{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:IN6YAQCHLNPFHUS6HRMI5H7JLH","short_pith_number":"pith:IN6YAQCH","schema_version":"1.0","canonical_sha256":"437d8040475b5e53d25e3c588e9fe959d398b8b3183926421863a88a5883ab93","source":{"kind":"arxiv","id":"2608.09285","version":1},"attestation_state":"computed","paper":{"title":"GLocFM: A Geometry-Aware Foundation Model for 3D Indoor Wireless Localization","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"eess.SP","authors_text":"Chaozheng Wen, Chenghong Bian, Hongze Chen, Jun Zhang","submitted_at":"2026-08-10T08:39:15Z","abstract_excerpt":"Learning-based wireless localizers often fail to utilize geometric information about the propagation environment, limiting their ability to exploit non-line-of-sight (NLoS) propagation and generalize across scenes. To bridge this gap, we propose GLocFM, a Geometry-aware Localization Foundation Model, which jointly exploits WiFi measurements and scene geometry represented as a 3D point cloud. We formulate localization as a maximum-likelihood (ML) estimation problem, where the goal is to find a transmitter position that maximizes the likelihood of the wireless observations conditioned on the sce"},"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":"2608.09285","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"eess.SP","submitted_at":"2026-08-10T08:39:15Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"65367f4768ea8195416a113c4d9f4b4313d40e893b7aaf7e8dedea4c3040d4f6","abstract_canon_sha256":"5e1b8efdd70a523591a96fc86a53b1397f0114570ce75c5bfcefdc8ca5b7fee1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-11T02:22:15.253025Z","signature_b64":"TT+LN8VVJfovupPIXPk8cTCw6N2sH1Xcg9XUszVVEEYTcp4oLQA2LZvcobK8xHOVKFrZ511XBVemoygZycqcAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"437d8040475b5e53d25e3c588e9fe959d398b8b3183926421863a88a5883ab93","last_reissued_at":"2026-08-11T02:22:15.251423Z","signature_status":"signed_v1","first_computed_at":"2026-08-11T02:22:15.251423Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GLocFM: A Geometry-Aware Foundation Model for 3D Indoor Wireless Localization","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"eess.SP","authors_text":"Chaozheng Wen, Chenghong Bian, Hongze Chen, Jun Zhang","submitted_at":"2026-08-10T08:39:15Z","abstract_excerpt":"Learning-based wireless localizers often fail to utilize geometric information about the propagation environment, limiting their ability to exploit non-line-of-sight (NLoS) propagation and generalize across scenes. To bridge this gap, we propose GLocFM, a Geometry-aware Localization Foundation Model, which jointly exploits WiFi measurements and scene geometry represented as a 3D point cloud. We formulate localization as a maximum-likelihood (ML) estimation problem, where the goal is to find a transmitter position that maximizes the likelihood of the wireless observations conditioned on the sce"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.09285","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/2608.09285/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":"2608.09285","created_at":"2026-08-11T02:22:15.252040+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.09285v1","created_at":"2026-08-11T02:22:15.252040+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.09285","created_at":"2026-08-11T02:22:15.252040+00:00"},{"alias_kind":"pith_short_12","alias_value":"IN6YAQCHLNPF","created_at":"2026-08-11T02:22:15.252040+00:00"},{"alias_kind":"pith_short_16","alias_value":"IN6YAQCHLNPFHUS6","created_at":"2026-08-11T02:22:15.252040+00:00"},{"alias_kind":"pith_short_8","alias_value":"IN6YAQCH","created_at":"2026-08-11T02:22:15.252040+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/IN6YAQCHLNPFHUS6HRMI5H7JLH","json":"https://pith.science/pith/IN6YAQCHLNPFHUS6HRMI5H7JLH.json","graph_json":"https://pith.science/api/pith-number/IN6YAQCHLNPFHUS6HRMI5H7JLH/graph.json","events_json":"https://pith.science/api/pith-number/IN6YAQCHLNPFHUS6HRMI5H7JLH/events.json","paper":"https://pith.science/paper/IN6YAQCH"},"agent_actions":{"view_html":"https://pith.science/pith/IN6YAQCHLNPFHUS6HRMI5H7JLH","download_json":"https://pith.science/pith/IN6YAQCHLNPFHUS6HRMI5H7JLH.json","view_paper":"https://pith.science/paper/IN6YAQCH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.09285&json=true","fetch_graph":"https://pith.science/api/pith-number/IN6YAQCHLNPFHUS6HRMI5H7JLH/graph.json","fetch_events":"https://pith.science/api/pith-number/IN6YAQCHLNPFHUS6HRMI5H7JLH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IN6YAQCHLNPFHUS6HRMI5H7JLH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IN6YAQCHLNPFHUS6HRMI5H7JLH/action/storage_attestation","attest_author":"https://pith.science/pith/IN6YAQCHLNPFHUS6HRMI5H7JLH/action/author_attestation","sign_citation":"https://pith.science/pith/IN6YAQCHLNPFHUS6HRMI5H7JLH/action/citation_signature","submit_replication":"https://pith.science/pith/IN6YAQCHLNPFHUS6HRMI5H7JLH/action/replication_record"}},"created_at":"2026-08-11T02:22:15.252040+00:00","updated_at":"2026-08-11T02:22:15.252040+00:00"}