{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:JC2E4TWDYF3FADUOF6YKWPDTH5","short_pith_number":"pith:JC2E4TWD","canonical_record":{"source":{"id":"2607.27643","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2026-07-30T03:53:58Z","cross_cats_sorted":[],"title_canon_sha256":"083849370facd69b2c301c44592cab8243ee29a414faba1a8944ecf2a7ec9bb3","abstract_canon_sha256":"5fe891aab9d6cda1925766cfdcde8fdd64c7f7e942e7eaf6a707029e25f7f202"},"schema_version":"1.0"},"canonical_sha256":"48b44e4ec3c176500e8e2fb0ab3c733f57bbdb8c7a5a23054fbd7e341ff10688","source":{"kind":"arxiv","id":"2607.27643","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.27643","created_at":"2026-07-31T01:29:07Z"},{"alias_kind":"arxiv_version","alias_value":"2607.27643v1","created_at":"2026-07-31T01:29:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.27643","created_at":"2026-07-31T01:29:07Z"},{"alias_kind":"pith_short_12","alias_value":"JC2E4TWDYF3F","created_at":"2026-07-31T01:29:07Z"},{"alias_kind":"pith_short_16","alias_value":"JC2E4TWDYF3FADUO","created_at":"2026-07-31T01:29:07Z"},{"alias_kind":"pith_short_8","alias_value":"JC2E4TWD","created_at":"2026-07-31T01:29:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:JC2E4TWDYF3FADUOF6YKWPDTH5","target":"record","payload":{"canonical_record":{"source":{"id":"2607.27643","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2026-07-30T03:53:58Z","cross_cats_sorted":[],"title_canon_sha256":"083849370facd69b2c301c44592cab8243ee29a414faba1a8944ecf2a7ec9bb3","abstract_canon_sha256":"5fe891aab9d6cda1925766cfdcde8fdd64c7f7e942e7eaf6a707029e25f7f202"},"schema_version":"1.0"},"canonical_sha256":"48b44e4ec3c176500e8e2fb0ab3c733f57bbdb8c7a5a23054fbd7e341ff10688","receipt":{"kind":"pith_receipt","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"48b44e4ec3c176500e8e2fb0ab3c733f57bbdb8c7a5a23054fbd7e341ff10688","last_reissued_at":"2026-07-31T01:29:07.748626Z","signature_status":"unsigned_v0","first_computed_at":"2026-07-31T01:29:07.748626Z"},"source_kind":"arxiv","source_id":"2607.27643","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":"2026-07-31T01:29:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iC5pz8V0QTVUMLBz6eBsfLyrrC5DYpEluQqrvV47HrCyA4DCqAx7CvAruWceJXpQW+YToVqFRnxkdGQN2PvGCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T00:12:59.013098Z"},"content_sha256":"92dda6ab9ae4eb03d01ad3cb75bd0d62926df9dd8c3d278e48bf1ab54a2ab914","schema_version":"1.0","event_id":"sha256:92dda6ab9ae4eb03d01ad3cb75bd0d62926df9dd8c3d278e48bf1ab54a2ab914"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:JC2E4TWDYF3FADUOF6YKWPDTH5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Radar-Aided Near-Field Beam Prediction via Beam Map Learning for XL-MIMO V2I Communications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Chao-Kai Wen, Jiali Nie, Shi Jin, Xiaojie Li, Yuanhao Cui, Yu Han","submitted_at":"2026-07-30T03:53:58Z","abstract_excerpt":"Near-field beam training in extremely large-scale multiple-input multiple-output (XL-MIMO) vehicle-to-infrastructure (V2I) systems incurs high overhead due to large range-angle codebooks and rapid channel variation. This paper proposes a passive radar-aided framework for near-field beam prediction based on radar-to-beam map learning. By exploiting the spatial correlation between radar observations and communication signals, the proposed method maps radar Bartlett spectra to communication beam maps using a lightweight encoder-decoder convolutional neural network. Gaussian soft supervision is fu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.27643","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/2607.27643/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":"2026-07-31T01:29:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GU5YWBxjmKFHPhAieUgTcSVaUAjs7SglS5FVMSGOPfoC4zjHvrs7bh6X/naVdnq4R18mDdBUpPRa3d+VqRLLCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T00:12:59.013606Z"},"content_sha256":"a6ac2ea447043e05f79fcf076b280243dc0af7753f6ca8bae00bb7ff06ae9a85","schema_version":"1.0","event_id":"sha256:a6ac2ea447043e05f79fcf076b280243dc0af7753f6ca8bae00bb7ff06ae9a85"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JC2E4TWDYF3FADUOF6YKWPDTH5/bundle.json","state_url":"https://pith.science/pith/JC2E4TWDYF3FADUOF6YKWPDTH5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JC2E4TWDYF3FADUOF6YKWPDTH5/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":"2026-08-08T00:12:59Z","links":{"resolver":"https://pith.science/pith/JC2E4TWDYF3FADUOF6YKWPDTH5","bundle":"https://pith.science/pith/JC2E4TWDYF3FADUOF6YKWPDTH5/bundle.json","state":"https://pith.science/pith/JC2E4TWDYF3FADUOF6YKWPDTH5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JC2E4TWDYF3FADUOF6YKWPDTH5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:JC2E4TWDYF3FADUOF6YKWPDTH5","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":"5fe891aab9d6cda1925766cfdcde8fdd64c7f7e942e7eaf6a707029e25f7f202","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2026-07-30T03:53:58Z","title_canon_sha256":"083849370facd69b2c301c44592cab8243ee29a414faba1a8944ecf2a7ec9bb3"},"schema_version":"1.0","source":{"id":"2607.27643","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.27643","created_at":"2026-07-31T01:29:07Z"},{"alias_kind":"arxiv_version","alias_value":"2607.27643v1","created_at":"2026-07-31T01:29:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.27643","created_at":"2026-07-31T01:29:07Z"},{"alias_kind":"pith_short_12","alias_value":"JC2E4TWDYF3F","created_at":"2026-07-31T01:29:07Z"},{"alias_kind":"pith_short_16","alias_value":"JC2E4TWDYF3FADUO","created_at":"2026-07-31T01:29:07Z"},{"alias_kind":"pith_short_8","alias_value":"JC2E4TWD","created_at":"2026-07-31T01:29:07Z"}],"graph_snapshots":[{"event_id":"sha256:a6ac2ea447043e05f79fcf076b280243dc0af7753f6ca8bae00bb7ff06ae9a85","target":"graph","created_at":"2026-07-31T01:29:07Z","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/2607.27643/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Near-field beam training in extremely large-scale multiple-input multiple-output (XL-MIMO) vehicle-to-infrastructure (V2I) systems incurs high overhead due to large range-angle codebooks and rapid channel variation. This paper proposes a passive radar-aided framework for near-field beam prediction based on radar-to-beam map learning. By exploiting the spatial correlation between radar observations and communication signals, the proposed method maps radar Bartlett spectra to communication beam maps using a lightweight encoder-decoder convolutional neural network. Gaussian soft supervision is fu","authors_text":"Chao-Kai Wen, Jiali Nie, Shi Jin, Xiaojie Li, Yuanhao Cui, Yu Han","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2026-07-30T03:53:58Z","title":"Radar-Aided Near-Field Beam Prediction via Beam Map Learning for XL-MIMO V2I Communications"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.27643","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":"sha256:92dda6ab9ae4eb03d01ad3cb75bd0d62926df9dd8c3d278e48bf1ab54a2ab914","target":"record","created_at":"2026-07-31T01:29:07Z","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":"5fe891aab9d6cda1925766cfdcde8fdd64c7f7e942e7eaf6a707029e25f7f202","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2026-07-30T03:53:58Z","title_canon_sha256":"083849370facd69b2c301c44592cab8243ee29a414faba1a8944ecf2a7ec9bb3"},"schema_version":"1.0","source":{"id":"2607.27643","kind":"arxiv","version":1}},"canonical_sha256":"48b44e4ec3c176500e8e2fb0ab3c733f57bbdb8c7a5a23054fbd7e341ff10688","receipt":{"builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"48b44e4ec3c176500e8e2fb0ab3c733f57bbdb8c7a5a23054fbd7e341ff10688","first_computed_at":"2026-07-31T01:29:07.748626Z","kind":"pith_receipt","last_reissued_at":"2026-07-31T01:29:07.748626Z","receipt_version":"0.3","signature_status":"unsigned_v0"},"source_id":"2607.27643","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:92dda6ab9ae4eb03d01ad3cb75bd0d62926df9dd8c3d278e48bf1ab54a2ab914","sha256:a6ac2ea447043e05f79fcf076b280243dc0af7753f6ca8bae00bb7ff06ae9a85"],"state_sha256":"e3f79936f55528dfda95fac1ff534a49622a2fc3759abc0e157c8fc644a8f31f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UoO9APHstKl+E+sD9h0lv66d+9bI70+MLy80W5XkNPrArb5X+0/uLR+mhIJ/+MdjHWPFLXu/wOGkkXwtt5alDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T00:12:59.017943Z","bundle_sha256":"1841b2fa1134a8f97dd96174233cc89b8de4590c456b472dbebfe44c8540afba"}}