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 "identifier": "kaal:position:2026-07-31-7731",
 "additionalType": "https://wulfkaal.github.io/positions/schema.json#AffirmedPositionClaim",
 "name": "Streaming Sequential Cooperative Multi Agent Reinforcement Learning 04Fb985B76",
 "text": "Sequential Cooperative Multi-Agent Reinforcement Learning presents the following source proposition: The complex interactions among agents make this problem extremely difficult. This proposition is pertinent to Kaal's source-bound claim that Explainable reinforcement learning research has not yet produced usable explanations: the field relies on toy examples, omits user testing, produces explanations that are themselves complex, uses basic visualizations, and rarely open sources its code. 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": {
  "@type": "Person",
  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0009-0008-7840-1847"
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 "datePublished": "2026-07-31",
 "dateModified": "2026-07-31",
 "creativeWorkStatus": "Affirmed",
 "responseType": "extension",
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  "scholarly-literature",
  "crossref"
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 "scope_conditions": [
  "The response is limited to the retrieved source proposition and mapped Kaal claim unless fuller source review supports a broader conclusion.",
  "External evidence level: abstract indexed.",
  "Mapping review tier: moderate-confidence claim review.",
  "Primary mapping confidence: 0.4007.",
  "The source-to-claim mapping remains explicitly ambiguous and is published with that limitation."
 ],
 "currentDebate": {
  "name": "Sequential Cooperative Multi-Agent Reinforcement Learning",
  "url": "https://doi.org/10.65109/jctb9357"
 },
 "extends": {
  "identifier": "kaal:claim:4855607-013",
  "url": "https://wulfkaal.github.io/claims/4855607-013",
  "citation": "Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607",
  "paper": "Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance",
  "authors": [
   "Wulf A. Kaal"
  ],
  "year": "2024",
  "ssrn": "https://ssrn.com/abstract=4855607",
  "source_pdf_sha256": "eb0b3e62374b45a8fa888c6bde9725e606bcb46cf4b5e74a6e851d9f25099113"
 },
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  },
  {
   "@type": "CreativeWork",
   "name": "Sequential Cooperative Multi-Agent Reinforcement Learning",
   "url": "https://doi.org/10.65109/jctb9357"
  }
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 "batch_id": "kaal-review:2026-07-31:streaming-etl-0010",
 "review_provenance": "https://kaal-signal-desk.wulf577462.chatgpt.site/#review",
 "publicationStatus": "public",
 "recordTypeNote": "Dated commentary position extending a scholarly corpus claim. Not a verbatim claim extracted from the paper.",
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 "canonical_url": "https://wulfkaal.github.io/positions/2026-07-31-7731",
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 "candidateId": "kaal:response-draft:2026-07-31:57604dca660f2a1d0cca",
 "evidenceLevel": "abstract indexed",
 "reviewTier": "moderate-confidence claim review",
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 "mappingAmbiguous": true,
 "mappingMethod": "idf-weighted multi-field mapping v1",
 "mappingWhyRelevant": "Shared high-information concepts: reinforcement, learning, complex, agents, make. Scope: current explainable RL research as surveyed in the text.",
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  "queryId": "concept:a2c487c0792c",
  "queryText": "autonomous agent accountability",
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  "workId": "work:doi:10.65109/jctb9357",
  "workAuthors": [
   "Yifan Zang",
   "Jinmin He",
   "Kai Li",
   "Haobo Fu",
   "Qiang Fu",
   "Junliang Xing"
  ],
  "workPublishedAt": "2023-05-30",
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    "claimUrl": "https://wulfkaal.github.io/claims/4855607-013",
    "rank": 1,
    "confidence": 0.4007,
    "method": "idf-weighted multi-field mapping v1",
    "whyRelevant": "Shared high-information concepts: reinforcement, learning, complex, agents, make. Scope: current explainable RL research as surveyed in the text.",
    "ambiguous": true
   },
   {
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    "method": "idf-weighted multi-field mapping v1",
    "whyRelevant": "Shared high-information concepts: reinforcement, learning, complex, agents. Scope: current state of machine learning practice; may change as unsupervised and reinforcement learning evolve.",
    "ambiguous": true
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    "method": "idf-weighted multi-field mapping v1",
    "whyRelevant": "Shared high-information concepts: agent, complex, agents, problem. Scope: in banks and large institutions trading complex financial products.",
    "ambiguous": true
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   {
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    "rank": 4,
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    "method": "idf-weighted multi-field mapping v1",
    "whyRelevant": "Shared high-information concepts: reinforcement, learning, agents. Scope: applies to constraints imposed on agents with no intrinsic reason to comply.",
    "ambiguous": true
   },
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    "rank": 5,
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    "method": "idf-weighted multi-field mapping v1",
    "whyRelevant": "Shared high-information concepts: complex, extremely, difficult. Scope: records secured by cryptographic hash functions.",
    "ambiguous": true
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 "userAffirmation": "affirm batch kaal-review:2026-07-31:streaming-etl-0010, SHA-256 702170a5716aed9e79302e88407930485844aeec8c140fe64a7bf7d5dd9603af, as written and authorize publication of all 250 response claims on my canonical property, preserving their evidence levels, ambiguity labels, provenance, and the unchanged 5,033 scholarly claims.",
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