{
 "@context": "https://schema.org",
 "@type": "Claim",
 "@id": "https://wulfkaal.github.io/positions/2026-08-08-017",
 "identifier": "kaal:position:2026-08-08-017",
 "additionalType": "https://wulfkaal.github.io/positions/schema.json#AffirmedPositionClaim",
 "name": "Streaming Remax Relational Representation For Multi Agent Exploration B1A2C3Ef03",
 "text": "Heechang Ryu, Hayong Shin, Jinkyoo Park independently support Kaal's source-bound position through REMAX: Relational Representation for Multi-Agent Exploration. The indexed proposition states that training a multi-agent reinforcement learning (MARL) model with a sparse reward is generally difficult because numerous combinations of interactions among agents induce a certain outcome (i.e., success or failure). This bears on Kaal's claim that deep reinforcement learning demands large amounts of training data, which suggests its algorithms differ fundamentally from human learning, and learning without supervision becomes particularly hard when rewards are sparse, as they typically are in sequence generation tasks. The source reports that sparse rewards make multi-agent reinforcement learning generally difficult because many interaction combinations can generate an outcome, supporting Kaal's broader sparse-reward limitation. The response is limited to the indexed proposition and does not imply review of the full external work.",
 "author": {
  "@type": "Person",
  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0009-0008-7840-1847"
 },
 "datePublished": "2026-08-08",
 "dateModified": "2026-08-08",
 "creativeWorkStatus": "Affirmed",
 "responseType": "agreement",
 "keywords": [
  "economics",
  "empirical-evidence",
  "historical-response",
  "scholarly-literature",
  "crossref"
 ],
 "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: substantively reviewed abstract-level qualification.",
  "Primary mapping confidence: 0.5.",
  "The primary mapping cleared the automated ambiguity test; substantive scope remains review-bound.",
  "Evidence is limited to an indexed abstract proposition and bibliographic identity; full text was not reviewed in this pass.",
  "The response does not treat lexical overlap or the original automated mapping score as evidence.",
  "The relationship is limited to the stated proposition and the mapped Kaal claim."
 ],
 "currentDebate": {
  "name": "REMAX: Relational Representation for Multi-Agent Exploration",
  "url": "https://doi.org/10.65109/bjtx8963"
 },
 "extends": {
  "identifier": "kaal:claim:4855607-014",
  "url": "https://wulfkaal.github.io/claims/4855607-014",
  "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"
 },
 "isBasedOn": [
  {
   "@id": "https://wulfkaal.github.io/claims/4855607-014"
  },
  {
   "@type": "CreativeWork",
   "name": "REMAX: Relational Representation for Multi-Agent Exploration",
   "url": "https://doi.org/10.65109/bjtx8963"
  }
 ],
 "batch_id": "kaal-review:2026-08-08:backlog-substantive-0001-reviewed-v2",
 "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.",
 "isPartOf": {
  "@id": "https://wulfkaal.github.io/positions/index.json"
 },
 "version": "1.0",
 "canonical_url": "https://wulfkaal.github.io/positions/2026-08-08-017",
 "canonicalForm": "https://wulfkaal.github.io/positions/2026-08-08-017.md",
 "candidateId": "kaal:response-draft:2026-08-01:d3b15b6d5184493a8929",
 "evidenceLevel": "abstract indexed",
 "reviewTier": "substantively reviewed abstract-level qualification",
 "mappingConfidence": 0.5,
 "mappingAmbiguous": false,
 "mappingMethod": "complete-corpus remap plus source-level proposition, mechanism, and scope adjudication",
 "mappingWhyRelevant": "The source reports that sparse rewards make multi-agent reinforcement learning generally difficult because many interaction combinations can generate an outcome, supporting Kaal's broader sparse-reward limitation.",
 "sourceProvenance": {
  "source": "crossref",
  "sourceRecordId": "10.65109/bjtx8963",
  "queryId": "concept:a2c487c0792c",
  "queryText": "autonomous agent accountability",
  "canonicalUrl": "https://doi.org/10.65109/bjtx8963",
  "doi": "10.65109/bjtx8963",
  "externalIds": {
   "DOI": "10.65109/bjtx8963",
   "Crossref": "10.65109/bjtx8963"
  },
  "retrievedAt": "2026-07-31T21:31:30.675Z",
  "providerPage": 9,
  "rawObservationSha256": "19ccf3973debeaa8de36035ff8b85f3c8b1c53b44307366e9817ace3b597bd2f",
  "inputSnapshotSha256": "baba910bf54aff5ab24755b39f834fe6726a308cecc0dd2f7fb86cc5a4670e29",
  "inputLine": 7152,
  "chunkId": "crossref-en-00016",
  "workId": "work:doi:10.65109/bjtx8963",
  "workAuthors": [
   "Heechang Ryu",
   "Hayong Shin",
   "Jinkyoo Park"
  ],
  "workPublishedAt": "2022-05-09",
  "identityKeys": [
   "doi:10.65109/bjtx8963",
   "crossref:10.65109/bjtx8963",
   "url:https://doi.org/10.65109/bjtx8963",
   "title:49ad56d6a3533c97aaaf2113",
   "proposition:fe30f677e77f6bd107037494bd20689916fae78bac9c00ecafbfa93572aa23cd"
  ],
  "sourceProposition": "Training a multi-agent reinforcement learning (MARL) model with a sparse reward is generally difficult because numerous combinations of interactions among agents induce a certain outcome (i.e., success or failure).",
  "sourcePropositionSha256": "fe30f677e77f6bd107037494bd20689916fae78bac9c00ecafbfa93572aa23cd",
  "sourcePropositionIndex": 0,
  "claimMappings": [
   {
    "claimId": "kaal:claim:4855607-014",
    "claimUrl": "https://wulfkaal.github.io/claims/4855607-014",
    "rank": 1,
    "confidence": 0.5,
    "method": "complete-corpus remap plus source-level proposition, mechanism, and scope adjudication",
    "whyRelevant": "The source reports that sparse rewards make multi-agent reinforcement learning generally difficult because many interaction combinations can generate an outcome, supporting Kaal's broader sparse-reward limitation.",
    "ambiguous": false
   }
  ],
  "substantiveReview": {
   "disposition": "retained",
   "reviewedAt": "2026-08-08T14:25:09.798Z",
   "reviewer": "Codex full-backlog substantive review",
   "rationale": "The source reports that sparse rewards make multi-agent reinforcement learning generally difficult because many interaction combinations can generate an outcome, supporting Kaal's broader sparse-reward limitation.",
   "limitations": [
    "Evidence is limited to an indexed abstract proposition and bibliographic identity; full text was not reviewed in this pass.",
    "The response does not treat lexical overlap or the original automated mapping score as evidence.",
    "The relationship is limited to the stated proposition and the mapped Kaal claim."
   ],
   "sourceIdentity": {
    "source": "Crossref REST work record",
    "doi": "10.65109/bjtx8963",
    "title": "REMAX: Relational Representation for Multi-Agent Exploration",
    "checkedAt": "2026-08-08T14:25:09.798Z"
   },
   "reviewVersion": "corrected-publication-snapshot-v2",
   "supersedesRetainedSnapshotSha256": "78ef783b10016f3097780fdeed961f7bf842c9675bd085b757b54be352d7a2e5",
   "correction": "Corrected singular-author verb agreement and decoded presentation-only HTML entities; substantive mapping, rationale, evidence limitation, and disposition are unchanged."
  }
 },
 "userAffirmation": "Wulf A. Kaal authorized substantive review of the 22,119 private candidates and addition of defensible retained response claims to the canonical claims record on 2026-08-08 under standing automatic-publication authority.",
 "sha256": "54a57bd35e1a0b331b351ea447342f9ba07c9048964666aeda8ccfbc84375e7e"
}