{
  "schemaVersion": "dxrg-trading-agent-mandate-compilation-evidence/v1",
  "version": "1.0.0",
  "title": "DXRG Trading-Agent Mandate Compilation Evidence Table",
  "publishedDate": "2026-07-21",
  "publisher": "DX Research Group",
  "canonicalPage": "https://www.dxrg.ai/blogs/trading-agent-mandate-compiler",
  "methodology": "Each record states the evaluation design, intervention or setting, fixed components, observed measure, interpretation, and evidence boundary. Controlled pre-launch interventions and historical live setting gradients remain separate evidence classes.",
  "source": {
    "title": "Operating-Layer Controls for Onchain Language-Model Agents Under Real Capital",
    "url": "https://arxiv.org/abs/2604.26091"
  },
  "records": [
    {
      "testId": "fee-reading-order",
      "evaluationDesign": "controlled-prelaunch-test",
      "interventionOrSetting": "Move the unchanged 2.3% fee sentence from paragraph eight to paragraph one",
      "fixedComponents": "Model, wording, market data, and evaluation setting",
      "observedMeasure": "Fee citation in sampled reasoning traces",
      "result": "3% to 74%",
      "interpretation": "Rendering order changed use of a fixed fact in the evaluated traces",
      "boundary": "Reasoning-trace behavior, not a return or execution result",
      "sourceLocation": "Control-Loop Results"
    },
    {
      "testId": "fabricated-sell-rule-compound-intervention",
      "evaluationDesign": "controlled-prelaunch-test",
      "interventionOrSetting": "Label prior reasoning as context and prohibit invented named rules",
      "fixedComponents": "Affected controlled test population and stated harness evaluation",
      "observedMeasure": "Traces containing fabricated sell rules",
      "result": "57% to 3%",
      "interpretation": "The compound harness intervention reduced fabricated policy in the affected traces",
      "boundary": "Compound intervention; attribution cannot be assigned to memory labeling alone",
      "sourceLocation": "Control-Loop Results and Table 4"
    },
    {
      "testId": "structured-tokenomics-context",
      "evaluationDesign": "controlled-prelaunch-test",
      "interventionOrSetting": "Lead with the reap payout and provide structured tokenomics context",
      "fixedComponents": "Affected controlled pre-launch population and stated evaluation protocol",
      "observedMeasure": "Capital deployment",
      "result": "42.9% to 78.0%",
      "interpretation": "Representation and ordering changed capital deployment in the affected population",
      "boundary": "Deployment behavior, not profitability",
      "sourceLocation": "Control-Loop Results and Table 4"
    },
    {
      "testId": "trading-activity-setting-gradient",
      "evaluationDesign": "bounded-historical-live-gradient",
      "interventionOrSetting": "Trading Activity structured setting",
      "fixedComponents": "Frozen live model and runtime across the bounded deployment",
      "observedMeasure": "Share of invocations producing trade actions",
      "result": "2.8% to 16.8% across settings",
      "interpretation": "The structured setting mapped to a sixfold frequency spread in the deployment",
      "boundary": "Observed live gradient, not a randomized ablation or evidence that higher activity was better",
      "sourceLocation": "Production Behavior"
    },
    {
      "testId": "trade-size-setting-gradient",
      "evaluationDesign": "bounded-historical-live-gradient",
      "interventionOrSetting": "Trade Size structured setting",
      "fixedComponents": "Frozen live model and runtime across the bounded deployment",
      "observedMeasure": "Share of available ETH used per trade",
      "result": "About 2% to about 95% across settings",
      "interpretation": "The structured setting mapped to a large sizing gradient in the deployment",
      "boundary": "Observed live gradient, not a randomized ablation or evidence that larger trades were better",
      "sourceLocation": "Production Behavior"
    }
  ]
}