we focus on incorporating semantic priors in the task of semantic navigation. We propose to use Graph Convolutional Networks for incorporating the prior knowledge into a deep reinforcement learning framework. The agent uses the features from the knowledge graph to predict the actions. For evaluation, we use the AI2","authors_text":"Abhinav Gupta, Ali Farhadi, Roozbeh Mottaghi, Wei Yang, Xiaolong Wang","cross_cats":["cs.AI","cs.RO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-15T17:45:02Z","title":"Visual Semantic Navigation using Scene Priors"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.06543","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":"sha256:32fedbbc01fedc011520f43f7748a9ff2c00d9bb6f93cdf20571c21bd22e1384","target":"record","created_at":"2026-05-18T00:03:20Z","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":"00c32565e8823fe67b3f60f7b0950a8cb0a91bb45ebdc75c49cdea8ef3712dc9","cross_cats_sorted":["cs.AI","cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-15T17:45:02Z","title_canon_sha256":"4ac8ef565d4499140a1a7264836966555029bc50a5a1af3b708df04f2ef298bc"},"schema_version":"1.0","source":{"id":"1810.06543","kind":"arxiv","version":1}},"canonical_sha256":"e59b93a6843b5b8621299033fe0e4cb34d069e9bc3b22556eb1fe3fa43d296e0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e59b93a6843b5b8621299033fe0e4cb34d069e9bc3b22556eb1fe3fa43d296e0","first_computed_at":"2026-05-18T00:03:20.305557Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:03:20.305557Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"l9t8eO1LKUSB9c4dDu5kfKuhSpzSdCI6gq2AUNbiylgkgQMwU1re1JO3r7Br5xM+5TSKFdeROK6MPPvh4U+XCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:03:20.306188Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.06543","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:32fedbbc01fedc011520f43f7748a9ff2c00d9bb6f93cdf20571c21bd22e1384","sha256:99fc7f6946a2f547a5844d2d42698662e95d5547d643024f78fd9d5b743c258f"],"state_sha256":"8357362d8b0efe57a8f0a686338edd1815125eed6410f549ef9d54387a33323e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"V+zTqG2/UwyOcW+xjeysLt5jH68zlhAC0YraGU0M2Al7v2jI/+CW/hz552pb0kRyYJi0P/ZhDviTlUJd95G0AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T21:08:13.606361Z","bundle_sha256":"4130ce684d3f97a32815138fc7969698d4f7a820e0d77ec272250cb1a6dc9ed0"}}