{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:PHEXOPLXGCWTCU3QEHDRXRNUUV","short_pith_number":"pith:PHEXOPLX","canonical_record":{"source":{"id":"2606.00506","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-30T03:39:15Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0620bca2188d1d26be2b63078c8e0ec59cec905f2b0c2ac9d68dbf91486ec7ef","abstract_canon_sha256":"d4b2a79602fed0093c2684591369ccdc4ab3cd77103d6be156b48a3a5fc9c8c2"},"schema_version":"1.0"},"canonical_sha256":"79c9773d7730ad31537021c71bc5b4a5715be7565937b18862b64d987ad4f2cf","source":{"kind":"arxiv","id":"2606.00506","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.00506","created_at":"2026-06-02T01:03:56Z"},{"alias_kind":"arxiv_version","alias_value":"2606.00506v1","created_at":"2026-06-02T01:03:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.00506","created_at":"2026-06-02T01:03:56Z"},{"alias_kind":"pith_short_12","alias_value":"PHEXOPLXGCWT","created_at":"2026-06-02T01:03:56Z"},{"alias_kind":"pith_short_16","alias_value":"PHEXOPLXGCWTCU3Q","created_at":"2026-06-02T01:03:56Z"},{"alias_kind":"pith_short_8","alias_value":"PHEXOPLX","created_at":"2026-06-02T01:03:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:PHEXOPLXGCWTCU3QEHDRXRNUUV","target":"record","payload":{"canonical_record":{"source":{"id":"2606.00506","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-30T03:39:15Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0620bca2188d1d26be2b63078c8e0ec59cec905f2b0c2ac9d68dbf91486ec7ef","abstract_canon_sha256":"d4b2a79602fed0093c2684591369ccdc4ab3cd77103d6be156b48a3a5fc9c8c2"},"schema_version":"1.0"},"canonical_sha256":"79c9773d7730ad31537021c71bc5b4a5715be7565937b18862b64d987ad4f2cf","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T01:03:56.746894Z","signature_b64":"0Tjt7oSgsCua/F6AiFEx/cxDGK0mSqk4eFsR59V+KZYNZr/i5zugECb5D8QWMv92EJer47C7m46Bts8U1lrZBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"79c9773d7730ad31537021c71bc5b4a5715be7565937b18862b64d987ad4f2cf","last_reissued_at":"2026-06-02T01:03:56.746477Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T01:03:56.746477Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.00506","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":"2026-06-02T01:03:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JyvJEF2rFxttxPXVKVnWXnXZoMc09DLzsfgmF0rNum5pDc5BhWpUVt+T9REWX+7rup27YkalOhZIOGYwmMPNBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T01:57:19.793196Z"},"content_sha256":"c0f0d5b78931d910886f86ce5f4e39c5614c29eb229cddd25ca67203d83c3535","schema_version":"1.0","event_id":"sha256:c0f0d5b78931d910886f86ce5f4e39c5614c29eb229cddd25ca67203d83c3535"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:PHEXOPLXGCWTCU3QEHDRXRNUUV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"EnergyMamba: An Uncertainty-Aware Graph-Enhanced Selective State Space Model for Energy Consumption Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Dahai Yu, Guang Wang, Lin Jiang, Rongchao Xu","submitted_at":"2026-05-30T03:39:15Z","abstract_excerpt":"Energy consumption prediction is essential for efficient grid management, demand-side optimization, and sustainable energy planning. Although advanced machine learning methods have been employed for better prediction performance, existing works have two key limitations: (1) they usually formulate this task as a purely time-series prediction problem without explicitly modeling the spatial dependencies among different regions, and (2) they fail to provide reliable predictions with uncertainty estimates under abnormal situations such as extreme weather events. To advance existing research, we pro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.00506","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/2606.00506/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":"2026-06-02T01:03:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2vydk986c0X+8NSbIIVCFRY3xtNlG82M+/FtYKI8sP0Cb5UDaqQxUD9Rte5De48mYwQPVGMgQeZcibkMBRf0BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T01:57:19.793575Z"},"content_sha256":"c1b1a0cbcc442b69e4eaadb9c22958f2782380c9a5517c29134ca82110aeb2ce","schema_version":"1.0","event_id":"sha256:c1b1a0cbcc442b69e4eaadb9c22958f2782380c9a5517c29134ca82110aeb2ce"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PHEXOPLXGCWTCU3QEHDRXRNUUV/bundle.json","state_url":"https://pith.science/pith/PHEXOPLXGCWTCU3QEHDRXRNUUV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PHEXOPLXGCWTCU3QEHDRXRNUUV/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":"2026-06-29T01:57:19Z","links":{"resolver":"https://pith.science/pith/PHEXOPLXGCWTCU3QEHDRXRNUUV","bundle":"https://pith.science/pith/PHEXOPLXGCWTCU3QEHDRXRNUUV/bundle.json","state":"https://pith.science/pith/PHEXOPLXGCWTCU3QEHDRXRNUUV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PHEXOPLXGCWTCU3QEHDRXRNUUV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:PHEXOPLXGCWTCU3QEHDRXRNUUV","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":"d4b2a79602fed0093c2684591369ccdc4ab3cd77103d6be156b48a3a5fc9c8c2","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-30T03:39:15Z","title_canon_sha256":"0620bca2188d1d26be2b63078c8e0ec59cec905f2b0c2ac9d68dbf91486ec7ef"},"schema_version":"1.0","source":{"id":"2606.00506","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.00506","created_at":"2026-06-02T01:03:56Z"},{"alias_kind":"arxiv_version","alias_value":"2606.00506v1","created_at":"2026-06-02T01:03:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.00506","created_at":"2026-06-02T01:03:56Z"},{"alias_kind":"pith_short_12","alias_value":"PHEXOPLXGCWT","created_at":"2026-06-02T01:03:56Z"},{"alias_kind":"pith_short_16","alias_value":"PHEXOPLXGCWTCU3Q","created_at":"2026-06-02T01:03:56Z"},{"alias_kind":"pith_short_8","alias_value":"PHEXOPLX","created_at":"2026-06-02T01:03:56Z"}],"graph_snapshots":[{"event_id":"sha256:c1b1a0cbcc442b69e4eaadb9c22958f2782380c9a5517c29134ca82110aeb2ce","target":"graph","created_at":"2026-06-02T01:03:56Z","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/2606.00506/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Energy consumption prediction is essential for efficient grid management, demand-side optimization, and sustainable energy planning. Although advanced machine learning methods have been employed for better prediction performance, existing works have two key limitations: (1) they usually formulate this task as a purely time-series prediction problem without explicitly modeling the spatial dependencies among different regions, and (2) they fail to provide reliable predictions with uncertainty estimates under abnormal situations such as extreme weather events. To advance existing research, we pro","authors_text":"Dahai Yu, Guang Wang, Lin Jiang, Rongchao Xu","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-30T03:39:15Z","title":"EnergyMamba: An Uncertainty-Aware Graph-Enhanced Selective State Space Model for Energy Consumption Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.00506","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:c0f0d5b78931d910886f86ce5f4e39c5614c29eb229cddd25ca67203d83c3535","target":"record","created_at":"2026-06-02T01:03:56Z","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":"d4b2a79602fed0093c2684591369ccdc4ab3cd77103d6be156b48a3a5fc9c8c2","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-30T03:39:15Z","title_canon_sha256":"0620bca2188d1d26be2b63078c8e0ec59cec905f2b0c2ac9d68dbf91486ec7ef"},"schema_version":"1.0","source":{"id":"2606.00506","kind":"arxiv","version":1}},"canonical_sha256":"79c9773d7730ad31537021c71bc5b4a5715be7565937b18862b64d987ad4f2cf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"79c9773d7730ad31537021c71bc5b4a5715be7565937b18862b64d987ad4f2cf","first_computed_at":"2026-06-02T01:03:56.746477Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-02T01:03:56.746477Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0Tjt7oSgsCua/F6AiFEx/cxDGK0mSqk4eFsR59V+KZYNZr/i5zugECb5D8QWMv92EJer47C7m46Bts8U1lrZBw==","signature_status":"signed_v1","signed_at":"2026-06-02T01:03:56.746894Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.00506","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c0f0d5b78931d910886f86ce5f4e39c5614c29eb229cddd25ca67203d83c3535","sha256:c1b1a0cbcc442b69e4eaadb9c22958f2782380c9a5517c29134ca82110aeb2ce"],"state_sha256":"4140b99426ed77306208c52f21dafe54c04569821fd8da84ea29a7175e6a5d7e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ED5KzoaYfq8fkADpOzj4Rt1DaiD0H6M5pMoFPqHx5eELECCdVdqW1rvdGpKbgBcqcBHFULqYbErd5A0B6nRaBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-29T01:57:19.795434Z","bundle_sha256":"4b1a5b352dd5f73072af8fb7eab2095b8be79dd7abe08a8a21881a8a14f3a46d"}}