{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:UOWD6SRBXXG73X56NZM7I73ZFS","short_pith_number":"pith:UOWD6SRB","canonical_record":{"source":{"id":"2507.05221","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-07T17:33:20Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1e7a252474ba03b3239835a291edb98fd087ea7630ad1f9a65ae4dcb4b753e62","abstract_canon_sha256":"fde92eb10847e79b26d11c863f7adfe9dfc47f70ef0d308266a4f88a7121e10d"},"schema_version":"1.0"},"canonical_sha256":"a3ac3f4a21bdcdfddfbe6e59f47f792cb0b20c918e9bcd88a730a409ba3f2900","source":{"kind":"arxiv","id":"2507.05221","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.05221","created_at":"2026-07-05T11:33:27Z"},{"alias_kind":"arxiv_version","alias_value":"2507.05221v2","created_at":"2026-07-05T11:33:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.05221","created_at":"2026-07-05T11:33:27Z"},{"alias_kind":"pith_short_12","alias_value":"UOWD6SRBXXG7","created_at":"2026-07-05T11:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"UOWD6SRBXXG73X56","created_at":"2026-07-05T11:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"UOWD6SRB","created_at":"2026-07-05T11:33:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:UOWD6SRBXXG73X56NZM7I73ZFS","target":"record","payload":{"canonical_record":{"source":{"id":"2507.05221","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-07T17:33:20Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1e7a252474ba03b3239835a291edb98fd087ea7630ad1f9a65ae4dcb4b753e62","abstract_canon_sha256":"fde92eb10847e79b26d11c863f7adfe9dfc47f70ef0d308266a4f88a7121e10d"},"schema_version":"1.0"},"canonical_sha256":"a3ac3f4a21bdcdfddfbe6e59f47f792cb0b20c918e9bcd88a730a409ba3f2900","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:33:27.107217Z","signature_b64":"6QP5eq0fBWj4qvoVKgPsYXZmAw6C5st4U9Obo0x47jQnStY8ukuq0EPc9UVFLlD1nWSaRy20Bgc2WiQrk6hpCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a3ac3f4a21bdcdfddfbe6e59f47f792cb0b20c918e9bcd88a730a409ba3f2900","last_reissued_at":"2026-07-05T11:33:27.106690Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:33:27.106690Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.05221","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":"2026-07-05T11:33:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZQtV/EHyU/+x7K9BsjrufecMG7UTSv7ciM9NECLTACURDpsteZ5yBdcSFrp/q7IrlShYWwnSCUxichTAPZybAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T02:42:48.354867Z"},"content_sha256":"600928093757d72c114fccb5fc54b531c126f8b5a59a4e55b1195e37f2a0a78c","schema_version":"1.0","event_id":"sha256:600928093757d72c114fccb5fc54b531c126f8b5a59a4e55b1195e37f2a0a78c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:UOWD6SRBXXG73X56NZM7I73ZFS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CTA: Cross-Task Alignment for Better Test Time Training","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Ali Bahri, Christian Desrosiers, David Osowiechi, Masih Aminbeidokhti, Moslem Yazdanpanah, Pedram Fekri, Samuel Barbeau","submitted_at":"2025-07-07T17:33:20Z","abstract_excerpt":"Deep learning models have demonstrated exceptional performance across a wide range of computer vision tasks. However, their performance often degrades significantly when faced with distribution shifts, such as domain or dataset changes. Test-Time Training (TTT) has emerged as an effective method to enhance model robustness by incorporating an auxiliary unsupervised task during training and leveraging it for model updates at test time. In this work, we introduce CTA (Cross-Task Alignment), a novel approach for improving TTT. Unlike existing TTT methods, CTA does not require a specialized model "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.05221","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/2507.05221/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":"2026-07-05T11:33:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"s5mo1mbMEkwICEWOgkJ//j2MnRScrnE0d4+SLDhOI48gmHNXyYelUsK+94Nt0C+xF7UqUXpADPRz481A9NcKAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T02:42:48.355368Z"},"content_sha256":"24688d5b680b6d265bb9e301b34f6928a133bd22b8b391b155836f3b055f7b6c","schema_version":"1.0","event_id":"sha256:24688d5b680b6d265bb9e301b34f6928a133bd22b8b391b155836f3b055f7b6c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UOWD6SRBXXG73X56NZM7I73ZFS/bundle.json","state_url":"https://pith.science/pith/UOWD6SRBXXG73X56NZM7I73ZFS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UOWD6SRBXXG73X56NZM7I73ZFS/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":"2026-08-10T02:42:48Z","links":{"resolver":"https://pith.science/pith/UOWD6SRBXXG73X56NZM7I73ZFS","bundle":"https://pith.science/pith/UOWD6SRBXXG73X56NZM7I73ZFS/bundle.json","state":"https://pith.science/pith/UOWD6SRBXXG73X56NZM7I73ZFS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UOWD6SRBXXG73X56NZM7I73ZFS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:UOWD6SRBXXG73X56NZM7I73ZFS","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":"fde92eb10847e79b26d11c863f7adfe9dfc47f70ef0d308266a4f88a7121e10d","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-07T17:33:20Z","title_canon_sha256":"1e7a252474ba03b3239835a291edb98fd087ea7630ad1f9a65ae4dcb4b753e62"},"schema_version":"1.0","source":{"id":"2507.05221","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.05221","created_at":"2026-07-05T11:33:27Z"},{"alias_kind":"arxiv_version","alias_value":"2507.05221v2","created_at":"2026-07-05T11:33:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.05221","created_at":"2026-07-05T11:33:27Z"},{"alias_kind":"pith_short_12","alias_value":"UOWD6SRBXXG7","created_at":"2026-07-05T11:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"UOWD6SRBXXG73X56","created_at":"2026-07-05T11:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"UOWD6SRB","created_at":"2026-07-05T11:33:27Z"}],"graph_snapshots":[{"event_id":"sha256:24688d5b680b6d265bb9e301b34f6928a133bd22b8b391b155836f3b055f7b6c","target":"graph","created_at":"2026-07-05T11:33:27Z","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/2507.05221/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep learning models have demonstrated exceptional performance across a wide range of computer vision tasks. However, their performance often degrades significantly when faced with distribution shifts, such as domain or dataset changes. Test-Time Training (TTT) has emerged as an effective method to enhance model robustness by incorporating an auxiliary unsupervised task during training and leveraging it for model updates at test time. In this work, we introduce CTA (Cross-Task Alignment), a novel approach for improving TTT. Unlike existing TTT methods, CTA does not require a specialized model ","authors_text":"Ali Bahri, Christian Desrosiers, David Osowiechi, Masih Aminbeidokhti, Moslem Yazdanpanah, Pedram Fekri, Samuel Barbeau","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-07T17:33:20Z","title":"CTA: Cross-Task Alignment for Better Test Time Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.05221","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":"sha256:600928093757d72c114fccb5fc54b531c126f8b5a59a4e55b1195e37f2a0a78c","target":"record","created_at":"2026-07-05T11:33:27Z","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":"fde92eb10847e79b26d11c863f7adfe9dfc47f70ef0d308266a4f88a7121e10d","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-07T17:33:20Z","title_canon_sha256":"1e7a252474ba03b3239835a291edb98fd087ea7630ad1f9a65ae4dcb4b753e62"},"schema_version":"1.0","source":{"id":"2507.05221","kind":"arxiv","version":2}},"canonical_sha256":"a3ac3f4a21bdcdfddfbe6e59f47f792cb0b20c918e9bcd88a730a409ba3f2900","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a3ac3f4a21bdcdfddfbe6e59f47f792cb0b20c918e9bcd88a730a409ba3f2900","first_computed_at":"2026-07-05T11:33:27.106690Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:33:27.106690Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6QP5eq0fBWj4qvoVKgPsYXZmAw6C5st4U9Obo0x47jQnStY8ukuq0EPc9UVFLlD1nWSaRy20Bgc2WiQrk6hpCw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:33:27.107217Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.05221","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:600928093757d72c114fccb5fc54b531c126f8b5a59a4e55b1195e37f2a0a78c","sha256:24688d5b680b6d265bb9e301b34f6928a133bd22b8b391b155836f3b055f7b6c"],"state_sha256":"c35b56a289c07802c1881e5f377d5b3f99369fbd20c565c81ae626de1c19f78b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wxOgBmn5AXj0WvP/KVPNTTq+wBo3T7wU/uzRmwRjcb6J9c5UeQt16zKYqyZnsSQw8sHIEGrhhpGrKypuUvOUBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T02:42:48.360375Z","bundle_sha256":"8cf4c67940bedd1b028e9104e8484f9c915c87148d995c2c98cd545dec9d5449"}}