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Long-range information propagation is a key challenge while learning representations, wherein it is important to retain and update information over long temporal horizons. Existing approaches restrict models to capture one-hop or local temporal neighborhoods and fail to capture multi-hop or global structural patterns. To mitigate this, we derive a parameter-efficient state-space modeling framework for continuous-time dynamic graphs (CTDG-SSM) from first principles. We first introduce continuous-time Topology-Aware higher order polynomial projection operator (CTT-HiPPO), a novel memory-based reformulation of HiPPO to jointly encode temporal dynamics and graph structure. The solution from CTT-HiPPO is obtained by projecting the classical HiPPO solution through a polynomial of the Laplacian matrix, yield","title":"Learning Long Range Spatio-Temporal Representations over Continuous Time Dynamic Graphs with State Space Models","url":"https://arxiv.org/abs/2606.04672","vendor":"arxiv_cs_ai"},"summary":"arXiv:2606.04672v2 Announce Type: cross \nAbstract: Continuous-time dynamic graphs (CTDGs) provide a richer framework to capture fine-grained temporal patterns in evolving relational data. Long-range information propagation is a key challenge while learning representations, wherein it is important to retain and update information over long temporal horizons. Existing approaches restrict models to capture one-hop or local temporal neighborhoods and fail to capture multi-hop or global structural patterns. To mitigate this, we derive a parameter-efficient state-space modeling framework for continuous-time dynamic graphs (CTDG-SSM) from first principles. We first introduce continuous-time Topology-Aware higher order polynomial projection operator (CTT-HiPPO), a novel memory-based reformulation of HiPPO to jointly encode temporal dynamics and graph structure. The solution from CTT-HiPPO is obtained by projecting the classical HiPPO solution through a polynomial of the Laplacian matrix, yield","title":"Learning Long Range Spatio-Temporal Representations over Continuous Time Dynamic Graphs with State Space Models","vendor":"arxiv_cs_ai"},"confidence":{"method":"deterministic"},"decision":"POSITIVE","issued_at":"2026-06-05T04:43:18Z","notes":"Spider vendor_press (news) news.vendor_press.v1","object":{"captured_by":"crovia.spider.vendor_press","primary_source_url":"https://arxiv.org/abs/2606.04672"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:5c97ebfb46905de4c5739d873c7de2c45b0a219fbeec36773685c03286658fbcd58ca36b82fad2e44f8ca6eb1dee3692208873c8826e0262352d572da7e5b40d","signer":"crovia.substrate","subject":{"observed_at":"2026-06-05T04:43:18Z","source_collector":"spider:vendor_press","target_id":"https://arxiv.org/abs/2606.04672"},"tsa":{"authority":"crovia.substrate.bootstrap","rfc3161_token":"{\"kind\":\"crovia.bootstrap.tsa\",\"source_jsonl\":\"/opt/crovia/spider/data/news/vendor_press_v1.jsonl\",\"source_seal_merkle_root\":\"spider_vendor_press_v1\",\"upgrade_path\":\"Sessione H \\u2014 OpenTimestamps weekly anchor\"}"},"zk_mode":"clear","zk_proof":null},"ledger":{"leaf_hash":"6ba854a4af06921848952c3224d2d4d63615526b3a5807502bb2cf4082034345","leaf_index":216051,"ledger_path":"/opt/crovia/substrate/axiom_ledger.jsonl"},"merkle_proof":{"hash_alg":"sha256","leaf_prefix":"0x00","node_prefix":"0x01","odd_leaf_rule":"duplicate_last","path":[{"sibling":"d51423f4e5f4fd8a3a8ef6037aad35e4a484e4eb483e286e1d4f5ed71b3e6180","side":"left"},{"sibling":"84cdee7d9d3c8ceadaadc36a380d35a3f9d9329e8877317ce6f516ff2c325a06","side":"left"},{"sibling":"2518dcbb97417f5a8ea01e8bebabcc94bf89d1686d7ef6da23ceeb1b68d1efa4","side":"right"},{"sibling":"7db2dee1fbb6ef14733f4ef13a9243fe2b897a936ae26935d1d3e410aa6d8d22","side":"right"},{"sibling":"8bd2ae69e60460a30f0ed8f9b5a70d96bc4bd43656446e864a9d8c7f84b01b6d","side":"left"},{"sibling":"fa2fd112d39dac7cdc18ac1779f97d20ea942ce9f9a19774a22dcca910601984","side":"left"},{"sibling":"21ba3aa71af1cd7179c6bc067241197a4e092e6c68977def9c245081227eb701","side":"left"},{"sibling":"c232f0a98c6fd02eabc3155eb94d48261422543316c6f3198a92f09295fa8b65","side":"left"},{"sibling":"80c8f198b42e8ab9cecdef375223da498ae1c8691834ed79774638c44919af1a","side":"left"},{"sibling":"f71e5c82f0914ff67c825d0f6af89d5d4bac7dcf562dec6401a8c8ca4cb8b205","side":"left"},{"sibling":"16f9b88f45c6a3d5fd8e7a2c3900e7e76360c2a1a16cc1cd2ca83272e56ae03d","side":"right"},{"sibling":"37f2470d3b1c888c0179581620fded264990c2f86e80df0b5c379f1bd64cdb41","side":"left"},{"sibling":"24923c25478a136cad4a294856cb961d86e6a8aeb5b3c49e6e498039915fcf21","side":"right"},{"sibling":"10e59757ce3fa727404b5c4439d7171bdba5009288900a100ca9c9133d992bd6","side":"right"},{"sibling":"b2590791b920ca2a4ed39de126d2c0b1a10d9e7e62f572f12425f214e767b6e1","side":"left"},{"sibling":"084df6220452656a13c111887e1d2f105bb69fbb1e32c7e2ea96213f61503243","side":"right"},{"sibling":"c300cf0154c136afc09b1702a0be98f4ba5b6dc5cf57e8cc714ec1eaf4196eff","side":"left"},{"sibling":"d841ad93efda0869e5eb97678f348f03f5caab4353e05ff4bf18f47fb945b822","side":"left"}]},"schema":"crovia.axiom_proof.v1","seal":{"first_collector_run_id":"","first_receipt_hash":"","jsonl_path":"/opt/crovia/substrate/axiom_ledger.jsonl","key_id":"430895f101d38164","last_collector_run_id":"","last_receipt_hash":"","leaf_count":216172,"merkle_root":"459571be344a3b31e70215c839ba3e664ec8adfe4a34b6f4f0840892215ddbfd","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","run_id":"hourly_json_retrofit_20260605T053701Z","schema":"crovia.seal.v1","seal_family_version":"crovia-seal-family/1","seal_kind":"substrate_batch","sealed_at":"2026-06-05T05:38:28Z","sig_algorithm":"ed25519","signature":"d05ae4e1f30ece667b6ae35dce5276e69368ad7c979df791d19de6b9daf150d2ac5be528d75ff13fb5734608cc1c7b74afd6781d390d84517c87818d7d302d02","signer_version":"1.1.0"},"trust_root":{"key_id":"430895f101d38164","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","signature_algorithm":"ed25519","url":"/registry/canon/TRUST_ROOT.md"},"verifier":{"spec":"/registry/canon/AXIOM_RECEIPT_v1.md","url":"/v/axm_3d7ccb891d8d0f4ccf781a4ef14a9ba2f88646e22c374fb504afc3e91c258d0f"}}