{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:VXDAIVY5JX2CVITZPEC7735GRE","short_pith_number":"pith:VXDAIVY5","canonical_record":{"source":{"id":"2501.13988","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-01-23T08:27:15Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"e98949d34d4003263e5bf9f6d4ab84dac29f6b87ffe87e0e16b2065731dfbc74","abstract_canon_sha256":"81942b6ac5aa3bd5abc53082f06ddddc1e137f090a402afada1949f26195eb9c"},"schema_version":"1.0"},"canonical_sha256":"adc604571d4df42aa2797905ffefa68929210cb97d54136c26ed9c25feb2c037","source":{"kind":"arxiv","id":"2501.13988","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.13988","created_at":"2026-07-05T10:04:43Z"},{"alias_kind":"arxiv_version","alias_value":"2501.13988v1","created_at":"2026-07-05T10:04:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.13988","created_at":"2026-07-05T10:04:43Z"},{"alias_kind":"pith_short_12","alias_value":"VXDAIVY5JX2C","created_at":"2026-07-05T10:04:43Z"},{"alias_kind":"pith_short_16","alias_value":"VXDAIVY5JX2CVITZ","created_at":"2026-07-05T10:04:43Z"},{"alias_kind":"pith_short_8","alias_value":"VXDAIVY5","created_at":"2026-07-05T10:04:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:VXDAIVY5JX2CVITZPEC7735GRE","target":"record","payload":{"canonical_record":{"source":{"id":"2501.13988","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-01-23T08:27:15Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"e98949d34d4003263e5bf9f6d4ab84dac29f6b87ffe87e0e16b2065731dfbc74","abstract_canon_sha256":"81942b6ac5aa3bd5abc53082f06ddddc1e137f090a402afada1949f26195eb9c"},"schema_version":"1.0"},"canonical_sha256":"adc604571d4df42aa2797905ffefa68929210cb97d54136c26ed9c25feb2c037","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:04:43.541790Z","signature_b64":"lSmWoPdAvE/WGNmoz3SUvmRP/vo0Ij8IWlEIjDGrTDORMEXaDnrXbPlqisZPOUm3rqzwPb2XrkRBsZOZArWEDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"adc604571d4df42aa2797905ffefa68929210cb97d54136c26ed9c25feb2c037","last_reissued_at":"2026-07-05T10:04:43.541384Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:04:43.541384Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.13988","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":"2026-07-05T10:04:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hk+VWJ5NE91VOqURvxh0dqfVozZi2QbjOD2yjZRdyR23JGYlKW60TCefJA/FSYb1LIgdf+Qayvjv3p6jbDmaCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T08:30:48.204341Z"},"content_sha256":"768a9e4d9712d9a6f0f1b1850105d46faba13d366adac0014b0d550ead3f7d36","schema_version":"1.0","event_id":"sha256:768a9e4d9712d9a6f0f1b1850105d46faba13d366adac0014b0d550ead3f7d36"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:VXDAIVY5JX2CVITZPEC7735GRE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MCRL4OR: Multimodal Contrastive Representation Learning for Off-Road Environmental Perception","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.RO","authors_text":"Liang Wang, Yi Yang, Zhang Zhang","submitted_at":"2025-01-23T08:27:15Z","abstract_excerpt":"Most studies on environmental perception for autonomous vehicles (AVs) focus on urban traffic environments, where the objects/stuff to be perceived are mainly from man-made scenes and scalable datasets with dense annotations can be used to train supervised learning models. By contrast, it is hard to densely annotate a large-scale off-road driving dataset manually due to the inherently unstructured nature of off-road environments. In this paper, we propose a Multimodal Contrastive Representation Learning approach for Off-Road environmental perception, namely MCRL4OR. This approach aims to joint"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.13988","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/2501.13988/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":"2026-07-05T10:04:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5LFFQ1T8XHjGN/wveZDeMEOWDs7aX3IB27aVp57yCdUpSIHPJ538bbznCyFUjUAA2LMos3fZ3dUOR8TsEmlbCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T08:30:48.215646Z"},"content_sha256":"97e0f13414023f7b4f3cd08c2b44411e408890e03d4c6426d9e2c04d6daea5de","schema_version":"1.0","event_id":"sha256:97e0f13414023f7b4f3cd08c2b44411e408890e03d4c6426d9e2c04d6daea5de"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VXDAIVY5JX2CVITZPEC7735GRE/bundle.json","state_url":"https://pith.science/pith/VXDAIVY5JX2CVITZPEC7735GRE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VXDAIVY5JX2CVITZPEC7735GRE/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":"2026-08-14T08:30:48Z","links":{"resolver":"https://pith.science/pith/VXDAIVY5JX2CVITZPEC7735GRE","bundle":"https://pith.science/pith/VXDAIVY5JX2CVITZPEC7735GRE/bundle.json","state":"https://pith.science/pith/VXDAIVY5JX2CVITZPEC7735GRE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VXDAIVY5JX2CVITZPEC7735GRE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:VXDAIVY5JX2CVITZPEC7735GRE","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":"81942b6ac5aa3bd5abc53082f06ddddc1e137f090a402afada1949f26195eb9c","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-01-23T08:27:15Z","title_canon_sha256":"e98949d34d4003263e5bf9f6d4ab84dac29f6b87ffe87e0e16b2065731dfbc74"},"schema_version":"1.0","source":{"id":"2501.13988","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.13988","created_at":"2026-07-05T10:04:43Z"},{"alias_kind":"arxiv_version","alias_value":"2501.13988v1","created_at":"2026-07-05T10:04:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.13988","created_at":"2026-07-05T10:04:43Z"},{"alias_kind":"pith_short_12","alias_value":"VXDAIVY5JX2C","created_at":"2026-07-05T10:04:43Z"},{"alias_kind":"pith_short_16","alias_value":"VXDAIVY5JX2CVITZ","created_at":"2026-07-05T10:04:43Z"},{"alias_kind":"pith_short_8","alias_value":"VXDAIVY5","created_at":"2026-07-05T10:04:43Z"}],"graph_snapshots":[{"event_id":"sha256:97e0f13414023f7b4f3cd08c2b44411e408890e03d4c6426d9e2c04d6daea5de","target":"graph","created_at":"2026-07-05T10:04:43Z","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/2501.13988/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Most studies on environmental perception for autonomous vehicles (AVs) focus on urban traffic environments, where the objects/stuff to be perceived are mainly from man-made scenes and scalable datasets with dense annotations can be used to train supervised learning models. By contrast, it is hard to densely annotate a large-scale off-road driving dataset manually due to the inherently unstructured nature of off-road environments. In this paper, we propose a Multimodal Contrastive Representation Learning approach for Off-Road environmental perception, namely MCRL4OR. This approach aims to joint","authors_text":"Liang Wang, Yi Yang, Zhang Zhang","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-01-23T08:27:15Z","title":"MCRL4OR: Multimodal Contrastive Representation Learning for Off-Road Environmental Perception"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.13988","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":"sha256:768a9e4d9712d9a6f0f1b1850105d46faba13d366adac0014b0d550ead3f7d36","target":"record","created_at":"2026-07-05T10:04:43Z","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":"81942b6ac5aa3bd5abc53082f06ddddc1e137f090a402afada1949f26195eb9c","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-01-23T08:27:15Z","title_canon_sha256":"e98949d34d4003263e5bf9f6d4ab84dac29f6b87ffe87e0e16b2065731dfbc74"},"schema_version":"1.0","source":{"id":"2501.13988","kind":"arxiv","version":1}},"canonical_sha256":"adc604571d4df42aa2797905ffefa68929210cb97d54136c26ed9c25feb2c037","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"adc604571d4df42aa2797905ffefa68929210cb97d54136c26ed9c25feb2c037","first_computed_at":"2026-07-05T10:04:43.541384Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:04:43.541384Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lSmWoPdAvE/WGNmoz3SUvmRP/vo0Ij8IWlEIjDGrTDORMEXaDnrXbPlqisZPOUm3rqzwPb2XrkRBsZOZArWEDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:04:43.541790Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.13988","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:768a9e4d9712d9a6f0f1b1850105d46faba13d366adac0014b0d550ead3f7d36","sha256:97e0f13414023f7b4f3cd08c2b44411e408890e03d4c6426d9e2c04d6daea5de"],"state_sha256":"3225483596ddf27a239f9f32424279e3f17c67d5feff4953ae3ce01b576094b8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vfugFrHE3eju1oEUffUbeylM1sUshIztpQtpMXLrzsbog2jCjpvoD/rxt7S42piwwJ6hLJZPrGWkiIDLKYByBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T08:30:48.222429Z","bundle_sha256":"ce1ef39f54479e68088c48d92857ac4a8f35f079af7530b8d2f600247fedc878"}}