{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:L3JF2JY3CAOKCOHNGASBB4XPDV","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"981d0567238b2480aeb767f974b8a940411e44437362b68962d5941afe0869d6","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2026-05-02T10:29:40Z","title_canon_sha256":"88da9ded9568f2d079fc04ca23fc3d58397ea1b4e0a735e7ecbea01ac5eb7567"},"schema_version":"1.0","source":{"id":"2605.01369","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.01369","created_at":"2026-05-22T02:04:41Z"},{"alias_kind":"arxiv_version","alias_value":"2605.01369v2","created_at":"2026-05-22T02:04:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.01369","created_at":"2026-05-22T02:04:41Z"},{"alias_kind":"pith_short_12","alias_value":"L3JF2JY3CAOK","created_at":"2026-05-22T02:04:41Z"},{"alias_kind":"pith_short_16","alias_value":"L3JF2JY3CAOKCOHN","created_at":"2026-05-22T02:04:41Z"},{"alias_kind":"pith_short_8","alias_value":"L3JF2JY3","created_at":"2026-05-22T02:04:41Z"}],"graph_snapshots":[{"event_id":"sha256:a270a358c0462cf318b8fedaf0db12741eeaf75dd88c1a773df7737d140f73c7","target":"graph","created_at":"2026-05-22T02:04:41Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"MU-SHOT-Fi effectively recovers multi-user exact-activity classification performance under large domain shifts while maintaining accurate occupancy estimation and preventing collapse toward dominant classes."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the pre-trained source model provides a sufficiently rich feature backbone that frozen-classifier adaptation plus the proposed self-supervision signals can recover performance without any target labels or source data access."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"MU-SHOT-Fi recovers multi-user activity classification accuracy under domain shifts in WiFi CSI sensing using source-free adaptation with Hungarian matching, occupancy-weighted entropy regularization, and rotation prediction self-supervision."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"MU-SHOT-Fi adapts a pre-trained Wi-Fi model to new rooms and frequencies using only unlabeled target CSI and self-supervision."}],"snapshot_sha256":"a0a4b92ef26cbd98b4fac6079dee81764581237f31c3babd3b60467ad96e852b"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-20T18:34:35.230989Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T17:19:52.161541Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.01369/integrity.json","findings":[],"snapshot_sha256":"d9e6b8e3d7e17331639e87be08fe9c79bc96bedef8d3b16d350b5e262e097bdc","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep learning has been widely adopted for WiFi CSI-based human activity recognition (HAR) due to its ability to learn spatio-temporal features in a privacy-preserving and cost-effective manner. However, DL-based models generalize poorly across environments, a challenge amplified in multi-user settings where overlapping activities cause CSI entanglement and domain shifts. Practical deployments often limit access to labeled source data due to privacy constraints, motivating source-free adaptation using only unlabeled target-domain CSI and a pre-trained source model. In this paper, we propose MU-","authors_text":"Ahmed Y. Radwan, Hina Tabassum","cross_cats":["cs.AI","cs.LG"],"headline":"MU-SHOT-Fi adapts a pre-trained Wi-Fi model to new rooms and frequencies using only unlabeled target CSI and self-supervision.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2026-05-02T10:29:40Z","title":"MU-SHOT-Fi: Self-Supervised Multi-User Wi-Fi Sensing with Source-free Unsupervised Domain Adaptation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.01369","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-09T18:30:53.813995Z","id":"85633487-9b57-4685-bc60-cdcde02dd6ac","model_set":{"reader":"grok-4.3"},"one_line_summary":"MU-SHOT-Fi recovers multi-user activity classification accuracy under domain shifts in WiFi CSI sensing using source-free adaptation with Hungarian matching, occupancy-weighted entropy regularization, and rotation prediction self-supervision.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"MU-SHOT-Fi adapts a pre-trained Wi-Fi model to new rooms and frequencies using only unlabeled target CSI and self-supervision.","strongest_claim":"MU-SHOT-Fi effectively recovers multi-user exact-activity classification performance under large domain shifts while maintaining accurate occupancy estimation and preventing collapse toward dominant classes.","weakest_assumption":"That the pre-trained source model provides a sufficiently rich feature backbone that frozen-classifier adaptation plus the proposed self-supervision signals can recover performance without any target labels or source data access."}},"verdict_id":"85633487-9b57-4685-bc60-cdcde02dd6ac"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b44fe80ba4c4a150339dfb6064a31967d6062e8c3bf16a7cbfcec35e667ff982","target":"record","created_at":"2026-05-22T02:04:41Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"981d0567238b2480aeb767f974b8a940411e44437362b68962d5941afe0869d6","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2026-05-02T10:29:40Z","title_canon_sha256":"88da9ded9568f2d079fc04ca23fc3d58397ea1b4e0a735e7ecbea01ac5eb7567"},"schema_version":"1.0","source":{"id":"2605.01369","kind":"arxiv","version":2}},"canonical_sha256":"5ed25d271b101ca138ed302410f2ef1d7659bc407134c20f1c0edb65950c79ea","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5ed25d271b101ca138ed302410f2ef1d7659bc407134c20f1c0edb65950c79ea","first_computed_at":"2026-05-22T02:04:41.494382Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-22T02:04:41.494382Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UQgNofGFTF24qbA/Y8mDSJ0t6jRum79gtkQapeP7rLA1kJvvmKueuZxsXAo+uUH/GL2l8e3fLIlJ9bWzMUZ1DQ==","signature_status":"signed_v1","signed_at":"2026-05-22T02:04:41.495188Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.01369","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b44fe80ba4c4a150339dfb6064a31967d6062e8c3bf16a7cbfcec35e667ff982","sha256:a270a358c0462cf318b8fedaf0db12741eeaf75dd88c1a773df7737d140f73c7"],"state_sha256":"1ad93b34e3aa68efbe416fed16c952af9005fb8ccb1a2bf7104fc2639bf8e105"}