{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MM455M4LDFIDPWOFWQ3GASMI6T","short_pith_number":"pith:MM455M4L","schema_version":"1.0","canonical_sha256":"6339deb38b195037d9c5b436604988f4edf70f6d7c6f95b3f16b88d5a18b1ef6","source":{"kind":"arxiv","id":"2406.13618","version":2},"attestation_state":"computed","paper":{"title":"In-Context Former: Lightning-fast Compressing Context for Large Language Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Enhong Chen, Tong Xu, Xiangfeng Wang, Yongyi He, Zaiyi Chen, Zheyong Xie","submitted_at":"2024-06-19T15:14:55Z","abstract_excerpt":"With the rising popularity of Transformer-based large language models (LLMs), reducing their high inference costs has become a significant research focus. One effective approach is to compress the long input contexts. Existing methods typically leverage the self-attention mechanism of the LLM itself for context compression. While these methods have achieved notable results, the compression process still involves quadratic time complexity, which limits their applicability. To mitigate this limitation, we propose the In-Context Former (IC-Former). Unlike previous methods, IC-Former does not depe"},"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":"2406.13618","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-06-19T15:14:55Z","cross_cats_sorted":[],"title_canon_sha256":"f8a5180afd41f5c06fb26ec5f7cfab1f814cbaf0d35b8f9fc9d13b4950a330a5","abstract_canon_sha256":"c0f7ae4a81e195372b96a14ce174d36d310543d5c922788aacded4c62c5f8ccc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:31:04.480866Z","signature_b64":"ZB3OHhfisEOuDI5xV+KB3tDGehO8EU8dchYSffNPD6NOuOEHvOKSSoOIxn5A6ShW/l4kYni8BnMmSP/9bANhBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6339deb38b195037d9c5b436604988f4edf70f6d7c6f95b3f16b88d5a18b1ef6","last_reissued_at":"2026-07-05T09:31:04.480378Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:31:04.480378Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"In-Context Former: Lightning-fast Compressing Context for Large Language Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Enhong Chen, Tong Xu, Xiangfeng Wang, Yongyi He, Zaiyi Chen, Zheyong Xie","submitted_at":"2024-06-19T15:14:55Z","abstract_excerpt":"With the rising popularity of Transformer-based large language models (LLMs), reducing their high inference costs has become a significant research focus. One effective approach is to compress the long input contexts. Existing methods typically leverage the self-attention mechanism of the LLM itself for context compression. While these methods have achieved notable results, the compression process still involves quadratic time complexity, which limits their applicability. To mitigate this limitation, we propose the In-Context Former (IC-Former). Unlike previous methods, IC-Former does not depe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.13618","kind":"arxiv","version":2},"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/2406.13618/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":"2406.13618","created_at":"2026-07-05T09:31:04.480436+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.13618v2","created_at":"2026-07-05T09:31:04.480436+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.13618","created_at":"2026-07-05T09:31:04.480436+00:00"},{"alias_kind":"pith_short_12","alias_value":"MM455M4LDFID","created_at":"2026-07-05T09:31:04.480436+00:00"},{"alias_kind":"pith_short_16","alias_value":"MM455M4LDFIDPWOF","created_at":"2026-07-05T09:31:04.480436+00:00"},{"alias_kind":"pith_short_8","alias_value":"MM455M4L","created_at":"2026-07-05T09:31:04.480436+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.17670","citing_title":"Towards General Continuous Memory for Vision-Language Models","ref_index":57,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MM455M4LDFIDPWOFWQ3GASMI6T","json":"https://pith.science/pith/MM455M4LDFIDPWOFWQ3GASMI6T.json","graph_json":"https://pith.science/api/pith-number/MM455M4LDFIDPWOFWQ3GASMI6T/graph.json","events_json":"https://pith.science/api/pith-number/MM455M4LDFIDPWOFWQ3GASMI6T/events.json","paper":"https://pith.science/paper/MM455M4L"},"agent_actions":{"view_html":"https://pith.science/pith/MM455M4LDFIDPWOFWQ3GASMI6T","download_json":"https://pith.science/pith/MM455M4LDFIDPWOFWQ3GASMI6T.json","view_paper":"https://pith.science/paper/MM455M4L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.13618&json=true","fetch_graph":"https://pith.science/api/pith-number/MM455M4LDFIDPWOFWQ3GASMI6T/graph.json","fetch_events":"https://pith.science/api/pith-number/MM455M4LDFIDPWOFWQ3GASMI6T/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MM455M4LDFIDPWOFWQ3GASMI6T/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MM455M4LDFIDPWOFWQ3GASMI6T/action/storage_attestation","attest_author":"https://pith.science/pith/MM455M4LDFIDPWOFWQ3GASMI6T/action/author_attestation","sign_citation":"https://pith.science/pith/MM455M4LDFIDPWOFWQ3GASMI6T/action/citation_signature","submit_replication":"https://pith.science/pith/MM455M4LDFIDPWOFWQ3GASMI6T/action/replication_record"}},"created_at":"2026-07-05T09:31:04.480436+00:00","updated_at":"2026-07-05T09:31:04.480436+00:00"}