diff --git a/dsv4/kernels/indexer/csa_indexer.py b/dsv4/kernels/indexer/csa_indexer.py
index 6b5cf021..b736b663 100644
--- a/dsv4/kernels/indexer/csa_indexer.py
+++ b/dsv4/kernels/indexer/csa_indexer.py
@@ -4,9 +4,19 @@ Paper §2.3.1, eq. 13–17:
   c_Q = h_t · W_DQ    (shared with main queries)
   q^I_t = c_Q · W_IUQ  (low-rank indexer queries)
   w^I_t = h_t · W_w    (per-head weights)
-  I[t,s] = Σ_h w^I_t,h · ReLU(q^I_t,h · K^IComp[s])
+  I[t,s] = Σ_h w^I_t,h · ReLU(q^I_t,h · K^IComp[s])  (MQA: shared key K)
   Selected = TopK(I[t,:])
 
+Key layout: K^IComp[s] is shared across indexer heads (MQA, NOT per-head).
+The dot product is: q^I_t,h (per-head) · K^IComp[s] (shared).
+This matches the production Indexer.forward() einsum 'tnd,cd->tnc'.
+
+RoPE: Neither indexer queries nor keys have RoPE applied.
+The indexer is a lightweight scoring mechanism for block selection,
+not a full attention layer. If the HF reference applies RoPE to
+indexer keys, the stored FP4 keys would need it baked in at
+compression time. VERIFY THIS AGAINST THE REFERENCE BEFORE PRODUCTION.
+
 The indexer only exists in CSA layers. HCA and SWA layers don't have
 an indexer (they do dense attention).
 """
@@ -47,14 +57,22 @@ class CSAIndexer:
         # For now, use a simple torch linear; will swap to Nvfp4Linear
         # with FP4 output in Phase 2.
         if not hasattr(self, '_q_up_weight'):
-            # Lazy init — weights would be loaded from checkpoint
-            d_c = self.config.query_compression_dim
-            n_ih = self.config.indexer_num_heads
-            c_i = self.config.indexer_head_dim
-            self._q_up_weight = torch.randn(
-                d_c, n_ih * c_i, dtype=torch.bfloat16, device='cuda') * 0.02
-            self._w_head_weight = torch.randn(
-                self.config.hidden_size, n_ih, dtype=torch.bfloat16, device='cuda') * 0.02
+            # WARNING: USING RANDOM WEIGHTS — csa_indexer.py has NO weight loading.
+            # The production path uses the Indexer class in single_shot_inference.py
+            # which loads real weights from the checkpoint via Nvfp4Linear.
+            # This CSAIndexer class should NOT be used for production inference.
+            # If you see this message, you need to wire up checkpoint weight loading
+            # or use the production Indexer instead.
+            raise RuntimeError(
+                "CSAIndexer has no checkpoint weight loading. "
+                "Use the production Indexer class (single_shot_inference.py) instead, "
+                "or implement weight loading for CSAIndexer.")
+            # Old code (random weights — removed to prevent silent incorrect behavior):
+            # d_c = self.config.query_compression_dim
+            # n_ih = self.config.indexer_num_heads
+            # c_i = self.config.indexer_head_dim
+            # self._q_up_weight = torch.randn(d_c, n_ih * c_i, ...) * 0.02
+            # self._w_head_weight = torch.randn(hidden_size, n_ih, ...) * 0.02
 
         q_I = torch.nn.functional.linear(c_Q, self._q_up_weight.T)  # [T, n_ih * c_i] BF16
         w_h = torch.nn.functional.linear(h_t, self._w_head_weight.T).float()  # [T, n_ih] FP32
diff --git a/dsv4/kernels/indexer/score_topk.py b/dsv4/kernels/indexer/score_topk.py
index 9b0a8227..517d926b 100644
--- a/dsv4/kernels/indexer/score_topk.py
+++ b/dsv4/kernels/indexer/score_topk.py
@@ -39,10 +39,14 @@ def run_indexer_score_topk(
 ) -> torch.Tensor:
     """Returns [T, top_k] int32 of selected compressed entry indices.
 
-    The kernel computes:
-      I[t,s] = Σ_h w_h[t,h] · ReLU(q_I[t,h] · K^IComp[s,h])
+    The kernel computes (MQA — shared key across indexer heads):
+      I[t,s] = Σ_h w_h[t,h] · ReLU(q_I[t,h] · K^IComp[s])
       topk_indices = argtopk(I[t,:], k=top_k)
 
+    Note: K^IComp[s] is shared across heads (MQA), NOT per-head K^IComp[s,h].
+    This matches the .cu kernel and the production Indexer.forward() einsum.
+    The paper (eq. 16) uses the shared-key form.
+
     q_I is passed as BF16 and dequantized to FP32 before the kernel.
     The indexer keys are stored FP4 in the cache and dequantized
     inside the kernel.
@@ -61,7 +65,9 @@ def run_indexer_score_topk(
     # Simplification: assume T == B for now (one token per request in decode).
     if valid_lens.shape[0] != T:
         # Prefill: T > B. We need to map tokens to requests.
-        # For now, broadcast the first request's valid_lens.
+        # WARNING: broadcasting request 0's valid_lens is WRONG for batched
+        # or multi-request prefill — it selects from wrong key ranges per token.
+        # This is only correct for single-request bring-up.
         # TODO: proper per-token valid_lens from request_ids mapping.
         valid_lens = valid_lens[:1].expand(T).contiguous()