diff --git a/src/models/glm5-next.cpp b/src/models/glm5-next.cpp
index 5b11c77c34..cb1c6fed5e 100644
--- a/src/models/glm5-next.cpp
+++ b/src/models/glm5-next.cpp
@@ -273,7 +273,7 @@ public:
     ggml_tensor * pool_idxs     = nullptr; // I32     [kpool, n_pool]  member cells per pool, n_kv sentinel for the padded pools
     ggml_tensor * pool_mask     = nullptr; // F32/F16 [n_pool, n_tokens]
     ggml_tensor * tail_idxs     = nullptr; // I32     [kpool - 1, n_tokens]
-    ggml_tensor * gather_mask   = nullptr; // F32     [n_sel, 1, 1, n_tokens]
+    ggml_tensor * gather_mask   = nullptr; // F32     [n_sel, 1, 1, n_tokens] 0 for live selection slots, -inf for dead ones
     // n_new is never below 1, see build_inp_kpool
     ggml_tensor * new_pool_idxs = nullptr; // I32     [kpool, n_new]   members of the pools completed this ubatch
     ggml_tensor * new_pool_rep  = nullptr; // I64     [n_new]          cell to write each new pooled key into
@@ -330,12 +330,11 @@ llama_model_glm5_next::llm_graph_input_kpool * llama_model_glm5_next::graph::bui
         inp->n_sel = (uint32_t) n_sel;
         inp->gather = (int64_t) n_tokens <= max_ub && (int64_t) n_kv > n_sel;
 
-        if (inp->gather) {
-            inp->gather_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_sel, 1, 1, n_tokens);
-            ggml_set_input(inp->gather_mask);
-            // Keep the mask allocated even when no op reads it, because set_input_kpool always fills it.
-            ggml_build_forward_expand(gf, inp->gather_mask);
-        }
+        // Both paths read the slot mask: gather adds it to the scores, scatter maps its dead slots to dump rows.
+        inp->gather_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_sel, 1, 1, n_tokens);
+        ggml_set_input(inp->gather_mask);
+        // Keep the mask allocated even when no op reads it, because set_input_kpool always fills it.
+        ggml_build_forward_expand(gf, inp->gather_mask);
     }
 
     inp->n_new = n_new;
@@ -892,13 +891,22 @@ ggml_tensor * llama_model_glm5_next::graph::build_kpool_select(
 
     ggml_tensor * mask_seed = kq_mask->type == GGML_TYPE_F32 ? seed : ggml_cast(ctx0, seed, kq_mask->type);
     mask_seed = ggml_fill(ctx0, mask_seed, -INFINITY);
-    ggml_tensor * mask_all = ggml_repeat_4d(ctx0, mask_seed, 1, n_kv + 1, n_tokens, 1);
-    mask_all = ggml_reshape_3d(ctx0, mask_all, 1, n_kv + 1, n_tokens);
+    ggml_tensor * mask_all = ggml_repeat_4d(ctx0, mask_seed, 1, n_kv + n_sel, n_tokens, 1);
+    mask_all = ggml_reshape_3d(ctx0, mask_all, 1, n_kv + n_sel, n_tokens);
 
     ggml_tensor * zero_seed = ggml_fill(ctx0, seed, 0.0f);
     ggml_tensor * zeros = ggml_repeat_4d(ctx0, zero_seed, 1, n_sel, n_tokens, 1);
     zeros = ggml_reshape_3d(ctx0, zeros, 1, n_sel, n_tokens);
 
+    // Live slots (visible pools, real tail cells) address disjoint cells. Each dead slot writes its own dump row
+    // n_kv + slot, so the scatter indices of a token are unique: idx = dump + live*(idx - dump), live = exp(mask).
+    GGML_ASSERT(inp_kpool->gather_mask->ne[0] == n_sel && inp_kpool->gather_mask->ne[3] == n_tokens);
+    ggml_tensor * live  = ggml_exp(ctx0, ggml_reshape_2d(ctx0, inp_kpool->gather_mask, n_sel, n_tokens));
+    ggml_tensor * dump  = ggml_arange(ctx0, (float) n_kv, (float) (n_kv + n_sel), 1.0f);
+    ggml_tensor * idx_f = ggml_cast(ctx0, sel_idx, GGML_TYPE_F32);
+    idx_f   = ggml_add(ctx0, ggml_mul(ctx0, ggml_sub(ctx0, idx_f, dump), live), dump);
+    sel_idx = ggml_cast(ctx0, idx_f, GGML_TYPE_I32);
+
     ggml_tensor * sel = ggml_set_rows(ctx0, mask_all, zeros, ggml_reshape_3d(ctx0, sel_idx, n_sel, n_tokens, 1));
     sel = ggml_view_2d(ctx0, sel, n_kv, n_tokens, sel->nb[2], 0);