{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:QYVCKSH77GLKUE6CH7AJSH6XR4","short_pith_number":"pith:QYVCKSH7","canonical_record":{"source":{"id":"2302.05738","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-11T16:32:28Z","cross_cats_sorted":[],"title_canon_sha256":"618abb72ff61df02a84417cd316af5cfe4e69c47cc5f89425f8fc470cd406075","abstract_canon_sha256":"241d8bc3edc74aaf7acdb02fc42d3e40805f7d6ccd50378df7e753418460e5e5"},"schema_version":"1.0"},"canonical_sha256":"862a2548fff996aa13c23fc0991fd78f265ef99ab58630d9eee6af17a9c875e2","source":{"kind":"arxiv","id":"2302.05738","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.05738","created_at":"2026-07-05T05:52:26Z"},{"alias_kind":"arxiv_version","alias_value":"2302.05738v2","created_at":"2026-07-05T05:52:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.05738","created_at":"2026-07-05T05:52:26Z"},{"alias_kind":"pith_short_12","alias_value":"QYVCKSH77GLK","created_at":"2026-07-05T05:52:26Z"},{"alias_kind":"pith_short_16","alias_value":"QYVCKSH77GLKUE6C","created_at":"2026-07-05T05:52:26Z"},{"alias_kind":"pith_short_8","alias_value":"QYVCKSH7","created_at":"2026-07-05T05:52:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:QYVCKSH77GLKUE6CH7AJSH6XR4","target":"record","payload":{"canonical_record":{"source":{"id":"2302.05738","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-11T16:32:28Z","cross_cats_sorted":[],"title_canon_sha256":"618abb72ff61df02a84417cd316af5cfe4e69c47cc5f89425f8fc470cd406075","abstract_canon_sha256":"241d8bc3edc74aaf7acdb02fc42d3e40805f7d6ccd50378df7e753418460e5e5"},"schema_version":"1.0"},"canonical_sha256":"862a2548fff996aa13c23fc0991fd78f265ef99ab58630d9eee6af17a9c875e2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:52:26.012608Z","signature_b64":"iQ4Ryo95NcyOdrI1C28mrIgMD4Ofj/BAMhJKic7X8XdaDHufavTp4J85t+7+9egSZ/ysy/CCInIwRvva+z8/DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"862a2548fff996aa13c23fc0991fd78f265ef99ab58630d9eee6af17a9c875e2","last_reissued_at":"2026-07-05T05:52:26.012156Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:52:26.012156Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2302.05738","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":"2026-07-05T05:52:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VcNhBcoe/MtWNKWwjbQvt2CNo0WKwVDY7QdfYiSczfybSG0Tsm02qr85+1td489ZOLPaC0y/X0JyciNGEiFtCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T14:35:26.776142Z"},"content_sha256":"e2536f07c02df0dca6b1e530430d3918a8dc0ca26e3dba55a5cd936a8e3ab827","schema_version":"1.0","event_id":"sha256:e2536f07c02df0dca6b1e530430d3918a8dc0ca26e3dba55a5cd936a8e3ab827"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:QYVCKSH77GLKUE6CH7AJSH6XR4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Cross-Modal Fine-Tuning: Align then Refine","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ameet Talwalkar, Corey Staten, Graham Neubig, Junhong Shen, Liam Li, Lucio M. Dery, Mikhail Khodak","submitted_at":"2023-02-11T16:32:28Z","abstract_excerpt":"Fine-tuning large-scale pretrained models has led to tremendous progress in well-studied modalities such as vision and NLP. However, similar gains have not been observed in many other modalities due to a lack of relevant pretrained models. In this work, we propose ORCA, a general cross-modal fine-tuning framework that extends the applicability of a single large-scale pretrained model to diverse modalities. ORCA adapts to a target task via an align-then-refine workflow: given the target input, ORCA first learns an embedding network that aligns the embedded feature distribution with the pretrain"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.05738","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/2302.05738/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":"2026-07-05T05:52:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"h+ZqYG/Fl8v1vta9hjqTSdIbSgLg6CgSjkKre6q9F2gFAGKEUirX4kZSYdPGQCosF3msOrTQF+veO299tbVrDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T14:35:26.776668Z"},"content_sha256":"c3c056009d8bcb04fa384d3414a7f4615af725d6580b8e177d17be6f1d66b248","schema_version":"1.0","event_id":"sha256:c3c056009d8bcb04fa384d3414a7f4615af725d6580b8e177d17be6f1d66b248"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QYVCKSH77GLKUE6CH7AJSH6XR4/bundle.json","state_url":"https://pith.science/pith/QYVCKSH77GLKUE6CH7AJSH6XR4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QYVCKSH77GLKUE6CH7AJSH6XR4/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":"2026-08-12T14:35:26Z","links":{"resolver":"https://pith.science/pith/QYVCKSH77GLKUE6CH7AJSH6XR4","bundle":"https://pith.science/pith/QYVCKSH77GLKUE6CH7AJSH6XR4/bundle.json","state":"https://pith.science/pith/QYVCKSH77GLKUE6CH7AJSH6XR4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QYVCKSH77GLKUE6CH7AJSH6XR4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:QYVCKSH77GLKUE6CH7AJSH6XR4","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":"241d8bc3edc74aaf7acdb02fc42d3e40805f7d6ccd50378df7e753418460e5e5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-11T16:32:28Z","title_canon_sha256":"618abb72ff61df02a84417cd316af5cfe4e69c47cc5f89425f8fc470cd406075"},"schema_version":"1.0","source":{"id":"2302.05738","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.05738","created_at":"2026-07-05T05:52:26Z"},{"alias_kind":"arxiv_version","alias_value":"2302.05738v2","created_at":"2026-07-05T05:52:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.05738","created_at":"2026-07-05T05:52:26Z"},{"alias_kind":"pith_short_12","alias_value":"QYVCKSH77GLK","created_at":"2026-07-05T05:52:26Z"},{"alias_kind":"pith_short_16","alias_value":"QYVCKSH77GLKUE6C","created_at":"2026-07-05T05:52:26Z"},{"alias_kind":"pith_short_8","alias_value":"QYVCKSH7","created_at":"2026-07-05T05:52:26Z"}],"graph_snapshots":[{"event_id":"sha256:c3c056009d8bcb04fa384d3414a7f4615af725d6580b8e177d17be6f1d66b248","target":"graph","created_at":"2026-07-05T05:52:26Z","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/2302.05738/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Fine-tuning large-scale pretrained models has led to tremendous progress in well-studied modalities such as vision and NLP. However, similar gains have not been observed in many other modalities due to a lack of relevant pretrained models. In this work, we propose ORCA, a general cross-modal fine-tuning framework that extends the applicability of a single large-scale pretrained model to diverse modalities. ORCA adapts to a target task via an align-then-refine workflow: given the target input, ORCA first learns an embedding network that aligns the embedded feature distribution with the pretrain","authors_text":"Ameet Talwalkar, Corey Staten, Graham Neubig, Junhong Shen, Liam Li, Lucio M. Dery, Mikhail Khodak","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-11T16:32:28Z","title":"Cross-Modal Fine-Tuning: Align then Refine"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.05738","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:e2536f07c02df0dca6b1e530430d3918a8dc0ca26e3dba55a5cd936a8e3ab827","target":"record","created_at":"2026-07-05T05:52:26Z","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":"241d8bc3edc74aaf7acdb02fc42d3e40805f7d6ccd50378df7e753418460e5e5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-11T16:32:28Z","title_canon_sha256":"618abb72ff61df02a84417cd316af5cfe4e69c47cc5f89425f8fc470cd406075"},"schema_version":"1.0","source":{"id":"2302.05738","kind":"arxiv","version":2}},"canonical_sha256":"862a2548fff996aa13c23fc0991fd78f265ef99ab58630d9eee6af17a9c875e2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"862a2548fff996aa13c23fc0991fd78f265ef99ab58630d9eee6af17a9c875e2","first_computed_at":"2026-07-05T05:52:26.012156Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:52:26.012156Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"iQ4Ryo95NcyOdrI1C28mrIgMD4Ofj/BAMhJKic7X8XdaDHufavTp4J85t+7+9egSZ/ysy/CCInIwRvva+z8/DA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:52:26.012608Z","signed_message":"canonical_sha256_bytes"},"source_id":"2302.05738","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e2536f07c02df0dca6b1e530430d3918a8dc0ca26e3dba55a5cd936a8e3ab827","sha256:c3c056009d8bcb04fa384d3414a7f4615af725d6580b8e177d17be6f1d66b248"],"state_sha256":"8628e8fe000dc2f022aaec7e087cbcb274e3c7580c5830367742b3aa9b04a751"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t78Wz2BdzT9e6yTd0bbA+VR96OLeeezD6jRUB85NdDYQjLb+/8SVexIztQvNJC3s+hxBqDm7/aOMuqBHKl49Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T14:35:26.780623Z","bundle_sha256":"0370b8866a617788fb2e51815c06a612c2a74da4593a07d62b95d3656839f77d"}}