{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:FW7IZPO67ZWEOZTQMPHDELMS7N","short_pith_number":"pith:FW7IZPO6","schema_version":"1.0","canonical_sha256":"2dbe8cbddefe6c47667063ce322d92fb7c67af6eca67e6e1c95a9999d54fc1c3","source":{"kind":"arxiv","id":"2504.14100","version":1},"attestation_state":"computed","paper":{"title":"6G WavesFM: A Foundation Model for Sensing, Communication, and Localization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"eess.SP","authors_text":"Ahmed Aboulfotouh, Elsayed Mohammed, Hatem Abou-Zeid","submitted_at":"2025-04-18T22:51:35Z","abstract_excerpt":"This paper introduces WavesFM, a novel Wireless Foundation Model (WFM) framework, capable of supporting a wide array of communication, sensing, and localization tasks. Our proposed architecture combines a shared Vision Transformer (ViT) backbone with task-specific multi-layer perceptron (MLP) heads and incorporates Low-Rank Adaptation (LoRA) for parameter-efficient fine-tuning. This design promotes full parameter sharing across tasks, significantly reducing the computational and memory footprint without sacrificing performance. The model processes both image-like wireless modalities, such as s"},"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":"2504.14100","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2025-04-18T22:51:35Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"1de790df6dc985bbc2bfe7d7c975a39d610c751b6b109fca34440db5ffc67ff3","abstract_canon_sha256":"64181c4d33146aa4c33ecdcd50c198b3bca813b39046839eaab716703bc07562"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:51:10.835389Z","signature_b64":"saEWK3Vpy3DRxXWncBhAdLZ8ViYLBVR1dFk+pL50YPMaQFNqZrdYV8V3WE64vTX3LatDUoPiAlbDeVK3uKaYDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2dbe8cbddefe6c47667063ce322d92fb7c67af6eca67e6e1c95a9999d54fc1c3","last_reissued_at":"2026-07-05T10:51:10.834882Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:51:10.834882Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"6G WavesFM: A Foundation Model for Sensing, Communication, and Localization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"eess.SP","authors_text":"Ahmed Aboulfotouh, Elsayed Mohammed, Hatem Abou-Zeid","submitted_at":"2025-04-18T22:51:35Z","abstract_excerpt":"This paper introduces WavesFM, a novel Wireless Foundation Model (WFM) framework, capable of supporting a wide array of communication, sensing, and localization tasks. Our proposed architecture combines a shared Vision Transformer (ViT) backbone with task-specific multi-layer perceptron (MLP) heads and incorporates Low-Rank Adaptation (LoRA) for parameter-efficient fine-tuning. This design promotes full parameter sharing across tasks, significantly reducing the computational and memory footprint without sacrificing performance. The model processes both image-like wireless modalities, such as s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.14100","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/2504.14100/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":"2504.14100","created_at":"2026-07-05T10:51:10.834959+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.14100v1","created_at":"2026-07-05T10:51:10.834959+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.14100","created_at":"2026-07-05T10:51:10.834959+00:00"},{"alias_kind":"pith_short_12","alias_value":"FW7IZPO67ZWE","created_at":"2026-07-05T10:51:10.834959+00:00"},{"alias_kind":"pith_short_16","alias_value":"FW7IZPO67ZWEOZTQ","created_at":"2026-07-05T10:51:10.834959+00:00"},{"alias_kind":"pith_short_8","alias_value":"FW7IZPO6","created_at":"2026-07-05T10:51:10.834959+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.18785","citing_title":"EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding","ref_index":13,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FW7IZPO67ZWEOZTQMPHDELMS7N","json":"https://pith.science/pith/FW7IZPO67ZWEOZTQMPHDELMS7N.json","graph_json":"https://pith.science/api/pith-number/FW7IZPO67ZWEOZTQMPHDELMS7N/graph.json","events_json":"https://pith.science/api/pith-number/FW7IZPO67ZWEOZTQMPHDELMS7N/events.json","paper":"https://pith.science/paper/FW7IZPO6"},"agent_actions":{"view_html":"https://pith.science/pith/FW7IZPO67ZWEOZTQMPHDELMS7N","download_json":"https://pith.science/pith/FW7IZPO67ZWEOZTQMPHDELMS7N.json","view_paper":"https://pith.science/paper/FW7IZPO6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.14100&json=true","fetch_graph":"https://pith.science/api/pith-number/FW7IZPO67ZWEOZTQMPHDELMS7N/graph.json","fetch_events":"https://pith.science/api/pith-number/FW7IZPO67ZWEOZTQMPHDELMS7N/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FW7IZPO67ZWEOZTQMPHDELMS7N/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FW7IZPO67ZWEOZTQMPHDELMS7N/action/storage_attestation","attest_author":"https://pith.science/pith/FW7IZPO67ZWEOZTQMPHDELMS7N/action/author_attestation","sign_citation":"https://pith.science/pith/FW7IZPO67ZWEOZTQMPHDELMS7N/action/citation_signature","submit_replication":"https://pith.science/pith/FW7IZPO67ZWEOZTQMPHDELMS7N/action/replication_record"}},"created_at":"2026-07-05T10:51:10.834959+00:00","updated_at":"2026-07-05T10:51:10.834959+00:00"}