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 "name": "Peer Rated Task Performance Changes Agent Reputation",
 "text": "Lou et al. provide a narrow operational analogue to Kaal's standing mechanism. Their DRF framework raises an LLM agent's reputation when its peer-derived task score meets the task threshold and lowers reputation when the score falls below it. The source supports performance-contingent gains and losses in standing. It does not establish independent or cryptographic verification, and it tests a simulated rating network rather than Kaal's substrate.",
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  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0009-0008-7840-1847"
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 "datePublished": "2026-08-08",
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  "llm-agents"
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  "External evidence level: complete public arXiv v1 manuscript bound through title, seven authors, category, date, PDF hash, HTML identity, extracted text, section, equations, and printed-page locators.",
  "Mapping review tier: independent substantive scholarly-growth qualification.",
  "The rating network evaluates agent task plans and performance through peer agents rather than independent or cryptographic verification.",
  "The task threshold is set from task requirements and empirical experience rather than derived from Kaal's validation design.",
  "The experiments use simulated LLM-agent teams and do not evaluate Kaal's cohort or reference implementation.",
  "The source does not establish production security, resistance to collusion, or identity-replacement resistance.",
  "Semantic Scholar fresh search returned HTTP 429, while the complete arXiv manuscript remained publicly accessible."
 ],
 "currentDebate": {
  "name": "DRF: LLM-AGENT Dynamic Reputation Filtering Framework",
  "url": "https://arxiv.org/abs/2509.05764"
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  "identifier": "kaal:claim:7261018-024",
  "url": "https://wulfkaal.github.io/claims/7261018-024",
  "citation": "Wulf A. Kaal, Empirical Evaluation of the Agentic Reputation Substrate: Deliberation, the Composition of Error, and the Registered Measurement of Agency Costs in a Controlled Multi-Model Cohort (2026). SSRN: https://ssrn.com/abstract=7261018",
  "paper": "Wulf A. Kaal, Empirical Evaluation of the Agentic Reputation Substrate: Deliberation, the Composition of Error, and the Registered Measurement of Agency Costs in a Controlled Multi-Model Cohort",
  "authors": [
   "Wulf A. Kaal"
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  "year": "2026",
  "ssrn": "https://ssrn.com/abstract=7261018",
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   "name": "DRF: LLM-AGENT Dynamic Reputation Filtering Framework",
   "url": "https://arxiv.org/abs/2509.05764"
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 "publicationStatus": "public",
 "recordTypeNote": "Dated commentary position extending a scholarly corpus claim. Not a verbatim claim extracted from the paper.",
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 "evidenceLevel": "complete public arXiv v1 manuscript bound through title, seven authors, category, date, PDF hash, HTML identity, extracted text, section, equations, and printed-page locators",
 "reviewTier": "independent substantive scholarly-growth qualification",
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 "mappingAmbiguous": false,
 "mappingMethod": "independent substantive scholarly-growth one-to-one review",
 "mappingWhyRelevant": "The source expressly increases or decreases an LLM agent's reputation according to an evaluated task score, while exposing the narrower peer-rating verification model.",
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  "sourceProposition": "Lou et al. assign reputation to LLM agents after a multi-agent rating round. Their mechanism increases reputation when a peer-derived task score meets the task threshold and decreases reputation when the score falls below it. This independently supports Kaal's proposition that standing can rise or fall through evaluated work. The source uses peer ratings rather than independent or cryptographic verification, and its experiments test a simulated LLM-agent framework rather than Kaal's substrate.",
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    "text": "it indicates that agent i has performed well in that task time interval and its reputation should be increased, as shown in Equation (7).",
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     "section": "4.2 LLM-Agent Reputation Iteration Mechanism",
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   {
    "text": "it indicates that agent i has performed poorly in that task time interval, likely due to malicious interference or inherently being a subpar agent. Therefore, we need to use Equation (8) to decrease the reputation of the agent.",
    "locator": {
     "version": "arXiv:2509.05764v1",
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  "workAuthors": [
   "Yuwei Lou",
   "Hao Hu",
   "Shaocong Ma",
   "Zongfei Zhang",
   "Liang Wang",
   "Jidong Ge",
   "Xianping Tao"
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    "The experiments use simulated LLM-agent teams and do not evaluate Kaal's cohort or reference implementation.",
    "The source does not establish production security, resistance to collusion, or identity-replacement resistance.",
    "Semantic Scholar fresh search returned HTTP 429, while the complete arXiv manuscript remained publicly accessible."
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   "v1Correction": "V1 passage fields accurately paraphrased the evidence. V2 replaces them with exact contiguous source excerpts before publication."
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