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diff --git a/src/personalization/feedback/handlers.py b/src/personalization/feedback/handlers.py
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+++ b/src/personalization/feedback/handlers.py
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+from typing import Tuple, List, Optional
+import numpy as np
+
+from personalization.retrieval.preference_store.schemas import MemoryCard
+from personalization.feedback.schemas import TurnSample
+from personalization.feedback.reward_model import estimate_reward
+from personalization.feedback.gating import estimate_retrieval_gating
+
+def eval_step(
+    q_t: str,
+    answer_t: str,
+    q_t1: str,
+    memories_t: List[MemoryCard],
+    query_embedding_t: Optional[np.ndarray] = None,
+    query_embedding_t1: Optional[np.ndarray] = None,
+) -> Tuple[float, float]:
+    """
+    Unified evaluation interface.
+    Given (q_t, a_t, q_{t+1}, memories), returns (reward_hat, gating_hat).
+    """
+    
+    # Construct a lightweight TurnSample
+    # We might need embeddings for gating. If not provided, gating might return default.
+    
+    # Ensure memories have embeddings for gating
+    mem_embs = None
+    if memories_t and memories_t[0].embedding_e:
+        try:
+            mem_embs = np.array([m.embedding_e for m in memories_t])
+        except:
+            pass
+            
+    sample = TurnSample(
+        user_id="", # Not needed for simple eval
+        session_id="",
+        turn_id=0,
+        query_t=q_t,
+        answer_t=answer_t,
+        query_t1=q_t1,
+        memories=memories_t,
+        query_embedding_t=query_embedding_t,
+        query_embedding_t1=query_embedding_t1,
+        memory_embeddings=mem_embs
+    )
+    
+    r_hat = estimate_reward(sample)
+    g_hat = estimate_retrieval_gating(sample, r_hat)
+    
+    return r_hat, g_hat
+