{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:LSUWAGBQZ532JSPHQSL3BXSWRN","short_pith_number":"pith:LSUWAGBQ","canonical_record":{"source":{"id":"2511.22222","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2025-11-27T08:42:44Z","cross_cats_sorted":[],"title_canon_sha256":"7e2f7896285daacf95ba36441aef2cd1a4beb2d6df71f05aa8843f8744b619bd","abstract_canon_sha256":"19c5569c1ffde4801fdcea499f169098bb76719685092bf40e6c467020583a57"},"schema_version":"1.0"},"canonical_sha256":"5ca9601830cf77a4c9e78497b0de568b43b66d0772f8c9c9564ef061fe2bfed7","source":{"kind":"arxiv","id":"2511.22222","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2511.22222","created_at":"2026-07-27T01:19:52Z"},{"alias_kind":"arxiv_version","alias_value":"2511.22222v3","created_at":"2026-07-27T01:19:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.22222","created_at":"2026-07-27T01:19:52Z"},{"alias_kind":"pith_short_12","alias_value":"LSUWAGBQZ532","created_at":"2026-07-27T01:19:52Z"},{"alias_kind":"pith_short_16","alias_value":"LSUWAGBQZ532JSPH","created_at":"2026-07-27T01:19:52Z"},{"alias_kind":"pith_short_8","alias_value":"LSUWAGBQ","created_at":"2026-07-27T01:19:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:LSUWAGBQZ532JSPHQSL3BXSWRN","target":"record","payload":{"canonical_record":{"source":{"id":"2511.22222","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2025-11-27T08:42:44Z","cross_cats_sorted":[],"title_canon_sha256":"7e2f7896285daacf95ba36441aef2cd1a4beb2d6df71f05aa8843f8744b619bd","abstract_canon_sha256":"19c5569c1ffde4801fdcea499f169098bb76719685092bf40e6c467020583a57"},"schema_version":"1.0"},"canonical_sha256":"5ca9601830cf77a4c9e78497b0de568b43b66d0772f8c9c9564ef061fe2bfed7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-27T01:19:52.875762Z","signature_b64":"2Wp1naYwXyh8w2shrFSGU5Ccpv4hlU/RHELWJQuA5ZeU5Jwkpsu1FTR5lc37O0eOrikfkH/XU9qRQ1exUe7cBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5ca9601830cf77a4c9e78497b0de568b43b66d0772f8c9c9564ef061fe2bfed7","last_reissued_at":"2026-07-27T01:19:52.874814Z","signature_status":"signed_v1","first_computed_at":"2026-07-27T01:19:52.874814Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2511.22222","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-27T01:19:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wG4a3IlVGf7erqmzgTeqb2E52gURGO0abRoV+sAn1zi+T3W9wWup48ZWEpNhPfZKFT8esva1DZHXsYAxoVDBCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T21:01:00.713705Z"},"content_sha256":"5e02cce4d0ea02d5449703a99409eb2220f65390e819b9ff3c6cf0e5457c1508","schema_version":"1.0","event_id":"sha256:5e02cce4d0ea02d5449703a99409eb2220f65390e819b9ff3c6cf0e5457c1508"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:LSUWAGBQZ532JSPHQSL3BXSWRN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"WiFo-2: a generalist foundation model unifies heterogeneous wireless system design","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"WiFo-2 pretrained on 11.6 billion channel measurements unifies design for heterogeneous wireless systems via zero-shot reconstruction.","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Boxun Liu, Liuqing Yang, Shijian Gao, Xiang Cheng, Xuanyu Liu, Xuesong Cai","submitted_at":"2025-11-27T08:42:44Z","abstract_excerpt":"Emerging sixth-generation wireless systems are increasingly heterogeneous, with compatibility across diverse configurations, ubiquitous coverage, and expanded functionalities. Although deep learning has substantially benefited wireless system design, existing approaches are typically trained for specific system settings and scenarios with limited generalizability. Here we present WiFo-2, a space-time-frequency foundation model for unified wireless communications and sensing system design. Pretrained on a heterogeneous dataset of 11.6 billion channel state information (CSI) points, WiFo-2 learn"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"WiFo-2 achieves reliable and accurate zero-shot channel reconstruction, outperforming fully supervised task-specific models. With only 1% of the training samples required by supervised AI models, WiFo-2 achieves state-of-the-art performance across 9 distinct wireless tasks.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That a single model pretrained on the collected heterogeneous CSI dataset will continue to generalize reliably to new scenarios, configurations, and tasks not represented in the 11.6 billion training points.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"WiFo-2 is a space-time-frequency foundation model pretrained on heterogeneous CSI data that delivers strong zero-shot and few-shot performance across wireless communications and sensing tasks.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"WiFo-2 pretrained on 11.6 billion channel measurements unifies design for heterogeneous wireless systems via zero-shot reconstruction.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"9a476e941ae850d27bad26e2d949dbc2e49dca292b540377aedeab307c5eb930"},"source":{"id":"2511.22222","kind":"arxiv","version":3},"verdict":{"id":"1f984c5c-8649-4052-8e4f-fb9ef6265480","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-17T05:12:35.203995Z","strongest_claim":"WiFo-2 achieves reliable and accurate zero-shot channel reconstruction, outperforming fully supervised task-specific models. With only 1% of the training samples required by supervised AI models, WiFo-2 achieves state-of-the-art performance across 9 distinct wireless tasks.","one_line_summary":"WiFo-2 is a space-time-frequency foundation model pretrained on heterogeneous CSI data that delivers strong zero-shot and few-shot performance across wireless communications and sensing tasks.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That a single model pretrained on the collected heterogeneous CSI dataset will continue to generalize reliably to new scenarios, configurations, and tasks not represented in the 11.6 billion training points.","pith_extraction_headline":"WiFo-2 pretrained on 11.6 billion channel measurements unifies design for heterogeneous wireless systems via zero-shot reconstruction."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2511.22222/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":2,"snapshot_sha256":"0931d8a55412ec4215158dd73a9442957b274e0ffe13885862ceae4e9c1b3f69"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"1f984c5c-8649-4052-8e4f-fb9ef6265480"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-27T01:19:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TLW8WpKrMMddrESlZePsKlMZI0BxsraP3huEpJjwVxyDb01n4irjG1hprkaYqpgLLdMB8KEpZOCHD7xxeSn8Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T21:01:00.714737Z"},"content_sha256":"56fbbd01576ce40e1e1619053f83df9b11b65b65daff5a57f646068f43272591","schema_version":"1.0","event_id":"sha256:56fbbd01576ce40e1e1619053f83df9b11b65b65daff5a57f646068f43272591"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LSUWAGBQZ532JSPHQSL3BXSWRN/bundle.json","state_url":"https://pith.science/pith/LSUWAGBQZ532JSPHQSL3BXSWRN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LSUWAGBQZ532JSPHQSL3BXSWRN/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-04T21:01:00Z","links":{"resolver":"https://pith.science/pith/LSUWAGBQZ532JSPHQSL3BXSWRN","bundle":"https://pith.science/pith/LSUWAGBQZ532JSPHQSL3BXSWRN/bundle.json","state":"https://pith.science/pith/LSUWAGBQZ532JSPHQSL3BXSWRN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LSUWAGBQZ532JSPHQSL3BXSWRN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:LSUWAGBQZ532JSPHQSL3BXSWRN","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":"19c5569c1ffde4801fdcea499f169098bb76719685092bf40e6c467020583a57","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2025-11-27T08:42:44Z","title_canon_sha256":"7e2f7896285daacf95ba36441aef2cd1a4beb2d6df71f05aa8843f8744b619bd"},"schema_version":"1.0","source":{"id":"2511.22222","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2511.22222","created_at":"2026-07-27T01:19:52Z"},{"alias_kind":"arxiv_version","alias_value":"2511.22222v3","created_at":"2026-07-27T01:19:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.22222","created_at":"2026-07-27T01:19:52Z"},{"alias_kind":"pith_short_12","alias_value":"LSUWAGBQZ532","created_at":"2026-07-27T01:19:52Z"},{"alias_kind":"pith_short_16","alias_value":"LSUWAGBQZ532JSPH","created_at":"2026-07-27T01:19:52Z"},{"alias_kind":"pith_short_8","alias_value":"LSUWAGBQ","created_at":"2026-07-27T01:19:52Z"}],"graph_snapshots":[{"event_id":"sha256:56fbbd01576ce40e1e1619053f83df9b11b65b65daff5a57f646068f43272591","target":"graph","created_at":"2026-07-27T01:19:52Z","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":"WiFo-2 achieves reliable and accurate zero-shot channel reconstruction, outperforming fully supervised task-specific models. With only 1% of the training samples required by supervised AI models, WiFo-2 achieves state-of-the-art performance across 9 distinct wireless tasks."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That a single model pretrained on the collected heterogeneous CSI dataset will continue to generalize reliably to new scenarios, configurations, and tasks not represented in the 11.6 billion training points."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"WiFo-2 is a space-time-frequency foundation model pretrained on heterogeneous CSI data that delivers strong zero-shot and few-shot performance across wireless communications and sensing tasks."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"WiFo-2 pretrained on 11.6 billion channel measurements unifies design for heterogeneous wireless systems via zero-shot reconstruction."}],"snapshot_sha256":"9a476e941ae850d27bad26e2d949dbc2e49dca292b540377aedeab307c5eb930"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"0931d8a55412ec4215158dd73a9442957b274e0ffe13885862ceae4e9c1b3f69"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2511.22222/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Emerging sixth-generation wireless systems are increasingly heterogeneous, with compatibility across diverse configurations, ubiquitous coverage, and expanded functionalities. Although deep learning has substantially benefited wireless system design, existing approaches are typically trained for specific system settings and scenarios with limited generalizability. Here we present WiFo-2, a space-time-frequency foundation model for unified wireless communications and sensing system design. Pretrained on a heterogeneous dataset of 11.6 billion channel state information (CSI) points, WiFo-2 learn","authors_text":"Boxun Liu, Liuqing Yang, Shijian Gao, Xiang Cheng, Xuanyu Liu, Xuesong Cai","cross_cats":[],"headline":"WiFo-2 pretrained on 11.6 billion channel measurements unifies design for heterogeneous wireless systems via zero-shot reconstruction.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2025-11-27T08:42:44Z","title":"WiFo-2: a generalist foundation model unifies heterogeneous wireless system design"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2511.22222","kind":"arxiv","version":3},"verdict":{"created_at":"2026-05-17T05:12:35.203995Z","id":"1f984c5c-8649-4052-8e4f-fb9ef6265480","model_set":{"reader":"grok-4.3"},"one_line_summary":"WiFo-2 is a space-time-frequency foundation model pretrained on heterogeneous CSI data that delivers strong zero-shot and few-shot performance across wireless communications and sensing tasks.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"WiFo-2 pretrained on 11.6 billion channel measurements unifies design for heterogeneous wireless systems via zero-shot reconstruction.","strongest_claim":"WiFo-2 achieves reliable and accurate zero-shot channel reconstruction, outperforming fully supervised task-specific models. With only 1% of the training samples required by supervised AI models, WiFo-2 achieves state-of-the-art performance across 9 distinct wireless tasks.","weakest_assumption":"That a single model pretrained on the collected heterogeneous CSI dataset will continue to generalize reliably to new scenarios, configurations, and tasks not represented in the 11.6 billion training points."}},"verdict_id":"1f984c5c-8649-4052-8e4f-fb9ef6265480"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:5e02cce4d0ea02d5449703a99409eb2220f65390e819b9ff3c6cf0e5457c1508","target":"record","created_at":"2026-07-27T01:19:52Z","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":"19c5569c1ffde4801fdcea499f169098bb76719685092bf40e6c467020583a57","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2025-11-27T08:42:44Z","title_canon_sha256":"7e2f7896285daacf95ba36441aef2cd1a4beb2d6df71f05aa8843f8744b619bd"},"schema_version":"1.0","source":{"id":"2511.22222","kind":"arxiv","version":3}},"canonical_sha256":"5ca9601830cf77a4c9e78497b0de568b43b66d0772f8c9c9564ef061fe2bfed7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5ca9601830cf77a4c9e78497b0de568b43b66d0772f8c9c9564ef061fe2bfed7","first_computed_at":"2026-07-27T01:19:52.874814Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-27T01:19:52.874814Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2Wp1naYwXyh8w2shrFSGU5Ccpv4hlU/RHELWJQuA5ZeU5Jwkpsu1FTR5lc37O0eOrikfkH/XU9qRQ1exUe7cBw==","signature_status":"signed_v1","signed_at":"2026-07-27T01:19:52.875762Z","signed_message":"canonical_sha256_bytes"},"source_id":"2511.22222","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5e02cce4d0ea02d5449703a99409eb2220f65390e819b9ff3c6cf0e5457c1508","sha256:56fbbd01576ce40e1e1619053f83df9b11b65b65daff5a57f646068f43272591"],"state_sha256":"63ac04e5a482fbb0ee488b149097fc1b49235aa31d87466a3b3da1021023cfef"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iF86JnpeSnXPVX5AeHGT1GnfdWGhgHfcSz81aKulh7HwsGhyijQnxX8vOrD/nuH7I2epxJsrniLWY4UjleUsBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T21:01:00.721268Z","bundle_sha256":"a6edf39f1e3ff4fcea17e14c8c35bac4e47364d6fc53a8bb00680f030e016d2a"}}