{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:5HFLHMFRDY2DJRVSZZPO32U6VI","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"ea4a5d4799bd99eb56f33e556b0bae36a83e4f89437ac5ed5c4a007da03187ed","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-11-21T04:59:17Z","title_canon_sha256":"bab6629cb6de884d8c27d1dde64583a50b07afcc76a578a203b1f60206dcb862"},"schema_version":"1.0","source":{"id":"2311.12351","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.12351","created_at":"2026-07-05T07:48:50Z"},{"alias_kind":"arxiv_version","alias_value":"2311.12351v2","created_at":"2026-07-05T07:48:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.12351","created_at":"2026-07-05T07:48:50Z"},{"alias_kind":"pith_short_12","alias_value":"5HFLHMFRDY2D","created_at":"2026-07-05T07:48:50Z"},{"alias_kind":"pith_short_16","alias_value":"5HFLHMFRDY2DJRVS","created_at":"2026-07-05T07:48:50Z"},{"alias_kind":"pith_short_8","alias_value":"5HFLHMFR","created_at":"2026-07-05T07:48:50Z"}],"graph_snapshots":[{"event_id":"sha256:190d3f265ca067628473f4be4a894ebe3cf9a9e9f8834d2cd08b5f8f8e7e2cbf","target":"graph","created_at":"2026-07-05T07:48:50Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2311.12351/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Transformer-based Large Language Models (LLMs) have been applied in diverse areas such as knowledge bases, human interfaces, and dynamic agents, and marking a stride towards achieving Artificial General Intelligence (AGI). However, current LLMs are predominantly pretrained on short text snippets, which compromises their effectiveness in processing the long-context prompts that are frequently encountered in practical scenarios. This article offers a comprehensive survey of the recent advancement in Transformer-based LLM architectures aimed at enhancing the long-context capabilities of LLMs thro","authors_text":"Hao Chen, Jingwei Xu, Junyu Lai, Lijuan Yang, Penghao Zhao, Shupeng Li, Taolue Chen, Xiaoxing Ma, Yuan Yao, Yunpeng Huang, Zenan Li, Zixu Jiang","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-11-21T04:59:17Z","title":"Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.12351","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:5ea0ccba12eac897efb05ecebf550557e5c6be0b8742d9e4cb60ca0254ecb2e9","target":"record","created_at":"2026-07-05T07:48:50Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"ea4a5d4799bd99eb56f33e556b0bae36a83e4f89437ac5ed5c4a007da03187ed","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-11-21T04:59:17Z","title_canon_sha256":"bab6629cb6de884d8c27d1dde64583a50b07afcc76a578a203b1f60206dcb862"},"schema_version":"1.0","source":{"id":"2311.12351","kind":"arxiv","version":2}},"canonical_sha256":"e9cab3b0b11e3434c6b2ce5eedea9eaa31f66e6d822144701fb2c78af33cd981","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e9cab3b0b11e3434c6b2ce5eedea9eaa31f66e6d822144701fb2c78af33cd981","first_computed_at":"2026-07-05T07:48:50.415017Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:48:50.415017Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"htLPAeBdjdc4ZsuZTINDtqnQDENI+gxvn0l5M53/cbguqh/BQp+4FEqAeGWJNApHMuEsdT8X278hjk85DlLZAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:48:50.415528Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.12351","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5ea0ccba12eac897efb05ecebf550557e5c6be0b8742d9e4cb60ca0254ecb2e9","sha256:190d3f265ca067628473f4be4a894ebe3cf9a9e9f8834d2cd08b5f8f8e7e2cbf"],"state_sha256":"ad3e6cf56fdfb39973037ff36ec566b1037d6f207782992739c5d20f38594cc9"}