{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:JGQL5GVTSLY7VQIEWDTXN4MM6N","short_pith_number":"pith:JGQL5GVT","schema_version":"1.0","canonical_sha256":"49a0be9ab392f1fac104b0e776f18cf34655a4bb825f809d410609a946ec027e","source":{"kind":"arxiv","id":"2503.10432","version":2},"attestation_state":"computed","paper":{"title":"BeamLLM: Vision-Empowered mmWave Beam Prediction with Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Can Zheng, Chung G. Kang, Guofa Cai, Jiguang He, Zitong Yu","submitted_at":"2025-03-13T14:55:59Z","abstract_excerpt":"In this paper, we propose BeamLLM, a vision-aided millimeter-wave (mmWave) beam prediction framework leveraging large language models (LLMs) to address the challenges of high training overhead and latency in mmWave communication systems. By combining computer vision (CV) with LLMs' cross-modal reasoning capabilities, the framework extracts user equipment (UE) positional features from RGB images and aligns visual-temporal features with LLMs' semantic space through reprogramming techniques. Evaluated on a realistic vehicle-to-infrastructure (V2I) scenario, the proposed method achieves 61.01% top"},"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":"2503.10432","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-13T14:55:59Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"1f5d04417df2a3756e8ae1aefd8430362bfef23a3caa93d0663c9cf642604ff7","abstract_canon_sha256":"3b31b57614d7b21afe439c1135afba72c5f0e7504a6017067c4dcdd1c1fc6ced"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:28:15.044062Z","signature_b64":"PTIbcUb2TChFqBCpNBxDQyaguZX7aSLG+0N4mzr41BE1bFX4PtjS2VGejrHaNtkeSMy6jsZRR9ulncVFt/QUDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"49a0be9ab392f1fac104b0e776f18cf34655a4bb825f809d410609a946ec027e","last_reissued_at":"2026-07-05T11:28:15.043495Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:28:15.043495Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BeamLLM: Vision-Empowered mmWave Beam Prediction with Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Can Zheng, Chung G. Kang, Guofa Cai, Jiguang He, Zitong Yu","submitted_at":"2025-03-13T14:55:59Z","abstract_excerpt":"In this paper, we propose BeamLLM, a vision-aided millimeter-wave (mmWave) beam prediction framework leveraging large language models (LLMs) to address the challenges of high training overhead and latency in mmWave communication systems. By combining computer vision (CV) with LLMs' cross-modal reasoning capabilities, the framework extracts user equipment (UE) positional features from RGB images and aligns visual-temporal features with LLMs' semantic space through reprogramming techniques. Evaluated on a realistic vehicle-to-infrastructure (V2I) scenario, the proposed method achieves 61.01% top"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.10432","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/2503.10432/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":"2503.10432","created_at":"2026-07-05T11:28:15.043557+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.10432v2","created_at":"2026-07-05T11:28:15.043557+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.10432","created_at":"2026-07-05T11:28:15.043557+00:00"},{"alias_kind":"pith_short_12","alias_value":"JGQL5GVTSLY7","created_at":"2026-07-05T11:28:15.043557+00:00"},{"alias_kind":"pith_short_16","alias_value":"JGQL5GVTSLY7VQIE","created_at":"2026-07-05T11:28:15.043557+00:00"},{"alias_kind":"pith_short_8","alias_value":"JGQL5GVT","created_at":"2026-07-05T11:28:15.043557+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2511.20265","citing_title":"Segment-Wise Flow Matching for Vision-Aided mmWave V2I Beam Prediction","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2604.22706","citing_title":"When AI Meets Terahertz: A Survey on the Symbiosis of Artificial Intelligence and Terahertz Networks","ref_index":282,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JGQL5GVTSLY7VQIEWDTXN4MM6N","json":"https://pith.science/pith/JGQL5GVTSLY7VQIEWDTXN4MM6N.json","graph_json":"https://pith.science/api/pith-number/JGQL5GVTSLY7VQIEWDTXN4MM6N/graph.json","events_json":"https://pith.science/api/pith-number/JGQL5GVTSLY7VQIEWDTXN4MM6N/events.json","paper":"https://pith.science/paper/JGQL5GVT"},"agent_actions":{"view_html":"https://pith.science/pith/JGQL5GVTSLY7VQIEWDTXN4MM6N","download_json":"https://pith.science/pith/JGQL5GVTSLY7VQIEWDTXN4MM6N.json","view_paper":"https://pith.science/paper/JGQL5GVT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.10432&json=true","fetch_graph":"https://pith.science/api/pith-number/JGQL5GVTSLY7VQIEWDTXN4MM6N/graph.json","fetch_events":"https://pith.science/api/pith-number/JGQL5GVTSLY7VQIEWDTXN4MM6N/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JGQL5GVTSLY7VQIEWDTXN4MM6N/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JGQL5GVTSLY7VQIEWDTXN4MM6N/action/storage_attestation","attest_author":"https://pith.science/pith/JGQL5GVTSLY7VQIEWDTXN4MM6N/action/author_attestation","sign_citation":"https://pith.science/pith/JGQL5GVTSLY7VQIEWDTXN4MM6N/action/citation_signature","submit_replication":"https://pith.science/pith/JGQL5GVTSLY7VQIEWDTXN4MM6N/action/replication_record"}},"created_at":"2026-07-05T11:28:15.043557+00:00","updated_at":"2026-07-05T11:28:15.043557+00:00"}