{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:PQDX4TSIBGRQYS5JEC7IR5MAYG","short_pith_number":"pith:PQDX4TSI","canonical_record":{"source":{"id":"2509.01297","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-09-01T09:33:52Z","cross_cats_sorted":[],"title_canon_sha256":"63f0565151bd7dc2d89a0b422fc38f1c9a9a446b32df9db09601d7e6950ea876","abstract_canon_sha256":"059567b4740b7bddd2c0649fe5527319513f1cbd3c1f31852d3880b1b9c9b63a"},"schema_version":"1.0"},"canonical_sha256":"7c077e4e4809a30c4ba920be88f580c19b90e71d8a77169fca5094047f6d0b26","source":{"kind":"arxiv","id":"2509.01297","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.01297","created_at":"2026-07-05T12:02:57Z"},{"alias_kind":"arxiv_version","alias_value":"2509.01297v1","created_at":"2026-07-05T12:02:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.01297","created_at":"2026-07-05T12:02:57Z"},{"alias_kind":"pith_short_12","alias_value":"PQDX4TSIBGRQ","created_at":"2026-07-05T12:02:57Z"},{"alias_kind":"pith_short_16","alias_value":"PQDX4TSIBGRQYS5J","created_at":"2026-07-05T12:02:57Z"},{"alias_kind":"pith_short_8","alias_value":"PQDX4TSI","created_at":"2026-07-05T12:02:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:PQDX4TSIBGRQYS5JEC7IR5MAYG","target":"record","payload":{"canonical_record":{"source":{"id":"2509.01297","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-09-01T09:33:52Z","cross_cats_sorted":[],"title_canon_sha256":"63f0565151bd7dc2d89a0b422fc38f1c9a9a446b32df9db09601d7e6950ea876","abstract_canon_sha256":"059567b4740b7bddd2c0649fe5527319513f1cbd3c1f31852d3880b1b9c9b63a"},"schema_version":"1.0"},"canonical_sha256":"7c077e4e4809a30c4ba920be88f580c19b90e71d8a77169fca5094047f6d0b26","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:02:57.821954Z","signature_b64":"SloODupCOm12+9Dh9K7CCeTvRdl7ySba80+a6KgFV3qe4/CAxA4BJEzT1kRS8mMxRIJR0kcIjU/pIEZBlv31AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7c077e4e4809a30c4ba920be88f580c19b90e71d8a77169fca5094047f6d0b26","last_reissued_at":"2026-07-05T12:02:57.821466Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:02:57.821466Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2509.01297","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":"2026-07-05T12:02:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+HMblTuURXx+1e3cQ2r+HNA9m3BcRqZGSzYFKyZnunpmuukhmjXv1ZdU706bhUOj93LgbvHfffRoWdNkJNHTAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T01:03:42.504930Z"},"content_sha256":"1ec8e44ea21b556d6f02e5b4d63973fd1fc2a26ba7e0cc9f97f79ae47af9f497","schema_version":"1.0","event_id":"sha256:1ec8e44ea21b556d6f02e5b4d63973fd1fc2a26ba7e0cc9f97f79ae47af9f497"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:PQDX4TSIBGRQYS5JEC7IR5MAYG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Disentangled Multi-Context Meta-Learning: Unlocking robust and Generalized Task Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Jun-Gill Kang, Seongil Hong, Seonsoo Kim, Taehong Kim","submitted_at":"2025-09-01T09:33:52Z","abstract_excerpt":"In meta-learning and its downstream tasks, many methods rely on implicit adaptation to task variations, where multiple factors are mixed together in a single entangled representation. This makes it difficult to interpret which factors drive performance and can hinder generalization. In this work, we introduce a disentangled multi-context meta-learning framework that explicitly assigns each task factor to a distinct context vector. By decoupling these variations, our approach improves robustness through deeper task understanding and enhances generalization by enabling context vector sharing acr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.01297","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/2509.01297/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":"2026-07-05T12:02:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dPmr80Z3AVaQG5LE+NZpe1VHs57Kg5uIOxUXAXKoep2FGG6YzTXnxriNv0U8npt/MK+pyZduMNQB57dXlHAUCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T01:03:42.506104Z"},"content_sha256":"9a4219193ee92736161dec5cb0eeec419b52294812ee336f0bcb481537453f6c","schema_version":"1.0","event_id":"sha256:9a4219193ee92736161dec5cb0eeec419b52294812ee336f0bcb481537453f6c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PQDX4TSIBGRQYS5JEC7IR5MAYG/bundle.json","state_url":"https://pith.science/pith/PQDX4TSIBGRQYS5JEC7IR5MAYG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PQDX4TSIBGRQYS5JEC7IR5MAYG/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":"2026-08-06T01:03:42Z","links":{"resolver":"https://pith.science/pith/PQDX4TSIBGRQYS5JEC7IR5MAYG","bundle":"https://pith.science/pith/PQDX4TSIBGRQYS5JEC7IR5MAYG/bundle.json","state":"https://pith.science/pith/PQDX4TSIBGRQYS5JEC7IR5MAYG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PQDX4TSIBGRQYS5JEC7IR5MAYG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:PQDX4TSIBGRQYS5JEC7IR5MAYG","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":"059567b4740b7bddd2c0649fe5527319513f1cbd3c1f31852d3880b1b9c9b63a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-09-01T09:33:52Z","title_canon_sha256":"63f0565151bd7dc2d89a0b422fc38f1c9a9a446b32df9db09601d7e6950ea876"},"schema_version":"1.0","source":{"id":"2509.01297","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.01297","created_at":"2026-07-05T12:02:57Z"},{"alias_kind":"arxiv_version","alias_value":"2509.01297v1","created_at":"2026-07-05T12:02:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.01297","created_at":"2026-07-05T12:02:57Z"},{"alias_kind":"pith_short_12","alias_value":"PQDX4TSIBGRQ","created_at":"2026-07-05T12:02:57Z"},{"alias_kind":"pith_short_16","alias_value":"PQDX4TSIBGRQYS5J","created_at":"2026-07-05T12:02:57Z"},{"alias_kind":"pith_short_8","alias_value":"PQDX4TSI","created_at":"2026-07-05T12:02:57Z"}],"graph_snapshots":[{"event_id":"sha256:9a4219193ee92736161dec5cb0eeec419b52294812ee336f0bcb481537453f6c","target":"graph","created_at":"2026-07-05T12:02:57Z","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/2509.01297/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In meta-learning and its downstream tasks, many methods rely on implicit adaptation to task variations, where multiple factors are mixed together in a single entangled representation. This makes it difficult to interpret which factors drive performance and can hinder generalization. In this work, we introduce a disentangled multi-context meta-learning framework that explicitly assigns each task factor to a distinct context vector. By decoupling these variations, our approach improves robustness through deeper task understanding and enhances generalization by enabling context vector sharing acr","authors_text":"Jun-Gill Kang, Seongil Hong, Seonsoo Kim, Taehong Kim","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-09-01T09:33:52Z","title":"Disentangled Multi-Context Meta-Learning: Unlocking robust and Generalized Task Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.01297","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":"sha256:1ec8e44ea21b556d6f02e5b4d63973fd1fc2a26ba7e0cc9f97f79ae47af9f497","target":"record","created_at":"2026-07-05T12:02:57Z","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":"059567b4740b7bddd2c0649fe5527319513f1cbd3c1f31852d3880b1b9c9b63a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-09-01T09:33:52Z","title_canon_sha256":"63f0565151bd7dc2d89a0b422fc38f1c9a9a446b32df9db09601d7e6950ea876"},"schema_version":"1.0","source":{"id":"2509.01297","kind":"arxiv","version":1}},"canonical_sha256":"7c077e4e4809a30c4ba920be88f580c19b90e71d8a77169fca5094047f6d0b26","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7c077e4e4809a30c4ba920be88f580c19b90e71d8a77169fca5094047f6d0b26","first_computed_at":"2026-07-05T12:02:57.821466Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:02:57.821466Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SloODupCOm12+9Dh9K7CCeTvRdl7ySba80+a6KgFV3qe4/CAxA4BJEzT1kRS8mMxRIJR0kcIjU/pIEZBlv31AA==","signature_status":"signed_v1","signed_at":"2026-07-05T12:02:57.821954Z","signed_message":"canonical_sha256_bytes"},"source_id":"2509.01297","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1ec8e44ea21b556d6f02e5b4d63973fd1fc2a26ba7e0cc9f97f79ae47af9f497","sha256:9a4219193ee92736161dec5cb0eeec419b52294812ee336f0bcb481537453f6c"],"state_sha256":"b2d18a00358f7ebc7b98e6cc6531eda6733c5135be2f187f54bba108d68fe6c7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Sm330AgkHBy2zrGVKS4BC/5w+rlAO8AxMYjVgcUt3SQUCgErxlD5XVnOxjOAJexU7Atj2JNXIgEIEIAgThELCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T01:03:42.512061Z","bundle_sha256":"786a77852d97650a0d749f903d881d3bce09105f446d72e46508c0422a91a58f"}}