{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:SKF2GZ3CU65GXXRU3LE7PDCQVK","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":"647845417740dda9367085d66ad4fca2fbcf6454f30458978424dcb8dd5109cb","cross_cats_sorted":["cs.AI","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2024-08-15T11:39:23Z","title_canon_sha256":"82d3d41aa3040000d901f282b7ace798b154903a29765c399fabc9ece587d83e"},"schema_version":"1.0","source":{"id":"2409.00005","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.00005","created_at":"2026-07-05T09:01:39Z"},{"alias_kind":"arxiv_version","alias_value":"2409.00005v1","created_at":"2026-07-05T09:01:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.00005","created_at":"2026-07-05T09:01:39Z"},{"alias_kind":"pith_short_12","alias_value":"SKF2GZ3CU65G","created_at":"2026-07-05T09:01:39Z"},{"alias_kind":"pith_short_16","alias_value":"SKF2GZ3CU65GXXRU","created_at":"2026-07-05T09:01:39Z"},{"alias_kind":"pith_short_8","alias_value":"SKF2GZ3C","created_at":"2026-07-05T09:01:39Z"}],"graph_snapshots":[{"event_id":"sha256:75d9c64b7916f81da2542f45685b30649281adddc82aa577bc4469c773bd7643","target":"graph","created_at":"2026-07-05T09:01:39Z","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/2409.00005/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Downlink channel temporal prediction is a critical technology in massive multiple-input multiple-output (MIMO) systems. However, existing methods that rely on fixed-step historical sequences significantly limit the accuracy, practicality, and scalability of channel prediction. Recent advances have shown that large language models (LLMs) exhibit strong pattern recognition and reasoning abilities over complex sequences. The challenge lies in effectively aligning wireless communication data with the modalities used in natural language processing to fully harness these capabilities. In this work, ","authors_text":"Haozhen Li, Shilong Fan, Xinyu Gu, Zhenyu Liu","cross_cats":["cs.AI","math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2024-08-15T11:39:23Z","title":"Csi-LLM: A Novel Downlink Channel Prediction Method Aligned with LLM Pre-Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.00005","kind":"arxiv","version":1},"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:b9b1708fc19ecf70f17bb1f318bc2bc850e2fd965cd719e72fff4c9926578aab","target":"record","created_at":"2026-07-05T09:01:39Z","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":"647845417740dda9367085d66ad4fca2fbcf6454f30458978424dcb8dd5109cb","cross_cats_sorted":["cs.AI","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2024-08-15T11:39:23Z","title_canon_sha256":"82d3d41aa3040000d901f282b7ace798b154903a29765c399fabc9ece587d83e"},"schema_version":"1.0","source":{"id":"2409.00005","kind":"arxiv","version":1}},"canonical_sha256":"928ba36762a7ba6bde34dac9f78c50aabed7ac8363d8ddf6d953862c26ff9ea9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"928ba36762a7ba6bde34dac9f78c50aabed7ac8363d8ddf6d953862c26ff9ea9","first_computed_at":"2026-07-05T09:01:39.187170Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:01:39.187170Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"U5wCrUV0AkVoDcrf8E/VuDQbhmDH+qa0Uq7m60E98OQu3HOB67PuSenIKCuqB62HGm/FAl2Dw2HIstE50L/xBw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:01:39.187651Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.00005","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b9b1708fc19ecf70f17bb1f318bc2bc850e2fd965cd719e72fff4c9926578aab","sha256:75d9c64b7916f81da2542f45685b30649281adddc82aa577bc4469c773bd7643"],"state_sha256":"9baef69f299827c51253c9c32fb110f9a0ab2e12990d8ae2c4f9e96a05f4bf01"}