{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:WCTY47T7UX26ZZ45BBF4LKFILP","short_pith_number":"pith:WCTY47T7","schema_version":"1.0","canonical_sha256":"b0a78e7e7fa5f5ece79d084bc5a8a85bc4f2f1267cf02242e4567d00e4cfb345","source":{"kind":"arxiv","id":"2605.25892","version":1},"attestation_state":"computed","paper":{"title":"SP-MoMamba: Superpixel-driven Mixture of State Space Experts for Efficient Image Super-Resolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guanbin Li, Huiping Zhuang, Jinshan Pan, Lap-Pui Chau, Liang Chen, Wenbin Zou, Yawen Cui, Yi Wang","submitted_at":"2026-05-25T14:19:59Z","abstract_excerpt":"State space models (SSMs) have emerged as a powerful paradigm for efficient single-image super-resolution (SR) due to their linear complexity and long-range modeling capabilities. However, existing Mamba-based methods typically rely on data-agnostic rigid scanning, which reshapes 2D images into 1D sequences over a fixed grid, inevitably disrupting spatial-semantic topology and introducing artifacts. Inspired by the \\textbf{Gestalt perceptual grouping theory}, we propose \\textbf{SP-MoMamba}, a superpixel-driven mixture of state space experts designed for content-aware SR. Our core idea is to tr"},"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":"2605.25892","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-25T14:19:59Z","cross_cats_sorted":[],"title_canon_sha256":"089fb3aee33972532da06d4f03649d755ed022a043e70282b88db9549dbecb0c","abstract_canon_sha256":"a19ace8d8efe0ab162133a21458768f73b9510ab5b6656c65605e123fac04b4b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T02:05:17.193310Z","signature_b64":"CS5x5q35goGpGS7gu0Jrebd5ELM9lIzYOAQFy0cF9tOfmYAQ+kvXoSdS86HlHrd9OhYeiiv5IuLzKoRI5TvrDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b0a78e7e7fa5f5ece79d084bc5a8a85bc4f2f1267cf02242e4567d00e4cfb345","last_reissued_at":"2026-05-26T02:05:17.192722Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T02:05:17.192722Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SP-MoMamba: Superpixel-driven Mixture of State Space Experts for Efficient Image Super-Resolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guanbin Li, Huiping Zhuang, Jinshan Pan, Lap-Pui Chau, Liang Chen, Wenbin Zou, Yawen Cui, Yi Wang","submitted_at":"2026-05-25T14:19:59Z","abstract_excerpt":"State space models (SSMs) have emerged as a powerful paradigm for efficient single-image super-resolution (SR) due to their linear complexity and long-range modeling capabilities. However, existing Mamba-based methods typically rely on data-agnostic rigid scanning, which reshapes 2D images into 1D sequences over a fixed grid, inevitably disrupting spatial-semantic topology and introducing artifacts. Inspired by the \\textbf{Gestalt perceptual grouping theory}, we propose \\textbf{SP-MoMamba}, a superpixel-driven mixture of state space experts designed for content-aware SR. Our core idea is to tr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.25892","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/2605.25892/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":"2605.25892","created_at":"2026-05-26T02:05:17.192794+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.25892v1","created_at":"2026-05-26T02:05:17.192794+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.25892","created_at":"2026-05-26T02:05:17.192794+00:00"},{"alias_kind":"pith_short_12","alias_value":"WCTY47T7UX26","created_at":"2026-05-26T02:05:17.192794+00:00"},{"alias_kind":"pith_short_16","alias_value":"WCTY47T7UX26ZZ45","created_at":"2026-05-26T02:05:17.192794+00:00"},{"alias_kind":"pith_short_8","alias_value":"WCTY47T7","created_at":"2026-05-26T02:05:17.192794+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/WCTY47T7UX26ZZ45BBF4LKFILP","json":"https://pith.science/pith/WCTY47T7UX26ZZ45BBF4LKFILP.json","graph_json":"https://pith.science/api/pith-number/WCTY47T7UX26ZZ45BBF4LKFILP/graph.json","events_json":"https://pith.science/api/pith-number/WCTY47T7UX26ZZ45BBF4LKFILP/events.json","paper":"https://pith.science/paper/WCTY47T7"},"agent_actions":{"view_html":"https://pith.science/pith/WCTY47T7UX26ZZ45BBF4LKFILP","download_json":"https://pith.science/pith/WCTY47T7UX26ZZ45BBF4LKFILP.json","view_paper":"https://pith.science/paper/WCTY47T7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.25892&json=true","fetch_graph":"https://pith.science/api/pith-number/WCTY47T7UX26ZZ45BBF4LKFILP/graph.json","fetch_events":"https://pith.science/api/pith-number/WCTY47T7UX26ZZ45BBF4LKFILP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WCTY47T7UX26ZZ45BBF4LKFILP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WCTY47T7UX26ZZ45BBF4LKFILP/action/storage_attestation","attest_author":"https://pith.science/pith/WCTY47T7UX26ZZ45BBF4LKFILP/action/author_attestation","sign_citation":"https://pith.science/pith/WCTY47T7UX26ZZ45BBF4LKFILP/action/citation_signature","submit_replication":"https://pith.science/pith/WCTY47T7UX26ZZ45BBF4LKFILP/action/replication_record"}},"created_at":"2026-05-26T02:05:17.192794+00:00","updated_at":"2026-05-26T02:05:17.192794+00:00"}