{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:3I4BQTQPLDOKN6F3CR42DA2JRA","short_pith_number":"pith:3I4BQTQP","schema_version":"1.0","canonical_sha256":"da38184e0f58dca6f8bb1479a18349880c37e8bc8af9e23210e83c4ae6f9f9a5","source":{"kind":"arxiv","id":"2606.19932","version":1},"attestation_state":"computed","paper":{"title":"Spatial-Aware Reduction Framework: Towards Efficient and Faithful Visual State Space Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Aoyu Li, Jiancheng Lv, Jindi Lv, Qing Ye, Wentao Feng, Xiaofeng Wang, Yueqi Duan, Yuhao Zhou, Zheng Zhu","submitted_at":"2026-06-18T08:31:11Z","abstract_excerpt":"Mamba demonstrates strong efficiency in modeling long visual sequences. However, when token reduction is applied to structurally enhanced Mamba variants, these models exhibit a severe performance collapse. We attribute this degradation to the spatially agnostic nature of existing reduction methods, which violate the two-dimensional structural premise required by the selective scanning mechanism. In this work, we propose STORM, a spatial-aware token reduction framework designed to maintain structural integrity throughout the compression process. STORM reformulates reduction into a structured op"},"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":"2606.19932","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-18T08:31:11Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"072922da6c76ddc17804420acc202ea1001231061c3a15afa7dc2244a893689e","abstract_canon_sha256":"2918e72cf6033d60aed58ad0da375d8e16751ff1ca2850d86e7a4fcb4fdf91d9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:12:39.123934Z","signature_b64":"Q+qUbH1VMt/yiicxAzn2BP16ngHWw1x5X+aHRjO8UpRSV76mSqKxo2RyCBeJwK4d3j0zDnbqFswRCB/gBQmQCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"da38184e0f58dca6f8bb1479a18349880c37e8bc8af9e23210e83c4ae6f9f9a5","last_reissued_at":"2026-06-19T16:12:39.123529Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:12:39.123529Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Spatial-Aware Reduction Framework: Towards Efficient and Faithful Visual State Space Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Aoyu Li, Jiancheng Lv, Jindi Lv, Qing Ye, Wentao Feng, Xiaofeng Wang, Yueqi Duan, Yuhao Zhou, Zheng Zhu","submitted_at":"2026-06-18T08:31:11Z","abstract_excerpt":"Mamba demonstrates strong efficiency in modeling long visual sequences. However, when token reduction is applied to structurally enhanced Mamba variants, these models exhibit a severe performance collapse. We attribute this degradation to the spatially agnostic nature of existing reduction methods, which violate the two-dimensional structural premise required by the selective scanning mechanism. In this work, we propose STORM, a spatial-aware token reduction framework designed to maintain structural integrity throughout the compression process. STORM reformulates reduction into a structured op"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.19932","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/2606.19932/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":"2606.19932","created_at":"2026-06-19T16:12:39.123590+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.19932v1","created_at":"2026-06-19T16:12:39.123590+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.19932","created_at":"2026-06-19T16:12:39.123590+00:00"},{"alias_kind":"pith_short_12","alias_value":"3I4BQTQPLDOK","created_at":"2026-06-19T16:12:39.123590+00:00"},{"alias_kind":"pith_short_16","alias_value":"3I4BQTQPLDOKN6F3","created_at":"2026-06-19T16:12:39.123590+00:00"},{"alias_kind":"pith_short_8","alias_value":"3I4BQTQP","created_at":"2026-06-19T16:12:39.123590+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/3I4BQTQPLDOKN6F3CR42DA2JRA","json":"https://pith.science/pith/3I4BQTQPLDOKN6F3CR42DA2JRA.json","graph_json":"https://pith.science/api/pith-number/3I4BQTQPLDOKN6F3CR42DA2JRA/graph.json","events_json":"https://pith.science/api/pith-number/3I4BQTQPLDOKN6F3CR42DA2JRA/events.json","paper":"https://pith.science/paper/3I4BQTQP"},"agent_actions":{"view_html":"https://pith.science/pith/3I4BQTQPLDOKN6F3CR42DA2JRA","download_json":"https://pith.science/pith/3I4BQTQPLDOKN6F3CR42DA2JRA.json","view_paper":"https://pith.science/paper/3I4BQTQP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.19932&json=true","fetch_graph":"https://pith.science/api/pith-number/3I4BQTQPLDOKN6F3CR42DA2JRA/graph.json","fetch_events":"https://pith.science/api/pith-number/3I4BQTQPLDOKN6F3CR42DA2JRA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3I4BQTQPLDOKN6F3CR42DA2JRA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3I4BQTQPLDOKN6F3CR42DA2JRA/action/storage_attestation","attest_author":"https://pith.science/pith/3I4BQTQPLDOKN6F3CR42DA2JRA/action/author_attestation","sign_citation":"https://pith.science/pith/3I4BQTQPLDOKN6F3CR42DA2JRA/action/citation_signature","submit_replication":"https://pith.science/pith/3I4BQTQPLDOKN6F3CR42DA2JRA/action/replication_record"}},"created_at":"2026-06-19T16:12:39.123590+00:00","updated_at":"2026-06-19T16:12:39.123590+00:00"}