fix: eliminate thinking-block poisoning + no-op navigation trap

ROOT CAUSE: qwen3.5 (reasoning model) returns response='' with thinking
block containing all reasoning. llm_provider.py line 352 silently
substituted the thinking block as the response via:
  content = raw_response or raw_thinking or ''
The Brain then extracted random actions from the reasoning text.

FIXES:
1. llm_provider.py: Conditional thinking isolation
   - format_json=True (SAE/perception): thinking fallback preserved
   - format_json=False (Brain): thinking NEVER substituted
   - Added think=false for Ollama free-text calls to force direct response

2. planner.py: No-Op Guard strips tab actions that navigate to
   the current screen (e.g. 'tap profile tab' on OWN_PROFILE)

3. test_brain_live.py: Stochastic testing (5 runs, 60% min valid)
   to handle non-deterministic LLM behavior reliably

4. tests/integration/test_llm_provider_pipeline.py: NEW test layer
   mocking at HTTP level (requests.post) to exercise the FULL
   llm_provider → Brain pipeline. This would have caught the
   thinking substitution bug from day one.

Suite: 168 passed, 0 failed

tests/integration/test_llm_provider_pipeline.py

@@ -0,0 +1,194 @@
+"""
+LLM Provider Integration Tests — The Missing Layer
+====================================================
+
+These tests exercise the ACTUAL llm_provider.py pipeline by mocking at
+the HTTP level (requests.post), NOT at the function level (query_llm).
+
+This is the layer that was untested and caused the 2026-04-28 production
+failures:
+  - llm_provider silently substituted thinking blocks as responses
+  - The Brain then extracted random actions from reasoning text
+
+Contract:
+  For format_json=False (Brain calls): thinking MUST NOT be substituted
+  For format_json=True (SAE/perception): thinking CAN be used as fallback
+"""
+
+import json
+
+import pytest
+
+from GramAddict.core.llm_provider import query_llm
+
+
+class TestLLMProviderThinkingIsolation:
+    """Contract: The llm_provider must NOT silently substitute thinking
+    blocks for empty responses in free-text mode."""
+
+    def _mock_ollama_response(self, monkeypatch, raw_response: str, raw_thinking: str):
+        """Mock requests.post to return a fake Ollama API response."""
+        import requests
+
+        class FakeResponse:
+            status_code = 200
+
+            def __init__(self, resp, think):
+                self._data = {"response": resp, "thinking": think, "done": True}
+
+            def json(self):
+                return self._data
+
+            def raise_for_status(self):
+                pass
+
+        def fake_post(url, **kwargs):
+            return FakeResponse(raw_response, raw_thinking)
+
+        monkeypatch.setattr(requests, "post", fake_post)
+
+    def test_empty_response_with_thinking_returns_empty_for_freetext(self, monkeypatch):
+        """REGRESSION: When Ollama returns response='' with thinking='...',
+        format_json=False callers must get '' — NOT the thinking block."""
+        self._mock_ollama_response(
+            monkeypatch,
+            raw_response="",
+            raw_thinking="I think I should tap profile tab because it would help...",
+        )
+
+        result = query_llm(
+            url="http://localhost:11434/api/generate",
+            model="qwen3.5:latest",
+            prompt="Choose an action",
+            system="You are an agent",
+            format_json=False,
+        )
+
+        assert result is not None
+        content = result["response"]
+        assert content == "", (
+            f"llm_provider returned thinking block as response in free-text mode! "
+            f"Got: '{content[:80]}...'"
+        )
+        # Specifically: MUST NOT contain thinking content
+        assert "tap profile tab" not in content, (
+            "Thinking block leaked into the response!"
+        )
+
+    def test_empty_response_with_thinking_uses_thinking_for_json(self, monkeypatch):
+        """For JSON-expecting callers, falling back to thinking IS correct."""
+        json_in_thinking = json.dumps({"classification": "obstacle_modal", "confidence": 0.9})
+        self._mock_ollama_response(
+            monkeypatch,
+            raw_response="",
+            raw_thinking=json_in_thinking,
+        )
+
+        result = query_llm(
+            url="http://localhost:11434/api/generate",
+            model="qwen3.5:latest",
+            prompt="Classify this screen",
+            system="You are a screen classifier",
+            format_json=True,
+        )
+
+        assert result is not None
+        content = result["response"]
+        parsed = json.loads(content)
+        assert parsed["classification"] == "obstacle_modal", (
+            "JSON mode should have extracted from thinking block"
+        )
+
+    def test_normal_response_is_passed_through(self, monkeypatch):
+        """When the LLM returns a clean response, it should pass through unchanged."""
+        self._mock_ollama_response(
+            monkeypatch,
+            raw_response="scroll down",
+            raw_thinking="I considered various options and decided to scroll down.",
+        )
+
+        result = query_llm(
+            url="http://localhost:11434/api/generate",
+            model="qwen3.5:latest",
+            prompt="Choose an action",
+            system="You are an agent",
+            format_json=False,
+        )
+
+        assert result is not None
+        assert result["response"] == "scroll down"
+
+
+class TestBrainFullPipeline:
+    """Integration test: the FULL pipeline from Ollama response → Brain action.
+    Mocked at the HTTP level, not at the function level."""
+
+    def _mock_ollama_response(self, monkeypatch, raw_response: str, raw_thinking: str):
+        import requests
+
+        class FakeResponse:
+            status_code = 200
+
+            def __init__(self, resp, think):
+                self._data = {"response": resp, "thinking": think, "done": True}
+
+            def json(self):
+                return self._data
+
+            def raise_for_status(self):
+                pass
+
+        def fake_post(url, **kwargs):
+            return FakeResponse(raw_response, raw_thinking)
+
+        monkeypatch.setattr(requests, "post", fake_post)
+
+    def test_thinking_block_with_empty_response_returns_none(self, monkeypatch):
+        """EXACT REPRODUCTION of the 2026-04-28 23:51 production failure.
+        The LLM returns response='' with thinking mentioning 'tap profile tab'.
+        The Brain MUST return None (not 'tap profile tab')."""
+        from GramAddict.core.navigation.brain import ask_brain_for_action
+
+        self._mock_ollama_response(
+            monkeypatch,
+            raw_response="",
+            raw_thinking=(
+                "The user wants to nurture their community. "
+                "I could tap profile tab but we're already on the profile. "
+                "Maybe tap messages tab would be better. "
+                "Actually I think press back is the best option."
+            ),
+        )
+
+        result = ask_brain_for_action(
+            goal="nurture community",
+            screen_type="OWN_PROFILE",
+            available_actions=["tap message button", "scroll down", "press back", "tap profile tab"],
+            explored_actions=set(),
+        )
+
+        # The Brain MUST return None because the LLM gave no actual response.
+        # It must NOT extract 'press back' or 'tap profile tab' from the thinking.
+        assert result is None, (
+            f"Brain returned '{result}' when LLM response was empty! "
+            f"The thinking block leaked through llm_provider into the Brain."
+        )
+
+    def test_clean_response_is_correctly_extracted(self, monkeypatch):
+        """When the LLM gives a clean response, the full pipeline works."""
+        from GramAddict.core.navigation.brain import ask_brain_for_action
+
+        self._mock_ollama_response(
+            monkeypatch,
+            raw_response="scroll down",
+            raw_thinking="I decided to scroll down to find more content.",
+        )
+
+        result = ask_brain_for_action(
+            goal="find content",
+            screen_type="HOME_FEED",
+            available_actions=["scroll down", "tap explore tab", "press back"],
+            explored_actions=set(),
+        )
+
+        assert result == "scroll down"