diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index 5e61f61f7f..1db1f1835d 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -1100,6 +1100,7 @@ bool llm_arch_supports_rs_rollback(const llm_arch & arch) {
     switch (arch) {
         case LLM_ARCH_QWEN35:
         case LLM_ARCH_QWEN35MOE:
+        case LLM_ARCH_QWEN4EXP:
         case LLM_ARCH_DEEPSEEK4:
         case LLM_ARCH_NEMOTRON_H:
         case LLM_ARCH_NEMOTRON_H_MOE:
diff --git a/src/models/qwen4exp.cpp b/src/models/qwen4exp.cpp
index 74235f2295..6f6bee0152 100644
--- a/src/models/qwen4exp.cpp
+++ b/src/models/qwen4exp.cpp
@@ -1087,20 +1087,28 @@ ggml_tensor * llama_model_qwen4exp::graph::build_conv_state_at(
 
     ggml_tensor * conv_input = ggml_concat(ctx0, state, ggml_transpose(ctx0, x), 0);
 
-    // keep the last state_cols columns for the next ubatch
+    // [TAG_RECURRENT_ROLLBACK_SPLITS] keep the last state_cols columns once per rollback slot,
+    // slot s ending s tokens earlier so a rollback of s tokens reads a history that never saw them
     const size_t row_size = ggml_row_size(conv_states_all->type, row_total);
+    const uint32_t mem_size = mctx_cur->get_size();
 
-    ggml_tensor * tail = ggml_view_3d(ctx0, conv_input,
-            state_cols, channels, n_seqs,
-            conv_input->nb[1], conv_input->nb[2],
-            ggml_row_size(conv_input->type, conv_input->ne[0] - state_cols));
+    const int64_t n_slots = (int64_t) cparams.n_rs_seq + 1;
 
-    ggml_tensor * dst = ggml_view_2d(ctx0, conv_states_all,
-            state_cols * channels, n_seqs,
-            conv_states_all->nb[1],
-            kv_head * row_size);
+    for (int64_t slot = 0; slot < n_slots; ++slot) {
+        const int64_t s_idx = std::max<int64_t>(0, conv_input->ne[0] - state_cols - slot);
 
-    ggml_build_forward_expand(gf, ggml_cpy(ctx0, ggml_cont(ctx0, tail), dst));
+        ggml_tensor * tail = ggml_view_3d(ctx0, conv_input,
+                state_cols, channels, n_seqs,
+                conv_input->nb[1], conv_input->nb[2],
+                ggml_row_size(conv_input->type, s_idx));
+
+        ggml_tensor * dst = ggml_view_2d(ctx0, conv_states_all,
+                state_cols * channels, n_seqs,
+                conv_states_all->nb[1],
+                (slot * mem_size + kv_head) * row_size);
+
+        ggml_build_forward_expand(gf, ggml_cpy(ctx0, ggml_cont(ctx0, tail), dst));
+    }
 
     return conv_input;
 }