feat: integrate safer vehicle CAN reasoning

apps/web-console/app.js

@@ -36,6 +36,7 @@ function loadHardwareClientId() {
 
 const state = {
   provider: "openai",
+  reasoningEffort: localStorage.getItem("cognitive-agent.reasoning-effort") || "medium",
   executionMode: "dry_run",
   conversationId: loadConversationId(),
   vehicleId: null,
@@ -3929,6 +3930,35 @@ function openModeGallery() {
     backdrop.classList.add("open");
     $("#modeGalleryClose").focus();
   });
+  syncReasoningControls();
+}
+
+function availableReasoningEfforts() {
+  const configured = state.runtime?.openai_reasoning_efforts;
+  return Array.isArray(configured) && configured.length ? configured : ["low", "medium", "high"];
+}
+
+function selectReasoningEffort(effort, { persist = true } = {}) {
+  if (!availableReasoningEfforts().includes(effort)) return false;
+  state.reasoningEffort = effort;
+  if (persist) localStorage.setItem("cognitive-agent.reasoning-effort", effort);
+  syncReasoningControls();
+  return true;
+}
+
+function syncReasoningControls() {
+  const enabled = state.provider === "openai" && state.runtime?.openai_available === true;
+  const descriptions = {
+    low: "极速模式优先降低指令解析延迟",
+    medium: "均衡模式兼顾自然语言解析精度与响应速度",
+    high: "深度模式适合多设备、相对动作与复杂约束指令",
+  };
+  $$('[data-reasoning-effort]').forEach((button) => {
+    button.disabled = !enabled || !availableReasoningEfforts().includes(button.dataset.reasoningEffort);
+    button.classList.toggle("active", button.dataset.reasoningEffort === state.reasoningEffort);
+  });
+  const note = $("#reasoningNote");
+  if (note) note.textContent = enabled ? descriptions[state.reasoningEffort] : "本地规则解析不调用模型推理";
 }
 
 function closeModeGallery({ restoreFocus = true } = {}) {
@@ -4292,6 +4322,10 @@ async function loadRuntime() {
     applyRuntimeTargetPolicy(state.runtime);
     populateVehicleSelector();
     populateModeChips();
+    const configuredEffort = state.runtime.openai_reasoning_effort_default || "medium";
+    if (!availableReasoningEfforts().includes(state.reasoningEffort)) {
+      state.reasoningEffort = configuredEffort;
+    }
     const hardware = state.runtime.hardware || {};
     const vehicleCan = state.runtime.vehicle_can || {};
     const linkedHardwareReady = Boolean(hardware.configured && vehicleCan.configured);
@@ -4315,6 +4349,7 @@ async function loadRuntime() {
       $("#providerNote").textContent = "未设置 OPENAI_API_KEY，已切换为本地规则解析";
       selectProvider("local");
     }
+    syncReasoningControls();
     $("#matrixCount").textContent = `${state.runtime.matrix.messages} / ${state.runtime.matrix.signals}`;
     const hardwareButton = $("#hardwareMode");
     hardwareButton.disabled = !hardwareExecutionReady;
@@ -5575,6 +5610,9 @@ function renderResult(result) {
   const conversationLabel = result.conversation_id?.slice(-8) || "no-context";
   $("#requestMeta").textContent = `${result.request_id} / context:${conversationLabel} / turn:${result.conversation_turn} / ${result.provider_used || result.provider_requested}`;
   $("#requestMeta").textContent += ` / ${result.execution_report?.spec?.mode || result.execution_mode || "dry_run"}`;
+  if (result.reasoning_effort_used) {
+    $("#requestMeta").textContent += ` / reasoning:${result.reasoning_effort_used}`;
+  }
   setRunStatus(
     partiallySucceeded ? "partial" : executionSucceeded ? "succeeded" : "failed",
     result.status.toUpperCase(),
@@ -5647,6 +5685,7 @@ async function submitRequest(event) {
         conversation_id: state.conversationId,
         vehicle_id: state.vehicleId || state.runtime?.default_vehicle_id,
         execution_mode: state.executionMode,
+        ...(state.provider === "openai" ? { reasoning_effort: state.reasoningEffort } : {}),
         ...(state.executionMode === "hardware" && !driverOnlyCh1
           ? { hardware_client_id: state.hardwareClientId }
           : {}),
@@ -5892,6 +5931,7 @@ document.addEventListener("DOMContentLoaded", async () => {
     }
   });
   $$(".segment[data-provider]").forEach((button) => button.addEventListener("click", () => selectProvider(button.dataset.provider)));
+  $$('[data-reasoning-effort]').forEach((button) => button.addEventListener("click", () => selectReasoningEffort(button.dataset.reasoningEffort)));
   $$('[data-execution-mode]').forEach((button) => button.addEventListener("click", () => selectExecutionMode(button.dataset.executionMode)));
   $$(".example-command").forEach((button) => button.addEventListener("click", () => {
     $("#utterance").value = button.dataset.example;

apps/web-console/console-v2.css

@@ -3283,3 +3283,209 @@
     font-size: 10px;
   }
 }
+.reasoning-segmented {
+  grid-template-columns: repeat(3, minmax(0, 1fr));
+}
+
+.reasoning-option > span > strong {
+  font: inherit;
+}
+
+.reasoning-option > span > small,
+.reasoning-option > .reasoning-check {
+  display: none;
+}
+
+.reasoning-option:disabled {
+  cursor: not-allowed;
+  opacity: 0.45;
+}
+
+/* Market-style model intelligence picker: a compact composer pill expands
+   into an overlay only when the user needs to change the reasoning level. */
+.console-v2 .v2-reasoning-field {
+  width: 320px;
+  min-width: 250px;
+  position: fixed;
+  z-index: 120;
+  margin: 0;
+  padding: 12px;
+  background: rgba(255, 255, 255, 0.98);
+  border: 1px solid color-mix(in srgb, var(--v2-accent) 18%, #e6eceb);
+  border-radius: 14px;
+  box-shadow: 0 16px 38px rgba(28, 61, 62, 0.2);
+}
+
+.console-v2 .v2-reasoning-field[hidden] {
+  display: none !important;
+}
+
+.console-v2 .v2-reasoning-field > .field-label {
+  margin: 0 0 8px;
+  color: #263b3d;
+  font-size: 11px;
+  font-weight: 850;
+}
+
+.console-v2 .v2-reasoning-field .reasoning-segmented {
+  width: 100%;
+  min-width: 0;
+  display: grid;
+  grid-template-columns: minmax(0, 1fr);
+  gap: 5px;
+  padding: 0;
+  background: transparent;
+  border: 0;
+}
+
+.console-v2 .v2-reasoning-field .reasoning-option {
+  width: 100%;
+  min-width: 0;
+  min-height: 42px;
+  display: grid;
+  grid-template-columns: 24px minmax(0, 1fr) 18px;
+  align-items: center;
+  justify-content: initial;
+  gap: 8px;
+  padding: 6px 9px;
+  color: #536466;
+  border: 1px solid transparent;
+  border-radius: 9px;
+  text-align: left;
+}
+
+.console-v2 .v2-reasoning-field .reasoning-option:hover {
+  color: var(--v2-accent-strong);
+  background: color-mix(in srgb, var(--v2-accent-soft) 38%, white);
+}
+
+.console-v2 .v2-reasoning-field .reasoning-option.active {
+  color: var(--v2-accent-strong);
+  background: color-mix(in srgb, var(--v2-accent-soft) 58%, white);
+  border-color: color-mix(in srgb, var(--v2-accent) 16%, transparent);
+  box-shadow: none;
+}
+
+.console-v2 .v2-reasoning-field .reasoning-option > svg:first-child {
+  width: 16px;
+  height: 16px;
+  justify-self: center;
+}
+
+.console-v2 .v2-reasoning-field .reasoning-option > span {
+  min-width: 0;
+  display: grid;
+  gap: 1px;
+}
+
+.console-v2 .v2-reasoning-field .reasoning-option > span > strong,
+.console-v2 .v2-reasoning-field .reasoning-option > span > small {
+  display: block;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+}
+
+.console-v2 .v2-reasoning-field .reasoning-option > span > strong {
+  color: inherit;
+  font-size: 10px;
+  font-weight: 850;
+}
+
+.console-v2 .v2-reasoning-field .reasoning-option > span > small {
+  color: #899697;
+  font-size: 8px;
+  font-weight: 600;
+}
+
+.console-v2 .v2-reasoning-field .reasoning-option > .reasoning-check {
+  width: 14px;
+  height: 14px;
+  display: block;
+  opacity: 0;
+}
+
+.console-v2 .v2-reasoning-field .reasoning-option.active > .reasoning-check {
+  opacity: 1;
+}
+
+.console-v2 .v2-reasoning-field > .provider-note {
+  min-height: 0;
+  margin: 8px 2px 0;
+  color: #829091;
+  font-size: 8px;
+  line-height: 1.45;
+}
+
+.console-v2 .v2-request-form .command-composer {
+  overflow: visible;
+}
+
+.console-v2 .v2-request-form #utterance {
+  padding-bottom: 42px;
+  border-radius: inherit;
+}
+
+.console-v2 .v2-reasoning-trigger {
+  max-width: 180px;
+  min-height: 26px;
+  position: absolute;
+  left: 14px;
+  bottom: 9px;
+  z-index: 12;
+  display: inline-grid;
+  grid-template-columns: 14px auto auto 12px;
+  align-items: center;
+  gap: 5px;
+  padding: 0 9px;
+  color: #526264;
+  background: rgba(241, 244, 243, 0.96);
+  border: 1px solid rgba(98, 127, 126, 0.11);
+  border-radius: 999px;
+  font-size: 9px;
+  font-weight: 700;
+  line-height: 1;
+  cursor: pointer;
+}
+
+.console-v2 .v2-reasoning-trigger:hover,
+.console-v2 .v2-reasoning-trigger.open {
+  color: var(--v2-accent-strong);
+  background: color-mix(in srgb, var(--v2-accent-soft) 52%, white);
+  border-color: color-mix(in srgb, var(--v2-accent) 18%, transparent);
+}
+
+.console-v2 .v2-reasoning-trigger.unavailable {
+  opacity: 0.62;
+}
+
+.console-v2 .v2-reasoning-trigger > svg {
+  width: 12px;
+  height: 12px;
+}
+
+.console-v2 .v2-reasoning-trigger > strong {
+  color: var(--v2-accent-strong);
+  font-size: 9px;
+}
+
+.console-v2 .v2-reasoning-trigger > .v2-reasoning-caret {
+  transition: transform 150ms ease;
+}
+
+.console-v2 .v2-reasoning-trigger.open > .v2-reasoning-caret {
+  transform: rotate(180deg);
+}
+
+.console-v2 .v2-request-form .composer-hint {
+  max-width: 150px;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+}
+
+@media (max-height: 820px) {
+  .console-v2 .v2-reasoning-trigger {
+    bottom: 7px;
+  }
+}

apps/web-console/console-v2.js

@@ -33,6 +33,7 @@
   };
 
   const dropdownControls = new Map();
+  let reasoningPickerControl = null;
 
   function make(tag, className, html) {
     const node = document.createElement(tag);
@@ -47,6 +48,99 @@
     });
   }
 
+  function closeReasoningPicker() {
+    reasoningPickerControl?.close();
+  }
+
+  function installReasoningPicker(reasoningField, originalForm) {
+    const composer = $(".command-composer", originalForm);
+    if (!composer || !reasoningField) return;
+
+    reasoningField.classList.add("v2-reasoning-field");
+    reasoningField.hidden = true;
+    const trigger = make("button", "v2-reasoning-trigger", `
+      <i data-lucide="sparkles"></i>
+      <span>模型智能</span>
+      <strong id="v2ReasoningValue">均衡</strong>
+      <i class="v2-reasoning-caret" data-lucide="chevron-up"></i>`);
+    trigger.id = "v2ReasoningTrigger";
+    trigger.type = "button";
+    trigger.setAttribute("aria-haspopup", "listbox");
+    trigger.setAttribute("aria-expanded", "false");
+    trigger.setAttribute("aria-controls", "reasoningField");
+    trigger.setAttribute("aria-label", "选择模型智能程度，当前均衡");
+    $(".reasoning-segmented", reasoningField)?.setAttribute("role", "listbox");
+
+    const options = () => $$('[data-reasoning-effort]', reasoningField);
+    const place = () => {
+      if (reasoningField.hidden) return;
+      const triggerBox = trigger.getBoundingClientRect();
+      const width = Math.min(320, Math.max(250, window.innerWidth - 24));
+      const left = Math.min(
+        Math.max(12, triggerBox.left),
+        Math.max(12, window.innerWidth - width - 12),
+      );
+      reasoningField.style.width = `${width}px`;
+      reasoningField.style.left = `${left}px`;
+      const height = reasoningField.offsetHeight;
+      const above = triggerBox.top - height - 8;
+      const top = above >= 12
+        ? above
+        : Math.min(triggerBox.bottom + 8, window.innerHeight - height - 12);
+      reasoningField.style.top = `${Math.max(12, top)}px`;
+    };
+    const sync = () => {
+      const selected = options().find((button) => button.classList.contains("active"));
+      const label = selected?.querySelector("strong")?.textContent?.trim() || "均衡";
+      const unavailable = options().length > 0 && options().every((button) => button.disabled);
+      $("#v2ReasoningValue", trigger).textContent = label;
+      trigger.classList.toggle("unavailable", unavailable);
+      trigger.setAttribute("aria-disabled", String(unavailable));
+      trigger.setAttribute("aria-label", `选择模型智能程度，当前${label}`);
+      options().forEach((button) => {
+        button.setAttribute("role", "option");
+        button.setAttribute("aria-selected", String(button === selected));
+      });
+    };
+    const close = () => {
+      reasoningField.hidden = true;
+      trigger.classList.remove("open");
+      trigger.setAttribute("aria-expanded", "false");
+    };
+    const open = () => {
+      closeCustomDropdowns();
+      reasoningField.hidden = false;
+      trigger.classList.add("open");
+      trigger.setAttribute("aria-expanded", "true");
+      place();
+    };
+    reasoningPickerControl = { close, open, place, sync, trigger };
+    composer.append(trigger);
+    document.body.append(reasoningField);
+
+    trigger.addEventListener("click", (event) => {
+      event.stopPropagation();
+      if (reasoningField.hidden) open();
+      else close();
+    });
+    reasoningField.addEventListener("click", (event) => {
+      event.stopPropagation();
+      if (!event.target.closest("[data-reasoning-effort]")) return;
+      requestAnimationFrame(() => {
+        sync();
+        close();
+        trigger.focus({ preventScroll: true });
+      });
+    });
+    new MutationObserver(sync).observe(reasoningField, {
+      attributes: true,
+      attributeFilter: ["class", "disabled"],
+      subtree: true,
+    });
+    window.addEventListener("resize", place);
+    sync();
+  }
+
   function enhanceSelect(select) {
     if (!select || dropdownControls.has(select)) return;
     const shell = select.parentElement;
@@ -207,6 +301,7 @@
   function buildConfigFields(configStack, originalForm) {
     const vehicleField = $(".vehicle-field", originalForm);
     const providerField = $(".field-group:has(.segmented [data-provider])", originalForm);
+    const reasoningField = $("#reasoningField", originalForm);
     const executionField = $(".field-group:has(.execution-mode-options)", originalForm);
     const simulationFaultField = $("#simulationFaultField", originalForm);
 
@@ -238,6 +333,7 @@
       <p class="provider-note" id="v2ModelNote">模型由服务端安全配置</p>`);
     modelField.id = "v2ModelField";
     configStack.append(modelField);
+    installReasoningPicker(reasoningField, originalForm);
 
     const gestureModelField = make("div", "v2-config-field v2-config-card v2-gesture-model-field", `
       <i data-lucide="scan-line"></i>
@@ -773,9 +869,15 @@
       "click",
       () => setSidebarCollapsed(!ui.sidebarCollapsed),
     );
-    document.addEventListener("click", () => closeCustomDropdowns());
+    document.addEventListener("click", () => {
+      closeCustomDropdowns();
+      closeReasoningPicker();
+    });
     document.addEventListener("keydown", (event) => {
-      if (event.key === "Escape") closeCustomDropdowns();
+      if (event.key === "Escape") {
+        closeCustomDropdowns();
+        closeReasoningPicker();
+      }
     });
     window.addEventListener("resize", updateScrollCues);
     window.addEventListener("resize", updateModeEdgeHints);

apps/web-console/index.html

@@ -10,7 +10,7 @@
     <link rel="icon" href="/icons/app-icon-192.png" />
     <title>智舱随行 · 整车智能控制台</title>
     <link rel="stylesheet" href="/styles.css?v=dual-seat-9" />
-    <link rel="stylesheet" href="/console-v2.css?v=desktop-refactor-87" />
+    <link rel="stylesheet" href="/console-v2.css?v=desktop-refactor-89" />
     <link rel="stylesheet" href="/self-extension.css?v=extension-12" />
     <link rel="stylesheet" href="/self-calibration.css?v=calibration-6" />
     <link rel="stylesheet" href="/self-repair.css?v=repair-17" />
@@ -22,7 +22,7 @@
     <script src="/app.js?v=dual-seat-67" defer></script>
     <script type="module" src="/gesture-control.js?v=android-pad-7"></script>
     <script src="/pwa.js?v=android-pad-1" defer></script>
-    <script src="/console-v2.js?v=desktop-refactor-28" defer></script>
+    <script src="/console-v2.js?v=desktop-refactor-30" defer></script>
     <script src="/self-extension.js?v=extension-18" defer></script>
     <script src="/self-calibration.js?v=calibration-3" defer></script>
     <script src="/self-repair.js?v=repair-17" defer></script>
@@ -112,6 +112,22 @@
             <p class="provider-note" id="providerNote">正在读取运行时配置</p>
           </div>
 
+          <div class="field-group" id="reasoningField">
+            <span class="field-label">模型推理强度</span>
+            <div class="segmented reasoning-segmented" role="group" aria-label="模型推理强度">
+              <button type="button" class="segment reasoning-option" data-reasoning-effort="low">
+                <i data-lucide="zap"></i><span><strong>极速</strong><small>简单指令，优先响应速度</small></span><i class="reasoning-check" data-lucide="check"></i>
+              </button>
+              <button type="button" class="segment reasoning-option active" data-reasoning-effort="medium">
+                <i data-lucide="scale"></i><span><strong>均衡</strong><small>日常控制，兼顾速度与理解</small></span><i class="reasoning-check" data-lucide="check"></i>
+              </button>
+              <button type="button" class="segment reasoning-option" data-reasoning-effort="high">
+                <i data-lucide="brain"></i><span><strong>深度</strong><small>复杂联动、相对动作与约束</small></span><i class="reasoning-check" data-lucide="check"></i>
+              </button>
+            </div>
+            <p class="provider-note" id="reasoningNote">均衡模式兼顾自然语言解析精度与响应速度</p>
+          </div>
+
           <div class="field-group">
             <span class="field-label">执行目标</span>
             <div class="segmented execution-mode-options" role="group" aria-label="执行目标">

config/native_can_relative_controls.yaml

@@ -0,0 +1,32 @@
+apiVersion: cognitive-agent/v1alpha1
+kind: NativeCanRelativeControlPolicy
+metadata:
+  id: denza-n7-relative-controls
+  version: 1.0.0
+spec:
+  controls:
+    climate_temperature:
+      default_step: 1
+      minimum: 18
+      maximum: 32
+      unit: celsius
+    climate_fan:
+      default_step: 1
+      minimum: 1
+      maximum: 7
+      unit: level
+    ambient_brightness:
+      default_step: 1
+      minimum: 1
+      maximum: 5
+      unit: level
+    shade_position:
+      default_step: 10
+      minimum: 0
+      maximum: 100
+      unit: percentage_point
+    window_position:
+      default_step: 10
+      minimum: 0
+      maximum: 100
+      unit: percentage_point

contracts/schemas/intent-ir.schema.json

@@ -12,7 +12,7 @@
     "spec": {
       "type": "object",
       "additionalProperties": false,
-      "required": ["utterance", "language", "mode", "detected_domains", "requests", "confidence", "requires_confirmation"],
+      "required": ["utterance", "language", "mode", "detected_domains", "requests", "actions", "constraints", "ambiguities", "unresolved_spans", "coverage", "confidence", "requires_confirmation", "execution_eligible"],
       "properties": {
         "utterance": {"type": "string", "minLength": 1, "maxLength": 4000},
         "language": {"type": "string", "pattern": "^[a-z]{2}(?:-[A-Z]{2})?$"},
@@ -39,6 +39,45 @@
             }
           }
         },
+        "actions": {
+          "type": "array",
+          "items": {
+            "type": "object",
+            "additionalProperties": false,
+            "required": ["id", "domain", "capability", "target", "operation", "parameters", "source_span", "confidence", "provenance", "risk_level", "confirmation_policy"],
+            "properties": {
+              "id": {"type": "string", "pattern": "^[a-z][a-z0-9_-]*$"},
+              "domain": {"type": "string", "pattern": "^[a-z][a-z0-9_]*$"},
+              "capability": {"type": "string", "minLength": 1},
+              "target": {"$ref": "common.schema.json#/$defs/target"},
+              "operation": {"type": "string", "minLength": 1},
+              "parameters": {"type": "object"},
+              "source_span": {"$ref": "#/$defs/sourceSpan"},
+              "confidence": {"type": "object", "additionalProperties": {"type": "number", "minimum": 0, "maximum": 1}},
+              "provenance": {"type": "object", "required": ["parser", "rule_ids"], "properties": {"parser": {"type": "string"}, "rule_ids": {"type": "array", "items": {"type": "string"}}}, "additionalProperties": true},
+              "risk_level": {"type": "string", "enum": ["low", "medium", "high", "critical"]},
+              "confirmation_policy": {"type": "string", "enum": ["never", "on_ambiguity", "always"]},
+              "resolution": {"type": "object"}
+            }
+          }
+        },
+        "constraints": {
+          "type": "array",
+          "items": {
+            "type": "object",
+            "additionalProperties": true,
+            "required": ["id", "type", "source_span"],
+            "properties": {
+              "id": {"type": "string"},
+              "type": {"type": "string"},
+              "domain": {"type": "string"},
+              "capability": {"type": "string"},
+              "target": {"$ref": "common.schema.json#/$defs/target"},
+              "parameters": {"type": "object"},
+              "source_span": {"$ref": "#/$defs/sourceSpan"}
+            }
+          }
+        },
         "ambiguities": {
           "type": "array",
           "items": {
@@ -48,13 +87,51 @@
             "properties": {
               "field": {"type": "string", "minLength": 1},
               "question": {"type": "string", "minLength": 1},
-              "options": {"type": "array", "items": {"type": ["string", "number", "boolean"]}}
+              "options": {"type": "array", "items": {"type": ["string", "number", "boolean"]}},
+              "reason": {"type": "string"},
+              "risk_level": {"type": "string"}
             }
           },
           "default": []
         },
+        "unresolved_spans": {
+          "type": "array",
+          "items": {
+            "type": "object",
+            "additionalProperties": false,
+            "required": ["text", "start", "end", "reason"],
+            "properties": {
+              "text": {"type": "string"},
+              "start": {"type": "integer", "minimum": 0},
+              "end": {"type": "integer", "minimum": 0},
+              "reason": {"type": "string"}
+            }
+          }
+        },
+        "coverage": {
+          "type": "object",
+          "additionalProperties": false,
+          "required": ["complete", "covered_spans"],
+          "properties": {
+            "complete": {"type": "boolean"},
+            "covered_spans": {"type": "array", "items": {"$ref": "#/$defs/sourceSpan"}}
+          }
+        },
         "confidence": {"type": "number", "minimum": 0, "maximum": 1},
-        "requires_confirmation": {"type": "boolean"}
+        "requires_confirmation": {"type": "boolean"},
+        "execution_eligible": {"type": "boolean"}
+      }
+    }
+  },
+  "$defs": {
+    "sourceSpan": {
+      "type": "object",
+      "additionalProperties": false,
+      "required": ["text", "start", "end"],
+      "properties": {
+        "text": {"type": "string"},
+        "start": {"type": "integer", "minimum": 0},
+        "end": {"type": "integer", "minimum": 0}
       }
     }
   }

domains/seat/src/seat_domain/generator.py

@@ -8,6 +8,7 @@ from typing import Any
 
 TARGETS = {
     "passenger": {"device": "seat", "zone": "front_right", "instance": "passenger"},
+    "driver": {"device": "seat", "zone": "front_left", "instance": "driver"},
 }
 CAPABILITIES = {
     "ventilation": "seat.set_ventilation",
@@ -29,8 +30,145 @@ def _now() -> str:
     return datetime.now(UTC).isoformat().replace("+00:00", "Z")
 
 
+def _source_span(utterance: str, source: Any = None) -> dict[str, Any]:
+    text = source if isinstance(source, str) and source else utterance
+    start = utterance.find(text)
+    if start < 0:
+        text = utterance
+        start = 0
+    return {"text": text, "start": start, "end": start + len(text)}
+
+
+def _semantic_target(domain: str, target: str) -> dict[str, str]:
+    if domain == "seat" and target in TARGETS:
+        return dict(TARGETS[target])
+    return {"device": domain, "zone": target}
+
+
+def _canonical_semantic_actions(
+    parsed: dict[str, Any],
+    utterance: str,
+    provider: str,
+    capability_metadata: dict[str, dict[str, str]],
+) -> list[dict[str, Any]]:
+    actions: list[dict[str, Any]] = []
+
+    def append(
+        *,
+        domain: str,
+        capability: str,
+        target: str,
+        operation: str,
+        parameters: dict[str, Any],
+        source: dict[str, Any],
+    ) -> None:
+        policy = capability_metadata.get(capability, {})
+        confidence = source.get("confidence")
+        if not isinstance(confidence, dict):
+            confidence = {"overall": float(parsed.get("confidence", 0))}
+        provenance = source.get("provenance")
+        if not isinstance(provenance, dict):
+            provenance = {"parser": provider, "rule_ids": []}
+        semantic: dict[str, Any] = {
+            "id": f"action_{len(actions) + 1}",
+            "domain": domain,
+            "capability": capability,
+            "target": _semantic_target(domain, target),
+            "operation": operation,
+            "parameters": parameters,
+            "source_span": _source_span(utterance, source.get("source_span")),
+            "confidence": confidence,
+            "provenance": provenance,
+            "risk_level": str(policy.get("risk_level", "high")),
+            "confirmation_policy": str(
+                policy.get("confirmation_policy", "on_ambiguity")
+            ),
+        }
+        resolution = source.get("relative_resolution", source.get("motion_intent"))
+        if isinstance(resolution, dict):
+            semantic["resolution"] = dict(resolution)
+        actions.append(semantic)
+
+    for action in parsed.get("actions", []):
+        target = str(action["target"])
+        if action.get("action_type") == "position":
+            parameters = {"position_pct": action["position_pct"]}
+            if action.get("angle_deg") is not None:
+                parameters["angle_deg"] = action["angle_deg"]
+            append(
+                domain="seat",
+                capability=MOTION_CAPABILITIES[str(action["axis"])],
+                target=target,
+                operation="set",
+                parameters=parameters,
+                source=action,
+            )
+        elif action.get("action_type") == "dynamic_mode":
+            append(
+                domain="seat",
+                capability=DYNAMIC_MODE_CAPABILITY,
+                target=target,
+                operation="activate",
+                parameters={"mode_id": action["mode_id"]},
+                source=action,
+            )
+        elif action.get("action_type") == "dynamic_mode_stop":
+            append(
+                domain="seat",
+                capability=DYNAMIC_MODE_STOP_CAPABILITY,
+                target=target,
+                operation="deactivate",
+                parameters={},
+                source=action,
+            )
+        else:
+            parameters = {"state": action["state"]}
+            if action.get("requested_level") is not None:
+                parameters["level"] = action["requested_level"]
+            append(
+                domain="seat",
+                capability=CAPABILITIES[str(action["function"])],
+                target=target,
+                operation="set",
+                parameters=parameters,
+                source=action,
+            )
+
+    for action in [
+        *parsed.get("domain_actions", []),
+        *parsed.get("native_can_actions", []),
+    ]:
+        append(
+            domain=str(action["domain"]),
+            capability=str(action["capability"]),
+            target=str(action["target"]),
+            operation=str(action.get("operation", "set")),
+            parameters=dict(action.get("parameters", {})),
+            source=action,
+        )
+
+    unique: list[dict[str, Any]] = []
+    signatures: set[str] = set()
+    for action in actions:
+        signature = repr(
+            (
+                action["domain"], action["capability"], action["target"],
+                action["operation"], action["parameters"],
+            )
+        )
+        if signature not in signatures:
+            signatures.add(signature)
+            action["id"] = f"action_{len(unique) + 1}"
+            unique.append(action)
+    return unique
+
+
 def build_intent_ir(
-    parsed: dict[str, Any], utterance: str, request_id: str, provider: str
+    parsed: dict[str, Any],
+    utterance: str,
+    request_id: str,
+    provider: str,
+    capability_metadata: dict[str, dict[str, str]] | None = None,
 ) -> dict[str, Any]:
     requests = []
     for action in parsed["actions"]:
@@ -154,7 +292,57 @@ def build_intent_ir(
             }
         )
 
-    detected_domains = parsed.get("detected_domains", ["seat"])
+    semantic_actions = _canonical_semantic_actions(
+        parsed, utterance, provider, capability_metadata or {}
+    )
+    constraints = []
+    for index, raw in enumerate(parsed.get("semantic_constraints", []), start=1):
+        constraint = dict(raw)
+        constraint["id"] = str(constraint.get("id") or f"constraint_{index}")
+        if not isinstance(constraint.get("source_span"), dict):
+            constraint["source_span"] = _source_span(
+                utterance, constraint.get("source_span")
+            )
+        if isinstance(constraint.get("target"), str):
+            constraint["target"] = _semantic_target(
+                str(constraint.get("domain", "seat")), constraint["target"]
+            )
+        constraints.append(constraint)
+
+    unresolved_spans = []
+    for raw in parsed.get("unresolved_spans", []):
+        item = {"text": raw} if isinstance(raw, str) else dict(raw)
+        unresolved_spans.append(
+            {
+                **_source_span(utterance, item.get("text")),
+                "reason": str(
+                    item.get("reason", "not_mapped_to_registered_capability")
+                ),
+            }
+        )
+
+    covered_spans = []
+    covered_signatures: set[tuple[int, int]] = set()
+    for item in [*semantic_actions, *constraints]:
+        span = item["source_span"]
+        signature = (int(span["start"]), int(span["end"]))
+        if signature not in covered_signatures:
+            covered_signatures.add(signature)
+            covered_spans.append(dict(span))
+
+    detected_domains = list(parsed.get("detected_domains", ["seat"]))
+    for action in semantic_actions:
+        if action["domain"] not in detected_domains:
+            detected_domains.append(action["domain"])
+    ambiguities = list(parsed.get("ambiguities", []))
+    requires_confirmation = bool(
+        ambiguities
+        or unresolved_spans
+        or any(
+            action["confirmation_policy"] == "always"
+            for action in semantic_actions
+        )
+    )
 
     return {
         "apiVersion": "cognitive-agent/v1alpha1",
@@ -173,9 +361,17 @@ def build_intent_ir(
             "mode": "cross_domain" if len(detected_domains) > 1 else "single_domain",
             "detected_domains": detected_domains,
             "requests": requests,
-            "ambiguities": parsed["ambiguities"],
+            "actions": semantic_actions,
+            "constraints": constraints,
+            "ambiguities": ambiguities,
+            "unresolved_spans": unresolved_spans,
+            "coverage": {
+                "complete": not unresolved_spans,
+                "covered_spans": covered_spans,
+            },
             "confidence": parsed["confidence"],
-            "requires_confirmation": bool(parsed["ambiguities"]),
+            "requires_confirmation": requires_confirmation,
+            "execution_eligible": bool(semantic_actions) and not requires_confirmation,
         },
     }
 

src/cognitive_agent/application/native_can_intent.py

@@ -26,6 +26,11 @@ WINDOW_TARGETS = {
     "右后": "rear_right",
     "后右": "rear_right",
 }
+WINDOW_GROUP_MEMBERS = {
+    "all": ["front_left", "front_right", "rear_left", "rear_right"],
+    "front": ["front_left", "front_right"],
+    "rear": ["rear_left", "rear_right"],
+}
 OUTLET_TARGETS = {
     "主驾左": "driver_left",
     "主驾右": "driver_right",
@@ -543,24 +548,55 @@ def _parse_chinese_commands(text: str) -> list[dict[str, Any]]:
                 break
 
     if "车窗" in compact:
-        explicit_target = next(
-            (value for label, value in WINDOW_TARGETS.items() if label in compact),
+        explicit_targets = []
+        for label, target_name in WINDOW_TARGETS.items():
+            if target_name in WINDOW_GROUP_MEMBERS or label not in compact:
+                continue
+            if target_name not in explicit_targets:
+                explicit_targets.append(target_name)
+        broad_target = next(
+            (
+                target_name
+                for label, target_name in WINDOW_TARGETS.items()
+                if target_name in WINDOW_GROUP_MEMBERS and label in compact
+            ),
             None,
         )
-        target = explicit_target or "front_left"
+        exclusions = {
+            target_name
+            for label, target_name in WINDOW_TARGETS.items()
+            if target_name not in WINDOW_GROUP_MEMBERS
+            and re.search(
+                rf"{re.escape(label)}(?:车窗)?(?:除外|除去|不包括|不含)",
+                compact,
+            )
+        }
+        if broad_target and exclusions:
+            targets = [
+                target_name
+                for target_name in WINDOW_GROUP_MEMBERS[broad_target]
+                if target_name not in exclusions
+            ]
+        elif explicit_targets:
+            targets = [target_name for target_name in explicit_targets if target_name not in exclusions]
+        else:
+            targets = [broad_target or "front_left"]
         position = re.search(
             r"(?:位置|开度|调到|设为|设置为|开启到|打开到)(\d{1,3})%",
             compact,
         )
         half_position = bool(re.search(r"(?:开启|打开|开到|开度)?(?:一半|半开)", compact))
         position_pct = int(position[1]) if position else 50 if half_position else None
-        if position_pct is not None and target not in {"all", "front", "rear"} and position_pct <= 100:
+        if position_pct is not None and all(target not in WINDOW_GROUP_MEMBERS for target in targets) and position_pct <= 100:
             reason = (
                 "用户指定了单个车窗的绝对开度。"
-                if explicit_target
+                if explicit_targets or broad_target
                 else "用户未指定车窗目标，按驾驶台默认选择主驾左前窗。"
             )
-            actions.append(_action("window", "window.set_position", target, {"position_pct": position_pct}, reason))
+            actions.extend(
+                _action("window", "window.set_position", target, {"position_pct": position_pct}, reason)
+                for target in targets
+            )
         elif position_pct is not None:
             # Absolute position feedback is verified for one window only.  Do
             # not turn a half-open request for a broad scope into full motion.
@@ -575,10 +611,13 @@ def _parse_chinese_commands(text: str) -> list[dict[str, Any]]:
             if motion:
                 reason = (
                     "用户指定了车窗目标和动作。"
-                    if explicit_target
+                    if explicit_targets or broad_target
                     else "用户未指定车窗目标，按驾驶台默认选择主驾左前窗。"
                 )
-                actions.append(_action("window", "window.set_motion", target, {"motion": motion}, reason))
+                actions.extend(
+                    _action("window", "window.set_motion", target, {"motion": motion}, reason)
+                    for target in targets
+                )
 
     vehicle_lock_request = bool(
         re.search(
@@ -786,9 +825,40 @@ def _conflicts_with_driver_native(
     return False
 
 
-def merge_native_can_intent(parsed: dict[str, Any], utterance: str) -> dict[str, Any]:
+def merge_native_can_intent(
+    parsed: dict[str, Any],
+    utterance: str,
+    *,
+    relative_actions: list[dict[str, Any]] | None = None,
+    relative_ambiguities: list[dict[str, Any]] | None = None,
+    relative_claimed_controls: set[tuple[str, str]] | None = None,
+) -> dict[str, Any]:
     """Add deterministic vehicle-CAN actions without duplicating model requests."""
 
+    relative_actions = list(relative_actions or [])
+    relative_ambiguities = list(relative_ambiguities or [])
+    relative_claimed_controls = set(relative_claimed_controls or set())
+
+    def is_claimed(candidate: dict[str, Any]) -> bool:
+        capability = str(candidate.get("capability", ""))
+        target = str(candidate.get("target", candidate.get("target_scope", "")))
+        return (
+            (capability, target) in relative_claimed_controls
+            or (capability, "*") in relative_claimed_controls
+        )
+
+    if relative_claimed_controls:
+        parsed = dict(parsed)
+        for key in (
+            "domain_actions",
+            "native_can_actions",
+            "deferred_requests",
+            "model_requests",
+        ):
+            parsed[key] = [
+                item for item in parsed.get(key, []) if not is_claimed(item)
+            ]
+
     clauses = [part for part in re.split(r"[，,。；;！!？?]+", utterance) if part.strip()]
     exact_clause_actions = [
         parse_exact_native_can_command(clause) for clause in clauses
@@ -797,7 +867,12 @@ def merge_native_can_intent(parsed: dict[str, Any], utterance: str) -> dict[str,
         action for clause_actions in exact_clause_actions for action in clause_actions
     ]
     fully_exact = bool(clauses) and all(exact_clause_actions)
-    actions = exact_actions or _parse_chinese_commands(utterance)
+    actions = [
+        item
+        for item in (exact_actions or _parse_chinese_commands(utterance))
+        if not is_claimed(item)
+    ]
+    actions.extend(relative_actions)
     implicit_default_capabilities = {
         str(action.get("capability", ""))
         for action in actions
@@ -833,7 +908,7 @@ def merge_native_can_intent(parsed: dict[str, Any], utterance: str) -> dict[str,
             parsed,
             unresolved=not has_default_driver_seat_action,
         )
-    if not actions:
+    if not actions and not relative_ambiguities:
         return parsed
 
     result = dict(parsed)
@@ -946,6 +1021,11 @@ def merge_native_can_intent(parsed: dict[str, Any], utterance: str) -> dict[str,
                 "parameters": dict(item["parameters"]),
                 "reason": item["reason"],
                 "execution_backend": "native_can",
+                **(
+                    {"relative_resolution": dict(item["relative_resolution"])}
+                    if item.get("relative_resolution")
+                    else {}
+                ),
             }
             for item in unique_actions
         ],
@@ -963,10 +1043,42 @@ def merge_native_can_intent(parsed: dict[str, Any], utterance: str) -> dict[str,
                     for name, value in item["parameters"].items()
                 ],
                 "reason": item["reason"],
+                **(
+                    {"relative_resolution": dict(item["relative_resolution"])}
+                    if item.get("relative_resolution")
+                    else {}
+                ),
             }
             for item in unique_actions
         ],
     ]
+    constraints = list(result.get("semantic_constraints", []))
+    for match in re.finditer(
+        r"(?P<target>左前|前左|右前|前右|左后|后左|右后|后右)(?:车窗)?"
+        r"(?:除外|除去|不包括|不含)",
+        utterance,
+    ):
+        constraints.append(
+            {
+                "type": "exclude_target",
+                "domain": "window",
+                "target": WINDOW_TARGETS[match.group("target")],
+                "source_span": {
+                    "text": match.group(0),
+                    "start": match.start(),
+                    "end": match.end(),
+                },
+            }
+        )
+    result["semantic_constraints"] = constraints
+    result["ambiguities"] = list(result.get("ambiguities", []))
+    for ambiguity in relative_ambiguities:
+        signature = (ambiguity.get("field"), ambiguity.get("reason"))
+        if not any(
+            (item.get("field"), item.get("reason")) == signature
+            for item in result["ambiguities"]
+        ):
+            result["ambiguities"].append(ambiguity)
     domains = list(result.get("detected_domains", []))
     for item in unique_actions:
         if item["domain"] not in domains:
@@ -986,6 +1098,12 @@ def merge_native_can_intent(parsed: dict[str, Any], utterance: str) -> dict[str,
         item
         for item in result.get("ambiguities", [])
         if item.get("field") != "capability"
+        and not (
+            unique_actions
+            and not _looks_like_seat_request(utterance)
+            and str(item.get("field")) in {"target", "target_scope"}
+            and "当前台架仅支持副驾座椅" in str(item.get("question", ""))
+        )
         and not (
             resolved_driver_seat
             and (
@@ -1006,6 +1124,11 @@ def merge_native_can_intent(parsed: dict[str, Any], utterance: str) -> dict[str,
     result["decision_summary"] = [
         *existing_summaries,
         *[item["reason"] for item in unique_actions],
+        *[
+            f"相对指令未执行：{ambiguity['question']}"
+            for ambiguity in relative_ambiguities
+        ],
     ]
-    result["confidence"] = max(float(result.get("confidence", 0)), 0.98)
+    if unique_actions:
+        result["confidence"] = max(float(result.get("confidence", 0)), 0.98)
     return result

src/cognitive_agent/application/native_can_relative.py

@@ -0,0 +1,424 @@
+"""Resolve relative vehicle controls against the current synchronized state."""
+
+from __future__ import annotations
+
+import re
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any
+
+import yaml
+
+
+CLAUSE_SEPARATOR = re.compile(r"[，,；;。！？!?\n]+")
+NUMBER_TOKEN = r"(?:\d{1,3}|[一二两三四五六七八九十]+)"
+GENERIC_INCREASE = r"增加|提高|升高|上调|调高|增大|加大|调大|再高|更高"
+GENERIC_DECREASE = r"减少|降低|下降|下调|调低|减小|调小|再低|更低"
+AMBIENT_INCREASE = rf"{GENERIC_INCREASE}|调亮|变亮|更亮|再亮|亮一点"
+AMBIENT_DECREASE = rf"{GENERIC_DECREASE}|调暗|变暗|更暗|再暗|暗一点"
+OPENING_INCREASE = (
+    r"(?:增加|提高|增大|调大)(?:开度|开启程度)|"
+    r"(?:开度|开启程度)(?:增加|提高|增大|调大)|"
+    r"开大|多开|再打开|再开启|再开"
+)
+OPENING_DECREASE = (
+    r"(?:减少|降低|减小|调小)(?:开度|开启程度)|"
+    r"(?:开度|开启程度)(?:减少|降低|减小|调小)|"
+    r"关小|少开|再关闭|再关上|再关"
+)
+WINDOW_INCREASE = rf"{OPENING_INCREASE}|再降下|再下降|多降"
+WINDOW_DECREASE = rf"{OPENING_DECREASE}|再升起|再上升|多升"
+
+CHINESE_DIGITS = {
+    "一": 1, "二": 2, "两": 2, "三": 3, "四": 4, "五": 5,
+    "六": 6, "七": 7, "八": 8, "九": 9, "十": 10,
+}
+WINDOW_TARGET_LABELS = {
+    "左前": ("front_left",), "前左": ("front_left",),
+    "右前": ("front_right",), "前右": ("front_right",),
+    "左后": ("rear_left",), "后左": ("rear_left",),
+    "右后": ("rear_right",), "后右": ("rear_right",),
+    "前排": ("front_left", "front_right"),
+    "后排": ("rear_left", "rear_right"),
+    "全部": ("front_left", "front_right", "rear_left", "rear_right"),
+    "所有": ("front_left", "front_right", "rear_left", "rear_right"),
+    "四个": ("front_left", "front_right", "rear_left", "rear_right"),
+    "全车": ("front_left", "front_right", "rear_left", "rear_right"),
+}
+
+
+@dataclass(frozen=True)
+class RelativePolicy:
+    default_step: int
+    minimum: int
+    maximum: int
+    unit: str
+
+
+@dataclass
+class RelativeResolution:
+    actions: list[dict[str, Any]] = field(default_factory=list)
+    ambiguities: list[dict[str, Any]] = field(default_factory=list)
+    claimed_controls: set[tuple[str, str]] = field(default_factory=set)
+
+
+class NativeCanRelativeResolver:
+    """Convert relative language into bounded absolute native-CAN actions."""
+
+    def __init__(self, repository_root: Path) -> None:
+        document = yaml.safe_load(
+            (repository_root / "config" / "native_can_relative_controls.yaml").read_text(
+                encoding="utf-8"
+            )
+        ) or {}
+        if document.get("kind") != "NativeCanRelativeControlPolicy":
+            raise ValueError("unsupported native CAN relative-control policy")
+        raw_controls = document.get("spec", {}).get("controls", {})
+        self.policies = {
+            name: self._policy(name, raw_controls.get(name))
+            for name in (
+                "climate_temperature", "climate_fan", "ambient_brightness",
+                "shade_position", "window_position",
+            )
+        }
+
+    @staticmethod
+    def _policy(name: str, raw: Any) -> RelativePolicy:
+        if not isinstance(raw, dict):
+            raise TypeError(f"relative-control policy {name} must be a mapping")
+        values = {key: raw.get(key) for key in ("default_step", "minimum", "maximum")}
+        if any(isinstance(value, bool) or not isinstance(value, int) for value in values.values()):
+            raise TypeError(f"relative-control policy {name} values must be integers")
+        if values["default_step"] <= 0 or values["minimum"] >= values["maximum"]:
+            raise ValueError(f"relative-control policy {name} has invalid range")
+        unit = raw.get("unit")
+        if not isinstance(unit, str) or not unit:
+            raise TypeError(f"relative-control policy {name} unit must be a string")
+        return RelativePolicy(unit=unit, **values)
+
+    @staticmethod
+    def _clauses(utterance: str) -> list[str]:
+        return [
+            re.sub(r"\s+", "", clause)
+            for clause in CLAUSE_SEPARATOR.split(utterance)
+            if clause.strip()
+        ]
+
+    def matches(self, utterance: str) -> bool:
+        clauses = self._clauses(utterance)
+        for index, clause in enumerate(clauses):
+            if (
+                ("温度" in clause and self._operation(clause, GENERIC_INCREASE, GENERIC_DECREASE))
+                or (re.search(r"风量|风速", clause) and self._operation(clause, GENERIC_INCREASE, GENERIC_DECREASE))
+                or (self._ambient_clause(clauses, index) and self._operation(clause, AMBIENT_INCREASE, AMBIENT_DECREASE))
+                or (re.search(r"遮阳帘|天幕帘", clause) and self._operation(clause, OPENING_INCREASE, OPENING_DECREASE))
+                or ("车窗" in clause and self._operation(clause, WINDOW_INCREASE, WINDOW_DECREASE))
+            ):
+                return True
+        return False
+
+    def resolve(self, utterance: str, context: dict[str, Any] | None) -> RelativeResolution:
+        resolution = RelativeResolution()
+        domain_state = (
+            context.get("domain_devices", {}) if isinstance(context, dict) else {}
+        )
+        clauses = self._clauses(utterance)
+        for index, clause in enumerate(clauses):
+            if "温度" in clause:
+                self._resolve_temperature(clause, domain_state, resolution)
+            if re.search(r"风量|风速", clause):
+                self._resolve_fan(clause, domain_state, resolution)
+            if self._ambient_clause(clauses, index):
+                self._resolve_ambient(clause, domain_state, resolution)
+            if re.search(r"遮阳帘|天幕帘", clause):
+                self._resolve_shade(clause, domain_state, resolution)
+            if "车窗" in clause:
+                window_clause = clause
+                if index + 1 < len(clauses) and re.search(r"除外|除去|不包括|不含", clauses[index + 1]):
+                    window_clause += clauses[index + 1]
+                self._resolve_windows(window_clause, domain_state, resolution)
+        resolution.actions = self._deduplicate_actions(resolution.actions)
+        return resolution
+
+    @staticmethod
+    def _ambient_clause(clauses: list[str], index: int) -> bool:
+        clause = clauses[index]
+        if "氛围灯" in clause:
+            return not re.search(r"颜色|色号|色温", clause) and bool(
+                re.search(r"亮度|强度|调亮|变亮|更亮|再亮|调暗|变暗|更暗|再暗", clause)
+            )
+        return index > 0 and "氛围灯" in clauses[index - 1] and bool(re.match(r"(?:亮度|强度)", clause))
+
+    @staticmethod
+    def _operation(clause: str, increase_pattern: str, decrease_pattern: str) -> tuple[str, re.Match[str]] | None:
+        if re.search(rf"(?:{increase_pattern}|{decrease_pattern})(?:到|至|为){NUMBER_TOKEN}", clause):
+            return None
+        increase = re.search(increase_pattern, clause)
+        decrease = re.search(decrease_pattern, clause)
+        if bool(increase) == bool(decrease):
+            return None
+        return ("increase", increase) if increase else ("decrease", decrease)  # type: ignore[arg-type]
+
+    @staticmethod
+    def _number(text: str) -> int | None:
+        if text.isdigit():
+            return int(text)
+        if text in CHINESE_DIGITS:
+            return CHINESE_DIGITS[text]
+        if "十" not in text:
+            return None
+        tens, _, ones = text.partition("十")
+        return CHINESE_DIGITS.get(tens, 1) * 10 + CHINESE_DIGITS.get(ones, 0)
+
+    @classmethod
+    def _delta(cls, clause: str, match: re.Match[str], policy: RelativePolicy) -> int:
+        tail = clause[match.end():]
+        if re.match(r"(?:了)?一点(?:点)?", tail):
+            return policy.default_step
+        explicit = re.match(rf"(?:了)?(?P<number>{NUMBER_TOKEN})(?:档|级|度|%|个百分点)?", tail)
+        return cls._number(explicit.group("number")) if explicit else policy.default_step
+
+    @staticmethod
+    def _claim(resolution: RelativeResolution, capability: str, *targets: str) -> None:
+        resolution.claimed_controls.update((capability, target) for target in targets)
+
+    @staticmethod
+    def _ambiguity(resolution: RelativeResolution, *, field_name: str, question: str, reason: str) -> None:
+        resolution.ambiguities.append({"field": field_name, "question": question, "reason": reason})
+
+    @staticmethod
+    def _feedback_is_physical(value: Any) -> bool:
+        return isinstance(value, dict) and value.get("feedback_source") == "physical_can_ch1"
+
+    @classmethod
+    def _value(
+        cls,
+        resolution: RelativeResolution,
+        value: Any,
+        *,
+        field_name: str,
+        label: str,
+        require_physical: bool,
+    ) -> int | None:
+        if isinstance(value, dict):
+            raw = value.get("value", value.get("position_pct", value.get("temperature_c")))
+            physical = cls._feedback_is_physical(value)
+        else:
+            raw = value
+            physical = False
+        if isinstance(raw, bool) or not isinstance(raw, (int, float)):
+            cls._ambiguity(
+                resolution,
+                field_name=field_name,
+                question=f"缺少{label}的有效状态，无法计算相对目标。",
+                reason="native_can_relative_state_unknown",
+            )
+            return None
+        if require_physical and not physical:
+            cls._ambiguity(
+                resolution,
+                field_name=field_name,
+                question=f"{label}尚无 CH1 实车反馈，真实执行不能依据虚拟状态计算相对目标。",
+                reason="native_can_relative_state_not_physical",
+            )
+            return None
+        return int(raw)
+
+    @staticmethod
+    def _target(current: int, delta: int, direction: str, policy: RelativePolicy) -> tuple[int, bool]:
+        requested = current + delta if direction == "increase" else current - delta
+        target = min(policy.maximum, max(policy.minimum, requested))
+        return target, target != requested
+
+    @classmethod
+    def _at_limit(
+        cls,
+        resolution: RelativeResolution,
+        *,
+        current: int,
+        direction: str,
+        policy: RelativePolicy,
+        field_name: str,
+        label: str,
+    ) -> bool:
+        at_limit = (direction == "increase" and current >= policy.maximum) or (
+            direction == "decrease" and current <= policy.minimum
+        )
+        if at_limit:
+            cls._ambiguity(
+                resolution,
+                field_name=field_name,
+                question=f"{label}当前已在{'上限' if direction == 'increase' else '下限'} {current}，不会重复发送控制命令。",
+                reason="native_can_relative_target_at_limit",
+            )
+        return at_limit
+
+    @staticmethod
+    def _action(
+        *,
+        domain: str,
+        capability: str,
+        target: str,
+        parameters: dict[str, Any],
+        current: int,
+        delta: int,
+        direction: str,
+        label: str,
+        clamped: bool,
+        state_source: str,
+    ) -> dict[str, Any]:
+        verb = "提高" if direction == "increase" else "降低"
+        target_value = next(iter(parameters.values()))
+        return {
+            "domain": domain,
+            "capability": capability,
+            "target": target,
+            "operation": "set",
+            "parameters": parameters,
+            "reason": f"用户要求{label}{verb}{delta}；基于当前状态 {current} 计算绝对目标 {target_value}{'，已按允许范围收敛' if clamped else ''}。",
+            "relative_resolution": {
+                "direction": direction,
+                "current": current,
+                "delta": delta,
+                "target": target_value,
+                "clamped": clamped,
+                "state_source": state_source,
+            },
+        }
+
+    @staticmethod
+    def _require_physical(domain_state: dict[str, Any]) -> bool:
+        return domain_state.get("vehicle_status", {}).get("feedback_source") == "physical_can_ch1"
+
+    def _resolve_temperature(self, clause: str, state: dict[str, Any], resolution: RelativeResolution) -> None:
+        operation = self._operation(clause, GENERIC_INCREASE, GENERIC_DECREASE)
+        if operation is None:
+            return
+        direction, match = operation
+        target = "passenger" if re.search(r"副驾|乘客位", clause) else "driver"
+        self._claim(resolution, "climate.set_temperature", target)
+        zone = state.get("climate", {}).get("zones", {}).get(target, {})
+        current = self._value(
+            resolution,
+            {"value": zone.get("temperature_c"), "feedback_source": state.get("climate", {}).get("feedback_source")},
+            field_name=f"climate.{target}_temperature_c",
+            label=f"{'副驾' if target == 'passenger' else '主驾'}温度",
+            require_physical=self._require_physical(state),
+        )
+        if current is None:
+            return
+        policy = self.policies["climate_temperature"]
+        if self._at_limit(resolution, current=current, direction=direction, policy=policy, field_name=f"climate.{target}_temperature_c", label="空调温度"):
+            return
+        delta = self._delta(clause, match, policy)
+        target_value, clamped = self._target(current, delta, direction, policy)
+        resolution.actions.append(self._action(domain="climate", capability="climate.set_temperature", target=target, parameters={"temperature_c": target_value}, current=current, delta=delta, direction=direction, label="空调温度", clamped=clamped, state_source=state.get("climate", {}).get("feedback_source", "conversation_state")))
+
+    def _resolve_fan(self, clause: str, state: dict[str, Any], resolution: RelativeResolution) -> None:
+        operation = self._operation(clause, GENERIC_INCREASE, GENERIC_DECREASE)
+        if operation is None:
+            return
+        direction, match = operation
+        self._claim(resolution, "climate.set_fan_level", "vehicle")
+        climate = state.get("climate", {})
+        current = self._value(resolution, {"value": climate.get("fan_level"), "feedback_source": climate.get("feedback_source")}, field_name="climate.fan_level", label="空调风量", require_physical=self._require_physical(state))
+        if current is None:
+            return
+        policy = self.policies["climate_fan"]
+        if self._at_limit(resolution, current=current, direction=direction, policy=policy, field_name="climate.fan_level", label="空调风量"):
+            return
+        delta = self._delta(clause, match, policy)
+        target_value, clamped = self._target(current, delta, direction, policy)
+        resolution.actions.append(self._action(domain="climate", capability="climate.set_fan_level", target="vehicle", parameters={"level": target_value}, current=current, delta=delta, direction=direction, label="空调风量", clamped=clamped, state_source=climate.get("feedback_source", "conversation_state")))
+
+    def _resolve_ambient(self, clause: str, state: dict[str, Any], resolution: RelativeResolution) -> None:
+        operation = self._operation(clause, AMBIENT_INCREASE, AMBIENT_DECREASE)
+        if operation is None:
+            return
+        direction, match = operation
+        capability = "ambient_light.set_brightness_level"
+        self._claim(resolution, capability, "vehicle")
+        ambient = state.get("ambient_light", {})
+        level = ambient.get("front_brightness_level")
+        if level is None:
+            brightness_pct = ambient.get("brightness_pct")
+            level = round(float(brightness_pct) * 5 / 100) if isinstance(brightness_pct, (int, float)) else None
+        current = self._value(resolution, {"value": level, "feedback_source": ambient.get("feedback_source")}, field_name="ambient_light.brightness_level", label="氛围灯亮度", require_physical=self._require_physical(state))
+        if current is None:
+            return
+        policy = self.policies["ambient_brightness"]
+        if self._at_limit(resolution, current=current, direction=direction, policy=policy, field_name="ambient_light.brightness_level", label="氛围灯亮度"):
+            return
+        delta = self._delta(clause, match, policy)
+        target_value, clamped = self._target(current, delta, direction, policy)
+        resolution.actions.append(self._action(domain="ambient_light", capability=capability, target="vehicle", parameters={"brightness_level": target_value}, current=current, delta=delta, direction=direction, label="氛围灯亮度", clamped=clamped, state_source=ambient.get("feedback_source", "conversation_state")))
+
+    def _resolve_shade(self, clause: str, state: dict[str, Any], resolution: RelativeResolution) -> None:
+        operation = self._operation(clause, OPENING_INCREASE, OPENING_DECREASE)
+        if operation is None:
+            return
+        direction, match = operation
+        self._claim(resolution, "roof.set_shade_position", "vehicle")
+        roof = state.get("roof", {})
+        current = self._value(resolution, {"value": roof.get("shade_position_pct"), "feedback_source": roof.get("feedback_source")}, field_name="roof.shade_position_pct", label="遮阳帘开度", require_physical=self._require_physical(state))
+        if current is None:
+            return
+        policy = self.policies["shade_position"]
+        if self._at_limit(resolution, current=current, direction=direction, policy=policy, field_name="roof.shade_position_pct", label="遮阳帘开度"):
+            return
+        delta = self._delta(clause, match, policy)
+        target_value, clamped = self._target(current, delta, direction, policy)
+        resolution.actions.append(self._action(domain="roof", capability="roof.set_shade_position", target="vehicle", parameters={"position_pct": target_value}, current=current, delta=delta, direction=direction, label="遮阳帘开度", clamped=clamped, state_source=roof.get("feedback_source", "conversation_state")))
+
+    @staticmethod
+    def _window_targets(clause: str) -> tuple[str, ...]:
+        individual = []
+        for label, targets in tuple(WINDOW_TARGET_LABELS.items())[:8]:
+            if label in clause and targets[0] not in individual:
+                individual.append(targets[0])
+        if individual:
+            return tuple(individual)
+        for label in ("全部", "所有", "四个", "全车", "前排", "后排"):
+            if label in clause:
+                return WINDOW_TARGET_LABELS[label]
+        return ()
+
+    def _resolve_windows(self, clause: str, state: dict[str, Any], resolution: RelativeResolution) -> None:
+        operation = self._operation(clause, WINDOW_INCREASE, WINDOW_DECREASE)
+        if operation is None:
+            return
+        direction, match = operation
+        targets = self._window_targets(clause)
+        if re.search(r"除外|除去|不包括|不含", clause):
+            self._ambiguity(resolution, field_name="window.target", question="相对调整多个车窗时暂不支持排除目标，请明确列出需要调整的车窗。", reason="native_can_relative_exclusion_unsupported")
+            return
+        if not targets:
+            self._ambiguity(resolution, field_name="window.target", question="相对调整车窗开度需要明确单窗、前排、后排或全部车窗。", reason="native_can_relative_target_unknown")
+            return
+        policy = self.policies["window_position"]
+        delta = self._delta(clause, match, policy)
+        windows = state.get("window", {})
+        positions = windows.get("positions", {})
+        resolved: list[dict[str, Any]] = []
+        for target in targets:
+            self._claim(resolution, "window.set_position", target)
+            position = positions.get(target, {})
+            current = self._value(resolution, position, field_name=f"window.{target}_position_pct", label=f"{target}车窗开度", require_physical=self._require_physical(state))
+            if current is None:
+                return
+            if self._at_limit(resolution, current=current, direction=direction, policy=policy, field_name=f"window.{target}_position_pct", label=f"{target}车窗开度"):
+                return
+            target_value, clamped = self._target(current, delta, direction, policy)
+            resolved.append(self._action(domain="window", capability="window.set_position", target=target, parameters={"position_pct": target_value}, current=current, delta=delta, direction=direction, label=f"{target}车窗开度", clamped=clamped, state_source=position.get("feedback_source", windows.get("feedback_source", "conversation_state"))))
+        resolution.actions.extend(resolved)
+
+    @staticmethod
+    def _deduplicate_actions(actions: list[dict[str, Any]]) -> list[dict[str, Any]]:
+        unique: list[dict[str, Any]] = []
+        seen: set[tuple[str, str, str]] = set()
+        for action in actions:
+            key = (action["capability"], action["target"], repr(action["parameters"]))
+            if key not in seen:
+                seen.add(key)
+                unique.append(action)
+        return unique

src/cognitive_agent/application/seat_service.py

@@ -53,6 +53,12 @@ from cognitive_agent.application.model_plan_adapter import (  # noqa: E402
     SEAT_MOTION_CAPABILITIES,
 )
 from cognitive_agent.application.native_can_intent import merge_native_can_intent  # noqa: E402
+from cognitive_agent.application.native_can_relative import (  # noqa: E402
+    NativeCanRelativeResolver,
+)
+from cognitive_agent.application.semantic_ir import (  # noqa: E402
+    apply_language_safety_guards,
+)
 from cognitive_agent.application.native_can_feedback import (  # noqa: E402
     VehicleSafetyPreflightError,
     authoritative_physical_ch1_feedback_state,
@@ -75,6 +81,7 @@ from cognitive_agent.application.zcan_seat_executor import (  # noqa: E402
 from cognitive_agent.infrastructure.llm.openai_responses import (  # noqa: E402
     OpenAIIntentError,
     OpenAIIntentInterpreter,
+    REASONING_EFFORTS,
 )
 from cognitive_agent.infrastructure.can.hardware import (  # noqa: E402
     HardwareSafetyError,
@@ -202,6 +209,7 @@ class SeatDemoService:
         self.hardware_settings = SeatHardwareSettings.from_env(repository_root)
         self.vehicle_can_settings = VehicleCanSettings.from_env(repository_root)
         self.native_can_runtime = NativeCanRuntime(repository_root)
+        self.native_can_relative_resolver = NativeCanRelativeResolver(repository_root)
         self.hardware_execution = PHYSICAL_BENCH_COORDINATOR
         self.hardware_access = PHYSICAL_BENCH_ACCESS
         # Shared by the short-lived hardware executor instances. A process must
@@ -217,6 +225,16 @@ class SeatDemoService:
             repository_root, {"seat", *self.domain_runtimes}
         )
         self.capability_context = self.capability_registry.snapshot()
+        self.semantic_capability_metadata = {
+            capability["name"]: {
+                "risk_level": capability.get("risk_level", "high"),
+                "confirmation_policy": capability.get(
+                    "confirmation_policy", "on_ambiguity"
+                ),
+            }
+            for domain in self.capability_context["domains"]
+            for capability in domain["capabilities"]
+        }
         self.model_plan_adapter = ModelPlanAdapter(self.capability_context)
         self.vehicle_profiles = VehicleProfileRegistry(repository_root / "config" / "vehicles")
         self.mode_profiles = ModeProfileRegistry(repository_root / "config" / "modes")
@@ -316,6 +334,9 @@ class SeatDemoService:
             },
             "openai_available": self.openai.available,
             "openai_model": self.openai.model,
+            "openai_models": self.openai.model_catalog,
+            "openai_reasoning_effort_default": self.openai.reasoning_effort,
+            "openai_reasoning_efforts": list(REASONING_EFFORTS),
             "openai_responses_endpoint": self.openai.responses_endpoint,
             "conversation_memory": True,
             "default_vehicle_id": default_vehicle.id,
@@ -633,6 +654,7 @@ class SeatDemoService:
         *,
         hardware_client_id: str | None = None,
         hardware_access_bypass: bool = True,
+        reasoning_effort: str | None = None,
     ) -> dict[str, Any]:
         # Creating a reusable scene is a configuration workflow.  Synthesize
         # and validate it in dry-run even when the console currently targets
@@ -649,6 +671,7 @@ class SeatDemoService:
                 approval_token,
                 vehicle_id,
                 simulation_fault,
+                reasoning_effort,
             )
         if hardware_access_bypass or self.driver_only_ch1_execution_active:
             with self.hardware_execution.reserve():
@@ -660,6 +683,7 @@ class SeatDemoService:
                     approval_token,
                     vehicle_id,
                     simulation_fault,
+                    reasoning_effort,
                 )
         authorized_client_id = self.hardware_access.ensure_authorized(hardware_client_id)
         with self.hardware_execution.reserve():
@@ -672,6 +696,7 @@ class SeatDemoService:
                     approval_token,
                     vehicle_id,
                     simulation_fault,
+                    reasoning_effort,
                 )
 
     def _process_once(
@@ -683,6 +708,7 @@ class SeatDemoService:
         approval_token: str | None = None,
         vehicle_id: str | None = None,
         simulation_fault: str = "normal",
+        reasoning_effort: str | None = None,
     ) -> dict[str, Any]:
         utterance = utterance.strip()
         if not utterance:
@@ -691,6 +717,11 @@ class SeatDemoService:
             raise ValueError("utterance must be at most 1000 characters")
         if provider not in {"auto", "openai", "local"}:
             raise ValueError("provider must be auto, openai, or local")
+        effective_reasoning_effort = (
+            self.openai.reasoning_effort
+            if reasoning_effort is None
+            else self.openai.validate_reasoning_effort(reasoning_effort)
+        )
         if execution_mode not in {"dry_run", "simulated_hardware", "hardware"}:
             raise ValueError(
                 "execution_mode must be dry_run, simulated_hardware, or hardware"
@@ -755,6 +786,8 @@ class SeatDemoService:
             "conversation_turn": context["turn_count"],
             "status": "processing",
             "provider_requested": provider,
+            "reasoning_effort_requested": effective_reasoning_effort,
+            "reasoning_effort_used": None,
             "execution_mode": execution_mode,
             "hardware_execution_allowed": (
                 hardware_request and not control_plane_request
@@ -828,6 +861,7 @@ class SeatDemoService:
             provider,
             context,
             vehicle_profile,
+            reasoning_effort=reasoning_effort,
         )
         if self._is_dynamic_mode_stop_request(utterance):
             parsed = self._apply_dynamic_mode_stop_request(parsed, utterance)
@@ -835,7 +869,25 @@ class SeatDemoService:
             parsed = self._apply_mode_profiles(
                 parsed, matched_modes, context, vehicle_profile
             )
-        parsed = merge_native_can_intent(parsed, utterance)
+        relative_resolution = (
+            self.native_can_relative_resolver.resolve(utterance, context)
+            if self.native_can_relative_resolver.matches(utterance)
+            else None
+        )
+        parsed = merge_native_can_intent(
+            parsed,
+            utterance,
+            relative_actions=(
+                relative_resolution.actions if relative_resolution else None
+            ),
+            relative_ambiguities=(
+                relative_resolution.ambiguities if relative_resolution else None
+            ),
+            relative_claimed_controls=(
+                relative_resolution.claimed_controls if relative_resolution else None
+            ),
+        )
+        parsed = apply_language_safety_guards(parsed, utterance)
         parsed = self._partition_native_actions(
             parsed,
             promote_supported=(hardware_request and self.vehicle_can_settings.enabled),
@@ -891,10 +943,43 @@ class SeatDemoService:
         if provider_notice:
             parsed["decision_summary"].append(provider_notice)
         result["provider_used"] = provider_used
+        if provider_used.startswith("openai:"):
+            result["reasoning_effort_used"] = effective_reasoning_effort
         result["decision_summary"] = parsed["decision_summary"]
         result["deferred_requests"] = parsed.get("deferred_requests", [])
         result["model_requests"] = parsed.get("model_requests", [])
 
+        if (
+            parsed.get("semantic_constraints")
+            and not parsed.get("actions")
+            and not parsed.get("domain_actions")
+            and not parsed.get("native_can_actions")
+            and not parsed.get("ambiguities")
+            and not parsed.get("unresolved_spans")
+        ):
+            intent_ir = build_intent_ir(
+                parsed,
+                utterance,
+                request_id,
+                provider_used,
+                self.semantic_capability_metadata,
+            )
+            result["intent_ir"] = intent_ir
+            result["validation"] = self.contracts.validate(intent_ir)
+            result["status"] = "no_action"
+            self._trace(
+                traces,
+                request_id,
+                "safety_check",
+                "succeeded",
+                "检测到禁止或保持约束；未生成、编译或发送任何 CAN 动作。",
+                time.perf_counter(),
+                {"constraint_count": len(intent_ir["spec"]["constraints"]), "execution_eligible": False},
+                domain="orchestration",
+            )
+            self._persist(result)
+            return result
+
         context_started = time.perf_counter()
         self._trace(
             traces,
@@ -960,7 +1045,13 @@ class SeatDemoService:
                 vehicle_profile=vehicle_profile,
             )
 
-        intent_ir = build_intent_ir(parsed, utterance, request_id, provider_used)
+        intent_ir = build_intent_ir(
+            parsed,
+            utterance,
+            request_id,
+            provider_used,
+            self.semantic_capability_metadata,
+        )
         result["intent_ir"] = intent_ir
         generation_started = time.perf_counter()
         function_config = build_function_config(parsed, request_id, provider_used)
@@ -2791,7 +2882,13 @@ class SeatDemoService:
         """Run isolated adapters under the selected physical-bus policy."""
         vehicle_profile = vehicle_profile or self.vehicle_profiles.default()
         generation_started = time.perf_counter()
-        intent_ir = build_intent_ir(parsed, utterance, request_id, provider_used)
+        intent_ir = build_intent_ir(
+            parsed,
+            utterance,
+            request_id,
+            provider_used,
+            self.semantic_capability_metadata,
+        )
         intent_actions = [
             *parsed.get("domain_actions", []),
             *parsed.get("native_can_actions", []),
@@ -2823,6 +2920,47 @@ class SeatDemoService:
             )
         result["intent_ir"] = intent_ir
 
+        if intent_ir["spec"]["requires_confirmation"]:
+            validation = self.contracts.validate(intent_ir)
+            validation.extend(
+                {
+                    "severity": "error",
+                    "code": "AMBIGUOUS_INTENT",
+                    "path": item.get("field", "utterance"),
+                    "message": item.get("question", "控制目标需要确认。"),
+                }
+                for item in intent_ir["spec"]["ambiguities"]
+            )
+            validation.extend(
+                {
+                    "severity": "error",
+                    "code": "UNRESOLVED_UTTERANCE_SPAN",
+                    "path": "utterance",
+                    "message": (
+                        f"未解析原文“{item['text']}”：{item['reason']}。"
+                    ),
+                }
+                for item in intent_ir["spec"]["unresolved_spans"]
+            )
+            result["validation"] = validation
+            result["status"] = "needs_confirmation"
+            self._trace(
+                traces,
+                request_id,
+                "safety_check",
+                "failed",
+                "语义意图存在歧义或未覆盖片段；未进入 CAN 编译与执行。",
+                time.perf_counter(),
+                {
+                    "ambiguity_count": len(intent_ir["spec"]["ambiguities"]),
+                    "unresolved_span_count": len(intent_ir["spec"]["unresolved_spans"]),
+                    "execution_eligible": False,
+                },
+                domain="orchestration",
+            )
+            self._persist(result)
+            return result
+
         configs: dict[str, dict[str, Any]] = {}
         if parsed.get("actions"):
             seat_config = build_function_config(parsed, request_id, provider_used)
@@ -3531,6 +3669,8 @@ class SeatDemoService:
         provider: str,
         context: dict[str, Any],
         vehicle_profile: VehicleProfile | None = None,
+        *,
+        reasoning_effort: str | None = None,
     ) -> tuple[dict[str, Any], str, str | None]:
         vehicle_profile = vehicle_profile or self.vehicle_profiles.default()
         if provider == "local":
@@ -3539,7 +3679,15 @@ class SeatDemoService:
             if not self.openai.available:
                 raise ValueError("GPT 模式需要设置 OPENAI_API_KEY")
             try:
-                plan = self.openai.interpret(utterance, context, self.capability_context)
+                interpret_options = {}
+                if reasoning_effort is not None:
+                    interpret_options["reasoning_effort"] = reasoning_effort
+                plan = self.openai.interpret(
+                    utterance,
+                    context,
+                    self.capability_context,
+                    **interpret_options,
+                )
                 return (
                     self.model_plan_adapter.adapt(
                         plan,
@@ -3558,7 +3706,15 @@ class SeatDemoService:
                 )
         if self.openai.available:
             try:
-                plan = self.openai.interpret(utterance, context, self.capability_context)
+                interpret_options = {}
+                if reasoning_effort is not None:
+                    interpret_options["reasoning_effort"] = reasoning_effort
+                plan = self.openai.interpret(
+                    utterance,
+                    context,
+                    self.capability_context,
+                    **interpret_options,
+                )
                 return (
                     self.model_plan_adapter.adapt(
                         plan,

src/cognitive_agent/application/semantic_ir.py

@@ -0,0 +1,177 @@
+"""Fail-closed language guards applied before semantic IntentIR generation."""
+
+from __future__ import annotations
+
+import re
+from copy import deepcopy
+from typing import Any
+
+
+CLAUSE_SEPARATOR = re.compile(r"[，,；;。！？!?\n]+")
+NEGATED_OPERATION = re.compile(
+    r"(?:不要|别|不想|无需|不必|禁止)"
+    r"(?:再|去|把|将|给我|帮我|立即|马上){0,2}"
+    r"(?P<verb>打开|开启|启动|关闭|关掉|解锁|开锁|上锁|落锁|锁上|进入|"
+    r"切换到|调用|恢复|保存|升高|降低|后仰|前倾|前移|后移)"
+)
+PRESERVE_SEAT = re.compile(r"(?:不要|别|不想)(?:再)?动(?P<target>主驾|副驾)")
+CONTROL_VERB = re.compile(
+    r"打开|开启|启动|关闭|关掉|解锁|开锁|上锁|落锁|锁上|进入|切换到|"
+    r"调用|恢复|保存|升高|降低|后仰|前倾|前移|后移|调到|调为|设置为"
+)
+FUZZY_UNMAPPED_CONTROL = re.compile(
+    r"(?P<text>(?:座椅|靠背|腿托|空调|车窗|风口|灯光?)"
+    r"(?:调|弄|搞)(?:得|到)?(?:舒服|合适|好)(?:一点|点|些)?)"
+)
+
+
+def _span(utterance: str, match: re.Match[str]) -> dict[str, Any]:
+    return {"text": match.group(0), "start": match.start(), "end": match.end()}
+
+
+def _target_from_text(text: str, anchor: int = 0) -> tuple[str | None, str | None]:
+    candidates = [
+        (abs(match.start() - anchor), match.start(), domain, target)
+        for pattern, domain, target in (
+            (r"左前", "window", "front_left"),
+            (r"右前", "window", "front_right"),
+            (r"左后", "window", "rear_left"),
+            (r"右后", "window", "rear_right"),
+            (r"主驾|驾驶位|司机位", "seat", "driver"),
+            (r"副驾|副驾驶|乘客位", "seat", "passenger"),
+            (r"后排", "door", "rear"),
+        )
+        for match in re.finditer(pattern, text)
+    ]
+    if candidates:
+        _, _, domain, target = min(candidates)
+        return domain, target
+    if "车窗" in text:
+        return "window", "all" if re.search(r"全部|所有|四个|全车", text) else None
+    if re.search(r"遮阳帘|天幕帘", text):
+        return "roof", "vehicle"
+    if "空调" in text:
+        return "climate", "vehicle"
+    if "氛围灯" in text:
+        return "ambient_light", "vehicle"
+    return None, None
+
+
+def _constraint(utterance: str, match: re.Match[str]) -> dict[str, Any]:
+    clause = next(
+        (item for item in CLAUSE_SEPARATOR.split(utterance) if match.group(0) in item),
+        utterance,
+    )
+    domain, target = _target_from_text(clause, match.start())
+    verb = match.group("verb")
+    result: dict[str, Any] = {
+        "type": "prohibit_action",
+        "source_span": _span(utterance, match),
+    }
+    if domain:
+        result["domain"] = domain
+    if target:
+        result["target"] = target
+    parameters: dict[str, Any] = {}
+    if domain == "window":
+        result["capability"] = "window.set_motion"
+        parameters["motion"] = "open" if verb in {"打开", "开启", "启动"} else "close"
+    elif domain == "roof":
+        result["capability"] = "roof.set_shade_motion"
+        parameters["motion"] = "open" if verb in {"打开", "开启", "启动"} else "close"
+    elif domain == "climate":
+        result["capability"] = "climate.set_power"
+        parameters["state"] = "on" if verb in {"打开", "开启", "启动"} else "off"
+    elif domain == "ambient_light":
+        result["capability"] = "ambient_light.set_power"
+        parameters["state"] = "on" if verb in {"打开", "开启", "启动"} else "off"
+    elif domain == "seat":
+        if "通风" in clause:
+            result["capability"] = "seat.set_ventilation"
+        elif "加热" in clause:
+            result["capability"] = "seat.set_heating"
+        elif "靠背" in clause:
+            result["capability"] = "seat.set_backrest_recline"
+        if result.get("capability") in {"seat.set_ventilation", "seat.set_heating"}:
+            parameters["state"] = "on" if verb in {"打开", "开启", "启动"} else "off"
+    if parameters:
+        result["parameters"] = parameters
+    return result
+
+
+def _remove_seat_target(parsed: dict[str, Any], target: str) -> None:
+    parsed["actions"] = [
+        action for action in parsed.get("actions", []) if action.get("target") != target
+    ]
+    for key in ("domain_actions", "native_can_actions", "deferred_requests", "model_requests"):
+        parsed[key] = [
+            request
+            for request in parsed.get(key, [])
+            if not (
+                request.get("domain") == "seat"
+                and request.get("target", request.get("target_scope")) == target
+            )
+        ]
+
+
+def apply_language_safety_guards(parsed: dict[str, Any], utterance: str) -> dict[str, Any]:
+    """Preserve negation and unresolved control language without producing CAN."""
+
+    result = deepcopy(parsed)
+    constraints = list(result.get("semantic_constraints", []))
+    unresolved = list(result.get("unresolved_spans", []))
+
+    for match in PRESERVE_SEAT.finditer(utterance):
+        target = "driver" if match.group("target") == "主驾" else "passenger"
+        constraints.append(
+            {
+                "type": "prohibit_change",
+                "domain": "seat",
+                "target": target,
+                "source_span": _span(utterance, match),
+            }
+        )
+        _remove_seat_target(result, target)
+
+    negated = list(NEGATED_OPERATION.finditer(utterance))
+    if negated:
+        constraints.extend(_constraint(utterance, item) for item in negated)
+        for key in (
+            "actions", "domain_actions", "native_can_actions", "deferred_requests",
+            "model_requests", "preset_actions", "preset_events",
+        ):
+            result[key] = []
+        result["ambiguities"] = []
+        if len(CONTROL_VERB.findall(utterance)) > len(negated):
+            unresolved.append(
+                {
+                    "text": utterance,
+                    "reason": "mixed_negated_and_positive_operations_require_atomic_confirmation",
+                }
+            )
+            result.setdefault("ambiguities", []).append(
+                {
+                    "field": "utterance",
+                    "question": "同一句中同时包含否定和执行动作，请拆分或确认完整目标。",
+                    "reason": "mixed_negated_and_positive_operations",
+                }
+            )
+        result["decision_summary"] = [
+            *result.get("decision_summary", []),
+            "检测到否定控制约束；未把“不要/不想”错误转换为相反动作。",
+        ]
+        result["confidence"] = min(float(result.get("confidence", 0)), 0.8)
+
+    if not result.get("actions") and not result.get("domain_actions"):
+        for match in FUZZY_UNMAPPED_CONTROL.finditer(utterance):
+            if not any(item.get("text") == match.group("text") for item in unresolved):
+                unresolved.append(
+                    {
+                        "text": match.group("text"),
+                        "reason": "fuzzy_control_goal_not_resolved_to_absolute_target",
+                    }
+                )
+
+    result["semantic_constraints"] = constraints
+    result["unresolved_spans"] = unresolved
+    return result

src/cognitive_agent/infrastructure/llm/openai_responses.py

@@ -10,6 +10,9 @@ import urllib.request
 from typing import Any
 
 
+REASONING_EFFORTS = ("low", "medium", "high")
+
+
 def _env_text(name: str, default: str) -> str:
     """Return a non-blank environment value or its documented default."""
 
@@ -221,7 +224,9 @@ class OpenAIIntentInterpreter:
         ).rstrip("/")
         self.timeout_seconds = float(os.getenv("OPENAI_TIMEOUT_SECONDS", "30"))
         self.max_retries = max(0, int(os.getenv("OPENAI_MAX_RETRIES", "2")))
-        self.reasoning_effort = _env_text("OPENAI_REASONING_EFFORT", "medium")
+        self.reasoning_effort = self.validate_reasoning_effort(
+            _env_text("OPENAI_REASONING_EFFORT", "medium")
+        )
         self.streaming = os.getenv("OPENAI_STREAM", "false").strip().lower() in {
             "1",
             "true",
@@ -237,11 +242,31 @@ class OpenAIIntentInterpreter:
     def responses_endpoint(self) -> str:
         return f"{self.base_url}/responses"
 
+    @property
+    def model_catalog(self) -> list[dict[str, Any]]:
+        return [
+            {
+                "id": self.model,
+                "label": self.model,
+                "provider": "openai",
+                "reasoning_efforts": list(REASONING_EFFORTS),
+            }
+        ]
+
+    @staticmethod
+    def validate_reasoning_effort(value: str) -> str:
+        normalized = str(value).strip().lower()
+        if normalized not in REASONING_EFFORTS:
+            raise ValueError("reasoning_effort must be low, medium, or high")
+        return normalized
+
     def interpret(
         self,
         utterance: str,
         context: dict[str, Any] | None = None,
         capability_context: dict[str, Any] | None = None,
+        *,
+        reasoning_effort: str | None = None,
     ) -> dict[str, Any]:
         if not self.api_key:
             raise OpenAIIntentError("OPENAI_API_KEY is not configured")
@@ -256,10 +281,15 @@ class OpenAIIntentInterpreter:
                 }
             ],
         }
+        effective_reasoning_effort = (
+            self.reasoning_effort
+            if reasoning_effort is None
+            else self.validate_reasoning_effort(reasoning_effort)
+        )
         payload = {
             "model": self.model,
             "store": False,
-            "reasoning": {"effort": self.reasoning_effort},
+            "reasoning": {"effort": effective_reasoning_effort},
             "input": [
                 {
                     "role": "system",

src/cognitive_agent/server.py

@@ -313,6 +313,11 @@ class SeatDemoHandler(BaseHTTPRequestHandler):
                     else None
                 ),
                 hardware_access_bypass=self._is_unproxied_loopback_request(),
+                reasoning_effort=(
+                    str(payload["reasoning_effort"])
+                    if payload.get("reasoning_effort") is not None
+                    else None
+                ),
             )
             self._json(result, HTTPStatus.CREATED)
         except HardwareAccessRequiredError as exc:

tests/unit/test_can_test_branch_fusion.py

@@ -0,0 +1,123 @@
+from __future__ import annotations
+
+import tempfile
+import unittest
+from pathlib import Path
+from unittest.mock import patch
+
+from cognitive_agent.application.native_can_relative import NativeCanRelativeResolver
+from cognitive_agent.application.seat_service import REPOSITORY_ROOT, SeatDemoService
+
+
+class CanTestBranchFusionTests(unittest.TestCase):
+    def setUp(self) -> None:
+        self.service = SeatDemoService(REPOSITORY_ROOT)
+        self.temporary_directory = tempfile.TemporaryDirectory()
+        self.addCleanup(self.temporary_directory.cleanup)
+        self.service.artifact_root = Path(self.temporary_directory.name)
+
+    def test_negated_vehicle_control_never_compiles_or_executes(self) -> None:
+        result = self.service.process(
+            "不要打开左前车窗",
+            provider="local",
+            conversation_id="conv-negative-window",
+        )
+
+        self.assertEqual(result["status"], "no_action")
+        spec = result["intent_ir"]["spec"]
+        self.assertEqual(spec["actions"], [])
+        self.assertEqual(spec["constraints"][0]["capability"], "window.set_motion")
+        self.assertFalse(spec["execution_eligible"])
+        self.assertEqual(result["function_configs"], {})
+
+    def test_multiple_windows_and_exclusion_are_deterministic(self) -> None:
+        multiple = self.service.process(
+            "打开左前和右前车窗",
+            provider="local",
+            conversation_id="conv-two-windows",
+        )
+        self.assertEqual(multiple["status"], "succeeded")
+        self.assertEqual(
+            [item["target"]["zone"] for item in multiple["intent_ir"]["spec"]["actions"]],
+            ["front_left", "front_right"],
+        )
+
+        excluded = self.service.process(
+            "打开全部车窗，左前除外",
+            provider="local",
+            conversation_id="conv-excluded-window",
+        )
+        self.assertEqual(excluded["status"], "succeeded")
+        self.assertEqual(
+            [item["target"]["zone"] for item in excluded["intent_ir"]["spec"]["actions"]],
+            ["front_right", "rear_left", "rear_right"],
+        )
+        self.assertEqual(
+            excluded["intent_ir"]["spec"]["constraints"][0]["type"],
+            "exclude_target",
+        )
+
+    def test_unresolved_mixed_control_fails_closed_before_compile(self) -> None:
+        result = self.service.process(
+            "打开左前车窗，再把座椅调舒服点",
+            provider="local",
+            conversation_id="conv-unresolved-mixed",
+        )
+
+        self.assertEqual(result["status"], "needs_confirmation")
+        self.assertFalse(result["intent_ir"]["spec"]["execution_eligible"])
+        self.assertEqual(result["function_configs"], {})
+        self.assertIn(
+            "UNRESOLVED_UTTERANCE_SPAN",
+            {item["code"] for item in result["validation"]},
+        )
+
+    def test_relative_fan_uses_current_state_and_records_resolution(self) -> None:
+        result = self.service.process(
+            "把风量提高两档",
+            provider="local",
+            conversation_id="conv-relative-fan",
+        )
+
+        self.assertEqual(result["status"], "succeeded")
+        action = result["intent_ir"]["spec"]["actions"][0]
+        self.assertEqual(action["capability"], "climate.set_fan_level")
+        self.assertEqual(action["parameters"], {"level": 2})
+        self.assertEqual(action["resolution"]["current"], 0)
+        self.assertEqual(action["resolution"]["delta"], 2)
+
+    def test_relative_vehicle_control_requires_physical_feedback_in_hardware_context(self) -> None:
+        resolver = NativeCanRelativeResolver(REPOSITORY_ROOT)
+        context = self.service.conversations.snapshot("conv-relative-physical")
+        context["domain_devices"]["vehicle_status"]["feedback_source"] = "physical_can_ch1"
+
+        result = resolver.resolve("把风量提高一档", context)
+
+        self.assertEqual(result.actions, [])
+        self.assertEqual(
+            result.ambiguities[0]["reason"],
+            "native_can_relative_state_not_physical",
+        )
+
+    def test_reasoning_effort_reaches_openai_payload(self) -> None:
+        self.service.openai.api_key = "test-key"
+        plan = {
+            "detected_domains": ["window"],
+            "requests": [],
+            "ambiguities": [],
+            "decision_summary": ["no action"],
+            "confidence": 0.9,
+        }
+        with patch.object(self.service.openai, "interpret", return_value=plan) as interpret:
+            self.service.process(
+                "你好",
+                provider="openai",
+                conversation_id="conv-reasoning-effort",
+                reasoning_effort="high",
+            )
+
+        self.assertEqual(interpret.call_args.kwargs["reasoning_effort"], "high")
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/unit/test_frontend_reasoning_layout_contract.py

@@ -0,0 +1,31 @@
+from __future__ import annotations
+
+from pathlib import Path
+import unittest
+
+
+REPOSITORY_ROOT = Path(__file__).resolve().parents[2]
+CONSOLE_SOURCE = REPOSITORY_ROOT / "apps" / "web-console" / "console-v2.js"
+CONSOLE_STYLES = REPOSITORY_ROOT / "apps" / "web-console" / "console-v2.css"
+
+
+class FrontendReasoningLayoutContractTests(unittest.TestCase):
+    def test_reasoning_control_is_removed_from_fixed_height_request_form(self) -> None:
+        source = CONSOLE_SOURCE.read_text(encoding="utf-8")
+        self.assertIn('const reasoningField = $("#reasoningField", originalForm);', source)
+        self.assertIn('reasoningField.classList.add("v2-reasoning-field");', source)
+        self.assertIn("composer.append(trigger);", source)
+        self.assertIn("document.body.append(reasoningField);", source)
+        self.assertIn('trigger.setAttribute("aria-haspopup", "listbox");', source)
+
+    def test_v2_reasoning_control_has_compact_bounded_layout(self) -> None:
+        styles = CONSOLE_STYLES.read_text(encoding="utf-8")
+        self.assertIn(".console-v2 .v2-reasoning-field .reasoning-segmented", styles)
+        self.assertIn(".console-v2 .v2-reasoning-trigger", styles)
+        self.assertIn("position: fixed;", styles)
+        self.assertIn("min-height: 42px;", styles)
+        self.assertIn("white-space: nowrap;", styles)
+
+
+if __name__ == "__main__":
+    unittest.main()