{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:3L4V7X7Q2SRFSRLF2NR3PYTMGK","short_pith_number":"pith:3L4V7X7Q","canonical_record":{"source":{"id":"2509.00665","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-08-31T02:24:00Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"f0b4300c7a4b8157e0b9a4b20f5b8dddb9ad3938e90c4be5751d0d924f10ad81","abstract_canon_sha256":"da401c05fe410895795b512dc55ca844b59811a2c9edd7483229df2fb138866a"},"schema_version":"1.0"},"canonical_sha256":"daf95fdff0d4a2594565d363b7e26c32a91db19321a7d28de44f1ba492679b30","source":{"kind":"arxiv","id":"2509.00665","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.00665","created_at":"2026-07-05T12:06:19Z"},{"alias_kind":"arxiv_version","alias_value":"2509.00665v2","created_at":"2026-07-05T12:06:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.00665","created_at":"2026-07-05T12:06:19Z"},{"alias_kind":"pith_short_12","alias_value":"3L4V7X7Q2SRF","created_at":"2026-07-05T12:06:19Z"},{"alias_kind":"pith_short_16","alias_value":"3L4V7X7Q2SRFSRLF","created_at":"2026-07-05T12:06:19Z"},{"alias_kind":"pith_short_8","alias_value":"3L4V7X7Q","created_at":"2026-07-05T12:06:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:3L4V7X7Q2SRFSRLF2NR3PYTMGK","target":"record","payload":{"canonical_record":{"source":{"id":"2509.00665","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-08-31T02:24:00Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"f0b4300c7a4b8157e0b9a4b20f5b8dddb9ad3938e90c4be5751d0d924f10ad81","abstract_canon_sha256":"da401c05fe410895795b512dc55ca844b59811a2c9edd7483229df2fb138866a"},"schema_version":"1.0"},"canonical_sha256":"daf95fdff0d4a2594565d363b7e26c32a91db19321a7d28de44f1ba492679b30","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:06:19.270332Z","signature_b64":"MxVOEnchFEEljHtPcoUGED4Y1e7ighCpI7ZS5S+7TXIgp6Z6A+jzdgXzOtXV8kfG95aG/jUDeV6UCq/JAYiiCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"daf95fdff0d4a2594565d363b7e26c32a91db19321a7d28de44f1ba492679b30","last_reissued_at":"2026-07-05T12:06:19.269872Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:06:19.269872Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2509.00665","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T12:06:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kts+QxIi4tICO6vjzjsGnQ4iz9AhhvUiizH2sG1vnveDGCvZ1o/XR0yGV+UdaSCZBL7rcFQldT8o0nFlKxmVCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T07:13:46.240169Z"},"content_sha256":"427b6e96e6113ec483cd32554040b3e7bbf95f8c4897588e7649b1b8cbf65876","schema_version":"1.0","event_id":"sha256:427b6e96e6113ec483cd32554040b3e7bbf95f8c4897588e7649b1b8cbf65876"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:3L4V7X7Q2SRFSRLF2NR3PYTMGK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ER-LoRA: Effective-Rank Guided Adaptation for Weather-Generalized Depth Estimation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Robby T. Tan, Weilong Yan, Xin Zhang","submitted_at":"2025-08-31T02:24:00Z","abstract_excerpt":"Monocular depth estimation under adverse weather conditions (e.g.\\ rain, fog, snow, and nighttime) remains highly challenging due to the lack of reliable ground truth and the difficulty of learning from unlabeled real-world data. Existing methods often rely on synthetic adverse data with pseudo-labels, which suffer from domain gaps, or employ self-supervised learning, which violates photometric assumptions in adverse scenarios. In this work, we propose to achieve weather-generalized depth estimation by Parameter-Efficient Fine-Tuning (PEFT) of Vision Foundation Models (VFMs), using only a smal"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.00665","kind":"arxiv","version":2},"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/2509.00665/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":"2026-07-05T12:06:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AYS2Qli+t+N7Lqk1srMg2izhFCeQYGyepgytUVhLVEQIGlflHCBt8KArNbU4i2JRLSN9uyakmz2D4EAb7fTEDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T07:13:46.241164Z"},"content_sha256":"45a2e9727204da5d11fa81f9ef792a7fdf6e2df7ec6c6f5d4216651f32f479c0","schema_version":"1.0","event_id":"sha256:45a2e9727204da5d11fa81f9ef792a7fdf6e2df7ec6c6f5d4216651f32f479c0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3L4V7X7Q2SRFSRLF2NR3PYTMGK/bundle.json","state_url":"https://pith.science/pith/3L4V7X7Q2SRFSRLF2NR3PYTMGK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3L4V7X7Q2SRFSRLF2NR3PYTMGK/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":"2026-08-06T07:13:46Z","links":{"resolver":"https://pith.science/pith/3L4V7X7Q2SRFSRLF2NR3PYTMGK","bundle":"https://pith.science/pith/3L4V7X7Q2SRFSRLF2NR3PYTMGK/bundle.json","state":"https://pith.science/pith/3L4V7X7Q2SRFSRLF2NR3PYTMGK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3L4V7X7Q2SRFSRLF2NR3PYTMGK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:3L4V7X7Q2SRFSRLF2NR3PYTMGK","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":"da401c05fe410895795b512dc55ca844b59811a2c9edd7483229df2fb138866a","cross_cats_sorted":["cs.RO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-08-31T02:24:00Z","title_canon_sha256":"f0b4300c7a4b8157e0b9a4b20f5b8dddb9ad3938e90c4be5751d0d924f10ad81"},"schema_version":"1.0","source":{"id":"2509.00665","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.00665","created_at":"2026-07-05T12:06:19Z"},{"alias_kind":"arxiv_version","alias_value":"2509.00665v2","created_at":"2026-07-05T12:06:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.00665","created_at":"2026-07-05T12:06:19Z"},{"alias_kind":"pith_short_12","alias_value":"3L4V7X7Q2SRF","created_at":"2026-07-05T12:06:19Z"},{"alias_kind":"pith_short_16","alias_value":"3L4V7X7Q2SRFSRLF","created_at":"2026-07-05T12:06:19Z"},{"alias_kind":"pith_short_8","alias_value":"3L4V7X7Q","created_at":"2026-07-05T12:06:19Z"}],"graph_snapshots":[{"event_id":"sha256:45a2e9727204da5d11fa81f9ef792a7fdf6e2df7ec6c6f5d4216651f32f479c0","target":"graph","created_at":"2026-07-05T12:06:19Z","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/2509.00665/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Monocular depth estimation under adverse weather conditions (e.g.\\ rain, fog, snow, and nighttime) remains highly challenging due to the lack of reliable ground truth and the difficulty of learning from unlabeled real-world data. Existing methods often rely on synthetic adverse data with pseudo-labels, which suffer from domain gaps, or employ self-supervised learning, which violates photometric assumptions in adverse scenarios. In this work, we propose to achieve weather-generalized depth estimation by Parameter-Efficient Fine-Tuning (PEFT) of Vision Foundation Models (VFMs), using only a smal","authors_text":"Robby T. Tan, Weilong Yan, Xin Zhang","cross_cats":["cs.RO"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-08-31T02:24:00Z","title":"ER-LoRA: Effective-Rank Guided Adaptation for Weather-Generalized Depth Estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.00665","kind":"arxiv","version":2},"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":"sha256:427b6e96e6113ec483cd32554040b3e7bbf95f8c4897588e7649b1b8cbf65876","target":"record","created_at":"2026-07-05T12:06:19Z","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":"da401c05fe410895795b512dc55ca844b59811a2c9edd7483229df2fb138866a","cross_cats_sorted":["cs.RO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-08-31T02:24:00Z","title_canon_sha256":"f0b4300c7a4b8157e0b9a4b20f5b8dddb9ad3938e90c4be5751d0d924f10ad81"},"schema_version":"1.0","source":{"id":"2509.00665","kind":"arxiv","version":2}},"canonical_sha256":"daf95fdff0d4a2594565d363b7e26c32a91db19321a7d28de44f1ba492679b30","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"daf95fdff0d4a2594565d363b7e26c32a91db19321a7d28de44f1ba492679b30","first_computed_at":"2026-07-05T12:06:19.269872Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:06:19.269872Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MxVOEnchFEEljHtPcoUGED4Y1e7ighCpI7ZS5S+7TXIgp6Z6A+jzdgXzOtXV8kfG95aG/jUDeV6UCq/JAYiiCA==","signature_status":"signed_v1","signed_at":"2026-07-05T12:06:19.270332Z","signed_message":"canonical_sha256_bytes"},"source_id":"2509.00665","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:427b6e96e6113ec483cd32554040b3e7bbf95f8c4897588e7649b1b8cbf65876","sha256:45a2e9727204da5d11fa81f9ef792a7fdf6e2df7ec6c6f5d4216651f32f479c0"],"state_sha256":"8dca29dec6a949d11e101f60d3842873c0026616fb0663707c08f0985b9d0467"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"B5izI6EUNxh7qom6kKgd6JoORGFxzkX0eCBP4M59X6zWUObhH/5gN7ORQKvgZy4FDZsxUidpGqPa6y+ywfzRAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T07:13:46.249610Z","bundle_sha256":"6f29ae576d5d9b890ddf4bf9c4b792b2fdcea1d1a4b89008b6b4a9fc52d7160e"}}