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 "@id": "https://wulfkaal.github.io/positions/2026-07-31-2796",
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 "name": "Historical 732D82E85F1Ed38083C6",
 "text": "Beyond the Hype: Distinguishing LLM Agents from Gen-AI Startups Through Model Context Protocol should be assessed against Kaal's source-bound claim that The move by AI developers toward smaller training datasets raises the risk of overfitting, especially with complex models, which forces LLM developers to rely on regularization to counteract overfitting of the model to the training data. The current metadata indicates a plausible connection through model context protocol, but the defensible response is a qualification until the source text confirms agreement, scope, methods, and limitations.",
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  "identifier": "https://orcid.org/0009-0008-7840-1847"
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  "holds for smaller datasets used in LLM development",
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  "External evidence level: metadata only.",
  "Mapping review tier: ambiguity triage before claim review.",
  "The literature-to-claim mapping remains explicitly ambiguous and should not be treated as a settled equivalence."
 ],
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  "name": "Beyond the Hype: Distinguishing LLM Agents from Gen-AI Startups Through Model Context Protocol",
  "url": "https://www.semanticscholar.org/paper/bf4e99acb0e1e5e56e9efb2d5227603ef3bb128e"
 },
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  "identifier": "kaal:claim:4755632-003",
  "url": "https://wulfkaal.github.io/claims/4755632-003",
  "citation": "Wulf A. Kaal, AI Learning - Decentralized Governance to Optimize Human Output Datasets for AI Learning (2024). SSRN: https://ssrn.com/abstract=4755632",
  "paper": "AI Learning - Decentralized Governance to Optimize Human Output Datasets for AI Learning",
  "authors": [
   "Wulf A. Kaal"
  ],
  "year": "2024",
  "ssrn": "https://ssrn.com/abstract=4755632",
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 ],
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 "recordTypeNote": "Dated commentary position extending a scholarly corpus claim. Not a verbatim claim extracted from the paper.",
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 "version": "1.0",
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 "canonicalForm": "https://wulfkaal.github.io/positions/2026-07-31-2796.md",
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 "evidenceLevel": "metadata only",
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 "mappingConfidence": 0.2388,
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 "mappingWhyRelevant": "Shared high-information concepts: llm, agents. Scope: holds for smaller datasets used in LLM development; risk increases with model complexity.",
 "sourceProvenance": {
  "source": "Semantic Scholar",
  "api": "https://api.semanticscholar.org/graph/v1/paper/search/bulk",
  "query": "model context protocol",
  "queryId": "concept:9c5da690faad",
  "page": 1,
  "sourceRank": 742,
  "retrievedAt": "2026-07-31T13:58:04.059Z",
  "citationCount": 0,
  "venue": "Academy of Management Proceedings",
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