{"_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_2b6195ed16a5f0b1afd7b7ded55fa27ab0901fdd9afb7c292d3a055e70ebf9cc","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_2b6195ed16a5f0b1afd7b7ded55fa27ab0901fdd9afb7c292d3a055e70ebf9cc","axiom_type":"AX.OBS","body":{"axiom_subtype":"news.vendor_press.v1","category":"news","fingerprint":"f08122038d886f0927554a12d3063c4554adcbf7f21cc7e7d47dc126288b7d77","published":"Tue, 07 Jul 2026 00:00:00 -0400","receipt_hash":"f08122038d886f0927554a12d3063c4554adcbf7f21cc7e7d47dc126288b7d77","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":"f08122038d886f0927554a12d3063c4554adcbf7f21cc7e7d47dc126288b7d77","observed_at":"2026-07-07T04:43:08.294902Z","parent_run_hash":"fc40a96e5d33ecc82922806c3ad18de4725d7af03964570396c8af4e48fb5bc1","published":"Tue, 07 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:2607.04389v1 Announce Type: new \nAbstract: It is increasingly common to aggregate predictions from multiple LLMs, each with domain expertise or access to private tools and data, to improve collective prediction performance. In decentralized settings, aggregation weights need to be determined without access to models' private information and should remain robust to strategic reporting. We propose a family of advantage-aligned wagering mechanisms for LLM aggregation (WALLA), in which each model reports a prediction and a learned wager, and predictions are aggregated using wagers as weights. WALLA introduces a leave-one-out baseline into the net payout function, yielding three desirable properties: (1) dominant-strategy incentive compatibility of prediction under arbitrary belief structure, (2) advantage--wager alignment, where the optimal wager is proportional to the model's expected score advantage, and (3) prediction-agnostic wager optimization, enabling decentralized learning of","title":"Decentralized Aggregation of LLM Predictions via Wagering Mechanisms","url":"https://arxiv.org/abs/2607.04389","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.04389v1 Announce Type: new \nAbstract: It is increasingly common to aggregate predictions from multiple LLMs, each with domain expertise or access to private tools and data, to improve collective prediction performance. In decentralized settings, aggregation weights need to be determined without access to models' private information and should remain robust to strategic reporting. We propose a family of advantage-aligned wagering mechanisms for LLM aggregation (WALLA), in which each model reports a prediction and a learned wager, and predictions are aggregated using wagers as weights. WALLA introduces a leave-one-out baseline into the net payout function, yielding three desirable properties: (1) dominant-strategy incentive compatibility of prediction under arbitrary belief structure, (2) advantage--wager alignment, where the optimal wager is proportional to the model's expected score advantage, and (3) prediction-agnostic wager optimization, enabling decentralized learning of","title":"Decentralized Aggregation of LLM Predictions via Wagering Mechanisms","vendor":"arxiv_cs_ai"},"confidence":{"method":"deterministic"},"decision":"POSITIVE","issued_at":"2026-07-07T04:43:08Z","notes":"Spider vendor_press (news) news.vendor_press.v1","object":{"captured_by":"crovia.spider.vendor_press","primary_source_url":"https://arxiv.org/abs/2607.04389"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:3bb900db06adfc4324bcdcf7398d2cc9ae3ba05469f3919ba90ed6a3cb02a6bd69b68a6c9bedd5e7b7f644540df480db83da00e374562816f274c035edfd3d06","signer":"crovia.substrate","subject":{"observed_at":"2026-07-07T04:43:08Z","source_collector":"spider:vendor_press","target_id":"https://arxiv.org/abs/2607.04389"},"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":"f914a4db7f979d5185e27eb347aa917e986d542af3347899be5c2ec80c0b07e1","leaf_index":288764,"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":"dd8ca2d841d6ee4b8fe946844fd406aaad9327781952f405d4b2286034c1e2e7","side":"right"},{"sibling":"d77bad83dd53f5ccb4deecae08f847829d270d1276fe921a63525e126b017c2e","side":"right"},{"sibling":"9fc753066f94d39d5c228d5dfc26bb5c81a57e058b5f984c734588850e2d0061","side":"left"},{"sibling":"b78c4f3c976cfccd8682001674e9025e3cd2312dd9082d741027be89e3ba7168","side":"left"},{"sibling":"5547927da08df8599cc06965777697fcd81850beeacc9c257378b724de456bea","side":"left"},{"sibling":"6ce2490462f2183557d667ea543ad19b275e012200585fa65831bfe675408ade","side":"left"},{"sibling":"61f7272108de819a7a77493153b988651785961143dd7f7cbf21c3d662725b0d","side":"left"},{"sibling":"693d22f9477576140ca2120520ac7b4b633fb122e7f590d3abc01e28b77f9cfe","side":"left"},{"sibling":"2b24ae0b0e86a6bb85711be0b56eabcfea8026b8cad7364829595d5562ea5b79","side":"left"},{"sibling":"66acb8614a0400fe91b4430bfa4ca8f7f359fb95aea361dfea6a7c88a9fe24a7","side":"left"},{"sibling":"b76af82f0e95812185e25016354f4707e41c1bacfe819e4948e5e364745511e8","side":"left"},{"sibling":"175b61fd9088baa970ad449ad7fc5d7babb21d38120cfa8c28053ae9d448ac83","side":"right"},{"sibling":"aae716235efcb893a1f219dbcd5095070d08a497769fc6d50c14976aa26d5750","side":"right"},{"sibling":"a75ab4319e241beeddb1b3f5705febe0422937926c3479923ccfb0b0082fa4e3","side":"left"},{"sibling":"bd04fa605f883bfb2b81510d045b1e85e555a03da3be083619f61384dfe40ff8","side":"left"},{"sibling":"1b72ad8d12164fdf329e7871711be99d8569d140b21f94056e6962da21da9ce1","side":"right"},{"sibling":"5f5109c2bfdcc7a7e70554bba25862e2d7ce86b6b0cd48a72eb66d2eb735f321","side":"right"},{"sibling":"05fd8a05dddb2e7f72bbb5b290ca55c378f1aed709f132277908d9a5f30eb605","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":289613,"merkle_root":"dc428b9d9ba248d4f93f63147bf7c700bf5be7f500cec6c3507b9df6e9401601","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","run_id":"hourly_json_retrofit_20260707T053701Z","schema":"crovia.seal.v1","seal_family_version":"crovia-seal-family/1","seal_kind":"substrate_batch","sealed_at":"2026-07-07T05:38:15Z","sig_algorithm":"ed25519","signature":"c468b0e183383ab71992be40bda451093e6cd8cd8efb0d26f68e135a804b287c209d12a0f4fdd95c69c835c04b78df8cb1903dee1f53d4730b36f5332a29fe05","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_2b6195ed16a5f0b1afd7b7ded55fa27ab0901fdd9afb7c292d3a055e70ebf9cc"}}