{
 "@context": "https://schema.org",
 "@type": "DefinedTerm",
 "@id": "https://wulfkaal.github.io/entities/verifiers",
 "identifier": "kaal:entity:verifiers",
 "name": "Verifiers",
 "termCode": "verifiers",
 "inDefinedTermSet": {
  "@id": "https://wulfkaal.github.io/entities/index.json"
 },
 "author": {
  "@type": "Person",
  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0009-0008-7840-1847"
 },
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   "name": "status",
   "value": "derived"
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   "value": 4
  },
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   "name": "work_count",
   "value": 2
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   "name": "year_span",
   "value": [
    "2018",
    "2026"
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 "subjectOf": [
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/3128900-025",
   "identifier": "kaal:claim:3128900-025",
   "text": "Verifiers stake proportionally smaller amounts of reputation tokens than workers, because their higher reputation scores make them less likely to be malicious actors.",
   "abstract": "Similarly, verifiers stake (a proportionally smaller) amount of reputation tokens because verifiers have a higher reputation score and are therefore less likely to be malicious actors.",
   "citation": "Wulf A. Kaal, Decentralized Mechanical Turk Through Verified Reputation (2018). SSRN: https://ssrn.com/abstract=3128900",
   "datePublished": "2018",
   "claim_type": "mechanism",
   "confidence": "argued",
   "is_failure_mode": false,
   "scope_conditions": [
    "verifiers with higher reputation scores than the workers they verify"
   ],
   "source_pdf_sha256": "381d72e85d976e2af852e8ec3bba87bc6a05ccb3349a2c3dd6f9e64de96ab4b0",
   "status": "current"
  },
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/7598518-015",
   "identifier": "kaal:claim:7598518-015",
   "text": "Feng et al. (2025) show that verifier-selected synthetic data can prevent collapse, and the paper concedes the core of that result: verifier selection is an established remedy, and the oracle ceiling and the Gaussian setting are established.",
   "abstract": "Intelligence models increasingly learn from data that earlier Intelligence models produced, and when that loop repeats the rare cases disappear first. This paper preregisters a test of whether a reputation-weighted validator pool, filtering the training data of a recursive replacement loop, keeps those rare cases alive. The outcomes remain sealed until a replication of the original model-collapse result succeeds.",
   "citation": "Wulf A. Kaal, Can Reputation Prevent Model Collapse? A Preregistered Test of Reputation-Weighted Data Filtering (2026). SSRN: https://ssrn.com/abstract=7598518",
   "datePublished": "2026",
   "claim_type": "condition",
   "confidence": "reported",
   "is_failure_mode": false,
   "scope_conditions": [
    "The result is prior work, not a finding of this study.",
    "What this paper isolates is how to weight several assessors behind a validity filter, not whether verifier selection works."
   ],
   "source_pdf_sha256": "cd22d148b2b55c2e27f087dcaa61cdf44ad7022b400b355c31a9cd7e43a31749",
   "status": "current"
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  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/7598518-016",
   "identifier": "kaal:claim:7598518-016",
   "text": "Yi et al. (2026) report that an imperfect external verifier drives the model toward the verifier's own knowledge center.",
   "abstract": "Intelligence models increasingly learn from data that earlier Intelligence models produced, and when that loop repeats the rare cases disappear first. This paper preregisters a test of whether a reputation-weighted validator pool, filtering the training data of a recursive replacement loop, keeps those rare cases alive. The outcomes remain sealed until a replication of the original model-collapse result succeeds.",
   "citation": "Wulf A. Kaal, Can Reputation Prevent Model Collapse? A Preregistered Test of Reputation-Weighted Data Filtering (2026). SSRN: https://ssrn.com/abstract=7598518",
   "datePublished": "2026",
   "claim_type": "mechanism",
   "confidence": "reported",
   "is_failure_mode": true,
   "scope_conditions": [
    "The finding is prior work cited by the paper and is not measured in this study.",
    "It is a limit on verifier-based selection generally, including the pool under test."
   ],
   "source_pdf_sha256": "cd22d148b2b55c2e27f087dcaa61cdf44ad7022b400b355c31a9cd7e43a31749",
   "status": "current"
  },
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/7598518-018",
   "identifier": "kaal:claim:7598518-018",
   "text": "Qiao et al. (2026) report that verifiers which see only fragmented slices of the reference can accelerate tail loss.",
   "abstract": "Intelligence models increasingly learn from data that earlier Intelligence models produced, and when that loop repeats the rare cases disappear first. This paper preregisters a test of whether a reputation-weighted validator pool, filtering the training data of a recursive replacement loop, keeps those rare cases alive. The outcomes remain sealed until a replication of the original model-collapse result succeeds.",
   "citation": "Wulf A. Kaal, Can Reputation Prevent Model Collapse? A Preregistered Test of Reputation-Weighted Data Filtering (2026). SSRN: https://ssrn.com/abstract=7598518",
   "datePublished": "2026",
   "claim_type": "mechanism",
   "confidence": "reported",
   "is_failure_mode": true,
   "scope_conditions": [
    "The finding is prior work cited by the paper and is not measured in this study.",
    "The pool under test applies one domain-general validity bit, so this is not the failure mode it is exposed to."
   ],
   "source_pdf_sha256": "cd22d148b2b55c2e27f087dcaa61cdf44ad7022b400b355c31a9cd7e43a31749",
   "status": "current"
  }
 ],
 "description": "4 claims in the published works of Wulf A. Kaal carry the concept tag 'verifiers'. Derived node: a roster, not an adjudicated definition."
}