{"_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_1ad9689eb279a61d65e8bcf68f5fbca189ea6891366d71f669e0672ed8463fd8","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_1ad9689eb279a61d65e8bcf68f5fbca189ea6891366d71f669e0672ed8463fd8","axiom_type":"AX.OBS","body":{"axiom_subtype":"news.vendor_press.v1","category":"news","fingerprint":"42272d7006c70e58b734c1d3c2a3b0e65401b6191fc9f3b0da443308960ad271","published":"Tue, 30 Jun 2026 00:00:00 -0400","receipt_hash":"42272d7006c70e58b734c1d3c2a3b0e65401b6191fc9f3b0da443308960ad271","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":"42272d7006c70e58b734c1d3c2a3b0e65401b6191fc9f3b0da443308960ad271","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:2606.29150v1 Announce Type: new \nAbstract: Discrete flow models have recently shown promising performance on few-step text generation; however, when naively applied to structured reasoning tasks such as Sudoku and Zebra puzzles, they converge confidently to incorrect answers (solving only $\\sim$36% of Sudoku puzzles). We introduce Flow Reasoning Models (FRMs), a training and test-time-scaling framework for structured reasoning with flow models. We make the observation that, despite their poor solve rate, flow models can act as their own verifiers. A correct answer is a stable fixed point of the denoising dynamics, returning to itself when re-noised and re-solved. This enables a test-time-scaling paradigm: propose many candidate solutions and keep those that are dynamically stable, which alone reaches high solve rates on Sudoku-Shah (~$100\\%$) and Zebra ($95.9\\%$). This even generalizes to harder out-of-distribution puzzles like Sudoku-Extreme ($96.1\\%$), without ever training on ","title":"Flow Reasoning Models: Scaling Reasoning Through Iterative Self-Refinement","url":"https://arxiv.org/abs/2606.29150","vendor":"arxiv_cs_ai"},"summary":"arXiv:2606.29150v1 Announce Type: new \nAbstract: Discrete flow models have recently shown promising performance on few-step text generation; however, when naively applied to structured reasoning tasks such as Sudoku and Zebra puzzles, they converge confidently to incorrect answers (solving only $\\sim$36% of Sudoku puzzles). We introduce Flow Reasoning Models (FRMs), a training and test-time-scaling framework for structured reasoning with flow models. We make the observation that, despite their poor solve rate, flow models can act as their own verifiers. A correct answer is a stable fixed point of the denoising dynamics, returning to itself when re-noised and re-solved. This enables a test-time-scaling paradigm: propose many candidate solutions and keep those that are dynamically stable, which alone reaches high solve rates on Sudoku-Shah (~$100\\%$) and Zebra ($95.9\\%$). This even generalizes to harder out-of-distribution puzzles like Sudoku-Extreme ($96.1\\%$), without ever training on ","title":"Flow Reasoning Models: Scaling Reasoning Through Iterative Self-Refinement","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/2606.29150"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:cef2013ff10777032ef31fdd83c2fa98b90af061a996b27c6e2efc14474f0f358498928d674e8804312cc6a9b78526fa952cf1289ad48690d6b6e748c952b70f","signer":"crovia.substrate","subject":{"observed_at":"2026-06-30T04:43:04Z","source_collector":"spider:vendor_press","target_id":"https://arxiv.org/abs/2606.29150"},"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":"88ad22071ba74e6125cea24646ab51a64bd8257a42120a3c493b16940055c102","leaf_index":264628,"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":"001ccabf83b0c15494de94014456afc89e737225422be97167f5ab319dfdb91c","side":"right"},{"sibling":"c87b2b2d77436de8e564e8f04593b2e3016f70e509657034f5ce2f7529708829","side":"right"},{"sibling":"2ce29691dd2cbedb65faf494dc554a9227b0998afbcffc886908e30a8667a1ae","side":"left"},{"sibling":"46a378a40188ebea7f613a3739263e042ced0258c240399d152ac5d989764d75","side":"right"},{"sibling":"2f84e347b124338972e38d0f79c3f35c77dc90f644a079694d1c16b9ad760cde","side":"left"},{"sibling":"d5a6e24286f5c70bfa5d770b18f58bde2d10bd39fe152e050df615f3d76255b6","side":"left"},{"sibling":"72ca31d723b9813d5bddaf5e89440ca5d3bb8898e526bd86c03fbafd589a70de","side":"right"},{"sibling":"0adb33f90b5325f1bcd6d9d08abc79306f0275cec357b6a5d5a9aeb8dae006eb","side":"left"},{"sibling":"a18aac5bf38684ad71f816483beee037e0b60af41587ea19ba92b263e2076d3e","side":"left"},{"sibling":"e814db3b1e34c91aa2d674ee041f8e093e00869024c9f6f8c4d72cab70c3a87e","side":"right"},{"sibling":"23994bf0974e5c9c7f63a61b4f0a48b0ca756a4adc34a8f85f878e774c37dfbe","side":"right"},{"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_1ad9689eb279a61d65e8bcf68f5fbca189ea6891366d71f669e0672ed8463fd8"}}