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 "name": "Streaming Local Anomaly Detection With Partial Observation In Multi Agent Systems  C8A31D241B",
 "text": "Local Anomaly Detection with Partial Observation in Multi-agent Systems as a Data Matching Game presents the following source proposition: This paper proposes a distributed training method to address this question. This proposition is pertinent to Kaal's source-bound claim that Anomaly detection and behavioral analysis models trained on agent data may replicate the biases in that data and fail to detect novel deviations absent from the training set. The proposed response is an extension: the relationship should remain limited to the retrieved source proposition and the mapped Kaal claim unless fuller source review supports a broader conclusion.",
 "author": {
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  "identifier": "https://orcid.org/0009-0008-7840-1847"
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  "External evidence level: abstract indexed.",
  "Mapping review tier: moderate-confidence claim review.",
  "Primary mapping confidence: 0.3524.",
  "The source-to-claim mapping remains explicitly ambiguous and is published with that limitation."
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  "url": "https://doi.org/10.65109/kryz2031"
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  "url": "https://wulfkaal.github.io/claims/5245185-028",
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  "paper": "Wulf A. Kaal, How can we Best Monitor AI Agents",
  "authors": [
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  "workAuthors": [
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   "Tansu Alpcan",
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    "whyRelevant": "Shared high-information concepts: detection, multi, agent. Scope: validators actively penalize detected collusion; honest agents compete on quality in the same jobs.",
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