feat: carry the reasoning verdict through to LLMResponse

Both assembly paths fill it, streaming and non-streaming alike. Filling
only one is exactly the divergence this issue exposed: M3 returns
reasoning prose over SSE and nothing at all over the plain endpoint, so
a verdict computed on one path says nothing about the other.

The field defaults to UNKNOWN on both TransportResult and LLMResponse.
A transport that does not judge should not get to declare absence on
the provider's behalf, and a default that stays silent is the only one
that cannot lie.

src/polygateway/transports/openai_compat.py

@@ -28,6 +28,7 @@ from polygateway.thinking import (
     ThinkingCapability,
     ThinkingUnsupportedError,
     get_capability,
+    observe_thinking,
     resolve_thinking,
 )
 from polygateway.transports._http_errors import compose_message, summarize_body
@@ -459,6 +460,7 @@ class OpenAICompatTransport:
         content, thinking = self._finalize_text(content_parts, thinking_parts, profile)
         self._reject_empty_completion(content, source)
         prompt, completion, usage_source = _resolve_stream_usage(sink, salvaged)
+        reasoning_tokens = _coerce_reasoning_tokens(sink.get("usage"))
         return TransportResult(
             content=content,
             thinking=thinking,
@@ -470,7 +472,12 @@ class OpenAICompatTransport:
             raw={"usage": sink.get("usage")},
             cached_prompt_tokens=_coerce_cached_tokens(sink.get("usage")),
             model_reported=_coerce_model_reported(sink.get("model")),
-            reasoning_tokens=_coerce_reasoning_tokens(sink.get("usage")),
+            reasoning_tokens=reasoning_tokens,
+            # 两条组装路径必须同口径裁定: 只在一条路径上给结论,下游就得靠
+            # "这次是不是流式"去猜可观测性,那正是 issue #16/#17 的根因形态
+            thinking_observation=observe_thinking(
+                thinking=thinking, reasoning_tokens=reasoning_tokens
+            ),
         )
 
     def _check_done(
@@ -544,6 +551,7 @@ class OpenAICompatTransport:
         )
         self._reject_empty_completion(content, source)
         prompt, completion, usage_source = _resolve_usage(body.get("usage") or {})
+        reasoning_tokens = _coerce_reasoning_tokens(body.get("usage"))
         return TransportResult(
             content=content,
             thinking=thinking,
@@ -555,7 +563,12 @@ class OpenAICompatTransport:
             raw={"usage": body.get("usage")},
             cached_prompt_tokens=_coerce_cached_tokens(body.get("usage")),
             model_reported=_coerce_model_reported(body.get("model")),
-            reasoning_tokens=_coerce_reasoning_tokens(body.get("usage")),
+            reasoning_tokens=reasoning_tokens,
+            # 本路径的裁定多半落 UNKNOWN(M3 实测: 推理已计费却正文与 details 双
+            # 缺)。如实标记"观测不到",好过让下游误读成"没推理"
+            thinking_observation=observe_thinking(
+                thinking=thinking, reasoning_tokens=reasoning_tokens
+            ),
         )
 
     async def aclose(self) -> None: