{
  "version": 1,
  "language": "en",
  "title": "Effort guides learned model behavior",
  "description": "A supported effort mode guides learned model behavior but does not imply a fixed token count. A separate runtime cap sets a generation limit. Measured outcomes guide subsequent request settings.",
  "caption": "An effort signal selects learned behavior, with adaptive token use. Evaluation supplies data for subsequent settings. A hard runtime cap separately sets a generation limit. The author made this explanatory diagram with example playback, and animation time is not measured runtime.",
  "alt": "Flow: a supported effort mode guides model behavior. A separate runtime cap sets a generation limit. Evaluation supplies data for subsequent settings.",
  "sources": [
    {
      "path": "content/blog/2026-07-22_reasoning-effort-inference-systems/post.md",
      "description": "Article explanation and retained source citations"
    }
  ],
  "limitations": [
    "The execution is an example. Playback duration is not measured runtime.",
    "A supported effort setting does not promise a fixed quantity of work or a quality gain. This feedback changes request configuration, not model weights."
  ],
  "targets": {
    "effort": {
      "id": "mechanism-effort",
      "kind": "node",
      "label": "Effort mode"
    },
    "policy": {
      "id": "mechanism-policy",
      "kind": "node",
      "label": "Learned policy"
    },
    "work": {
      "id": "mechanism-work",
      "kind": "node",
      "label": "Generated work"
    },
    "outcome": {
      "id": "mechanism-outcome",
      "kind": "node",
      "label": "Task outcome"
    },
    "limit": {
      "id": "mechanism-limit",
      "kind": "node",
      "label": "Hard limit"
    },
    "evaluate": {
      "id": "mechanism-evaluate",
      "kind": "node",
      "label": "Evaluate"
    },
    "condition": {
      "id": "mechanism-condition",
      "kind": "edge",
      "label": "guide behavior"
    },
    "allocate": {
      "id": "mechanism-allocate",
      "kind": "edge",
      "label": "generate work"
    },
    "produce": {
      "id": "mechanism-produce",
      "kind": "edge",
      "label": "give the outcome"
    },
    "enforce": {
      "id": "mechanism-enforce",
      "kind": "edge",
      "label": "use a generation limit"
    },
    "measure": {
      "id": "mechanism-measure",
      "kind": "edge",
      "label": "evaluate the outcome"
    },
    "futureSetting": {
      "id": "mechanism-futureSetting",
      "kind": "edge",
      "label": "select subsequent effort"
    }
  },
  "stateLabels": {
    "effort": "Effort condition",
    "allocation": "Work policy",
    "work": "Generated reasoning path",
    "boundary": "Hard boundary",
    "outcome": "Evaluation"
  },
  "scenarios": [
    {
      "id": "effort-and-boundary",
      "title": "Work with a separate limit",
      "description": "The model allocates work with a separate runtime limit.",
      "initial": {
        "effort": "Not selected",
        "allocation": "The mode does not imply a fixed token count.",
        "work": "Not generated",
        "boundary": "Separate runtime control",
        "outcome": "Not measured"
      },
      "unselected": [],
      "steps": [
        {
          "title": "Select a supported mode",
          "description": "The request uses a supported effort mode.",
          "duration": 3000,
          "active": [
            "effort"
          ],
          "paths": [],
          "state": {
            "effort": "Supported effort selected",
            "allocation": "The mode does not imply a fixed token count.",
            "work": "Not generated",
            "boundary": "Separate runtime control",
            "outcome": "Not measured"
          }
        },
        {
          "title": "Condition the learned policy",
          "description": "The effort mode conditions the learned policy.",
          "duration": 3000,
          "active": [
            "policy"
          ],
          "paths": [
            "condition"
          ],
          "state": {
            "effort": "Supported effort selected",
            "allocation": "Learned work allocation",
            "work": "Not generated",
            "boundary": "Separate runtime control",
            "outcome": "Not measured"
          }
        },
        {
          "title": "Generate reasoning and calls",
          "description": "The model generates reasoning and tool calls.",
          "duration": 3000,
          "active": [
            "work"
          ],
          "paths": [
            "allocate"
          ],
          "state": {
            "effort": "Supported effort selected",
            "allocation": "Learned work allocation",
            "work": "Reasoning path from the model",
            "boundary": "Separate runtime control",
            "outcome": "Not measured"
          }
        },
        {
          "title": "Use the runtime limit",
          "description": "The separate runtime cap sets a generation limit.",
          "duration": 3000,
          "active": [
            "limit",
            "work"
          ],
          "paths": [
            "enforce"
          ],
          "state": {
            "effort": "Supported effort selected",
            "allocation": "Learned work allocation",
            "work": "Reasoning path from the model",
            "boundary": "Independent continuation limit",
            "outcome": "Not measured"
          }
        },
        {
          "title": "Measure the completed request",
          "description": "Evaluation measures the request outcome.",
          "duration": 3000,
          "active": [
            "outcome"
          ],
          "paths": [
            "produce"
          ],
          "state": {
            "effort": "Supported effort selected",
            "allocation": "Learned work allocation",
            "work": "Reasoning path from the model",
            "boundary": "Independent continuation limit",
            "outcome": "Quality, time, tokens and cost"
          }
        },
        {
          "title": "Compare results",
          "description": "Evaluation compares the task results.",
          "duration": 3000,
          "active": [
            "evaluate"
          ],
          "paths": [
            "measure"
          ],
          "state": {
            "effort": "Supported effort selected",
            "allocation": "Learned work allocation",
            "work": "Reasoning path from the model",
            "boundary": "Independent continuation limit",
            "outcome": "Task-specific evidence"
          }
        },
        {
          "title": "Select a subsequent setting",
          "description": "The next setting is selected from evaluation evidence.",
          "duration": 3000,
          "active": [
            "effort"
          ],
          "paths": [
            "futureSetting"
          ],
          "state": {
            "effort": "The next setting is selected from evaluation evidence.",
            "allocation": "This loop does not change model weights.",
            "work": "Reasoning path from the model",
            "boundary": "Independent continuation limit",
            "outcome": "Task-specific evidence"
          }
        }
      ]
    }
  ]
}