{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:4S4EZS6BQCLGT2IEHPD5MGFVM6","short_pith_number":"pith:4S4EZS6B","canonical_record":{"source":{"id":"2607.22637","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-06-22T13:20:01Z","cross_cats_sorted":["cs.IT","math.IT"],"title_canon_sha256":"0fa30a48efef791d2a9e706bc53dbdee11b3b17d7201893c4e798060879e3455","abstract_canon_sha256":"cb208dfffa6d5d0d7c0eb1e64a10a85c0afdc9ade7c3f984b0e0bb79d89b15ae"},"schema_version":"1.0"},"canonical_sha256":"e4b84ccbc1809669e9043bc7d618b567b6855bafecbde67be968bbdc0be9ac62","source":{"kind":"arxiv","id":"2607.22637","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.22637","created_at":"2026-07-28T00:21:46Z"},{"alias_kind":"arxiv_version","alias_value":"2607.22637v1","created_at":"2026-07-28T00:21:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.22637","created_at":"2026-07-28T00:21:46Z"},{"alias_kind":"pith_short_12","alias_value":"4S4EZS6BQCLG","created_at":"2026-07-28T00:21:46Z"},{"alias_kind":"pith_short_16","alias_value":"4S4EZS6BQCLGT2IE","created_at":"2026-07-28T00:21:46Z"},{"alias_kind":"pith_short_8","alias_value":"4S4EZS6B","created_at":"2026-07-28T00:21:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:4S4EZS6BQCLGT2IEHPD5MGFVM6","target":"record","payload":{"canonical_record":{"source":{"id":"2607.22637","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-06-22T13:20:01Z","cross_cats_sorted":["cs.IT","math.IT"],"title_canon_sha256":"0fa30a48efef791d2a9e706bc53dbdee11b3b17d7201893c4e798060879e3455","abstract_canon_sha256":"cb208dfffa6d5d0d7c0eb1e64a10a85c0afdc9ade7c3f984b0e0bb79d89b15ae"},"schema_version":"1.0"},"canonical_sha256":"e4b84ccbc1809669e9043bc7d618b567b6855bafecbde67be968bbdc0be9ac62","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-28T00:21:46.419665Z","signature_b64":"Na/40Xfc14tRL6rTI25fuURyflR+3rNskk5d1IOF0HA/oCkKBGjCIG9uGxWhBJfPLoCQmkbVQ4v1hVwWlJyXAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e4b84ccbc1809669e9043bc7d618b567b6855bafecbde67be968bbdc0be9ac62","last_reissued_at":"2026-07-28T00:21:46.418822Z","signature_status":"signed_v1","first_computed_at":"2026-07-28T00:21:46.418822Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.22637","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-28T00:21:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WQDirgc6cZehHiq9ibamL5vFIH/znG3OnEjXypFF1yL8zVk4bbT83mqf9eO/DybRUoEkx7iIkDLiY5K0Z3sCBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T06:21:49.260893Z"},"content_sha256":"5ee8d5a8c088e2680082ff95b5ab1e51f49dde46618be135a35c4c751e70e6f6","schema_version":"1.0","event_id":"sha256:5ee8d5a8c088e2680082ff95b5ab1e51f49dde46618be135a35c4c751e70e6f6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:4S4EZS6BQCLGT2IEHPD5MGFVM6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fast Cross-Scenario Adaptation of CSI Models via Channel Conditional Parameter Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT"],"primary_cat":"cs.AI","authors_text":"Jiacong Hu, Jie Song, Mingli Song, Siyu Wu, Xudong Zou, Yuanyu Wan, Zunlei Feng","submitted_at":"2026-06-22T13:20:01Z","abstract_excerpt":"Deep learning has shown strong potential for massive multiple-input multiple-output (Massive MIMO) physical-layer tasks, including channel state information (CSI) feedback and channel estimation. However, environmental heterogeneity can severely degrade CSI models in unseen scenarios, while conventional adaptation requires target-domain data and substantial computation. This paper proposes Channel Conditional Parameter Generation (CCPG), an end-to-end pipeline for rapid deployment of CSI models in dynamic wireless environments. CCPG identifies scene-sensitive adaptation bottlenecks through com"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.22637","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/2607.22637/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-28T00:21:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RslCV+k2Tq4ynmmQzmKGeqsJwEdgHrVN8e5UrF8fok3Zp6UpvJqfkyROqcuxgjbFcw+79SATEtlPOmF6rlpbAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T06:21:49.261417Z"},"content_sha256":"76dc2e4c8c9fce2fea55feb39dea63913f71f9abfd35afe3586727538e9eb024","schema_version":"1.0","event_id":"sha256:76dc2e4c8c9fce2fea55feb39dea63913f71f9abfd35afe3586727538e9eb024"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4S4EZS6BQCLGT2IEHPD5MGFVM6/bundle.json","state_url":"https://pith.science/pith/4S4EZS6BQCLGT2IEHPD5MGFVM6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4S4EZS6BQCLGT2IEHPD5MGFVM6/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":"2026-08-07T06:21:49Z","links":{"resolver":"https://pith.science/pith/4S4EZS6BQCLGT2IEHPD5MGFVM6","bundle":"https://pith.science/pith/4S4EZS6BQCLGT2IEHPD5MGFVM6/bundle.json","state":"https://pith.science/pith/4S4EZS6BQCLGT2IEHPD5MGFVM6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4S4EZS6BQCLGT2IEHPD5MGFVM6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:4S4EZS6BQCLGT2IEHPD5MGFVM6","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":"cb208dfffa6d5d0d7c0eb1e64a10a85c0afdc9ade7c3f984b0e0bb79d89b15ae","cross_cats_sorted":["cs.IT","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-06-22T13:20:01Z","title_canon_sha256":"0fa30a48efef791d2a9e706bc53dbdee11b3b17d7201893c4e798060879e3455"},"schema_version":"1.0","source":{"id":"2607.22637","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.22637","created_at":"2026-07-28T00:21:46Z"},{"alias_kind":"arxiv_version","alias_value":"2607.22637v1","created_at":"2026-07-28T00:21:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.22637","created_at":"2026-07-28T00:21:46Z"},{"alias_kind":"pith_short_12","alias_value":"4S4EZS6BQCLG","created_at":"2026-07-28T00:21:46Z"},{"alias_kind":"pith_short_16","alias_value":"4S4EZS6BQCLGT2IE","created_at":"2026-07-28T00:21:46Z"},{"alias_kind":"pith_short_8","alias_value":"4S4EZS6B","created_at":"2026-07-28T00:21:46Z"}],"graph_snapshots":[{"event_id":"sha256:76dc2e4c8c9fce2fea55feb39dea63913f71f9abfd35afe3586727538e9eb024","target":"graph","created_at":"2026-07-28T00:21:46Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2607.22637/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep learning has shown strong potential for massive multiple-input multiple-output (Massive MIMO) physical-layer tasks, including channel state information (CSI) feedback and channel estimation. However, environmental heterogeneity can severely degrade CSI models in unseen scenarios, while conventional adaptation requires target-domain data and substantial computation. This paper proposes Channel Conditional Parameter Generation (CCPG), an end-to-end pipeline for rapid deployment of CSI models in dynamic wireless environments. CCPG identifies scene-sensitive adaptation bottlenecks through com","authors_text":"Jiacong Hu, Jie Song, Mingli Song, Siyu Wu, Xudong Zou, Yuanyu Wan, Zunlei Feng","cross_cats":["cs.IT","math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-06-22T13:20:01Z","title":"Fast Cross-Scenario Adaptation of CSI Models via Channel Conditional Parameter Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.22637","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:5ee8d5a8c088e2680082ff95b5ab1e51f49dde46618be135a35c4c751e70e6f6","target":"record","created_at":"2026-07-28T00:21:46Z","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":"cb208dfffa6d5d0d7c0eb1e64a10a85c0afdc9ade7c3f984b0e0bb79d89b15ae","cross_cats_sorted":["cs.IT","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-06-22T13:20:01Z","title_canon_sha256":"0fa30a48efef791d2a9e706bc53dbdee11b3b17d7201893c4e798060879e3455"},"schema_version":"1.0","source":{"id":"2607.22637","kind":"arxiv","version":1}},"canonical_sha256":"e4b84ccbc1809669e9043bc7d618b567b6855bafecbde67be968bbdc0be9ac62","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e4b84ccbc1809669e9043bc7d618b567b6855bafecbde67be968bbdc0be9ac62","first_computed_at":"2026-07-28T00:21:46.418822Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-28T00:21:46.418822Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Na/40Xfc14tRL6rTI25fuURyflR+3rNskk5d1IOF0HA/oCkKBGjCIG9uGxWhBJfPLoCQmkbVQ4v1hVwWlJyXAQ==","signature_status":"signed_v1","signed_at":"2026-07-28T00:21:46.419665Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.22637","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5ee8d5a8c088e2680082ff95b5ab1e51f49dde46618be135a35c4c751e70e6f6","sha256:76dc2e4c8c9fce2fea55feb39dea63913f71f9abfd35afe3586727538e9eb024"],"state_sha256":"0ab67f78fc40479f90797491da964b19375cc823a725215a56e83bdf45ad2a17"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yL0g4y69DJI+cCUq1zVztDnrR+OdhHLvL5/6rEKINqlxHPlMmc1nC2aYBQrKuVmYt8SRSXrynlPLcAESpoQjDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T06:21:49.266586Z","bundle_sha256":"4f5fa0ea5621b3f4cff92bcc4ba8366396dba3a7bc27eea52bb6429c22fd452d"}}