{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:L4PSNZSARPVHKRJ75UDKRXAW6Y","short_pith_number":"pith:L4PSNZSA","schema_version":"1.0","canonical_sha256":"5f1f26e6408bea75453fed06a8dc16f61f25c2be96763757ddf79504759ad448","source":{"kind":"arxiv","id":"2606.29844","version":1},"attestation_state":"computed","paper":{"title":"MATCH: Modulating Attention via In-Context Retrieval for Long-Context Transformers","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Boxing Chen, Chengjun Zhan, Chun Hei Lo, Feng Wen, Hanlin Xu, Hanting Chen, Kai Han, Lifeng Shang, Linrui Ma, Peng Lu, Xihao Yuan, Xinghao Chen, Xinyu Wang, Yichun Yin, Yufei Cui","submitted_at":"2026-06-29T06:33:37Z","abstract_excerpt":"The quadratic computational cost of traditional attention mechanisms poses a major bottleneck to the scalability and practical deployment of large language models (LLMs), particularly in long-context scenarios. To improve efficiency, existing approaches often enforce rigid structural constraints such as local attention windows. However, these strategies typically lead to substantial performance degradation on tasks requiring precise long-range recall. In this work, we propose MATCH, a scalable and efficient framework that augments sparsified attention mechanisms with dynamically integrated in-"},"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":"2606.29844","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-29T06:33:37Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"d45c906a7fc337a4cdfac21535bb54fd07e02229a1edeb1712406d7f566d0a5b","abstract_canon_sha256":"244d926ee8e034adb3ddb657206f2e652684ab6f19b8c42d95f7f420d5c4bf5e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T02:17:37.805262Z","signature_b64":"J3kVTkxBMfpssoZovEodV5A9K+8zZDWmIfqOlSM9Jb7+21P6kxhWQpDIz64bT6Wv3dVnOjQH/hDtBQu7gLchCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5f1f26e6408bea75453fed06a8dc16f61f25c2be96763757ddf79504759ad448","last_reissued_at":"2026-06-30T02:17:37.804604Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T02:17:37.804604Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MATCH: Modulating Attention via In-Context Retrieval for Long-Context Transformers","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Boxing Chen, Chengjun Zhan, Chun Hei Lo, Feng Wen, Hanlin Xu, Hanting Chen, Kai Han, Lifeng Shang, Linrui Ma, Peng Lu, Xihao Yuan, Xinghao Chen, Xinyu Wang, Yichun Yin, Yufei Cui","submitted_at":"2026-06-29T06:33:37Z","abstract_excerpt":"The quadratic computational cost of traditional attention mechanisms poses a major bottleneck to the scalability and practical deployment of large language models (LLMs), particularly in long-context scenarios. To improve efficiency, existing approaches often enforce rigid structural constraints such as local attention windows. However, these strategies typically lead to substantial performance degradation on tasks requiring precise long-range recall. In this work, we propose MATCH, a scalable and efficient framework that augments sparsified attention mechanisms with dynamically integrated in-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.29844","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/2606.29844/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":"2606.29844","created_at":"2026-06-30T02:17:37.804710+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.29844v1","created_at":"2026-06-30T02:17:37.804710+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.29844","created_at":"2026-06-30T02:17:37.804710+00:00"},{"alias_kind":"pith_short_12","alias_value":"L4PSNZSARPVH","created_at":"2026-06-30T02:17:37.804710+00:00"},{"alias_kind":"pith_short_16","alias_value":"L4PSNZSARPVHKRJ7","created_at":"2026-06-30T02:17:37.804710+00:00"},{"alias_kind":"pith_short_8","alias_value":"L4PSNZSA","created_at":"2026-06-30T02:17:37.804710+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/L4PSNZSARPVHKRJ75UDKRXAW6Y","json":"https://pith.science/pith/L4PSNZSARPVHKRJ75UDKRXAW6Y.json","graph_json":"https://pith.science/api/pith-number/L4PSNZSARPVHKRJ75UDKRXAW6Y/graph.json","events_json":"https://pith.science/api/pith-number/L4PSNZSARPVHKRJ75UDKRXAW6Y/events.json","paper":"https://pith.science/paper/L4PSNZSA"},"agent_actions":{"view_html":"https://pith.science/pith/L4PSNZSARPVHKRJ75UDKRXAW6Y","download_json":"https://pith.science/pith/L4PSNZSARPVHKRJ75UDKRXAW6Y.json","view_paper":"https://pith.science/paper/L4PSNZSA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.29844&json=true","fetch_graph":"https://pith.science/api/pith-number/L4PSNZSARPVHKRJ75UDKRXAW6Y/graph.json","fetch_events":"https://pith.science/api/pith-number/L4PSNZSARPVHKRJ75UDKRXAW6Y/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L4PSNZSARPVHKRJ75UDKRXAW6Y/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L4PSNZSARPVHKRJ75UDKRXAW6Y/action/storage_attestation","attest_author":"https://pith.science/pith/L4PSNZSARPVHKRJ75UDKRXAW6Y/action/author_attestation","sign_citation":"https://pith.science/pith/L4PSNZSARPVHKRJ75UDKRXAW6Y/action/citation_signature","submit_replication":"https://pith.science/pith/L4PSNZSARPVHKRJ75UDKRXAW6Y/action/replication_record"}},"created_at":"2026-06-30T02:17:37.804710+00:00","updated_at":"2026-06-30T02:17:37.804710+00:00"}