{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:CA2MS7I437KBZXM2EHEI2LE2OQ","short_pith_number":"pith:CA2MS7I4","canonical_record":{"source":{"id":"2504.12436","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-04-16T19:10:34Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b0d206dbbb67257e874f4139c6cae05dcc5a0a7ce428b0254c68916915814ef0","abstract_canon_sha256":"a190d622707635c7066693a71a5a63745e8acf01be756339734a988527c296f6"},"schema_version":"1.0"},"canonical_sha256":"1034c97d1cdfd41cdd9a21c88d2c9a7426681da9f77ef4e343bf8067fb4e3662","source":{"kind":"arxiv","id":"2504.12436","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.12436","created_at":"2026-07-05T11:52:01Z"},{"alias_kind":"arxiv_version","alias_value":"2504.12436v2","created_at":"2026-07-05T11:52:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.12436","created_at":"2026-07-05T11:52:01Z"},{"alias_kind":"pith_short_12","alias_value":"CA2MS7I437KB","created_at":"2026-07-05T11:52:01Z"},{"alias_kind":"pith_short_16","alias_value":"CA2MS7I437KBZXM2","created_at":"2026-07-05T11:52:01Z"},{"alias_kind":"pith_short_8","alias_value":"CA2MS7I4","created_at":"2026-07-05T11:52:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:CA2MS7I437KBZXM2EHEI2LE2OQ","target":"record","payload":{"canonical_record":{"source":{"id":"2504.12436","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-04-16T19:10:34Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b0d206dbbb67257e874f4139c6cae05dcc5a0a7ce428b0254c68916915814ef0","abstract_canon_sha256":"a190d622707635c7066693a71a5a63745e8acf01be756339734a988527c296f6"},"schema_version":"1.0"},"canonical_sha256":"1034c97d1cdfd41cdd9a21c88d2c9a7426681da9f77ef4e343bf8067fb4e3662","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:52:01.950134Z","signature_b64":"1vzVt87OqW9uxC2gT+YYkln2DhZt5UGHDkXd3Y+hse2XWdPMsyne46cHz9tPhM43EGdJzltVgC+7VC1vGJmJDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1034c97d1cdfd41cdd9a21c88d2c9a7426681da9f77ef4e343bf8067fb4e3662","last_reissued_at":"2026-07-05T11:52:01.949685Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:52:01.949685Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.12436","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":"2026-07-05T11:52:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Wf7+5az1ANCakZS/IEGo6/qCOu8Vdrll2c+pSQKgM05SZUwGIwWPEuLiF10uplqWXofarJPcaM8CYGVew0ZbBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T07:29:56.774776Z"},"content_sha256":"4104a0054b30775aed94259ac85508be6b1a990012817e08ae06781dafd3115f","schema_version":"1.0","event_id":"sha256:4104a0054b30775aed94259ac85508be6b1a990012817e08ae06781dafd3115f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:CA2MS7I437KBZXM2EHEI2LE2OQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"\\'Eric Granger, Ismail Ben Ayed, Nairouz Mrabah, Nicolas Richet","submitted_at":"2025-04-16T19:10:34Z","abstract_excerpt":"Adapting Vision-Language Models (VLMs) to new domains with few labeled samples remains a significant challenge due to severe overfitting and computational constraints. State-of-the-art solutions, such as low-rank reparameterization, mitigate these issues but often struggle with generalization and require extensive hyperparameter tuning. In this paper, a novel Sparse Optimization (SO) framework is proposed. Unlike low-rank approaches that typically constrain updates to a fixed subspace, our SO method leverages high sparsity to dynamically adjust very few parameters. We introduce two key paradig"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.12436","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/2504.12436/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":"2026-07-05T11:52:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uRF3tWrXhtAbihmyHP6Ltw3T8+KMQes1dPtfRx5v3jnU6q9RfDc5tCL3/R8xlWcNGwHNWQ4zUGK5KUqxt4r8DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T07:29:56.775532Z"},"content_sha256":"8745c4d388328877d612874e382f9ae3cc9b483621563d5534a457853777d59d","schema_version":"1.0","event_id":"sha256:8745c4d388328877d612874e382f9ae3cc9b483621563d5534a457853777d59d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CA2MS7I437KBZXM2EHEI2LE2OQ/bundle.json","state_url":"https://pith.science/pith/CA2MS7I437KBZXM2EHEI2LE2OQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CA2MS7I437KBZXM2EHEI2LE2OQ/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":"2026-08-17T07:29:56Z","links":{"resolver":"https://pith.science/pith/CA2MS7I437KBZXM2EHEI2LE2OQ","bundle":"https://pith.science/pith/CA2MS7I437KBZXM2EHEI2LE2OQ/bundle.json","state":"https://pith.science/pith/CA2MS7I437KBZXM2EHEI2LE2OQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CA2MS7I437KBZXM2EHEI2LE2OQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:CA2MS7I437KBZXM2EHEI2LE2OQ","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":"a190d622707635c7066693a71a5a63745e8acf01be756339734a988527c296f6","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-04-16T19:10:34Z","title_canon_sha256":"b0d206dbbb67257e874f4139c6cae05dcc5a0a7ce428b0254c68916915814ef0"},"schema_version":"1.0","source":{"id":"2504.12436","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.12436","created_at":"2026-07-05T11:52:01Z"},{"alias_kind":"arxiv_version","alias_value":"2504.12436v2","created_at":"2026-07-05T11:52:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.12436","created_at":"2026-07-05T11:52:01Z"},{"alias_kind":"pith_short_12","alias_value":"CA2MS7I437KB","created_at":"2026-07-05T11:52:01Z"},{"alias_kind":"pith_short_16","alias_value":"CA2MS7I437KBZXM2","created_at":"2026-07-05T11:52:01Z"},{"alias_kind":"pith_short_8","alias_value":"CA2MS7I4","created_at":"2026-07-05T11:52:01Z"}],"graph_snapshots":[{"event_id":"sha256:8745c4d388328877d612874e382f9ae3cc9b483621563d5534a457853777d59d","target":"graph","created_at":"2026-07-05T11:52:01Z","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/2504.12436/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Adapting Vision-Language Models (VLMs) to new domains with few labeled samples remains a significant challenge due to severe overfitting and computational constraints. State-of-the-art solutions, such as low-rank reparameterization, mitigate these issues but often struggle with generalization and require extensive hyperparameter tuning. In this paper, a novel Sparse Optimization (SO) framework is proposed. Unlike low-rank approaches that typically constrain updates to a fixed subspace, our SO method leverages high sparsity to dynamically adjust very few parameters. We introduce two key paradig","authors_text":"\\'Eric Granger, Ismail Ben Ayed, Nairouz Mrabah, Nicolas Richet","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-04-16T19:10:34Z","title":"Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.12436","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":"sha256:4104a0054b30775aed94259ac85508be6b1a990012817e08ae06781dafd3115f","target":"record","created_at":"2026-07-05T11:52:01Z","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":"a190d622707635c7066693a71a5a63745e8acf01be756339734a988527c296f6","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-04-16T19:10:34Z","title_canon_sha256":"b0d206dbbb67257e874f4139c6cae05dcc5a0a7ce428b0254c68916915814ef0"},"schema_version":"1.0","source":{"id":"2504.12436","kind":"arxiv","version":2}},"canonical_sha256":"1034c97d1cdfd41cdd9a21c88d2c9a7426681da9f77ef4e343bf8067fb4e3662","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1034c97d1cdfd41cdd9a21c88d2c9a7426681da9f77ef4e343bf8067fb4e3662","first_computed_at":"2026-07-05T11:52:01.949685Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:52:01.949685Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1vzVt87OqW9uxC2gT+YYkln2DhZt5UGHDkXd3Y+hse2XWdPMsyne46cHz9tPhM43EGdJzltVgC+7VC1vGJmJDw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:52:01.950134Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.12436","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4104a0054b30775aed94259ac85508be6b1a990012817e08ae06781dafd3115f","sha256:8745c4d388328877d612874e382f9ae3cc9b483621563d5534a457853777d59d"],"state_sha256":"5296a3b46d88ef7aab1aeadcd5d1f72ecaf1aa85125d7a764296b5f3c0f5815c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"icT3uxZD7z+CKzJdlgTcQuf8SHr0MK+p620vI+PFFjpDwKJ9jycHl1adBGgENbTP108ZNWGW/KqckQbD2vZDAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T07:29:56.782800Z","bundle_sha256":"8732614be4c2a3a81faaff55b240545c9a62cf07794b127199d6230d1b14a242"}}