{"_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_0d2d2fa5fe0fe4b72d35e85f7cbb729387ad185cde6782b74408405cbf2a2c6f","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_0d2d2fa5fe0fe4b72d35e85f7cbb729387ad185cde6782b74408405cbf2a2c6f","axiom_type":"AX.OBS","body":{"axiom_subtype":"news.vendor_press.v1","category":"news","fingerprint":"99275d89cc2b29227a578bfb642d8faa243956bc7ee10cbb5b19490f758c2234","published":"Tue, 30 Jun 2026 00:00:00 -0400","receipt_hash":"99275d89cc2b29227a578bfb642d8faa243956bc7ee10cbb5b19490f758c2234","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":"99275d89cc2b29227a578bfb642d8faa243956bc7ee10cbb5b19490f758c2234","observed_at":"2026-06-30T04:43:04.087680Z","parent_run_hash":"74f7ab392cc702044101fe24a76a2fdad11164cd79ce725aad6c446a477e89c5","published":"Tue, 30 Jun 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:2606.27748v2 Announce Type: replace-cross \nAbstract: Transformer models rely on attention mechanism to capture long-range dependencies but suffer from quadratic complexity, limiting their scalability to long sequences. Kernel-based linear attention reduces this complexity but typically relies on fixed or weakly learnable kernels, restricting expressiveness and performance. In this work, we propose Flexformer, a flexible linear Transformer that learns attention kernels in a fully data-driven manner. Flexformer builds on random Fourier feature-based linear attention and treats spectral frequencies as trainable parameters, enabling the model to learn a broad family of attention kernels.\n  We develop both stationary and nonstationary variants, with the latter offering strictly greater expressiveness.\n  Extensive experiments on language modeling and sequence classification demonstrate that Flexformer consistently outperforms baselines. Moreover, Flexformer can be effectively distilled","title":"Flexformer: Flexible Linear Transformer with Learnable Attention Kernel","url":"https://arxiv.org/abs/2606.27748","vendor":"arxiv_cs_ai"},"summary":"arXiv:2606.27748v2 Announce Type: replace-cross \nAbstract: Transformer models rely on attention mechanism to capture long-range dependencies but suffer from quadratic complexity, limiting their scalability to long sequences. Kernel-based linear attention reduces this complexity but typically relies on fixed or weakly learnable kernels, restricting expressiveness and performance. In this work, we propose Flexformer, a flexible linear Transformer that learns attention kernels in a fully data-driven manner. Flexformer builds on random Fourier feature-based linear attention and treats spectral frequencies as trainable parameters, enabling the model to learn a broad family of attention kernels.\n  We develop both stationary and nonstationary variants, with the latter offering strictly greater expressiveness.\n  Extensive experiments on language modeling and sequence classification demonstrate that Flexformer consistently outperforms baselines. Moreover, Flexformer can be effectively distilled","title":"Flexformer: Flexible Linear Transformer with Learnable Attention Kernel","vendor":"arxiv_cs_ai"},"confidence":{"method":"deterministic"},"decision":"POSITIVE","issued_at":"2026-06-30T04:43:04Z","notes":"Spider vendor_press (news) news.vendor_press.v1","object":{"captured_by":"crovia.spider.vendor_press","primary_source_url":"https://arxiv.org/abs/2606.27748"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:5fab3c0cda84f35b88ef3a8b7523643027cd733a47a108f54750e89629b0e29726694d65b4b65b41cdf9f98a9a03064de2f3d0f08800bbb225a7975b5ed65a0c","signer":"crovia.substrate","subject":{"observed_at":"2026-06-30T04:43:04Z","source_collector":"spider:vendor_press","target_id":"https://arxiv.org/abs/2606.27748"},"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":"db040dffe9e53e94397a48aa9d152ad88105a0c57e722cce6fe1a280fbb27aed","leaf_index":265258,"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":"58c3fe2e2beda55d1420e0456bce7902c79c1a5679331cdf9e51669ef56a4f72","side":"right"},{"sibling":"c00b429e48f00df9b99c11561b3ce9690b5541c14cf7b097e912d94f52ae48aa","side":"left"},{"sibling":"8272a6bcfe3c7a7fc4b1228acb42323caee17988ed0c49f26073b63599f9b40e","side":"right"},{"sibling":"b602c16a606db10062a1bbb5763eac1f463da36cfcc543848b0353091e46d901","side":"left"},{"sibling":"af7c2de27919f42a0e65a43e1a9fafcbeedf56dae0dec0654b19aae478de2b70","side":"right"},{"sibling":"cccd81c33dafd50c7fdc64d63e8563daa68321bb44450ef3ec0b5f8628b5c9d2","side":"left"},{"sibling":"ff273c8201c286328de95b4c3f7ad04c9fb005c67fda3de45c54b72f9f5db5b3","side":"right"},{"sibling":"3cde187de7516743e6cd60012ce336384e260530220514cdd49599ef710e14e5","side":"right"},{"sibling":"2da116eff026ee371a88b8eb1a0aeb87679b487f1fbf1623083121809f754234","side":"right"},{"sibling":"eef13ac8a36eb836ad77846f6ef08d9afa4dcc380a64b88c542b57231522db0a","side":"right"},{"sibling":"a2cf20d97c095bc4fa7c4f5bb4f2266dd953e3d733d7e185aa164763491b312c","side":"left"},{"sibling":"f9b4bed84fa6990c71ad2887c91bda183001f05f6b648f21d1273045a26b11fd","side":"left"},{"sibling":"173d2dc4b29ee04ea41d6d0ebc334c4bc2d46e7ee4230c94765413f24fb4bc42","side":"right"},{"sibling":"112461f7c0ec411116fb5c6c90fe95cea9d8f188b9fe08afe25a837ac02d0071","side":"right"},{"sibling":"ea9488204352c49db8f7daf05eefcd7628ecf9413830346674801a99d0654a94","side":"right"},{"sibling":"6261c13b9922cb657f10d1e5d36ec15d8771cf8766e36c61dcbffb7bed57e396","side":"right"},{"sibling":"fa19aa3faf287618b820bcfceebb366152ad521dd20ef9f51e977816663e448b","side":"right"},{"sibling":"c32f943406b62d1fc59b7f7e243492174c8e1caba8c8a2705f86c773315736e0","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":265374,"merkle_root":"9636001ecab173cb6af10dc7c71eb14585daa62f9c0a6f027046f05633156891","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","run_id":"hourly_json_retrofit_20260630T053701Z","schema":"crovia.seal.v1","seal_family_version":"crovia-seal-family/1","seal_kind":"substrate_batch","sealed_at":"2026-06-30T05:38:03Z","sig_algorithm":"ed25519","signature":"6d4b8fd9b9da856cbb5fba7540877c6a63fa18a5ec3eaf28dc4d6d1c64921c9c0565f8c95f4b7aec0e7744fd7754844f051baf863db708cad768765b416a7b0c","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_0d2d2fa5fe0fe4b72d35e85f7cbb729387ad185cde6782b74408405cbf2a2c6f"}}