{"_canonicalization":{"envelope_id":"axm_ + sha256(envelope minus {signature, axiom_id, anchors})","envelope_signature":"ed25519(envelope minus {signature, axiom_id})","json":"sort_keys=True, separators=(',',':'), ensure_ascii=False, allow_nan=False, utf-8","leaf_hash":"sha256(0x00 || canonical_json(envelope_full))","seal_signature":"ed25519(seal minus {signature, sig_algorithm})"},"axiom_id":"axm_3e9ce99d9cff79e3ef10a83a1e3ff68016791c42f4d35cf3d668d7d023cdca94","bitcoin_anchor":{"bitcoin_attestations":[],"calendar_attestations":[],"ots_url":"","stamped_at":"","status":"pending_next_stamp"},"envelope":{"anchors":[{"chain":"crovia.axiom_graph","height":0,"merkle_proof":"spider_vendor_press_v1","root_at_anchor":"spider_vendor_press_v1"}],"axiom_id":"axm_3e9ce99d9cff79e3ef10a83a1e3ff68016791c42f4d35cf3d668d7d023cdca94","axiom_type":"AX.OBS","body":{"axiom_subtype":"news.vendor_press.v1","category":"news","fingerprint":"0640b7e2894f10dd9fb28706933ec980edffce3986b818a9a185a46b05979ff0","published":"Wed, 15 Jul 2026 00:00:00 -0400","receipt_hash":"0640b7e2894f10dd9fb28706933ec980edffce3986b818a9a185a46b05979ff0","schema":"spider.news.vendor_press.v1","spider":"vendor_press","spider_record":{"axiom_subtype":"news.vendor_press.v1","category":"news","decision_hint":"POSITIVE","envelope_target":"AX.OBS","fingerprint":"0640b7e2894f10dd9fb28706933ec980edffce3986b818a9a185a46b05979ff0","observed_at":"2026-07-15T04:44:03.592429Z","parent_run_hash":"d49a6cf532e74153266f377b7760fc948d950d80ed41fc3e3eb82b58f5597ead","published":"Wed, 15 Jul 2026 00:00:00 -0400","runtime_version":"0.1.0","schema":"spider.news.vendor_press.v1","source_status":200,"source_url":"https://export.arxiv.org/rss/cs.AI","spider":"vendor_press","summary_excerpt":"arXiv:2607.12462v1 Announce Type: new \nAbstract: Existing traffic forecasting models commonly focus on extracting spatial dependencies, particularly global spatial information, which characterizes the representations obtained through interactions between each individual node and all nodes across the traffic network. However, the underlying mechanism by which such global information is modeled and extracted remains insufficiently investigated. Whether global information must be extracted by high-degree-of-freedom adaptive attention or can be captured by a simple global aggregation operator remains unclear. For this purpose, we design a controlled ablation framework that replaces only the spatial mixing module to test attention-based global interaction. Across six traffic benchmarks, uniform full-range mixing and standard spatial attention each achieve lower MAE on three datasets, with only a 0.14% difference in mean MAE, while the former reduces node-scale spatial mixing complexity from","title":"Do We Really Need Transformers for Global Spatial Information Extraction in Traffic Forecasting?","url":"https://arxiv.org/abs/2607.12462","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.12462v1 Announce Type: new \nAbstract: Existing traffic forecasting models commonly focus on extracting spatial dependencies, particularly global spatial information, which characterizes the representations obtained through interactions between each individual node and all nodes across the traffic network. However, the underlying mechanism by which such global information is modeled and extracted remains insufficiently investigated. Whether global information must be extracted by high-degree-of-freedom adaptive attention or can be captured by a simple global aggregation operator remains unclear. For this purpose, we design a controlled ablation framework that replaces only the spatial mixing module to test attention-based global interaction. Across six traffic benchmarks, uniform full-range mixing and standard spatial attention each achieve lower MAE on three datasets, with only a 0.14% difference in mean MAE, while the former reduces node-scale spatial mixing complexity from","title":"Do We Really Need Transformers for Global Spatial Information Extraction in Traffic Forecasting?","vendor":"arxiv_cs_ai"},"confidence":{"method":"deterministic"},"decision":"POSITIVE","issued_at":"2026-07-15T04:44:03Z","notes":"Spider vendor_press (news) news.vendor_press.v1","object":{"captured_by":"crovia.spider.vendor_press","primary_source_url":"https://arxiv.org/abs/2607.12462"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:e48c2cfdb85d0938bed149a7ebffccc42264ca2c58f8f49d1f97d421579162b90e1ac146553ccd168b2892380b2955e74265ff90f39aa147deb99e8e646e5e01","signer":"crovia.substrate","subject":{"observed_at":"2026-07-15T04:44:03Z","source_collector":"spider:vendor_press","target_id":"https://arxiv.org/abs/2607.12462"},"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":"0383c22b370797ac9552c0f0f0c0ad4baa86c943ac42af71a0b4f0b6db1b6b77","leaf_index":316380,"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":"90c7d3a776f81f403198f2a6262b7566a6745e9c4a3521f4c53860a947f363e3","side":"right"},{"sibling":"3ee0793f0be2419781a2166dfb9420a747844c47c9f3cc7b60b9a2f984278a85","side":"right"},{"sibling":"17a7cbf1db1e8831ab6df6982f561cd50a1207cb214fc7805e10b7c6cb83d837","side":"left"},{"sibling":"c4623c782aae659cf528690b9ab8e7e17dbe0369eebd3f5da8258eb750191011","side":"left"},{"sibling":"afc9249eaf545ce549c76a86654c30dc381dc3a70f8b30b7d724127926786985","side":"left"},{"sibling":"2e6e43c7a0f7f6499621f0f438d8189318d67ceb374377041278440cc38473f6","side":"right"},{"sibling":"8dc6b151fc86f1b43f490c2263dd78189a9778cebe1755f2bcd01eb2c6239831","side":"left"},{"sibling":"9a178a31a5d97bdb1bf054b31ec2fcc6cf9b633c6d3f460e934451a35bac9c25","side":"left"},{"sibling":"31b0ff6eae164fcff8882af2b581471a08fffa2373505e1f33f01e361c94a42f","side":"left"},{"sibling":"e1ebad13fafe4a9a53c35e1f19fa67a19c940f70a4211d1fab42cf49be486a3c","side":"left"},{"sibling":"c265283bb70fb86740bdfa059cfe36cfea4a5f903b841822b2954814237de972","side":"right"},{"sibling":"0cb62c0ada57a2406a6bcb100889d3e8b29a15efeed07adaff5bb90a5e80612a","side":"right"},{"sibling":"0b69289b25462ddd6166f6f49004cfc8ada0ab4f10adafe188347817bdd46e37","side":"left"},{"sibling":"1418b281cd985b5ed411ef25f2017a1826cc14919b6fad3934e6ceeec693699b","side":"right"},{"sibling":"f302542c38ba7c3aab7c9280dd60259ecec777dca6e6f71b6f0729b0b8791b72","side":"left"},{"sibling":"d8b9143917b539c543cf4448cec00131f8b807bd8004979c54ebe09798748c66","side":"left"},{"sibling":"abe4a8c706e530484d1e96a8988cb09eab85928b2050985500ab289753fe3eec","side":"right"},{"sibling":"f436dccf82aa2c1eb7bfa3eb84316e116aaf64dc55cd9592597118f6cb0648f6","side":"right"},{"sibling":"1cecb7f447febd025aac272837c80de218aecc6485d2395a509b2a1f1b9c746e","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":316730,"merkle_root":"a8e6e5be81ea6f5b5f2227422459bf39455fe9f0b6602b4d1ce6977dbfd78bc7","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","run_id":"hourly_json_retrofit_20260715T053701Z","schema":"crovia.seal.v1","seal_family_version":"crovia-seal-family/1","seal_kind":"substrate_batch","sealed_at":"2026-07-15T05:38:24Z","sig_algorithm":"ed25519","signature":"df1678d268b5a07413e2ca6e748c3f40b6cfedea930a18d843489a4ab513da791bf0a886caab1b918d0989f8ebaaf3d0035ca2aa777913b1ad29979f1deb9a0c","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_3e9ce99d9cff79e3ef10a83a1e3ff68016791c42f4d35cf3d668d7d023cdca94"}}