Upload 2 files

env.py

@@ -100,10 +100,10 @@ class ClinicalTrialEnvironment(
             extracted_fields={},
             identified_deviations=[],
             final_decision=None,
-            grading_score=0.0,
+            grading_score=0.5,
         )
         return self._build_observation(
-            reward_details=ClinicalTrialReward(notes=["Episode reset."]),
+            reward_details=ClinicalTrialReward(notes=["Episode reset."], grader_score=0.5),
             done=False,
         )
 
@@ -146,13 +146,14 @@ class ClinicalTrialEnvironment(
             done = True
             terminal_reason = "max_steps_reached"
 
+        reward.grader_score = self.grader()
+        self._state.grading_score = reward.grader_score
+
         if done:
-            reward.grader_score = self.grader()
             if self._is_final_submission_correct():
                 reward.final_reward = FINAL_REWARD
                 reward.notes.append("Correct final screening decision.")
             reward.missing_items = self._missing_items()
-            self._state.grading_score = reward.grader_score
 
         reward.total_reward = round(
             reward.incremental_reward + reward.final_reward + reward.penalty, 4
@@ -344,4 +345,4 @@ class ClinicalTrialEnvironment(
 class ClinicalTrialEnv(ClinicalTrialEnvironment):
     """Compatibility alias for manifest entry points expecting env:ClinicalTrialEnv."""
 
-    pass
+    pass

models.py

@@ -21,7 +21,12 @@ class ClinicalTrialReward(BaseModel):
     final_reward: float = Field(default=0.0, description="Terminal reward for a correct final decision.")
     penalty: float = Field(default=0.0, description="Penalty for hallucinations or destructive actions.")
     total_reward: float = Field(default=0.0, description="Net reward for the step.")
-    grader_score: float = Field(default=0.0, ge=0.0, le=1.0, description="Deterministic task score.")
+    grader_score: float = Field(
+        default=0.5,
+        gt=0.0,
+        lt=1.0,
+        description="Deterministic task score in the strict open interval (0, 1).",
+    )
     matched_items: List[str] = Field(default_factory=list, description="Correctly matched items this step.")
     missing_items: List[str] = Field(default_factory=list, description="Expected items still missing at grading time.")
     notes: List[str] = Field(default_factory=list, description="Human-readable reward rationale.")
@@ -78,7 +83,12 @@ class ClinicalTrialState(State):
     extracted_fields: Dict[str, str] = Field(default_factory=dict, description="Accepted extracted fields.")
     identified_deviations: List[str] = Field(default_factory=list, description="Accepted deviations.")
     final_decision: Optional[str] = Field(default=None, description="Submitted terminal decision.")
-    grading_score: float = Field(default=0.0, ge=0.0, le=1.0, description="Latest grader output.")
+    grading_score: float = Field(
+        default=0.5,
+        gt=0.0,
+        lt=1.0,
+        description="Latest grader output in the strict open interval (0, 1).",
+    )
 
     def __call__(self) -> "ClinicalTrialState":
         """Support env.state() as a compatibility alias for the OpenEnv state property."""