{"_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_c1a32d4e98755b522407064146c27c868fdcfe39074fd89b58e6c2fa1e057553","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_c1a32d4e98755b522407064146c27c868fdcfe39074fd89b58e6c2fa1e057553","axiom_type":"AX.OBS","body":{"axiom_subtype":"news.vendor_press.v1","category":"news","fingerprint":"8534e7b7784a87f2032e7b44c3ff00f38916a0336dcb6176e11ef706f0f528b9","published":"Thu, 28 May 2026 00:00:00 -0400","receipt_hash":"8534e7b7784a87f2032e7b44c3ff00f38916a0336dcb6176e11ef706f0f528b9","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":"8534e7b7784a87f2032e7b44c3ff00f38916a0336dcb6176e11ef706f0f528b9","observed_at":"2026-05-28T04:43:38.862500Z","parent_run_hash":"58f8b4a134069e0a15ea3949252489597eb86dd27c9ca3fb15c6fb838ce49ef3","published":"Thu, 28 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:2605.27701v1 Announce Type: new \nAbstract: We present Frost Training, a method for improving Monte Carlo-based policy optimization for a large family of LLM-as-a-judge tasks called Cross-Entropy Games. The key idea is to exploit the gradient of the reward function in embedding space. This signal is used in the Greedy Coordinate Gradient (GCG) jailbreaking technique; we demonstrate for the first time that it can also be used to boost model training. We validate our method using GRPO training for maximum-likelihood infilling. Frost Training improves the model's ability to generate high-scoring outputs, reaching higher maximum scores in a best-of-k setting, and does so at an increased speed.","title":"Cross-Entropy Games and Frost Training","url":"https://arxiv.org/abs/2605.27701","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.27701v1 Announce Type: new \nAbstract: We present Frost Training, a method for improving Monte Carlo-based policy optimization for a large family of LLM-as-a-judge tasks called Cross-Entropy Games. The key idea is to exploit the gradient of the reward function in embedding space. This signal is used in the Greedy Coordinate Gradient (GCG) jailbreaking technique; we demonstrate for the first time that it can also be used to boost model training. We validate our method using GRPO training for maximum-likelihood infilling. Frost Training improves the model's ability to generate high-scoring outputs, reaching higher maximum scores in a best-of-k setting, and does so at an increased speed.","title":"Cross-Entropy Games and Frost Training","vendor":"arxiv_cs_ai"},"confidence":{"method":"deterministic"},"decision":"POSITIVE","issued_at":"2026-05-28T04:43:38Z","notes":"Spider vendor_press (news) news.vendor_press.v1","object":{"captured_by":"crovia.spider.vendor_press","primary_source_url":"https://arxiv.org/abs/2605.27701"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:db43037aa6fc37885336eeb24b4b5297831f5dc47899a88ece13c2fc5ee404457db16c53e8029ad555cc7ea6c8a7919e76df899657a7f1f5cdec7af780267e08","signer":"crovia.substrate","subject":{"observed_at":"2026-05-28T04:43:38Z","source_collector":"spider:vendor_press","target_id":"https://arxiv.org/abs/2605.27701"},"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":"f3de5bd5193f04e42375653605e99ce37fc438d8620b8477e0d13cc117677abe","leaf_index":155584,"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":"567a6eb1327c9756fc08e5dbef70cfb1511d2b74d876b1a652160418b72b90f0","side":"right"},{"sibling":"d78936746540faf3a1d332b5a8ef601d84c115b459a1a700657510b3dfdfd429","side":"right"},{"sibling":"ee09c2eeaf1d2ab3f71307d1909b0f9dfd8ad27703a25b1bcfd4fe41a98fd854","side":"right"},{"sibling":"bc19e91bb17e37438fc70837ae18f49c7e9ad39c4b4a5ea97f26631291a575d2","side":"right"},{"sibling":"21eb9ab4f038faf7e837ab4ef9fa38b24ab2059cec33667dab2cdb26f1163261","side":"right"},{"sibling":"9271164ea0f2c427fba10eb695cb98be464e920bb5d4d839cf341ff315753f23","side":"right"},{"sibling":"7252c2f7874601f6b2d750e8a0aa6cc72c6924d6614fe237cd5c97d2026c5c17","side":"left"},{"sibling":"70e73f197ecea5d12ff2f9cf2ca33087110a782ea956c539b3058422ada4b2bd","side":"left"},{"sibling":"8c9c7b64ca041ef0029c35e402972d0bebed706fe997b9c587da6f5c811dcce1","side":"left"},{"sibling":"aa7a574eaea239ab8851da225206477d62a6ea5a15b65834f22d1c10288348e7","side":"left"},{"sibling":"7fcba2ee8232873e5d864902f28968d2be38867751c238f238f629eddb98b226","side":"left"},{"sibling":"816f233274bb10f5a122aac086a0c8c697b78fec67a4af55190bb596b7506fab","side":"left"},{"sibling":"d415e6939aee710631f5062799379b547d2c3e3d9a68f263bbb5a693285ab2ca","side":"left"},{"sibling":"d422d38e0ffe6e849b6ce3259d90c9d36497b4d7391a003e15591668c79028a7","side":"right"},{"sibling":"35ca36cee447f0ef7064a25d55f59357c66901e31427729c4c1d8b14aa8adb6c","side":"left"},{"sibling":"7b6f0bea4291a5e63574dfca9aa0f9756f450c9478c3d07474d39a7ababb51f9","side":"right"},{"sibling":"1d39fe14b21e2ebbfb87e882423b24ee9469eae1e4c77af5b799ac4db9537467","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":156177,"merkle_root":"c5705a0243d16afd8b1ebfd731b7aa304079c442c2a7906493c5bbed374c69ec","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","run_id":"hourly_json_retrofit_20260528T053701Z","schema":"crovia.seal.v1","seal_family_version":"crovia-seal-family/1","seal_kind":"substrate_batch","sealed_at":"2026-05-28T05:37:36Z","sig_algorithm":"ed25519","signature":"f087e13febc8bb6a2e0812610de64cebc65be92915518d9c4b230799c3b161839b04c4eb1b02741f938f35545a76ab76555b04c782bdc2f9a44852d171d65909","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_c1a32d4e98755b522407064146c27c868fdcfe39074fd89b58e6c2fa1e057553"}}