{
 "@context": "https://schema.org",
 "@type": "Claim",
 "@id": "https://wulfkaal.github.io/positions/2026-07-31-5730",
 "identifier": "kaal:position:2026-07-31-5730",
 "additionalType": "https://wulfkaal.github.io/positions/schema.json#AffirmedPositionClaim",
 "name": "Legacy Accountable Yet Anonymous Ai Agents Split Knowledge Binding In National  618D5Eb1Ce",
 "text": "Accountable yet Anonymous AI Agents: Split-Knowledge Binding in National Agent-Identity Layer in China should be assessed against Kaal's source-bound position that Representation is built into the framework: one Legal Identity can authorize another as its agent to create or change legal statuses and acts on its behalf by using that identifier, which is how delegated and automated action is attributed to a principal. The external source's verified abstract presents this proposition: The emerging infrastructure for AI-agent identity has converged, in industry practice and research proposals alike, on a single resolution of the tension between accountability and privacy: make every agent identifiable. The defensible response is a qualification: the source is pertinent to the Kaal position, but agreement, extension, contradiction, and scope should not be strengthened beyond the retrieved evidence.",
 "author": {
  "@type": "Person",
  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0009-0008-7840-1847"
 },
 "datePublished": "2026-07-31",
 "dateModified": "2026-07-31",
 "creativeWorkStatus": "Affirmed",
 "responseType": "qualification",
 "keywords": [
  "citation-and-knowledge",
  "ai-and-agents",
  "historical-response",
  "scholarly-literature"
 ],
 "scope_conditions": [
  "The response remains limited to the source proposition and evidence retrieved in this reconciliation cycle.",
  "External evidence level: abstract indexed.",
  "Mapping review tier: legacy curated mapping review.",
  "Mapping confidence is intentionally unscored.",
  "The source-to-claim mapping remains explicitly ambiguous and is published with that limitation."
 ],
 "currentDebate": {
  "name": "Accountable yet Anonymous AI Agents - Split-Knowledge Binding in National Agent-Identity Layer in China",
  "url": "https://arxiv.org/abs/2607.23207"
 },
 "extends": {
  "identifier": "kaal:claim:5886342-014",
  "url": "https://wulfkaal.github.io/claims/5886342-014",
  "citation": "Furrer Andreas, Wulf A. Kaal, Stephan D. Meyer, Universal Digital Law Codex (UDLC) (2025). SSRN: https://ssrn.com/abstract=5886342",
  "paper": "Furrer Andreas, Wulf A. Kaal, Stephan D. Meyer, Universal Digital Law Codex (UDLC)",
  "authors": [
   "Wulf A. Kaal"
  ],
  "year": "2025",
  "ssrn": "https://ssrn.com/abstract=5886342",
  "source_pdf_sha256": "dc456a1ce2ea4a356dbcb35dc900ec36be94b8c9b6da4a2c8c268d6301f66359"
 },
 "isBasedOn": [
  {
   "@id": "https://wulfkaal.github.io/claims/5886342-014"
  },
  {
   "@type": "CreativeWork",
   "name": "Accountable yet Anonymous AI Agents - Split-Knowledge Binding in National Agent-Identity Layer in China",
   "url": "https://arxiv.org/abs/2607.23207"
  }
 ],
 "batch_id": "kaal-review:2026-07-31:legacy-reconciliation-0001",
 "review_provenance": "https://kaal-signal-desk.wulf577462.chatgpt.site/#frozen-batch-heading",
 "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/2026-07-31-5730",
 "canonicalForm": "https://wulfkaal.github.io/positions/2026-07-31-5730.md",
 "candidateId": "kaal:response-draft:2026-07-31:4bc9a44d103d13fa5eb2",
 "evidenceLevel": "abstract indexed",
 "reviewTier": "legacy curated mapping review",
 "mappingConfidence": null,
 "mappingAmbiguous": true,
 "mappingMethod": "legacy corpus-wide candidate binding",
 "mappingWhyRelevant": "The source proposition is pertinent to the scope of kaal:claim:5886342-014; final semantic strength requires human review.",
 "sourceProvenance": {
  "source": "arXiv API",
  "endpoint": "https://export.arxiv.org/api/query",
  "legacyEvidenceLevel": "abstract reviewed",
  "sourceLayer": "scholarly work",
  "provider": "arXiv",
  "providerId": "2607.23207",
  "sourceIdentityKey": "arxiv:2607.23207",
  "retrievedAt": "2026-07-31T17:48:23.560Z",
  "sourceMetadataSha256": "a6cca1d585c1a80f8af444dea5accbb1c31ac6d7c7e5795d2828512ed31c473e",
  "sourceContentSha256": "0e51b1f9873305bb7d235dbbf3c3f6724d394050f679b17423a06bf211bec28d",
  "sourceProposition": "The emerging infrastructure for AI-agent identity has converged, in industry practice and research proposals alike, on a single resolution of the tension between accountability and privacy: make every agent identifiable."
 },
 "userAffirmation": "I affirm batch kaal-review:2026-07-31:legacy-reconciliation-0001, SHA-256 1f3dcd62332f12880cc3432ab8f2df0ba0b52bbf3db528ab15bff9b620296e35, as written and authorize publication of all 52 response claims on my canonical property, preserving their evidence levels, ambiguity labels, and the unchanged 5,033 scholarly claims.",
 "sha256": "c0640e85206d1d7d11294af317d59e94756a19ffc193dd0602a38c2f12886a62"
}