{
  "name": "LLM Ambiguity Lab v1 Ambiguity Taxonomy",
  "version": "1.0",
  "creator": "Deshimaru Sakaguchi",
  "responsible_maintainer": "Deshimaru Sakaguchi",
  "project_url": "https://deshimarusakaguchi.com/llm-ambiguity-lab/",
  "canonical_url": "https://deshimarusakaguchi.com/llm-ambiguity-lab/data/ambiguity-taxonomy.json",
  "primary_language": "en",
  "scope": "Operational ambiguity affecting task identification or execution in the LLM Ambiguity Lab v1 simulation.",
  "categories": [
    {
      "id": "lexical",
      "label": "Lexical Ambiguity",
      "definition": "A word or expression permits multiple plausible meanings, and the available context does not sufficiently constrain which meaning is intended.",
      "operational_signal": "The same instruction supports materially different interpretations of a word or phrase.",
      "agent_risk": "The agent may select the wrong operation, tool, parameter, or interpretation.",
      "v1_free_text_coverage": "limited",
      "notes": "The v1 interface documents this category but the free-text heuristic is not a general lexical-disambiguation engine."
    },
    {
      "id": "referential",
      "label": "Referential Ambiguity",
      "definition": "An instruction depends on a referent such as 'it', 'that', or an omitted object that cannot be uniquely recovered from the available context.",
      "operational_signal": "The requested action may be identifiable while the object of that action remains unresolved.",
      "agent_risk": "The agent may operate on the wrong document, topic, task, or prior message.",
      "v1_free_text_coverage": "demonstrated",
      "example": "Summarize it."
    },
    {
      "id": "intent_task",
      "label": "Intent / Task Ambiguity",
      "definition": "The subject may be identifiable while the requested operation, goal, constraints, or expected output remain insufficiently specified.",
      "operational_signal": "Multiple materially different actions or goals could satisfy the wording.",
      "agent_risk": "The agent may produce a valid response that solves the wrong problem.",
      "v1_free_text_coverage": "demonstrated",
      "example": "Improve the previous section."
    },
    {
      "id": "mixed",
      "label": "Mixed Ambiguity",
      "definition": "More than one operational ambiguity category materially affects resolution.",
      "operational_signal": "Resolving one missing element would still leave another execution-relevant ambiguity unresolved.",
      "agent_risk": "The agent may both select the wrong target and apply the wrong operation or goal.",
      "v1_free_text_coverage": "demonstrated",
      "example": "Do that and make it better."
    },
    {
      "id": "none",
      "label": "No Material Ambiguity Detected",
      "definition": "The simulation policy considers execution-critical task information sufficiently resolved.",
      "operational_signal": "Task, target object, and required execution conditions are sufficiently specified under the v1 policy.",
      "agent_risk": "Residual uncertainty may still exist; this state is not a correctness or safety guarantee.",
      "v1_free_text_coverage": "demonstrated"
    }
  ],
  "non_exhaustive": true,
  "non_claims": [
    "The taxonomy is not claimed to exhaust all forms of linguistic, pragmatic, domain, temporal, multimodal, or authorization ambiguity.",
    "Category assignment does not reveal hidden model reasoning."
  ]
}