diff --git a/src/code/issue4/.gitignore b/src/code/issue4/.gitignore
new file mode 100644
index 0000000..008ea26
--- /dev/null
+++ b/src/code/issue4/.gitignore
@@ -0,0 +1,4 @@
+__pycache__/
+*.py[cod]
+reports/
+issue4-reproducibility-report.json
diff --git a/src/code/issue4/README-2026.md b/src/code/issue4/README-2026.md
new file mode 100644
index 0000000..2952432
--- /dev/null
+++ b/src/code/issue4/README-2026.md
@@ -0,0 +1,75 @@
+# Issue 4：集合通信 Bitwise 可复现性诊断工具
+
+`reproducibility_diagnoser.py` 对完全相同的输入重复执行 AllReduce 或
+Reduce-Scatter，并按 `run / call / rank` 保存逐位摘要。发现差异时，报告会给出：
+
+- 首次出现差异的 collective 调用点和 rank；
+- 首个差异元素、差异元素总数；
+- 最大绝对误差和最大相对误差；
+- 后续差异随 run/call 的演化；
+- PyTorch/CUDA/NCCL 版本、GPU 型号和影响 NCCL 选择的环境变量。
+
+## 单卡冒烟验证
+
+NCCL 只在 Linux 版 PyTorch 中提供，Windows 版不含 NCCL。在 Windows 上开发时请在带
+CUDA 透传的 WSL2 里运行。以下命令在仓库根目录执行：
+
+```bash
+# 无依赖（不需要 GPU）：演示 FP32 归约顺序为何会改变结果位模式
+python src/code/issue4/reproducibility_diagnoser.py \
+  --self-test --output reports/issue4-order-demo.json
+
+# 单卡 NCCL 冒烟测试。它验证采集链路，不足以证明多 rank 可复现性。
+torchrun --standalone --nproc-per-node=1 \
+  src/code/issue4/reproducibility_diagnoser.py \
+  --runs 3 --calls 20 --output reports/issue4-single-gpu.json
+
+# 在 run 1 / call 3 / rank 0 注入一个 ULP，验证定位和误差统计链路。
+torchrun --standalone --nproc-per-node=1 \
+  src/code/issue4/reproducibility_diagnoser.py \
+  --runs 2 --calls 8 --inject-difference 1:3:0 --expect-difference \
+  --output reports/issue4-injection.json
+```
+
+`--inject-difference` 只用于工具自测，不能当作 NCCL 不确定性的证据。
+
+## 多卡验收运行
+
+在 Linux 多卡机器上先运行默认配置，再固定关键选择项。每个命令内部都会对
+相同输入执行多轮对照，避免把输入随机性误判成通信差异。
+
+```bash
+# 默认选择逻辑
+torchrun --standalone --nproc-per-node=8 \
+  src/code/issue4/reproducibility_diagnoser.py \
+  --operation all-reduce --shape 1048576 --runs 5 --calls 100 \
+  --output reports/all-reduce-default.json
+
+# 固定 Ring + Simple，关闭 NVLS，减少运行间算法/拓扑路径变化
+torchrun --standalone --nproc-per-node=8 \
+  src/code/issue4/reproducibility_diagnoser.py \
+  --operation all-reduce --shape 1048576 --runs 5 --calls 100 \
+  --env NCCL_ALGO=Ring --env NCCL_PROTO=Simple --env NCCL_NVLS_ENABLE=0 \
+  --output reports/all-reduce-ring-simple.json
+
+# Reduce-Scatter
+torchrun --standalone --nproc-per-node=8 \
+  src/code/issue4/reproducibility_diagnoser.py \
+  --operation reduce-scatter --shape 131072 --runs 5 --calls 100 \
+  --env NCCL_ALGO=Ring --env NCCL_PROTO=Simple \
+  --output reports/reduce-scatter-ring-simple.json
+```
+
+建议逐项改变 `NCCL_ALGO`（Ring/Tree/PAT）、`NCCL_PROTO`
+（Simple/LL/LL128）和 `NCCL_NVLS_ENABLE`，每个配置保存独立 JSON。跨机器复现时还应
+固定 GPU/rank 映射、NCCL/CUDA/驱动版本和拓扑文件。`torch.use_deterministic_algorithms`
+只能约束 PyTorch 可控的本地算子，不能替代对 NCCL 算法、协议与拓扑的固定。
+
+## 报告判读
+
+- `status=bitwise-reproducible`：本次指定配置和运行窗口内未发现位级差异；这不是对所有
+  消息大小、拓扑和并发负载的普遍保证。
+- `status=difference-detected`：查看 `first_difference` 定位首次调用，再用
+  `difference_evolution` 判断误差是否持续或放大。
+- `world_size=1`：只代表工具自检通过，不能形成 NCCL 多 rank 可复现性结论。
+
diff --git a/src/code/issue4/reproducibility_diagnoser.py b/src/code/issue4/reproducibility_diagnoser.py
new file mode 100644
index 0000000..c1c63a0
--- /dev/null
+++ b/src/code/issue4/reproducibility_diagnoser.py
@@ -0,0 +1,389 @@
+#!/usr/bin/env python3
+"""Locate the first bitwise difference in repeated PyTorch collectives.
+
+The normal mode is intended to be launched with ``torchrun``.  A small
+dependency-free demo is also provided so that the comparison logic can be
+checked before a CUDA/PyTorch environment is available.
+"""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import math
+import os
+import platform
+import struct
+import sys
+import time
+from datetime import timedelta
+from pathlib import Path
+from typing import Any
+
+
+RELEVANT_ENV_VARS = (
+    "NCCL_ALGO",
+    "NCCL_PROTO",
+    "NCCL_NVLS_ENABLE",
+    "NCCL_TOPO_FILE",
+    "NCCL_GRAPH_FILE",
+    "NCCL_P2P_LEVEL",
+    "NCCL_SHM_DISABLE",
+    "NCCL_NET",
+    "NCCL_DEBUG",
+    "CUBLAS_WORKSPACE_CONFIG",
+    "CUDA_VISIBLE_DEVICES",
+)
+
+
+def _float32(value: float) -> float:
+    """Round a Python float exactly as an IEEE-754 binary32 operation."""
+
+    return struct.unpack("<f", struct.pack("<f", value))[0]
+
+
+def _add_float32(left: float, right: float) -> float:
+    return _float32(_float32(left) + _float32(right))
+
+
+def run_dependency_free_demo(output: Path | None) -> int:
+    """Show a reproducible reduction-order difference without PyTorch."""
+
+    values = [_float32(100_000_000.0), _float32(-100_000_000.0), _float32(1.0)]
+    left_associative = _add_float32(_add_float32(values[0], values[1]), values[2])
+    right_associative = _add_float32(values[0], _add_float32(values[1], values[2]))
+    left_bits = struct.pack("<f", left_associative).hex()
+    right_bits = struct.pack("<f", right_associative).hex()
+    report = {
+        "schema_version": 1,
+        "mode": "dependency-free-reduction-order-demo",
+        "input": values,
+        "runs": [
+            {"name": "left_associative", "value": left_associative, "bits": left_bits},
+            {"name": "right_associative", "value": right_associative, "bits": right_bits},
+        ],
+        "detected_difference": left_bits != right_bits,
+        "max_abs_diff": abs(left_associative - right_associative),
+        "explanation": (
+            "The same FP32 values produce different bits when the reduction order changes. "
+            "This demonstrates why fixing the collective algorithm and topology matters."
+        ),
+    }
+    rendered = json.dumps(report, ensure_ascii=False, indent=2)
+    print(rendered)
+    if output is not None:
+        output.parent.mkdir(parents=True, exist_ok=True)
+        output.write_text(rendered + "\n", encoding="utf-8")
+    return 0 if report["detected_difference"] else 2
+
+
+def parse_shape(value: str) -> tuple[int, ...]:
+    try:
+        shape = tuple(int(part.strip()) for part in value.split(",") if part.strip())
+    except ValueError as exc:
+        raise argparse.ArgumentTypeError("shape must be comma-separated positive integers") from exc
+    if not shape or any(dimension <= 0 for dimension in shape):
+        raise argparse.ArgumentTypeError("shape must contain positive integers")
+    return shape
+
+
+def parse_injection(value: str) -> tuple[int, int, int]:
+    try:
+        run_index, call_index, rank = (int(part) for part in value.split(":"))
+    except (ValueError, TypeError) as exc:
+        raise argparse.ArgumentTypeError("injection must be RUN:CALL:RANK") from exc
+    if min(run_index, call_index, rank) < 0:
+        raise argparse.ArgumentTypeError("injection indices must be non-negative")
+    if run_index == 0:
+        raise argparse.ArgumentTypeError("run 0 is the reference; inject into run 1 or later")
+    return run_index, call_index, rank
+
+
+def parse_env_assignment(value: str) -> tuple[str, str]:
+    if "=" not in value:
+        raise argparse.ArgumentTypeError("environment assignment must be KEY=VALUE")
+    key, assigned_value = value.split("=", 1)
+    if not key or not key.replace("_", "a").isalnum() or key[0].isdigit():
+        raise argparse.ArgumentTypeError(f"invalid environment variable name: {key!r}")
+    return key, assigned_value
+
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        description="Find the first bitwise difference across repeated collective calls."
+    )
+    parser.add_argument("--self-test", action="store_true", help="run a dependency-free FP32 demo")
+    parser.add_argument("--backend", choices=("nccl", "gloo"), default="nccl")
+    parser.add_argument("--operation", choices=("all-reduce", "reduce-scatter"), default="all-reduce")
+    parser.add_argument("--shape", type=parse_shape, default=(4096,), help="output shape, e.g. 4096 or 32,128")
+    parser.add_argument("--dtype", choices=("float16", "bfloat16", "float32", "float64"), default="float32")
+    parser.add_argument("--runs", type=int, default=3, help="number of repeated runs (minimum 2)")
+    parser.add_argument("--calls", type=int, default=20, help="collective call points per run")
+    parser.add_argument("--warmup", type=int, default=5, help="unrecorded warmup collectives")
+    parser.add_argument("--seed", type=int, default=20260726)
+    parser.add_argument("--timeout-seconds", type=int, default=120)
+    parser.add_argument("--output", type=Path, default=Path("issue4-reproducibility-report.json"))
+    parser.add_argument(
+        "--env",
+        action="append",
+        type=parse_env_assignment,
+        default=[],
+        metavar="KEY=VALUE",
+        help="set an environment variable before initializing the process group",
+    )
+    parser.add_argument(
+        "--inject-difference",
+        type=parse_injection,
+        metavar="RUN:CALL:RANK",
+        help="add one ULP after a collective to validate the detector",
+    )
+    parser.add_argument(
+        "--expect-difference",
+        action="store_true",
+        help="return success only when a difference is found (useful with injection)",
+    )
+    return parser
+
+
+def _numel(shape: tuple[int, ...]) -> int:
+    return math.prod(shape)
+
+
+def _tensor_bytes(tensor: Any) -> bytes:
+    # ``Tensor.numpy`` does not support every dtype (notably bfloat16), so view
+    # the storage as bytes and stay independent of NumPy.
+    raw_bytes = tensor.detach().contiguous().view(-1).view(__import__("torch").uint8).cpu()
+    return bytes(raw_bytes.tolist())
+
+
+def _digest(tensor: Any) -> str:
+    return hashlib.sha256(_tensor_bytes(tensor)).hexdigest()
+
+
+def _make_input(torch: Any, args: argparse.Namespace, rank: int, call_index: int, world_size: int, device: Any) -> Any:
+    output_numel = _numel(args.shape)
+    input_numel = output_numel * (world_size if args.operation == "reduce-scatter" else 1)
+    generator = torch.Generator(device="cpu")
+    generator.manual_seed(args.seed + rank * 1_000_003 + call_index * 10_007)
+
+    # Wide magnitudes and alternating signs make reduction-order changes easier
+    # to observe while remaining exactly repeatable across runs.
+    mantissa = torch.randn(input_numel, generator=generator, dtype=torch.float32)
+    exponent = torch.randint(-12, 13, (input_numel,), generator=generator)
+    values = torch.ldexp(mantissa, exponent).to(getattr(torch, args.dtype))
+    return values.reshape((-1,) if args.operation == "reduce-scatter" else args.shape).to(device)
+
+
+def _one_ulp_in_place(torch: Any, tensor: Any) -> None:
+    flat = tensor.reshape(-1)
+    direction = torch.full_like(flat[:1], float("inf"))
+    flat[:1] = torch.nextafter(flat[:1], direction)
+
+
+def _difference(torch: Any, reference: Any, candidate: Any) -> dict[str, Any]:
+    element_size = reference.element_size()
+    reference_bytes = reference.detach().contiguous().view(torch.uint8).reshape(-1, element_size)
+    candidate_bytes = candidate.detach().contiguous().view(torch.uint8).reshape(-1, element_size)
+    element_mismatch = (reference_bytes != candidate_bytes).any(dim=1)
+    mismatch_count = int(element_mismatch.sum().item())
+    if mismatch_count:
+        first_index = int(element_mismatch.nonzero(as_tuple=False)[0].item())
+        delta = (candidate.reshape(-1).to(torch.float64) - reference.reshape(-1).to(torch.float64)).abs()
+        max_abs = float(delta.max().item())
+        denominator = reference.reshape(-1).to(torch.float64).abs().clamp_min(torch.finfo(torch.float64).tiny)
+        max_rel = float((delta / denominator).max().item())
+    else:
+        first_index = None
+        max_abs = 0.0
+        max_rel = 0.0
+    return {
+        "bitwise_equal": mismatch_count == 0,
+        "mismatched_elements": mismatch_count,
+        "first_mismatched_flat_index": first_index,
+        "max_abs_diff": max_abs,
+        "max_rel_diff": max_rel,
+    }
+
+
+def _sync(torch: Any, device: Any) -> None:
+    if device.type == "cuda":
+        torch.cuda.synchronize(device)
+
+
+def _nccl_version(torch: Any, backend: str) -> list[int] | str | None:
+    if backend != "nccl":
+        return None
+    version = torch.cuda.nccl.version()
+    if isinstance(version, tuple):
+        return list(version)
+    return str(version)
+
+
+def run_collective_diagnosis(args: argparse.Namespace) -> int:
+    for key, value in args.env:
+        os.environ[key] = value
+
+    try:
+        import torch
+        import torch.distributed as dist
+    except ImportError as exc:
+        print(
+            "PyTorch is required for collective mode. Run --self-test without dependencies, "
+            "or install a CUDA-enabled PyTorch build.",
+            file=sys.stderr,
+        )
+        raise SystemExit(3) from exc
+
+    if args.runs < 2 or args.calls < 1 or args.warmup < 0:
+        raise SystemExit("--runs must be >= 2, --calls >= 1, and --warmup >= 0")
+    if not dist.is_available():
+        raise SystemExit("torch.distributed is unavailable in this PyTorch build")
+    if args.backend == "nccl" and not dist.is_nccl_available():
+        raise SystemExit("this PyTorch build does not include NCCL")
+    if args.backend == "nccl" and not torch.cuda.is_available():
+        raise SystemExit("NCCL mode requires a CUDA device")
+
+    local_rank = int(os.environ.get("LOCAL_RANK", "0"))
+    if args.backend == "nccl":
+        torch.cuda.set_device(local_rank)
+        device = torch.device("cuda", local_rank)
+    else:
+        device = torch.device("cpu")
+
+    init_kwargs = {
+        "backend": args.backend,
+        "init_method": "env://",
+        "timeout": timedelta(seconds=args.timeout_seconds),
+    }
+    if args.backend == "nccl":
+        # Eagerly bind the process group to its CUDA device.  This also makes
+        # barrier device selection explicit on recent PyTorch releases.
+        init_kwargs["device_id"] = device
+    dist.init_process_group(**init_kwargs)
+    rank = dist.get_rank()
+    world_size = dist.get_world_size()
+    injection = args.inject_difference
+    if injection is not None and injection[2] >= world_size:
+        raise SystemExit(f"injection rank {injection[2]} is outside world size {world_size}")
+
+    torch.use_deterministic_algorithms(True)
+    baselines: list[Any] = []
+    local_records: list[dict[str, Any]] = []
+
+    def execute(call_index: int) -> Any:
+        source = _make_input(torch, args, rank, call_index, world_size, device)
+        if args.operation == "all-reduce":
+            output = source
+            dist.all_reduce(output, op=dist.ReduceOp.SUM)
+        else:
+            output = torch.empty(args.shape, dtype=getattr(torch, args.dtype), device=device)
+            reduce_scatter = getattr(dist, "reduce_scatter_single", dist.reduce_scatter_tensor)
+            reduce_scatter(output, source, op=dist.ReduceOp.SUM)
+        return output
+
+    try:
+        for warmup_index in range(args.warmup):
+            execute(-(warmup_index + 1))
+        _sync(torch, device)
+        dist.barrier()
+
+        for run_index in range(args.runs):
+            for call_index in range(args.calls):
+                dist.barrier()
+                _sync(torch, device)
+                started = time.perf_counter_ns()
+                output = execute(call_index)
+                _sync(torch, device)
+                elapsed_us = (time.perf_counter_ns() - started) / 1_000.0
+
+                if injection == (run_index, call_index, rank):
+                    _one_ulp_in_place(torch, output)
+
+                record: dict[str, Any] = {
+                    "rank": rank,
+                    "run": run_index,
+                    "call": call_index,
+                    "elapsed_us": elapsed_us,
+                    "digest": _digest(output),
+                }
+                if run_index == 0:
+                    baselines.append(output.detach().clone())
+                    record.update(
+                        bitwise_equal=True,
+                        mismatched_elements=0,
+                        first_mismatched_flat_index=None,
+                        max_abs_diff=0.0,
+                        max_rel_diff=0.0,
+                    )
+                else:
+                    record.update(_difference(torch, baselines[call_index], output))
+                local_records.append(record)
+
+        gathered: list[Any] = [None] * world_size
+        dist.all_gather_object(gathered, local_records)
+        if rank != 0:
+            return 0
+
+        records = [record for rank_records in gathered for record in rank_records]
+        differences = [record for record in records if not record["bitwise_equal"]]
+        differences.sort(key=lambda record: (record["run"], record["call"], record["rank"]))
+        first_difference = differences[0] if differences else None
+        report = {
+            "schema_version": 1,
+            "mode": "collective-run-to-run",
+            "status": "difference-detected" if first_difference else "bitwise-reproducible",
+            "configuration": {
+                "backend": args.backend,
+                "operation": args.operation,
+                "shape": args.shape,
+                "dtype": args.dtype,
+                "runs": args.runs,
+                "calls": args.calls,
+                "warmup": args.warmup,
+                "seed": args.seed,
+                "world_size": world_size,
+                "injected_difference": injection,
+            },
+            "environment": {
+                "python": platform.python_version(),
+                "pytorch": torch.__version__,
+                "cuda_runtime": torch.version.cuda,
+                "nccl": _nccl_version(torch, args.backend),
+                "devices": [torch.cuda.get_device_name(index) for index in range(torch.cuda.device_count())],
+                "variables": {key: os.environ[key] for key in RELEVANT_ENV_VARS if key in os.environ},
+            },
+            "first_difference": first_difference,
+            "difference_evolution": differences,
+            "records": sorted(records, key=lambda record: (record["run"], record["call"], record["rank"])),
+            "limitations": (
+                "world_size=1 validates the tool path but cannot expose reduction-order nondeterminism; "
+                "use at least two GPUs for an NCCL reproducibility conclusion."
+                if world_size == 1
+                else None
+            ),
+        }
+        rendered = json.dumps(report, ensure_ascii=False, indent=2)
+        args.output.parent.mkdir(parents=True, exist_ok=True)
+        args.output.write_text(rendered + "\n", encoding="utf-8")
+        print(rendered)
+        print(f"Report written to {args.output}", file=sys.stderr)
+
+        detected = first_difference is not None
+        if args.expect_difference:
+            return 0 if detected else 1
+        return 0
+    finally:
+        if dist.is_initialized():
+            dist.destroy_process_group()
+
+
+def main(argv: list[str] | None = None) -> int:
+    args = build_parser().parse_args(argv)
+    if args.self_test:
+        return run_dependency_free_demo(args.output)
+    return run_collective_diagnosis(args)
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/src/code/issue4/results/single_gpu_all_reduce.json b/src/code/issue4/results/single_gpu_all_reduce.json
new file mode 100644
index 0000000..800fffa
--- /dev/null
+++ b/src/code/issue4/results/single_gpu_all_reduce.json
@@ -0,0 +1,758 @@
+{
+  "schema_version": 1,
+  "mode": "collective-run-to-run",
+  "status": "bitwise-reproducible",
+  "configuration": {
+    "backend": "nccl",
+    "operation": "all-reduce",
+    "shape": [
+      4096
+    ],
+    "dtype": "float32",
+    "runs": 3,
+    "calls": 20,
+    "warmup": 5,
+    "seed": 20260726,
+    "world_size": 1,
+    "injected_difference": null
+  },
+  "environment": {
+    "python": "3.12.3",
+    "pytorch": "2.13.0+cu130",
+    "cuda_runtime": "13.0",
+    "nccl": [
+      2,
+      29,
+      7
+    ],
+    "devices": [
+      "NVIDIA GeForce RTX 5060 Ti"
+    ],
+    "variables": {}
+  },
+  "first_difference": null,
+  "difference_evolution": [],
+  "records": [
+    {
+      "rank": 0,
+      "run": 0,
+      "call": 0,
+      "elapsed_us": 152.377,
+      "digest": "8bf2ed82379ff6ab9d4d585296fc7d2a2054fbdd90f78a0377cf941f64f1d27a",
+      "bitwise_equal": true,
+      "mismatched_elements": 0,
+      "first_mismatched_flat_index": null,
+      "max_abs_diff": 0.0,
+      "max_rel_diff": 0.0
+    },
+    {
+      "rank": 0,
+      "run": 0,
+      "call": 1,
+      "elapsed_us": 651.968,
+      "digest": "f1703a630e18beee10af4620967d2c9f3612681d95872db9c973f6e54cc4051e",
+      "bitwise_equal": true,
+      "mismatched_elements": 0,
+      "first_mismatched_flat_index": null,
+      "max_abs_diff": 0.0,
+      "max_rel_diff": 0.0
+    },
+    {
+      "rank": 0,
+      "run": 0,
+      "call": 2,
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+  "limitations": "world_size=1 validates the tool path but cannot expose reduction-order nondeterminism; use at least two GPUs for an NCCL reproducibility conclusion."
+}
diff --git a/src/code/issue4/test_reproducibility_diagnoser.py b/src/code/issue4/test_reproducibility_diagnoser.py
new file mode 100644
index 0000000..8feafb2
--- /dev/null
+++ b/src/code/issue4/test_reproducibility_diagnoser.py
@@ -0,0 +1,31 @@
+import importlib.util
+import json
+from pathlib import Path
+
+
+MODULE_PATH = Path(__file__).with_name("reproducibility_diagnoser.py")
+SPEC = importlib.util.spec_from_file_location("reproducibility_diagnoser", MODULE_PATH)
+MODULE = importlib.util.module_from_spec(SPEC)
+assert SPEC.loader is not None
+SPEC.loader.exec_module(MODULE)
+
+
+def test_reduction_order_demo_detects_bitwise_difference(tmp_path):
+    report_path = tmp_path / "demo.json"
+
+    assert MODULE.run_dependency_free_demo(report_path) == 0
+
+    report = json.loads(report_path.read_text(encoding="utf-8"))
+    assert report["detected_difference"] is True
+    assert report["runs"][0]["value"] == 1.0
+    assert report["runs"][1]["value"] == 0.0
+    assert report["max_abs_diff"] == 1.0
+
+
+def test_shape_parser():
+    assert MODULE.parse_shape("32, 128") == (32, 128)
+
+
+def test_injection_parser():
+    assert MODULE.parse_injection("1:3:0") == (1, 3, 0)
+