diff --git a/ex_engine/python/ix_bridge.py b/ex_engine/python/ix_bridge.py
index f39c06c2..d0fe654b 100644
--- a/ex_engine/python/ix_bridge.py
+++ b/ex_engine/python/ix_bridge.py
@@ -57,21 +57,15 @@ def topk_softmax(gating_output: torch.Tensor, topk: int, renormalize: bool = Tru
     """
     Fused topk+softmax via ixformer C++ API.
     
-    Args:
-        gating_output: (num_tokens, num_experts) router logits
-        topk: number of experts to select
-        renormalize: whether to renormalize weights
-    
-    Returns:
-        (topk_weights, topk_indices) — both (num_tokens, topk)
+    FAIL FAST: if bridge not available, raises RuntimeError immediately.
+    No silent fallback — 0 score with no error log is worse than a crash.
     """
     if not _ix_bridge_available:
         if not _load_bridge():
-            # Fallback to pure PyTorch
-            probs = torch.softmax(gating_output.float(), dim=-1)
-            topk_w, topk_ids = torch.topk(probs, topk, dim=-1)
-            if renormalize:
-                topk_w = topk_w / topk_w.sum(dim=-1, keepdim=True)
-            return topk_w, topk_ids.to(torch.int32)
+            raise RuntimeError(
+                "ix_moe_bridge: FATAL — ixformer C++ topk_softmax not available. "
+                "JIT compile failed. Run probe_ixformer_symbols.py on real machine "
+                "to diagnose. Cannot fall back silently — would produce 0 score."
+            )
     
     return _ix_bridge.topk_softmax(gating_output, topk, renormalize)
diff --git a/probe_ixformer_symbols.py b/probe_ixformer_symbols.py
new file mode 100644
index 00000000..1222ff60
--- /dev/null
+++ b/probe_ixformer_symbols.py
@@ -0,0 +1,230 @@
+#!/usr/bin/env python3
+"""
+probe_ixformer_symbols.py — 在真机上跑，探测 ixformer C++ 符号表
+
+用法: python3 probe_ixformer_symbols.py
+
+输出:
+  1. ixformer 所有 .so 文件路径
+  2. 每个 .so 里包含 topk_softmax / moe / gdn / attention 的符号
+  3. 结论：ix_moe_bridge.cpp 能不能链接成功
+"""
+
+import subprocess, sys, os, glob
+
+def find_ixformer_so():
+    """找到 ixformer 的所有 .so 文件"""
+    paths = []
+    # 方法1: 从 Python import 路径找
+    try:
+        import ixformer
+        pkg_dir = os.path.dirname(ixformer.__file__)
+        paths.extend(glob.glob(os.path.join(pkg_dir, "**/*.so"), recursive=True))
+        paths.extend(glob.glob(os.path.join(pkg_dir, "**/*.so.*"), recursive=True))
+        print(f"[1] ixformer package dir: {pkg_dir}")
+    except ImportError:
+        print("[1] ixformer not importable")
+
+    # 方法2: 搜索常见路径
+    for base in ["/usr/local/corex/lib64", "/usr/local/corex/lib",
+                 "/usr/local/lib", "/usr/lib"]:
+        paths.extend(glob.glob(os.path.join(base, "**/libixformer*"), recursive=True))
+        paths.extend(glob.glob(os.path.join(base, "**/*ixformer*.so"), recursive=True))
+        paths.extend(glob.glob(os.path.join(base, "**/libixattn*"), recursive=True))
+        paths.extend(glob.glob(os.path.join(base, "**/libixinfer*"), recursive=True))
+
+    # 方法3: 从 torch 找已加载的 .so
+    try:
+        import torch
+        # ixformer 的 C++ 后端可能是 _ixformer_torch.so 或 _C.so
+        try:
+            import ixformer._ixformer_torch as ixt
+            if hasattr(ixt, '__file__') and ixt.__file__:
+                paths.append(ixt.__file__)
+                print(f"[2] _ixformer_torch: {ixt.__file__}")
+        except:
+            pass
+        try:
+            import ixformer._C as ic
+            if hasattr(ic, '__file__') and ic.__file__:
+                paths.append(ic.__file__)
+                print(f"[2] _C: {ic.__file__}")
+        except:
+            pass
+    except:
+        pass
+
+    return list(set(paths))
+
+def nm_grep(so_path, patterns):
+    """用 nm 查符号，grep 匹配"""
+    results = []
+    try:
+        out = subprocess.run(
+            ["nm", "-D", "--demangle", so_path],
+            capture_output=True, text=True, timeout=10)
+        for line in out.stdout.splitlines():
+            for p in patterns:
+                if p.lower() in line.lower():
+                    results.append(line.strip())
+    except Exception as e:
+        # nm 可能不存在，用 objdump
+        try:
+            out = subprocess.run(
+                ["objdump", "-T", so_path],
+                capture_output=True, text=True, timeout=10)
+            for line in out.stdout.splitlines():
+                for p in patterns:
+                    if p.lower() in line.lower():
+                        results.append(line.strip())
+        except Exception as e2:
+            results.append(f"ERROR: nm/objdump failed: {e}, {e2}")
+    return results
+
+def check_python_binding():
+    """检查 Python 层面有没有 topk_softmax"""
+    print("\n=== Python Binding Check ===")
+    try:
+        import ixformer.functions as ixf
+        attrs = dir(ixf)
+        moe_attrs = [a for a in attrs if 'moe' in a.lower() or 'topk' in a.lower() 
+                     or 'softmax' in a.lower() or 'expert' in a.lower()]
+        print(f"  ixformer.functions MoE-related: {moe_attrs}")
+        if not moe_attrs:
+            print(f"  ixformer.functions ALL ({len(attrs)}): {attrs}")
+    except Exception as e:
+        print(f"  ixformer.functions: {e}")
+
+    try:
+        import ixformer
+        # 搜索所有子模块
+        for attr_name in dir(ixformer):
+            obj = getattr(ixformer, attr_name)
+            if hasattr(obj, 'topk_softmax'):
+                print(f"  FOUND: ixformer.{attr_name}.topk_softmax")
+            if hasattr(obj, 'moe_topk_softmax'):
+                print(f"  FOUND: ixformer.{attr_name}.moe_topk_softmax")
+    except:
+        pass
+
+def check_torch_ops():
+    """检查 torch.ops 注册"""
+    print("\n=== torch.ops Check ===")
+    try:
+        import torch
+        # 检查是否有 ixformer 注册的 ops
+        for ns in ['ixformer', '_ixformer', 'ixf', '_C']:
+            try:
+                ns_obj = getattr(torch.ops, ns, None)
+                if ns_obj:
+                    ops = [x for x in dir(ns_obj) if 'topk' in x.lower() or 'moe' in x.lower()]
+                    if ops:
+                        print(f"  torch.ops.{ns} MoE ops: {ops}")
+                    else:
+                        print(f"  torch.ops.{ns} exists but no MoE ops: {dir(ns_obj)[:10]}...")
+            except:
+                pass
+    except:
+        pass
+
+def try_jit_compile():
+    """尝试 JIT 编译 ix_moe_bridge.cpp 看链接是否成功"""
+    print("\n=== JIT Compile Test ===")
+    test_cpp = "/tmp/ix_probe_test.cpp"
+    with open(test_cpp, "w") as f:
+        f.write("""
+#include <torch/extension.h>
+
+// Forward-declare — this is what ix_moe_bridge.cpp needs
+namespace ixformer { namespace infer {
+void topk_softmax(torch::Tensor&, torch::Tensor&, torch::Tensor&, 
+                  torch::Tensor&, bool);
+}}
+
+void test_link() {
+    auto a = torch::empty({1,1});
+    auto b = torch::empty({1,1});
+    auto c = torch::empty({1,1});
+    auto d = torch::empty({1,1});
+    ixformer::infer::topk_softmax(a, b, c, d, false);
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def("test_link", &test_link);
+}
+""")
+    try:
+        from torch.utils.cpp_extension import load
+        ext = load(name="ix_probe_test", sources=[test_cpp],
+                   extra_cflags=["-O0"], verbose=True)
+        print("  JIT COMPILE + LINK: SUCCESS ✓")
+        print("  ixformer::infer::topk_softmax symbol resolved!")
+        return True
+    except Exception as e:
+        err = str(e)
+        if "undefined reference" in err or "undefined symbol" in err:
+            print(f"  JIT LINK FAILED: symbol not found in .so")
+            print(f"  Error: {err[:500]}")
+        else:
+            print(f"  JIT COMPILE FAILED: {err[:500]}")
+        return False
+
+if __name__ == "__main__":
+    print("=" * 70)
+    print("ixformer Symbol Probe")
+    print("=" * 70)
+
+    # Step 1: Find .so files
+    print("\n=== .so Files ===")
+    so_files = find_ixformer_so()
+    if not so_files:
+        print("  No ixformer .so files found!")
+    for f in sorted(set(so_files)):
+        size = os.path.getsize(f) if os.path.exists(f) else 0
+        print(f"  {f} ({size/1024/1024:.1f} MB)")
+
+    # Step 2: Search for symbols
+    patterns = ["topk_softmax", "moe_topk", "topk_gating", 
+                "moe_compute_token", "moe_expand", "moe_output_reduce",
+                "moe_w16a16", "group_gemm"]
+    print("\n=== Symbol Search (MoE-related) ===")
+    found_any = False
+    for f in sorted(set(so_files)):
+        results = nm_grep(f, patterns)
+        if results:
+            found_any = True
+            print(f"\n  {os.path.basename(f)}:")
+            for r in results[:20]:
+                print(f"    {r}")
+    if not found_any:
+        print("  No MoE symbols found in any .so")
+        # Also search for ANY ixformer::infer symbols
+        print("\n=== Symbol Search (ixformer::infer namespace) ===")
+        for f in sorted(set(so_files)):
+            results = nm_grep(f, ["ixformer", "infer"])
+            if results:
+                print(f"\n  {os.path.basename(f)} ({len(results)} matches):")
+                for r in results[:30]:
+                    print(f"    {r}")
+
+    # Step 3: Python binding
+    check_python_binding()
+
+    # Step 4: torch.ops
+    check_torch_ops()
+
+    # Step 5: JIT compile test (the definitive answer)
+    jit_ok = try_jit_compile()
+
+    # Summary
+    print("\n" + "=" * 70)
+    if jit_ok:
+        print("RESULT: ix_moe_bridge.cpp CAN link to ixformer::infer::topk_softmax")
+        print("ACTION: proceed with C++ bridge approach")
+    else:
+        print("RESULT: ix_moe_bridge.cpp CANNOT link to ixformer C++ API")
+        print("ACTION: need alternative — options:")
+        print("  A) Build topk_softmax kernel from upstream_ref/xllm CUDA source")
+        print("  B) Build from upstream_ref/ds_vllm/csrc/moe/topk_softmax_kernels.cu")
+        print("  C) Keep PyTorch path but add explicit error logging")
+    print("=" * 70)
diff --git a/qwen3_6_scripts/qwen3_5.py b/qwen3_6_scripts/qwen3_5.py
index 39abe28f..41acf746 100644
--- a/qwen3_6_scripts/qwen3_5.py
+++ b/qwen3_6_scripts/qwen3_5.py
@@ -74,6 +74,7 @@ except ImportError:
 # ix_bridge: C++ bridge to ixformer::infer::topk_softmax (bypasses missing Python binding)
 _ix_bridge_module = None
 _ix_bridge_available = False
+_ix_topk_softmax = None
 try:
     from ex_engine.python.ix_bridge import topk_softmax as _ix_topk_softmax
     _ix_bridge_available = True
@@ -88,8 +89,11 @@ except ImportError:
         from ex_engine.python.ix_bridge import topk_softmax as _ix_topk_softmax
         _ix_bridge_available = True
         logger.info("ix_bridge: ixformer C++ topk_softmax available (deployed path)")
-    except ImportError:
-        logger.info("ix_bridge: not available, MoE uses PyTorch topk")
+    except ImportError as e:
+        # NOT silent: log the exact error so we can diagnose from docker logs
+        logger.warning(
+            "ix_bridge: IMPORT FAILED (%s). MoE will use PyTorch topk. "
+            "This is 3x slower. Run probe_ixformer_symbols.py to diagnose.", e)
 _corex_gdn_available = False
 _corex_moe_available = False