{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:R5AUE6GKBJMWOWTKRGJRZVQWNU","short_pith_number":"pith:R5AUE6GK","canonical_record":{"source":{"id":"2405.10216","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-16T16:05:33Z","cross_cats_sorted":["cs.AI","eess.SP"],"title_canon_sha256":"cc793ced37bc472d4afeb46b6bb1e7abf4458b366b6a60dbe32fbd38f3c24d8e","abstract_canon_sha256":"1d5420412291237015f5207b8ef1c8014f4923bda9c85e181a3bd6b033aeecf5"},"schema_version":"1.0"},"canonical_sha256":"8f414278ca0a59675a6a89931cd6166d3a66cdeecb8d46db613a4034821ce293","source":{"kind":"arxiv","id":"2405.10216","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.10216","created_at":"2026-07-05T09:24:34Z"},{"alias_kind":"arxiv_version","alias_value":"2405.10216v1","created_at":"2026-07-05T09:24:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.10216","created_at":"2026-07-05T09:24:34Z"},{"alias_kind":"pith_short_12","alias_value":"R5AUE6GKBJMW","created_at":"2026-07-05T09:24:34Z"},{"alias_kind":"pith_short_16","alias_value":"R5AUE6GKBJMWOWTK","created_at":"2026-07-05T09:24:34Z"},{"alias_kind":"pith_short_8","alias_value":"R5AUE6GK","created_at":"2026-07-05T09:24:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:R5AUE6GKBJMWOWTKRGJRZVQWNU","target":"record","payload":{"canonical_record":{"source":{"id":"2405.10216","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-16T16:05:33Z","cross_cats_sorted":["cs.AI","eess.SP"],"title_canon_sha256":"cc793ced37bc472d4afeb46b6bb1e7abf4458b366b6a60dbe32fbd38f3c24d8e","abstract_canon_sha256":"1d5420412291237015f5207b8ef1c8014f4923bda9c85e181a3bd6b033aeecf5"},"schema_version":"1.0"},"canonical_sha256":"8f414278ca0a59675a6a89931cd6166d3a66cdeecb8d46db613a4034821ce293","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:24:34.995546Z","signature_b64":"7Bwo8aqtimDAKMzzcHWyHJlQiyOcihAJElpWBIixtja3CJYxKxhRzKk8jQAfAjd9jIj1zIV0Ss3ghUpFfCXABw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8f414278ca0a59675a6a89931cd6166d3a66cdeecb8d46db613a4034821ce293","last_reissued_at":"2026-07-05T09:24:34.995004Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:24:34.995004Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.10216","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":"2026-07-05T09:24:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"er5OaVZ0NddGk2D7F9I5fmK8MYfk6d5eLrV2erpmenlMSAE4pTciyNERforRSdtm5mNuMBD9R2x83VI1niA7Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T08:51:37.668209Z"},"content_sha256":"c8f5729d480ea53768256db2e90cad0f6a15123b457609574e564404a44d8496","schema_version":"1.0","event_id":"sha256:c8f5729d480ea53768256db2e90cad0f6a15123b457609574e564404a44d8496"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:R5AUE6GKBJMWOWTKRGJRZVQWNU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Low-Rank Adaptation of Time Series Foundational Models for Out-of-Domain Modality Forecasting","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","eess.SP"],"primary_cat":"cs.LG","authors_text":"Anubhav Bhatti, Bingjie Shen, Chen Dan, Divij Gupta, San Lee, Suraj Parmar, Yuwei Liu","submitted_at":"2024-05-16T16:05:33Z","abstract_excerpt":"Low-Rank Adaptation (LoRA) is a widely used technique for fine-tuning large pre-trained or foundational models across different modalities and tasks. However, its application to time series data, particularly within foundational models, remains underexplored. This paper examines the impact of LoRA on contemporary time series foundational models: Lag-Llama, MOIRAI, and Chronos. We demonstrate LoRA's fine-tuning potential for forecasting the vital signs of sepsis patients in intensive care units (ICUs), emphasizing the models' adaptability to previously unseen, out-of-domain modalities. Integrat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.10216","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/2405.10216/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":"2026-07-05T09:24:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Cm/ZD1IwusLTGG1AVUrrnecVkFOmSzFmALTu9iB1uHGbl5XwvJWrPmJkqqdxpJpWuo9yrmAzQ1nCHZAJ65xtAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T08:51:37.669121Z"},"content_sha256":"6cea8ba43d63289575b7dd9251d5a3965053378e4fcc0bfb747d6832bc9fca26","schema_version":"1.0","event_id":"sha256:6cea8ba43d63289575b7dd9251d5a3965053378e4fcc0bfb747d6832bc9fca26"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/R5AUE6GKBJMWOWTKRGJRZVQWNU/bundle.json","state_url":"https://pith.science/pith/R5AUE6GKBJMWOWTKRGJRZVQWNU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/R5AUE6GKBJMWOWTKRGJRZVQWNU/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":"2026-08-04T08:51:37Z","links":{"resolver":"https://pith.science/pith/R5AUE6GKBJMWOWTKRGJRZVQWNU","bundle":"https://pith.science/pith/R5AUE6GKBJMWOWTKRGJRZVQWNU/bundle.json","state":"https://pith.science/pith/R5AUE6GKBJMWOWTKRGJRZVQWNU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/R5AUE6GKBJMWOWTKRGJRZVQWNU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:R5AUE6GKBJMWOWTKRGJRZVQWNU","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":"1d5420412291237015f5207b8ef1c8014f4923bda9c85e181a3bd6b033aeecf5","cross_cats_sorted":["cs.AI","eess.SP"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-16T16:05:33Z","title_canon_sha256":"cc793ced37bc472d4afeb46b6bb1e7abf4458b366b6a60dbe32fbd38f3c24d8e"},"schema_version":"1.0","source":{"id":"2405.10216","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.10216","created_at":"2026-07-05T09:24:34Z"},{"alias_kind":"arxiv_version","alias_value":"2405.10216v1","created_at":"2026-07-05T09:24:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.10216","created_at":"2026-07-05T09:24:34Z"},{"alias_kind":"pith_short_12","alias_value":"R5AUE6GKBJMW","created_at":"2026-07-05T09:24:34Z"},{"alias_kind":"pith_short_16","alias_value":"R5AUE6GKBJMWOWTK","created_at":"2026-07-05T09:24:34Z"},{"alias_kind":"pith_short_8","alias_value":"R5AUE6GK","created_at":"2026-07-05T09:24:34Z"}],"graph_snapshots":[{"event_id":"sha256:6cea8ba43d63289575b7dd9251d5a3965053378e4fcc0bfb747d6832bc9fca26","target":"graph","created_at":"2026-07-05T09:24:34Z","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/2405.10216/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Low-Rank Adaptation (LoRA) is a widely used technique for fine-tuning large pre-trained or foundational models across different modalities and tasks. However, its application to time series data, particularly within foundational models, remains underexplored. This paper examines the impact of LoRA on contemporary time series foundational models: Lag-Llama, MOIRAI, and Chronos. We demonstrate LoRA's fine-tuning potential for forecasting the vital signs of sepsis patients in intensive care units (ICUs), emphasizing the models' adaptability to previously unseen, out-of-domain modalities. Integrat","authors_text":"Anubhav Bhatti, Bingjie Shen, Chen Dan, Divij Gupta, San Lee, Suraj Parmar, Yuwei Liu","cross_cats":["cs.AI","eess.SP"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-16T16:05:33Z","title":"Low-Rank Adaptation of Time Series Foundational Models for Out-of-Domain Modality Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.10216","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:c8f5729d480ea53768256db2e90cad0f6a15123b457609574e564404a44d8496","target":"record","created_at":"2026-07-05T09:24:34Z","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":"1d5420412291237015f5207b8ef1c8014f4923bda9c85e181a3bd6b033aeecf5","cross_cats_sorted":["cs.AI","eess.SP"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-16T16:05:33Z","title_canon_sha256":"cc793ced37bc472d4afeb46b6bb1e7abf4458b366b6a60dbe32fbd38f3c24d8e"},"schema_version":"1.0","source":{"id":"2405.10216","kind":"arxiv","version":1}},"canonical_sha256":"8f414278ca0a59675a6a89931cd6166d3a66cdeecb8d46db613a4034821ce293","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8f414278ca0a59675a6a89931cd6166d3a66cdeecb8d46db613a4034821ce293","first_computed_at":"2026-07-05T09:24:34.995004Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:24:34.995004Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7Bwo8aqtimDAKMzzcHWyHJlQiyOcihAJElpWBIixtja3CJYxKxhRzKk8jQAfAjd9jIj1zIV0Ss3ghUpFfCXABw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:24:34.995546Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.10216","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c8f5729d480ea53768256db2e90cad0f6a15123b457609574e564404a44d8496","sha256:6cea8ba43d63289575b7dd9251d5a3965053378e4fcc0bfb747d6832bc9fca26"],"state_sha256":"33619d00a9084ebc61976d0e9ce87e1e389d2f3b0276e9525869dce502f4dfe7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UdSBtUziaz02L+kzef1J8WdfG/WcYei7J8SOvcpMi+B9odb2b44/Z9xQk8tHyuUHTxyljj/uQ3A8EdUyEu6LCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T08:51:37.674514Z","bundle_sha256":"f5270edda02ec35be6ff5b17fd6d4b388cbdfd57745c05336a768ff09ca469be"}}