{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:E6QDQXSDRHKMJPOOXG4BPFAMNH","short_pith_number":"pith:E6QDQXSD","schema_version":"1.0","canonical_sha256":"27a0385e4389d4c4bdceb9b817940c69d8a0fa11f0ca7138e57c0f94a9ab5b83","source":{"kind":"arxiv","id":"2605.19997","version":1},"attestation_state":"computed","paper":{"title":"CAT-MoEformer: Context-Aware Temporal MoE Transformer for Beam Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Changkai Zhou, Cunhua Pan, Hong Ren, Jiangzhou Wang","submitted_at":"2026-05-19T15:32:22Z","abstract_excerpt":"This paper proposes CAT-MoEformer, a context-aware transformer with scene-conditioned mixture-of-experts (MoE) feed-forward networks, for proactive mmWave beam prediction from compressed uplink pilot observations. The spatial encoder comprises a three-layer asymmetric convolutional network followed by a squeeze-and-excitation recalibration block, which extracts frequency-beam correlation features from pilot tensors without explicit channel reconstruction. A truncated pretrained GPT-2 backbone models the temporal evolution of beam sequences, with the feed-forward networks in the upper three tra"},"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":"2605.19997","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2026-05-19T15:32:22Z","cross_cats_sorted":[],"title_canon_sha256":"99a85d1828b82ddac1ed1e47c2bfe63dc81fca78fe60357068508eafb0ee09a8","abstract_canon_sha256":"50cd8b15bfbde5873bb070063560309d481024e5c78a4a25fe022a6db7e78e0f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T02:05:58.503673Z","signature_b64":"FIJr038dnndKkmQQPXZ9VNVtxLwi7jEy5qPhzTBV2EzcAzWWyMuqdaEo4nY9kQif5dg9sX8KgrzLu7CfAjO6Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"27a0385e4389d4c4bdceb9b817940c69d8a0fa11f0ca7138e57c0f94a9ab5b83","last_reissued_at":"2026-05-20T02:05:58.503221Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T02:05:58.503221Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CAT-MoEformer: Context-Aware Temporal MoE Transformer for Beam Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Changkai Zhou, Cunhua Pan, Hong Ren, Jiangzhou Wang","submitted_at":"2026-05-19T15:32:22Z","abstract_excerpt":"This paper proposes CAT-MoEformer, a context-aware transformer with scene-conditioned mixture-of-experts (MoE) feed-forward networks, for proactive mmWave beam prediction from compressed uplink pilot observations. The spatial encoder comprises a three-layer asymmetric convolutional network followed by a squeeze-and-excitation recalibration block, which extracts frequency-beam correlation features from pilot tensors without explicit channel reconstruction. A truncated pretrained GPT-2 backbone models the temporal evolution of beam sequences, with the feed-forward networks in the upper three tra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.19997","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/2605.19997/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":"2605.19997","created_at":"2026-05-20T02:05:58.503282+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.19997v1","created_at":"2026-05-20T02:05:58.503282+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.19997","created_at":"2026-05-20T02:05:58.503282+00:00"},{"alias_kind":"pith_short_12","alias_value":"E6QDQXSDRHKM","created_at":"2026-05-20T02:05:58.503282+00:00"},{"alias_kind":"pith_short_16","alias_value":"E6QDQXSDRHKMJPOO","created_at":"2026-05-20T02:05:58.503282+00:00"},{"alias_kind":"pith_short_8","alias_value":"E6QDQXSD","created_at":"2026-05-20T02:05:58.503282+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/E6QDQXSDRHKMJPOOXG4BPFAMNH","json":"https://pith.science/pith/E6QDQXSDRHKMJPOOXG4BPFAMNH.json","graph_json":"https://pith.science/api/pith-number/E6QDQXSDRHKMJPOOXG4BPFAMNH/graph.json","events_json":"https://pith.science/api/pith-number/E6QDQXSDRHKMJPOOXG4BPFAMNH/events.json","paper":"https://pith.science/paper/E6QDQXSD"},"agent_actions":{"view_html":"https://pith.science/pith/E6QDQXSDRHKMJPOOXG4BPFAMNH","download_json":"https://pith.science/pith/E6QDQXSDRHKMJPOOXG4BPFAMNH.json","view_paper":"https://pith.science/paper/E6QDQXSD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.19997&json=true","fetch_graph":"https://pith.science/api/pith-number/E6QDQXSDRHKMJPOOXG4BPFAMNH/graph.json","fetch_events":"https://pith.science/api/pith-number/E6QDQXSDRHKMJPOOXG4BPFAMNH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E6QDQXSDRHKMJPOOXG4BPFAMNH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E6QDQXSDRHKMJPOOXG4BPFAMNH/action/storage_attestation","attest_author":"https://pith.science/pith/E6QDQXSDRHKMJPOOXG4BPFAMNH/action/author_attestation","sign_citation":"https://pith.science/pith/E6QDQXSDRHKMJPOOXG4BPFAMNH/action/citation_signature","submit_replication":"https://pith.science/pith/E6QDQXSDRHKMJPOOXG4BPFAMNH/action/replication_record"}},"created_at":"2026-05-20T02:05:58.503282+00:00","updated_at":"2026-05-20T02:05:58.503282+00:00"}