{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:EF2V7HYT7GPE77T3B52V5XUETX","short_pith_number":"pith:EF2V7HYT","canonical_record":{"source":{"id":"2411.17869","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-11-26T20:38:02Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"210a18ee0071ad7ac9d12b13b6dc49faef214775a717551db758a3177cf919f9","abstract_canon_sha256":"2372b6a8c63bcac099bedf0d53aae183c4b1135fc9c3bd41196fa83daaa4b97c"},"schema_version":"1.0"},"canonical_sha256":"21755f9f13f99e4ffe7b0f755ede849dcc1f7a50a0296ebfab93abf268a69b33","source":{"kind":"arxiv","id":"2411.17869","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.17869","created_at":"2026-07-05T09:41:08Z"},{"alias_kind":"arxiv_version","alias_value":"2411.17869v1","created_at":"2026-07-05T09:41:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.17869","created_at":"2026-07-05T09:41:08Z"},{"alias_kind":"pith_short_12","alias_value":"EF2V7HYT7GPE","created_at":"2026-07-05T09:41:08Z"},{"alias_kind":"pith_short_16","alias_value":"EF2V7HYT7GPE77T3","created_at":"2026-07-05T09:41:08Z"},{"alias_kind":"pith_short_8","alias_value":"EF2V7HYT","created_at":"2026-07-05T09:41:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:EF2V7HYT7GPE77T3B52V5XUETX","target":"record","payload":{"canonical_record":{"source":{"id":"2411.17869","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-11-26T20:38:02Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"210a18ee0071ad7ac9d12b13b6dc49faef214775a717551db758a3177cf919f9","abstract_canon_sha256":"2372b6a8c63bcac099bedf0d53aae183c4b1135fc9c3bd41196fa83daaa4b97c"},"schema_version":"1.0"},"canonical_sha256":"21755f9f13f99e4ffe7b0f755ede849dcc1f7a50a0296ebfab93abf268a69b33","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:41:08.298243Z","signature_b64":"bLqg19nq212le9j8FouAIkjlrco7kdN3zozeGMZtHNTPSLQUAHDx4tSNTdFDs4RAc9wcnmn+pwxGAbogC3GiBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"21755f9f13f99e4ffe7b0f755ede849dcc1f7a50a0296ebfab93abf268a69b33","last_reissued_at":"2026-07-05T09:41:08.297810Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:41:08.297810Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.17869","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":"2026-07-05T09:41:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"46ENSNbRv6KBrtKJQJgctTYfWEXCwSaVRHPD9tVxvhPlzSH3pt0jw5d8hvz89+kSeUZEwc3E4Cps/2Hg/mfBDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T07:48:27.238532Z"},"content_sha256":"2164548141994d99b5ef62b9dee5816b578fbbc42f677c73bbd8e96c8f81682b","schema_version":"1.0","event_id":"sha256:2164548141994d99b5ef62b9dee5816b578fbbc42f677c73bbd8e96c8f81682b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:EF2V7HYT7GPE77T3B52V5XUETX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ReC-TTT: Contrastive Feature Reconstruction for Test-Time Training","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Christian Desrosiers, Jose Dolz, Marco Colussi, Sergio Mascetti","submitted_at":"2024-11-26T20:38:02Z","abstract_excerpt":"The remarkable progress in deep learning (DL) showcases outstanding results in various computer vision tasks. However, adaptation to real-time variations in data distributions remains an important challenge. Test-Time Training (TTT) was proposed as an effective solution to this issue, which increases the generalization ability of trained models by adding an auxiliary task at train time and then using its loss at test time to adapt the model. Inspired by the recent achievements of contrastive representation learning in unsupervised tasks, we propose ReC-TTT, a test-time training technique that "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.17869","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/2411.17869/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":"2026-07-05T09:41:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TXvxBbSPhudbPa9geA1g8OLWw7qgVnd945o03oV7dqmoCRv58KvOMt84002rvOh4BuYamHZzJ919qnokGSM3Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T07:48:27.239094Z"},"content_sha256":"2cfed7f8239b914ef4c52c3632c2c9183feae9acca0d06c192a4298a453162de","schema_version":"1.0","event_id":"sha256:2cfed7f8239b914ef4c52c3632c2c9183feae9acca0d06c192a4298a453162de"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EF2V7HYT7GPE77T3B52V5XUETX/bundle.json","state_url":"https://pith.science/pith/EF2V7HYT7GPE77T3B52V5XUETX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EF2V7HYT7GPE77T3B52V5XUETX/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":"2026-08-20T07:48:27Z","links":{"resolver":"https://pith.science/pith/EF2V7HYT7GPE77T3B52V5XUETX","bundle":"https://pith.science/pith/EF2V7HYT7GPE77T3B52V5XUETX/bundle.json","state":"https://pith.science/pith/EF2V7HYT7GPE77T3B52V5XUETX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EF2V7HYT7GPE77T3B52V5XUETX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:EF2V7HYT7GPE77T3B52V5XUETX","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":"2372b6a8c63bcac099bedf0d53aae183c4b1135fc9c3bd41196fa83daaa4b97c","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-11-26T20:38:02Z","title_canon_sha256":"210a18ee0071ad7ac9d12b13b6dc49faef214775a717551db758a3177cf919f9"},"schema_version":"1.0","source":{"id":"2411.17869","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.17869","created_at":"2026-07-05T09:41:08Z"},{"alias_kind":"arxiv_version","alias_value":"2411.17869v1","created_at":"2026-07-05T09:41:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.17869","created_at":"2026-07-05T09:41:08Z"},{"alias_kind":"pith_short_12","alias_value":"EF2V7HYT7GPE","created_at":"2026-07-05T09:41:08Z"},{"alias_kind":"pith_short_16","alias_value":"EF2V7HYT7GPE77T3","created_at":"2026-07-05T09:41:08Z"},{"alias_kind":"pith_short_8","alias_value":"EF2V7HYT","created_at":"2026-07-05T09:41:08Z"}],"graph_snapshots":[{"event_id":"sha256:2cfed7f8239b914ef4c52c3632c2c9183feae9acca0d06c192a4298a453162de","target":"graph","created_at":"2026-07-05T09:41:08Z","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/2411.17869/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The remarkable progress in deep learning (DL) showcases outstanding results in various computer vision tasks. However, adaptation to real-time variations in data distributions remains an important challenge. Test-Time Training (TTT) was proposed as an effective solution to this issue, which increases the generalization ability of trained models by adding an auxiliary task at train time and then using its loss at test time to adapt the model. Inspired by the recent achievements of contrastive representation learning in unsupervised tasks, we propose ReC-TTT, a test-time training technique that ","authors_text":"Christian Desrosiers, Jose Dolz, Marco Colussi, Sergio Mascetti","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-11-26T20:38:02Z","title":"ReC-TTT: Contrastive Feature Reconstruction for Test-Time Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.17869","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":"sha256:2164548141994d99b5ef62b9dee5816b578fbbc42f677c73bbd8e96c8f81682b","target":"record","created_at":"2026-07-05T09:41:08Z","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":"2372b6a8c63bcac099bedf0d53aae183c4b1135fc9c3bd41196fa83daaa4b97c","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-11-26T20:38:02Z","title_canon_sha256":"210a18ee0071ad7ac9d12b13b6dc49faef214775a717551db758a3177cf919f9"},"schema_version":"1.0","source":{"id":"2411.17869","kind":"arxiv","version":1}},"canonical_sha256":"21755f9f13f99e4ffe7b0f755ede849dcc1f7a50a0296ebfab93abf268a69b33","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"21755f9f13f99e4ffe7b0f755ede849dcc1f7a50a0296ebfab93abf268a69b33","first_computed_at":"2026-07-05T09:41:08.297810Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:41:08.297810Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bLqg19nq212le9j8FouAIkjlrco7kdN3zozeGMZtHNTPSLQUAHDx4tSNTdFDs4RAc9wcnmn+pwxGAbogC3GiBg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:41:08.298243Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.17869","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2164548141994d99b5ef62b9dee5816b578fbbc42f677c73bbd8e96c8f81682b","sha256:2cfed7f8239b914ef4c52c3632c2c9183feae9acca0d06c192a4298a453162de"],"state_sha256":"585af2e168105c9640e49ec999c71c63cc78953930ffab22f0987df65f743ebe"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2Pcboy85m2YDLu9px2mILqMRNVTJhjsJVaWZbpHdbyVZ4zaRAiXue69LQlIyBxPs0MPBoXMqlfkmEhj0w5BFDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T07:48:27.295881Z","bundle_sha256":"8f3813a014a43bd46a89f8bfa657b2557fbd25cfbf364a887c35eb714b162e05"}}