{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:BQRY6DFK5VQNAUB76FFGW4CEDL","short_pith_number":"pith:BQRY6DFK","canonical_record":{"source":{"id":"2403.08109","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-03-12T22:33:08Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"03dc8293f59b6bd1faeb7f6e3af9fa94432cf90862205d1ed4408583234cd4cb","abstract_canon_sha256":"51284433b8c2841316932c14be27287feda0fbee6fa23fd085067aba0de20d40"},"schema_version":"1.0"},"canonical_sha256":"0c238f0caaed60d0503ff14a6b70441af456ccf33b2703966fb6b0c1d59abb8d","source":{"kind":"arxiv","id":"2403.08109","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.08109","created_at":"2026-07-05T09:55:32Z"},{"alias_kind":"arxiv_version","alias_value":"2403.08109v3","created_at":"2026-07-05T09:55:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.08109","created_at":"2026-07-05T09:55:32Z"},{"alias_kind":"pith_short_12","alias_value":"BQRY6DFK5VQN","created_at":"2026-07-05T09:55:32Z"},{"alias_kind":"pith_short_16","alias_value":"BQRY6DFK5VQNAUB7","created_at":"2026-07-05T09:55:32Z"},{"alias_kind":"pith_short_8","alias_value":"BQRY6DFK","created_at":"2026-07-05T09:55:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:BQRY6DFK5VQNAUB76FFGW4CEDL","target":"record","payload":{"canonical_record":{"source":{"id":"2403.08109","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-03-12T22:33:08Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"03dc8293f59b6bd1faeb7f6e3af9fa94432cf90862205d1ed4408583234cd4cb","abstract_canon_sha256":"51284433b8c2841316932c14be27287feda0fbee6fa23fd085067aba0de20d40"},"schema_version":"1.0"},"canonical_sha256":"0c238f0caaed60d0503ff14a6b70441af456ccf33b2703966fb6b0c1d59abb8d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:55:32.814511Z","signature_b64":"Ch75gZPEKBvhjBGLbadRoqbjNeYYZ0qlZ7FIVJMtE7yZcDLPPRnZUDLIzj0XvTQwtoh9hfqFu327NuGpVyyBDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0c238f0caaed60d0503ff14a6b70441af456ccf33b2703966fb6b0c1d59abb8d","last_reissued_at":"2026-07-05T09:55:32.813999Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:55:32.813999Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2403.08109","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:55:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"852iFdkTRRKx6hlPAS1ac7GC35fEzAw6LXx5Awc0M+LNXzsvkz0H/4RtiGvDRHZT9gQzgDJW+d5+mzEuYPTxAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T19:36:52.291393Z"},"content_sha256":"94bcd5e8a0db96c7d50cb09200b9d40a232a1f07010fa09671ea8f7b96065813","schema_version":"1.0","event_id":"sha256:94bcd5e8a0db96c7d50cb09200b9d40a232a1f07010fa09671ea8f7b96065813"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:BQRY6DFK5VQNAUB76FFGW4CEDL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"VANP: Learning Where to See for Navigation with Self-Supervised Vision-Action Pre-Training","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Amirreza Payandeh, Junzhe Wang, Mohammad Nazeri, Xuesu Xiao","submitted_at":"2024-03-12T22:33:08Z","abstract_excerpt":"Humans excel at efficiently navigating through crowds without collision by focusing on specific visual regions relevant to navigation. However, most robotic visual navigation methods rely on deep learning models pre-trained on vision tasks, which prioritize salient objects -- not necessarily relevant to navigation and potentially misleading. Alternative approaches train specialized navigation models from scratch, requiring significant computation. On the other hand, self-supervised learning has revolutionized computer vision and natural language processing, but its application to robotic navig"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.08109","kind":"arxiv","version":3},"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/2403.08109/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":"2026-07-05T09:55:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GtODwdSGJRqIzcv3jjYOkrEr0GhbByyUVMrtlYV2vyAj/rbRABuInYSOb4CDpk/zPl0gX2UGAnbj2rEdTdP+Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T19:36:52.292075Z"},"content_sha256":"ed00a04524cb286b14f36c471b97599080c0b6783c5a116080ccebe47cb3b65b","schema_version":"1.0","event_id":"sha256:ed00a04524cb286b14f36c471b97599080c0b6783c5a116080ccebe47cb3b65b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BQRY6DFK5VQNAUB76FFGW4CEDL/bundle.json","state_url":"https://pith.science/pith/BQRY6DFK5VQNAUB76FFGW4CEDL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BQRY6DFK5VQNAUB76FFGW4CEDL/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":"2026-08-13T19:36:52Z","links":{"resolver":"https://pith.science/pith/BQRY6DFK5VQNAUB76FFGW4CEDL","bundle":"https://pith.science/pith/BQRY6DFK5VQNAUB76FFGW4CEDL/bundle.json","state":"https://pith.science/pith/BQRY6DFK5VQNAUB76FFGW4CEDL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BQRY6DFK5VQNAUB76FFGW4CEDL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:BQRY6DFK5VQNAUB76FFGW4CEDL","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":"51284433b8c2841316932c14be27287feda0fbee6fa23fd085067aba0de20d40","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-03-12T22:33:08Z","title_canon_sha256":"03dc8293f59b6bd1faeb7f6e3af9fa94432cf90862205d1ed4408583234cd4cb"},"schema_version":"1.0","source":{"id":"2403.08109","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.08109","created_at":"2026-07-05T09:55:32Z"},{"alias_kind":"arxiv_version","alias_value":"2403.08109v3","created_at":"2026-07-05T09:55:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.08109","created_at":"2026-07-05T09:55:32Z"},{"alias_kind":"pith_short_12","alias_value":"BQRY6DFK5VQN","created_at":"2026-07-05T09:55:32Z"},{"alias_kind":"pith_short_16","alias_value":"BQRY6DFK5VQNAUB7","created_at":"2026-07-05T09:55:32Z"},{"alias_kind":"pith_short_8","alias_value":"BQRY6DFK","created_at":"2026-07-05T09:55:32Z"}],"graph_snapshots":[{"event_id":"sha256:ed00a04524cb286b14f36c471b97599080c0b6783c5a116080ccebe47cb3b65b","target":"graph","created_at":"2026-07-05T09:55:32Z","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/2403.08109/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Humans excel at efficiently navigating through crowds without collision by focusing on specific visual regions relevant to navigation. However, most robotic visual navigation methods rely on deep learning models pre-trained on vision tasks, which prioritize salient objects -- not necessarily relevant to navigation and potentially misleading. Alternative approaches train specialized navigation models from scratch, requiring significant computation. On the other hand, self-supervised learning has revolutionized computer vision and natural language processing, but its application to robotic navig","authors_text":"Amirreza Payandeh, Junzhe Wang, Mohammad Nazeri, Xuesu Xiao","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-03-12T22:33:08Z","title":"VANP: Learning Where to See for Navigation with Self-Supervised Vision-Action Pre-Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.08109","kind":"arxiv","version":3},"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":"sha256:94bcd5e8a0db96c7d50cb09200b9d40a232a1f07010fa09671ea8f7b96065813","target":"record","created_at":"2026-07-05T09:55:32Z","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":"51284433b8c2841316932c14be27287feda0fbee6fa23fd085067aba0de20d40","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-03-12T22:33:08Z","title_canon_sha256":"03dc8293f59b6bd1faeb7f6e3af9fa94432cf90862205d1ed4408583234cd4cb"},"schema_version":"1.0","source":{"id":"2403.08109","kind":"arxiv","version":3}},"canonical_sha256":"0c238f0caaed60d0503ff14a6b70441af456ccf33b2703966fb6b0c1d59abb8d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0c238f0caaed60d0503ff14a6b70441af456ccf33b2703966fb6b0c1d59abb8d","first_computed_at":"2026-07-05T09:55:32.813999Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:55:32.813999Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Ch75gZPEKBvhjBGLbadRoqbjNeYYZ0qlZ7FIVJMtE7yZcDLPPRnZUDLIzj0XvTQwtoh9hfqFu327NuGpVyyBDg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:55:32.814511Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.08109","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:94bcd5e8a0db96c7d50cb09200b9d40a232a1f07010fa09671ea8f7b96065813","sha256:ed00a04524cb286b14f36c471b97599080c0b6783c5a116080ccebe47cb3b65b"],"state_sha256":"708c6b714855936f445409b1f4843cb7c24dbbbcb0472ba7a2e7ad643ddc66fb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ekTxmNP4rKjHWNwksjGuhGblK6nDcOyPWTdJYDByuMm2YQ2DOTXmtijQvMzVtLMEKZA+owrsSc7yIyr3FBCODw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T19:36:52.296407Z","bundle_sha256":"9ae3c3ad29d5e0ed457543a4ed6940f2a042cb77c66c5f515dcbc22d24ed8495"}}