{"_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_aa056506dfaea293ecfa0269590f33be623a244dacf8b95f086f0a126dd9fb37","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_aa056506dfaea293ecfa0269590f33be623a244dacf8b95f086f0a126dd9fb37","axiom_type":"AX.OBS","body":{"axiom_subtype":"news.vendor_press.v1","category":"news","fingerprint":"0c1b73ea4470a8066bf8fda67a8ab2bd3b20474288b3b52522f8efa7254c76de","published":"Mon, 25 May 2026 00:00:00 -0400","receipt_hash":"0c1b73ea4470a8066bf8fda67a8ab2bd3b20474288b3b52522f8efa7254c76de","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":"0c1b73ea4470a8066bf8fda67a8ab2bd3b20474288b3b52522f8efa7254c76de","observed_at":"2026-05-25T04:43:48.841018Z","parent_run_hash":"56713422f06ad87427cd8cdcdb1ed341feb016b33c37198d9b168328d1df15fb","published":"Mon, 25 May 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:2602.05472v2 Announce Type: replace \nAbstract: The quest for expert-level reasoning in Large Language Models (LLMs) has been hampered by a persistent \\textit{reward bottleneck}: traditional reinforcement learning (RL) relies on scalar rewards that are \\textbf{costly} to scale, \\textbf{brittle} across domains, and \\textbf{blind} to the underlying logic of a solution. This reliance on external, impoverished signals prevents models from developing a deep, self-contained understanding of reasoning principles. We introduce \\textbf{ALIVE} (\\emph{Adversarial Learning with Instructive Verbal Evaluation}), a hands-free alignment framework that moves beyond scalar reward optimization toward intrinsic reasoning acquisition. Grounded in the principle of \\emph{Cognitive Synergy}, ALIVE unifies problem posing, solving, and judging within a single policy model to internalize the logic of correctness. By coupling adversarial learning with instructive verbal feedback, ALIVE enables models to inte","title":"ALIVE: Awakening LLM Reasoning via Adversarial Learning and Instructive Verbal Evaluation","url":"https://arxiv.org/abs/2602.05472","vendor":"arxiv_cs_ai"},"summary":"arXiv:2602.05472v2 Announce Type: replace \nAbstract: The quest for expert-level reasoning in Large Language Models (LLMs) has been hampered by a persistent \\textit{reward bottleneck}: traditional reinforcement learning (RL) relies on scalar rewards that are \\textbf{costly} to scale, \\textbf{brittle} across domains, and \\textbf{blind} to the underlying logic of a solution. This reliance on external, impoverished signals prevents models from developing a deep, self-contained understanding of reasoning principles. We introduce \\textbf{ALIVE} (\\emph{Adversarial Learning with Instructive Verbal Evaluation}), a hands-free alignment framework that moves beyond scalar reward optimization toward intrinsic reasoning acquisition. Grounded in the principle of \\emph{Cognitive Synergy}, ALIVE unifies problem posing, solving, and judging within a single policy model to internalize the logic of correctness. By coupling adversarial learning with instructive verbal feedback, ALIVE enables models to inte","title":"ALIVE: Awakening LLM Reasoning via Adversarial Learning and Instructive Verbal Evaluation","vendor":"arxiv_cs_ai"},"confidence":{"method":"deterministic"},"decision":"POSITIVE","issued_at":"2026-05-25T04:43:48Z","notes":"Spider vendor_press (news) news.vendor_press.v1","object":{"captured_by":"crovia.spider.vendor_press","primary_source_url":"https://arxiv.org/abs/2602.05472"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:6a435f183a0e1b0108cc04fc5c7e6dd99868472816bd1a3fab2e70363300c1201cd9e6af6add9e62cd4cc3de336bfe84bee6f2acdc54e080c1a9d622762f9d06","signer":"crovia.substrate","subject":{"observed_at":"2026-05-25T04:43:48Z","source_collector":"spider:vendor_press","target_id":"https://arxiv.org/abs/2602.05472"},"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":"a4e1a10800d0d5482dce95c0bbafd229c0b9059a679fbe00237c3c263473fe7b","leaf_index":149672,"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":"573df327d8ab6c2ff9f6ec8dde1ae660a868da990c4cea9c407112e35eb49377","side":"right"},{"sibling":"dc85d48f550431de08827d26181dc9edd5402ef9a5e610c780bad179d1a94e73","side":"right"},{"sibling":"04fb84a078d5f52d7d5679cea586512f9bc18027dd2eedfe971a8b8818a2c26f","side":"right"},{"sibling":"40f4a4dedb7380626ec4d82ae8c96e4342133529ea735a4460b9ca0e6eb69f92","side":"left"},{"sibling":"209f896313bfe6904ec0576b8a7d01c10ac62995548f4dd7deb1bce8ff42de3d","side":"right"},{"sibling":"a01bd5c21fe244fba996d404a0551dd1326f0ddd95e121df0ad296fc601b547f","side":"left"},{"sibling":"919f4560dbaa8584ff90fc1ba33dc74b59dff462d40837f8f3cbb7a4f3d2f375","side":"right"},{"sibling":"cb71a04113e54ec457f452a34b2778696e1747afe98ba9da255ce1f5fd8619d7","side":"left"},{"sibling":"e044acc52c31efe134c902c984299ea3452b42df993d096ed61ad8be9d530bbc","side":"right"},{"sibling":"6be461ecf12dedf98a31921aa7b5c32d4a32df6897e0f697b8bf1a4ba3e2d324","side":"right"},{"sibling":"c2861a8cef3eb66bf2726aa377c24a6bf6e8b7489dcd0e870b61d62a35ccadfb","side":"right"},{"sibling":"134949308b15cffd6792ee2cf678119af34d69a64764d7c89cd47573c94e1cda","side":"left"},{"sibling":"debbc1a6232ea7970009b91bdc2345041b2de5ac59551f955855d579001e512c","side":"right"},{"sibling":"216869846f40bd905626884f58cb67b9019e488b3656946a7808c436ab7339ac","side":"right"},{"sibling":"35ca36cee447f0ef7064a25d55f59357c66901e31427729c4c1d8b14aa8adb6c","side":"left"},{"sibling":"e734b0c6d6d13acb5f4cf00367b3913e5bbf1aa717377aa4e4820c6de24b7673","side":"right"},{"sibling":"4bbb7f78e96179bb9cdd06d7207b66b3503ab68e4880438263025b04ebe8f7ec","side":"right"},{"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":149835,"merkle_root":"51484a548bc0cf3d178eec868e98935c87f0aa83369143b7c96b048585e5ba01","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","run_id":"hourly_json_retrofit_20260525T053701Z","schema":"crovia.seal.v1","seal_family_version":"crovia-seal-family/1","seal_kind":"substrate_batch","sealed_at":"2026-05-25T05:37:33Z","sig_algorithm":"ed25519","signature":"99a657e53a015ace0bade5d7fbb3f60f950b6d1e1128251a63afcc454a492422cae5efdc0c18503b4f7e62f371bd48f2889be78d9048c4681d6c1e73f754d705","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_aa056506dfaea293ecfa0269590f33be623a244dacf8b95f086f0a126dd9fb37"}}