{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:VCP2PEULXI5Z55MGNLFRM53ASL","short_pith_number":"pith:VCP2PEUL","schema_version":"1.0","canonical_sha256":"a89fa7928bba3b9ef5866acb16776092c810aba5b40c11f3d158cec7d3b9a316","source":{"kind":"arxiv","id":"2507.09514","version":1},"attestation_state":"computed","paper":{"title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Diana Marculescu, Hung-Yueh Chiang, Kai-Chiang Wu, Tien-Yu Chi","submitted_at":"2025-07-13T06:49:32Z","abstract_excerpt":"State space models (SSMs) reduce the quadratic complexity of transformers by leveraging linear recurrence. Recently, VMamba has emerged as a strong SSM-based vision backbone, yet remains bottlenecked by spatial redundancy in its four-directional scan. We propose QuarterMap, a post-training activation pruning method that removes redundant spatial activations before scanning and restores dimensions via nearest-neighbor upsampling. Our method improves throughput without retraining. On ImageNet-1K, QuarterMap achieves up to 11% speedup on VMamba with less than 0.9% accuracy drop, and yields simila"},"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":"2507.09514","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-13T06:49:32Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"142e66e145ed245330573dce54508f8e14d1b4d985fa3530da4f67a1705281b6","abstract_canon_sha256":"4a8a3c660ed5793c97e660fe2e75e70ef7be46f88367787623c93603c639dbf2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:36:10.314393Z","signature_b64":"w/RfoQdu2kYBJyp1wxkZLTiNSdI1jkeyEu0hdodpmn9GcDHUsOMutRsK5xsGXLjIkn6o/Z2Hkzk/d6wjxPU4CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a89fa7928bba3b9ef5866acb16776092c810aba5b40c11f3d158cec7d3b9a316","last_reissued_at":"2026-07-05T11:36:10.313900Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:36:10.313900Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Diana Marculescu, Hung-Yueh Chiang, Kai-Chiang Wu, Tien-Yu Chi","submitted_at":"2025-07-13T06:49:32Z","abstract_excerpt":"State space models (SSMs) reduce the quadratic complexity of transformers by leveraging linear recurrence. Recently, VMamba has emerged as a strong SSM-based vision backbone, yet remains bottlenecked by spatial redundancy in its four-directional scan. We propose QuarterMap, a post-training activation pruning method that removes redundant spatial activations before scanning and restores dimensions via nearest-neighbor upsampling. Our method improves throughput without retraining. On ImageNet-1K, QuarterMap achieves up to 11% speedup on VMamba with less than 0.9% accuracy drop, and yields simila"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.09514","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/2507.09514/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2507.09514","created_at":"2026-07-05T11:36:10.313961+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.09514v1","created_at":"2026-07-05T11:36:10.313961+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.09514","created_at":"2026-07-05T11:36:10.313961+00:00"},{"alias_kind":"pith_short_12","alias_value":"VCP2PEULXI5Z","created_at":"2026-07-05T11:36:10.313961+00:00"},{"alias_kind":"pith_short_16","alias_value":"VCP2PEULXI5Z55MG","created_at":"2026-07-05T11:36:10.313961+00:00"},{"alias_kind":"pith_short_8","alias_value":"VCP2PEUL","created_at":"2026-07-05T11:36:10.313961+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.19932","citing_title":"Spatial-Aware Reduction Framework: Towards Efficient and Faithful Visual State Space Models","ref_index":93,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VCP2PEULXI5Z55MGNLFRM53ASL","json":"https://pith.science/pith/VCP2PEULXI5Z55MGNLFRM53ASL.json","graph_json":"https://pith.science/api/pith-number/VCP2PEULXI5Z55MGNLFRM53ASL/graph.json","events_json":"https://pith.science/api/pith-number/VCP2PEULXI5Z55MGNLFRM53ASL/events.json","paper":"https://pith.science/paper/VCP2PEUL"},"agent_actions":{"view_html":"https://pith.science/pith/VCP2PEULXI5Z55MGNLFRM53ASL","download_json":"https://pith.science/pith/VCP2PEULXI5Z55MGNLFRM53ASL.json","view_paper":"https://pith.science/paper/VCP2PEUL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.09514&json=true","fetch_graph":"https://pith.science/api/pith-number/VCP2PEULXI5Z55MGNLFRM53ASL/graph.json","fetch_events":"https://pith.science/api/pith-number/VCP2PEULXI5Z55MGNLFRM53ASL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VCP2PEULXI5Z55MGNLFRM53ASL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VCP2PEULXI5Z55MGNLFRM53ASL/action/storage_attestation","attest_author":"https://pith.science/pith/VCP2PEULXI5Z55MGNLFRM53ASL/action/author_attestation","sign_citation":"https://pith.science/pith/VCP2PEULXI5Z55MGNLFRM53ASL/action/citation_signature","submit_replication":"https://pith.science/pith/VCP2PEULXI5Z55MGNLFRM53ASL/action/replication_record"}},"created_at":"2026-07-05T11:36:10.313961+00:00","updated_at":"2026-07-05T11:36:10.313961+00:00"}