{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:7Z2RJ5YMVHEK4YZW4LIO6WSW3A","short_pith_number":"pith:7Z2RJ5YM","schema_version":"1.0","canonical_sha256":"fe7514f70ca9c8ae6336e2d0ef5a56d81282dacd92714acd0ec88903df0a897f","source":{"kind":"arxiv","id":"2605.17633","version":1},"attestation_state":"computed","paper":{"title":"SparseSAM: Structured Sparsification of Activations in Segment Anything Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Chi H. Nguyen, Duy M. H. Nguyen, Fan Lai, Hoai-Chau Tran, Khoa D. Doan, Mathias Niepert","submitted_at":"2026-05-17T19:54:22Z","abstract_excerpt":"The Segment Anything Model (SAM) achieves strong open-vocabulary segmentation, but its ViT-based image encoders dominate inference latency and memory. Existing activation compression methods, such as token merging, reduce the token length to process, yet introduce non-trivial runtime overhead and encounter catastrophic quality drop under high compression. Other methods applying Sparse Attention focus on attention alone, leaving the MLP fully dense and capping achievable speedup. We propose SparseSAM, a (i) training-free structured sparsification framework that jointly accelerates attention and"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.17633","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-17T19:54:22Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"369692ef9786c4195919ae456bf0d6b1a5bb05e56b05a9d05e43ccd488720fe8","abstract_canon_sha256":"9849d092cf9c826082e36c376a4e12f9529a88f401249fd61e89c30ce8779faf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:04:49.641922Z","signature_b64":"lyUjAcOwDZaHNtfoVs9C9YlitSbQUI3j+j8mN4xur6KLlx/8O+nOd2O9a2F3NI7bJPNXhusL2TEfgUejAN7wBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe7514f70ca9c8ae6336e2d0ef5a56d81282dacd92714acd0ec88903df0a897f","last_reissued_at":"2026-05-20T00:04:49.641186Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:04:49.641186Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SparseSAM: Structured Sparsification of Activations in Segment Anything Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Chi H. Nguyen, Duy M. H. Nguyen, Fan Lai, Hoai-Chau Tran, Khoa D. Doan, Mathias Niepert","submitted_at":"2026-05-17T19:54:22Z","abstract_excerpt":"The Segment Anything Model (SAM) achieves strong open-vocabulary segmentation, but its ViT-based image encoders dominate inference latency and memory. Existing activation compression methods, such as token merging, reduce the token length to process, yet introduce non-trivial runtime overhead and encounter catastrophic quality drop under high compression. Other methods applying Sparse Attention focus on attention alone, leaving the MLP fully dense and capping achievable speedup. We propose SparseSAM, a (i) training-free structured sparsification framework that jointly accelerates attention and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.17633","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/2605.17633/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"cited_work_retraction","ran_at":"2026-05-19T22:52:43.665350Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T21:33:23.557456Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T21:21:57.480906Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"5b9e7163cc8883cf6e1e8c4623ebf99f6f6a4ef48555d25085930f77b1e81e0a"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2605.17633","created_at":"2026-05-20T00:04:49.641337+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.17633v1","created_at":"2026-05-20T00:04:49.641337+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17633","created_at":"2026-05-20T00:04:49.641337+00:00"},{"alias_kind":"pith_short_12","alias_value":"7Z2RJ5YMVHEK","created_at":"2026-05-20T00:04:49.641337+00:00"},{"alias_kind":"pith_short_16","alias_value":"7Z2RJ5YMVHEK4YZW","created_at":"2026-05-20T00:04:49.641337+00:00"},{"alias_kind":"pith_short_8","alias_value":"7Z2RJ5YM","created_at":"2026-05-20T00:04:49.641337+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7Z2RJ5YMVHEK4YZW4LIO6WSW3A","json":"https://pith.science/pith/7Z2RJ5YMVHEK4YZW4LIO6WSW3A.json","graph_json":"https://pith.science/api/pith-number/7Z2RJ5YMVHEK4YZW4LIO6WSW3A/graph.json","events_json":"https://pith.science/api/pith-number/7Z2RJ5YMVHEK4YZW4LIO6WSW3A/events.json","paper":"https://pith.science/paper/7Z2RJ5YM"},"agent_actions":{"view_html":"https://pith.science/pith/7Z2RJ5YMVHEK4YZW4LIO6WSW3A","download_json":"https://pith.science/pith/7Z2RJ5YMVHEK4YZW4LIO6WSW3A.json","view_paper":"https://pith.science/paper/7Z2RJ5YM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.17633&json=true","fetch_graph":"https://pith.science/api/pith-number/7Z2RJ5YMVHEK4YZW4LIO6WSW3A/graph.json","fetch_events":"https://pith.science/api/pith-number/7Z2RJ5YMVHEK4YZW4LIO6WSW3A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7Z2RJ5YMVHEK4YZW4LIO6WSW3A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7Z2RJ5YMVHEK4YZW4LIO6WSW3A/action/storage_attestation","attest_author":"https://pith.science/pith/7Z2RJ5YMVHEK4YZW4LIO6WSW3A/action/author_attestation","sign_citation":"https://pith.science/pith/7Z2RJ5YMVHEK4YZW4LIO6WSW3A/action/citation_signature","submit_replication":"https://pith.science/pith/7Z2RJ5YMVHEK4YZW4LIO6WSW3A/action/replication_record"}},"created_at":"2026-05-20T00:04:49.641337+00:00","updated_at":"2026-05-20T00:04:49.641337+00:00"}