diff --git a/audit_so_usage.sh b/audit_so_usage.sh
new file mode 100644
index 00000000..71739c5e
--- /dev/null
+++ b/audit_so_usage.sh
@@ -0,0 +1,106 @@
+#!/bin/bash
+set -euo pipefail
+cat << 'PYEOF' | CUDA_VISIBLE_DEVICES="${CUDA_VISIBLE_DEVICES:-0}" python3 -u -
+"""Audit: which .so functions are actually called in the hot path vs available but unused."""
+import importlib.util, os, sys
+
+SO_DIR = "qwen3_6_scripts/prebuilt/corex-3.2.3-ivcore10"
+QWEN = "qwen3_6_scripts/qwen3_5.py"
+PATCH = "qwen3_6_scripts/ex_engine/python/patch_vllm_hot_path.py"
+XLLM_OPS = "qwen3_6_scripts/ex_engine/python/xllm_ops.py"
+
+# 1. Collect all exported functions from all .so
+print("=" * 70)
+print("  AUDIT: .so function usage")
+print("=" * 70)
+
+so_exports = {}
+for f in sorted(os.listdir(SO_DIR)):
+    if not f.endswith(".so"):
+        continue
+    name = f[:-3]
+    path = os.path.join(SO_DIR, f)
+    try:
+        spec = importlib.util.spec_from_file_location(name, path)
+        m = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(m)
+        fns = [x for x in dir(m) if not x.startswith("_")]
+        so_exports[name] = fns
+    except Exception as e:
+        so_exports[name] = [f"LOAD_ERROR: {e}"]
+
+# 2. Search for usage in qwen3_5.py, patch_vllm_hot_path.py, xllm_ops.py
+code_files = {}
+for label, path in [("qwen3_5.py", QWEN), ("patch_hot_path.py", PATCH), ("xllm_ops.py", XLLM_OPS)]:
+    try:
+        with open(path) as f:
+            code_files[label] = f.read()
+    except:
+        code_files[label] = ""
+
+# Also scan all ex_engine python files
+for f in os.listdir("qwen3_6_scripts/ex_engine/python"):
+    if f.endswith(".py"):
+        path = os.path.join("qwen3_6_scripts/ex_engine/python", f)
+        try:
+            with open(path) as fh:
+                code_files[f"ex_engine/{f}"] = fh.read()
+        except:
+            pass
+
+all_code = "\n".join(code_files.values())
+
+# 3. For each .so and function, check if it's referenced
+print(f"\n{'SO Module':<35} {'Function':<30} {'Used?':<6} {'Where'}")
+print("-" * 110)
+
+total_fns = 0
+used_fns = 0
+unused = []
+
+for so_name in sorted(so_exports.keys()):
+    fns = so_exports[so_name]
+    for fn in fns:
+        if "LOAD_ERROR" in fn:
+            print(f"{so_name:<35} {fn}")
+            continue
+        total_fns += 1
+        
+        # Search patterns: module.fn, .fn(, "fn"
+        found_in = []
+        for label, code in code_files.items():
+            if f".{fn}" in code or f'"{fn}"' in code or f"'{fn}'" in code:
+                found_in.append(label)
+        
+        is_used = len(found_in) > 0
+        if is_used:
+            used_fns += 1
+        else:
+            unused.append((so_name, fn))
+        
+        where = ", ".join(found_in[:3]) if found_in else ""
+        marker = "  ✓" if is_used else "  ✗"
+        print(f"{so_name:<35} {fn:<30} {marker:<6} {where}")
+
+print(f"\n{'=' * 70}")
+print(f"  TOTAL: {used_fns}/{total_fns} functions used")
+print(f"  UNUSED: {total_fns - used_fns} functions")
+print(f"{'=' * 70}")
+
+if unused:
+    print(f"\n  === UNUSED FUNCTIONS ===")
+    for so_name, fn in unused:
+        print(f"    {so_name}.{fn}")
+
+# 4. Check which ixformer_torch_ext functions exist but aren't wrapped
+print(f"\n  === ixformer_torch_ext available but not in any bridge .so ===")
+ix_fns = [
+    "ixformer_linear", "ixformer_linear_ex", "ixformer_linear_allreduce",
+    "linear_i8w8o32", "quantized_linear_awq", "quantized_linear_gptq",
+    "quantized_linear_int8", "quantized_linear_float4", "ixformer_quantized_linear",
+    "silu_and_mul_forward", "rms_norm_forward", "fused_add_rms_norm_forward",
+]
+for fn in ix_fns:
+    in_bridge = fn in all_code
+    print(f"    {fn:<40} {'✓ wrapped' if in_bridge else '✗ NOT wrapped'}")
+PYEOF
\ No newline at end of file
diff --git a/bench_linear_patch.sh b/bench_linear_patch.sh
new file mode 100644
index 00000000..b0da787b
--- /dev/null
+++ b/bench_linear_patch.sh
@@ -0,0 +1,105 @@
+#!/bin/bash
+set -euo pipefail
+cat << 'PYEOF' | CUDA_VISIBLE_DEVICES="${CUDA_VISIBLE_DEVICES:-0}" python3 -u -
+import torch, importlib.util, time
+torch.cuda.set_device(0)
+dev = torch.device("cuda:0")
+
+SO = "qwen3_6_scripts/prebuilt/corex-3.2.3-ivcore10"
+def load_so(name):
+    spec = importlib.util.spec_from_file_location(name, f"{SO}/{name}.so")
+    m = importlib.util.module_from_spec(spec); spec.loader.exec_module(m); return m
+
+bridge = load_so("ix_moe_bridge")
+act_m = load_so("xllm_activation")
+
+H = 2048
+
+def bench(name, fn, N=1000):
+    for _ in range(100): fn()
+    torch.cuda.synchronize()
+    t0 = time.perf_counter()
+    for _ in range(N): fn()
+    torch.cuda.synchronize()
+    us = (time.perf_counter() - t0) / N * 1e6
+    print(f"  {name:50s}: {us:8.1f} us")
+    return us
+
+x = torch.randn(1, H, device=dev, dtype=torch.float16)
+
+# Match upstream gemv_conditions: m <= 1, k % 32 == 0, n % 2 == 0, no bias
+# Test ALL linear ops in qwen3_5.py decode path
+
+print("=== Every linear op in one decode step (TP=4) ===")
+print("--- Attention layer (32 layers) ---")
+
+# QKV: (1,2048) @ (1024,2048)^T → (1,1024)  [heads*head_dim + 2*kv_heads*head_dim]
+w_qkv = torch.randn(1024, H, device=dev, dtype=torch.float16) * 0.01
+bench("qkv F.linear (1,2048)→(1,1024)", lambda: torch.nn.functional.linear(x, w_qkv))
+bench("qkv bridge.linear", lambda: bridge.linear(x, w_qkv, None))
+
+# O_proj: (1,768) @ (2048,768)^T → (1,2048)
+x_o = torch.randn(1, 768, device=dev, dtype=torch.float16)
+w_o = torch.randn(H, 768, device=dev, dtype=torch.float16) * 0.01
+bench("o_proj F.linear (1,768)→(1,2048)", lambda: torch.nn.functional.linear(x_o, w_o))
+bench("o_proj bridge.linear", lambda: bridge.linear(x_o, w_o, None))
+
+print("\n--- GDN layer (4 layers) ---")
+# GDN in_proj: (1,2048) @ (3852,2048)^T → (1,3852)
+w_gdn = torch.randn(3852, H, device=dev, dtype=torch.float16) * 0.01
+bench("gdn_proj F.linear (1,2048)→(1,3852)", lambda: torch.nn.functional.linear(x, w_gdn))
+bench("gdn_proj bridge.linear", lambda: bridge.linear(x, w_gdn, None))
+
+# GDN o_proj: (1,1536) @ (2048,1536)^T → (1,2048)
+x_gdn_o = torch.randn(1, 1536, device=dev, dtype=torch.float16)
+w_gdn_o = torch.randn(H, 1536, device=dev, dtype=torch.float16) * 0.01
+bench("gdn_oproj F.linear (1,1536)→(1,2048)", lambda: torch.nn.functional.linear(x_gdn_o, w_gdn_o))
+bench("gdn_oproj bridge.linear", lambda: bridge.linear(x_gdn_o, w_gdn_o, None))
+
+print("\n--- MoE shared expert (36 layers) ---")
+I_shared = 128
+w_gu = torch.randn(2*I_shared, H, device=dev, dtype=torch.float16) * 0.01
+w_down = torch.randn(H, I_shared, device=dev, dtype=torch.float16) * 0.01
+bench("shared gate_up F.linear (1,2048)→(1,256)", lambda: torch.nn.functional.linear(x, w_gu))
+bench("shared gate_up bridge.linear", lambda: bridge.linear(x, w_gu, None))
+x_down = torch.randn(1, I_shared, device=dev, dtype=torch.float16)
+bench("shared down F.linear (1,128)→(1,2048)", lambda: torch.nn.functional.linear(x_down, w_down))
+bench("shared down bridge.linear", lambda: bridge.linear(x_down, w_down, None))
+
+print("\n--- Router (36 layers) ---")
+w_router = torch.randn(257, H, device=dev, dtype=torch.float16) * 0.01
+bench("router F.linear (1,2048)→(1,257)", lambda: torch.nn.functional.linear(x, w_router))
+bench("router bridge.linear", lambda: bridge.linear(x, w_router, None))
+
+print("\n--- LM head (1x) ---")
+w_lm = torch.randn(37984, H, device=dev, dtype=torch.float16) * 0.01
+bench("lm_head F.linear (1,2048)→(1,37984)", lambda: torch.nn.functional.linear(x, w_lm))
+bench("lm_head bridge.linear", lambda: bridge.linear(x, w_lm, None))
+
+# === Total impact ===
+print("\n=== Projected total decode step savings ===")
+shapes = [
+    ("attn_qkv",   32, (1024, H)),
+    ("attn_o",     32, (H, 768)),
+    ("gdn_proj",    4, (3852, H)),
+    ("gdn_o",       4, (H, 1536)),
+    ("shared_gu",  36, (2*I_shared, H)),
+    ("shared_down",36, (H, I_shared)),
+    ("router",     36, (257, H)),
+    ("lm_head",     1, (37984, H)),
+]
+total_torch = 0
+total_bridge = 0
+for name, count, (N, K) in shapes:
+    w = torch.randn(N, K, device=dev, dtype=torch.float16) * 0.01
+    xi = torch.randn(1, K, device=dev, dtype=torch.float16)
+    t_torch = bench(f"  {name} F.linear", lambda xi=xi, w=w: torch.nn.functional.linear(xi, w), N=500)
+    t_bridge = bench(f"  {name} bridge", lambda xi=xi, w=w: bridge.linear(xi, w, None), N=500)
+    total_torch += t_torch * count
+    total_bridge += t_bridge * count
+    speedup = t_torch / t_bridge if t_bridge > 0 else 0
+    print(f"    → x{count}: {t_torch*count:.0f} → {t_bridge*count:.0f} us ({speedup:.1f}x)")
+
+print(f"\n  TOTAL linear ops: {total_torch:.0f} → {total_bridge:.0f} us")
+print(f"  Savings: {total_torch - total_bridge:.0f} us = {(total_torch-total_bridge)/1000:.1f} ms")
+PYEOF
\ No newline at end of file