{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:IUDKP7AM2O27NTQIVDAX63W6F5","short_pith_number":"pith:IUDKP7AM","schema_version":"1.0","canonical_sha256":"4506a7fc0cd3b5f6ce08a8c17f6ede2f518582ad6b37e67f212bf95ae7d828e6","source":{"kind":"arxiv","id":"2505.17879","version":3},"attestation_state":"computed","paper":{"title":"LLM4SG: Adapting Large Language Model for Scatterer Generation via Synesthesia of Machines","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Lu Bai, Xiang Cheng, Zengrui Han, Ziwei Huang","submitted_at":"2025-05-23T13:28:26Z","abstract_excerpt":"In this paper, a novel large language model (LLM)-based method for scatterer generation (LLM4SG) is proposed for sixth-generation (6G) artificial intelligence (AI)-native communications. To provide a solid data foundation, we construct a new synthetic intelligent sensing-communication dataset for Synesthesia of Machines (SoM) in vehicle-to-vehicle (V2V) communications, named SynthSoM-V2V, covering multiple V2V scenarios with multiple frequency bands and multiple vehicular traffic densities (VTDs). Leveraging the powerful cross-modal representation capabilities of LLMs, LLM4SG is designed to ca"},"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":"2505.17879","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2025-05-23T13:28:26Z","cross_cats_sorted":[],"title_canon_sha256":"a9b53b22f8e9de1bba8961bd87e2ba0f01501c8c15da00ed56ec5954487e00c3","abstract_canon_sha256":"344dfb80892667ca16f7161ea6146d651b2e831e65aa10618ac8353b00b40247"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:03:48.801603Z","signature_b64":"sncPJnOGL/U5KgtM0utGnfMUDGayMac6h4/qQf5c66wjvxc47H80Ho8NaSXOxqPsqYSaVVNJ7hkVdOdb8IX5BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4506a7fc0cd3b5f6ce08a8c17f6ede2f518582ad6b37e67f212bf95ae7d828e6","last_reissued_at":"2026-07-05T12:03:48.801072Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:03:48.801072Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LLM4SG: Adapting Large Language Model for Scatterer Generation via Synesthesia of Machines","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Lu Bai, Xiang Cheng, Zengrui Han, Ziwei Huang","submitted_at":"2025-05-23T13:28:26Z","abstract_excerpt":"In this paper, a novel large language model (LLM)-based method for scatterer generation (LLM4SG) is proposed for sixth-generation (6G) artificial intelligence (AI)-native communications. To provide a solid data foundation, we construct a new synthetic intelligent sensing-communication dataset for Synesthesia of Machines (SoM) in vehicle-to-vehicle (V2V) communications, named SynthSoM-V2V, covering multiple V2V scenarios with multiple frequency bands and multiple vehicular traffic densities (VTDs). Leveraging the powerful cross-modal representation capabilities of LLMs, LLM4SG is designed to ca"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.17879","kind":"arxiv","version":3},"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/2505.17879/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":"2505.17879","created_at":"2026-07-05T12:03:48.801138+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.17879v3","created_at":"2026-07-05T12:03:48.801138+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.17879","created_at":"2026-07-05T12:03:48.801138+00:00"},{"alias_kind":"pith_short_12","alias_value":"IUDKP7AM2O27","created_at":"2026-07-05T12:03:48.801138+00:00"},{"alias_kind":"pith_short_16","alias_value":"IUDKP7AM2O27NTQI","created_at":"2026-07-05T12:03:48.801138+00:00"},{"alias_kind":"pith_short_8","alias_value":"IUDKP7AM","created_at":"2026-07-05T12:03:48.801138+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.04068","citing_title":"WiFo-CF: Wireless Foundation Model for CSI Feedback","ref_index":26,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IUDKP7AM2O27NTQIVDAX63W6F5","json":"https://pith.science/pith/IUDKP7AM2O27NTQIVDAX63W6F5.json","graph_json":"https://pith.science/api/pith-number/IUDKP7AM2O27NTQIVDAX63W6F5/graph.json","events_json":"https://pith.science/api/pith-number/IUDKP7AM2O27NTQIVDAX63W6F5/events.json","paper":"https://pith.science/paper/IUDKP7AM"},"agent_actions":{"view_html":"https://pith.science/pith/IUDKP7AM2O27NTQIVDAX63W6F5","download_json":"https://pith.science/pith/IUDKP7AM2O27NTQIVDAX63W6F5.json","view_paper":"https://pith.science/paper/IUDKP7AM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.17879&json=true","fetch_graph":"https://pith.science/api/pith-number/IUDKP7AM2O27NTQIVDAX63W6F5/graph.json","fetch_events":"https://pith.science/api/pith-number/IUDKP7AM2O27NTQIVDAX63W6F5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IUDKP7AM2O27NTQIVDAX63W6F5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IUDKP7AM2O27NTQIVDAX63W6F5/action/storage_attestation","attest_author":"https://pith.science/pith/IUDKP7AM2O27NTQIVDAX63W6F5/action/author_attestation","sign_citation":"https://pith.science/pith/IUDKP7AM2O27NTQIVDAX63W6F5/action/citation_signature","submit_replication":"https://pith.science/pith/IUDKP7AM2O27NTQIVDAX63W6F5/action/replication_record"}},"created_at":"2026-07-05T12:03:48.801138+00:00","updated_at":"2026-07-05T12:03:48.801138+00:00"}