diff --git a/docs/research/julia_port_evaluation.md b/docs/research/julia_port_evaluation.md
index 0c5b17b..bc581a3 100644
--- a/docs/research/julia_port_evaluation.md
+++ b/docs/research/julia_port_evaluation.md
@@ -91,13 +91,37 @@ the heavier workload still does not reach near-linear 8-thread scaling.
 Julia delivers useful throughput scaling (4.76× at 8T), but this is not
 the decisive PASS threshold for the threading criterion.
 
-## Open evidence (criteria 4, 5)
+## Open evidence (criterion 5)
 
-- **Flux.jl + CUDA.jl MLP forward at bs={1, 64, 256}.** Compare to
-  PyTorch numbers in `docs/performance.md`. Criterion 4. Not started.
 - **Real-game-state slice port.** Port `play_card` + scoring, run on a
   fixed corpus of game states, compare to current Cython. Criterion 5.
-  Not started.
+  Not started. Skipped for now because criterion 4 failed; per the
+  decision rule, the full Julia port is no longer a GO candidate on the
+  current evidence.
+
+### 2026-05-07 — Flux.jl + CUDA.jl MLP forward (criterion 4)
+
+Path: `experiments/julia_flux_mlp/`.
+
+Same `DeepCFRMLP` shape as the current Deep CFR default MLP:
+365→512→512→512→22, ReLU, identical PyTorch-exported weights loaded
+into Flux. Output parity passed with max absolute difference
+`5.215e-08`. Timing uses 10 warmup forwards, then 100 timed forwards,
+with CUDA synchronized around the timed loop in both runtimes.
+
+| backend | batch | forward ms | μs/state | ratio vs PyTorch |
+| ---     | ---:  | ---:       | ---:     | ---:             |
+| PyTorch | 1     | 0.0829     | 82.8755  | 1.00×            |
+| Flux    | 1     | 0.1669     | 166.8867 | 2.01×            |
+| PyTorch | 64    | 0.0927     | 1.4477   | 1.00×            |
+| Flux    | 64    | 0.1909     | 2.9836   | 2.06×            |
+| PyTorch | 256   | 0.0877     | 0.3427   | 1.00×            |
+| Flux    | 256   | 0.1758     | 0.6867   | 2.00×            |
+
+**Verdict on criterion 4:** FAIL. bs=64 is ~2.06× slower than
+PyTorch, outside the ±20% PASS band. bs=1 and bs=256 are also ~2×
+slower, outside the ±30% bands. Criterion 5 was not run after this
+FAIL because the full Julia-port decision rule is already blocked.
 
 ## Pass/fail thresholds (decided in advance)
 
@@ -169,16 +193,17 @@ purpose.
 
 ## Decision posture
 
-Promising but not enough to justify a port yet. The completed benchmarks
-remove the main risk (GC under recursion) and confirm compute parity.
-Heavy thread scaling upgrades criterion 3 from inconclusive to PARTIAL:
-8T is 4.76× faster than 1T, but 59% efficiency is below the near-linear
-PASS threshold.
+No full Julia port on the current evidence. The completed benchmarks
+remove the main risk (GC under recursion) and confirm compute parity,
+but criterion 3 is only PARTIAL and criterion 4 is FAIL. Per the
+decision rule, a full port would spend months to replace a PyTorch GPU
+path that is already ~2× faster for the exact model shape we use.
 
-This means Julia remains a credible option, but not a slam dunk. ML-stack
-and game-state evidence (criteria 4, 5) must be positive before starting
-a serious port plan. If those are positive, criterion 3 should be revisited
-on a real traversal slice where each thread has substantially more work
-than this toy benchmark.
+Recommended next path: stay on Python/Cython and pursue Option B
+(per-worker interleaved traversal) as the GIL-escape path. A narrower
+Julia experiment could still be considered later for traversal-only
+logic, but it would need an explicit hybrid plan that keeps PyTorch for
+networks and separately proves PythonCall/PyCall overhead is acceptable.
 
-Do not commit to porting on the current evidence alone.
+Criterion 5 remains unrun because criterion 4 already blocks the full
+port decision.
diff --git a/experiments/julia_flux_mlp/Manifest.toml b/experiments/julia_flux_mlp/Manifest.toml
new file mode 100644
index 0000000..ef0b9b7
--- /dev/null
+++ b/experiments/julia_flux_mlp/Manifest.toml
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+deps = ["Adapt", "CEnum", "CUDACore", "CUDA_Runtime_Discovery", "CUDA_Runtime_jll", "GPUArrays", "GPUToolbox", "KernelAbstractions", "LinearAlgebra", "SparseArrays"]
+git-tree-sha1 = "5da59442cc13bb1a8647bbe20eba66917dc3a64b"
+uuid = "b26da814-b3bc-49ef-b0ee-c816305aa060"
+version = "6.1.0"
+
+    [deps.cuSPARSE.extensions]
+    SparseMatricesCSRExt = "SparseMatricesCSR"
+
+    [deps.cuSPARSE.weakdeps]
+    SparseMatricesCSR = "a0a7dd2c-ebf4-11e9-1f05-cf50bc540ca1"
+
+[[deps.demumble_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl"]
+git-tree-sha1 = "6498e3581023f8e530f34760d18f75a69e3a4ea8"
+uuid = "1e29f10c-031c-5a83-9565-69cddfc27673"
+version = "1.3.0+0"
+
+[[deps.libblastrampoline_jll]]
+deps = ["Artifacts", "Libdl"]
+uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"
+version = "5.11.0+0"
+
+[[deps.nghttp2_jll]]
+deps = ["Artifacts", "Libdl"]
+uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
+version = "1.59.0+0"
+
+[[deps.p7zip_jll]]
+deps = ["Artifacts", "Libdl"]
+uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
+version = "17.4.0+2"
diff --git a/experiments/julia_flux_mlp/Project.toml b/experiments/julia_flux_mlp/Project.toml
new file mode 100644
index 0000000..247ee89
--- /dev/null
+++ b/experiments/julia_flux_mlp/Project.toml
@@ -0,0 +1,8 @@
+name = "JuliaFluxMLP"
+uuid = "9fa932db-d4ab-4cbf-935e-32425028f0bc"
+version = "0.1.0"
+
+[deps]
+CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
+Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
+JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
diff --git a/experiments/julia_flux_mlp/README.md b/experiments/julia_flux_mlp/README.md
new file mode 100644
index 0000000..6910e7b
--- /dev/null
+++ b/experiments/julia_flux_mlp/README.md
@@ -0,0 +1,45 @@
+# Julia Flux MLP Forward Benchmark
+
+Criterion 4 for the Julia port evaluation.
+
+Compares PyTorch `DeepCFRMLP` against a Flux/CUDA implementation with the
+same shape and identical exported weights:
+
+- input dim: 365
+- hidden size: 512
+- hidden layers: 3
+- output dim: 22
+- activation: ReLU
+
+Run:
+
+```bash
+uv run python experiments/julia_flux_mlp/bench_pytorch_vs_flux.py
+```
+
+The runner exports PyTorch weights and inputs to `runs/tmp/`, runs Flux on the
+same payload, checks output equivalence, then writes `results.json`.
+
+## Results (2026-05-07)
+
+Host GPU: NVIDIA GeForce RTX 3090. Timing uses 10 warmup forwards, then 100
+timed forwards, with CUDA synchronized around the timed loop in both runtimes.
+
+| Backend | batch | forward ms | μs/state | ratio vs PyTorch |
+| --- | ---: | ---: | ---: | ---: |
+| PyTorch | 1 | 0.0829 | 82.8755 | 1.00× |
+| Flux | 1 | 0.1669 | 166.8867 | 2.01× |
+| PyTorch | 64 | 0.0927 | 1.4477 | 1.00× |
+| Flux | 64 | 0.1909 | 2.9836 | 2.06× |
+| PyTorch | 256 | 0.0877 | 0.3427 | 1.00× |
+| Flux | 256 | 0.1758 | 0.6867 | 2.00× |
+
+Maximum output difference: `5.215e-08`.
+
+Criterion 4 threshold:
+
+- bs=64 must be within ±20% of PyTorch.
+- bs=1 and bs=256 must be within ±30% of PyTorch.
+
+**Verdict:** FAIL. Flux/CUDA is ~2.0× slower than PyTorch at all measured
+batch sizes for this model shape.
diff --git a/experiments/julia_flux_mlp/bench_flux.jl b/experiments/julia_flux_mlp/bench_flux.jl
new file mode 100644
index 0000000..e9403f9
--- /dev/null
+++ b/experiments/julia_flux_mlp/bench_flux.jl
@@ -0,0 +1,120 @@
+using CUDA
+using Flux
+using JSON
+using Printf
+
+const WARMUP_ITERS = 10
+const TIMED_ITERS = 100
+
+function dense_from_payload(layer)
+    weight = Float32.(layer["weight"])
+    bias = Float32.(layer["bias"])
+    out_dim = Int(layer["out_dim"])
+    in_dim = Int(layer["in_dim"])
+    dense = Dense(in_dim => out_dim)
+    dense.weight .= reshape(weight, in_dim, out_dim)'
+    dense.bias .= bias
+    return dense
+end
+
+function build_model(payload)
+    layers = Any[]
+    dense_layers = payload["layers"]
+    for (index, layer) in enumerate(dense_layers)
+        push!(layers, dense_from_payload(layer))
+        if index < length(dense_layers)
+            push!(layers, relu)
+        end
+    end
+    return Flux.fmap(cu, Chain(layers...))
+end
+
+function input_matrix(batch_payload)
+    batch_size = Int(batch_payload["batch_size"])
+    input_dim = Int(batch_payload["input_dim"])
+    flat = Float32.(batch_payload["input"])
+    x = Matrix{Float32}(undef, input_dim, batch_size)
+    @inbounds for sample in 1:batch_size
+        source_base = (sample - 1) * input_dim
+        for feature in 1:input_dim
+            x[feature, sample] = flat[source_base + feature]
+        end
+    end
+    return cu(x)
+end
+
+function flatten_output(y)
+    cpu = Array(y)
+    out_dim, batch_size = size(cpu)
+    flat = Vector{Float32}(undef, batch_size * out_dim)
+    @inbounds for sample in 1:batch_size
+        dest_base = (sample - 1) * out_dim
+        for output in 1:out_dim
+            flat[dest_base + output] = cpu[output, sample]
+        end
+    end
+    return flat
+end
+
+function timed_forward_ms(model, x)
+    CUDA.synchronize()
+    for _ in 1:WARMUP_ITERS
+        model(x)
+    end
+    CUDA.synchronize()
+
+    elapsed = @elapsed begin
+        for _ in 1:TIMED_ITERS
+            model(x)
+        end
+        CUDA.synchronize()
+    end
+    return elapsed * 1000.0 / TIMED_ITERS
+end
+
+function benchmark(payload)
+    CUDA.allowscalar(false)
+    model = build_model(payload)
+    results = Dict{String,Any}()
+    max_abs_diff = 0.0
+    for batch_payload in payload["batches"]
+        batch_size = Int(batch_payload["batch_size"])
+        x = input_matrix(batch_payload)
+        y = model(x)
+        CUDA.synchronize()
+        actual = flatten_output(y)
+        expected = Float32.(batch_payload["expected_output"])
+        diff = maximum(abs.(actual .- expected))
+        max_abs_diff = max(max_abs_diff, Float64(diff))
+        ms = timed_forward_ms(model, x)
+        results[string(batch_size)] = Dict(
+            "forward_ms" => ms,
+            "us_per_state" => ms * 1000.0 / batch_size,
+            "max_abs_diff" => Float64(diff),
+        )
+    end
+    return Dict(
+        "lang" => "Julia/Flux",
+        "device" => string(CUDA.name(CUDA.device())),
+        "timed_iters" => TIMED_ITERS,
+        "warmup_iters" => WARMUP_ITERS,
+        "max_abs_diff" => max_abs_diff,
+        "batches" => results,
+    )
+end
+
+function print_json(result)
+    println(JSON.json(result))
+end
+
+function main()
+    if length(ARGS) != 1
+        println(stderr, "usage: julia --project=experiments/julia_flux_mlp experiments/julia_flux_mlp/bench_flux.jl <payload.json>")
+        exit(2)
+    end
+    payload = JSON.parsefile(ARGS[1])
+    result = benchmark(payload)
+    print_json(result)
+end
+
+main()
diff --git a/experiments/julia_flux_mlp/bench_pytorch_vs_flux.py b/experiments/julia_flux_mlp/bench_pytorch_vs_flux.py
new file mode 100644
index 0000000..4c74509
--- /dev/null
+++ b/experiments/julia_flux_mlp/bench_pytorch_vs_flux.py
@@ -0,0 +1,188 @@
+from __future__ import annotations
+
+import json
+import subprocess
+import time
+from pathlib import Path
+from typing import Any
+
+import torch
+
+from coolrl_lost_cities.games.classic.deep_cfr.networks import DeepCFRMLP
+
+ROOT = Path(__file__).resolve().parent
+REPO_ROOT = ROOT.parents[1]
+BATCH_SIZES = (1, 64, 256)
+INPUT_DIM = 365
+HIDDEN_SIZE = 512
+NUM_LAYERS = 3
+OUTPUT_DIM = 22
+WARMUP_ITERS = 10
+TIMED_ITERS = 100
+SEED = 20260507
+MAX_OUTPUT_DIFF = 1e-4
+
+
+def _julia_executable() -> str:
+    local = REPO_ROOT / "tools" / "julia" / "current" / "bin" / "julia"
+    if local.exists():
+        return str(local)
+    return "julia"
+
+
+def _linear_layers(model: DeepCFRMLP) -> list[torch.nn.Linear]:
+    return [layer for layer in model.net if isinstance(layer, torch.nn.Linear)]
+
+
+def _export_layers(model: DeepCFRMLP) -> list[dict[str, Any]]:
+    layers = []
+    for layer in _linear_layers(model):
+        weight = layer.weight.detach().cpu().contiguous()
+        bias = layer.bias.detach().cpu().contiguous()
+        layers.append(
+            {
+                "in_dim": int(weight.shape[1]),
+                "out_dim": int(weight.shape[0]),
+                "weight": weight.flatten().tolist(),
+                "bias": bias.flatten().tolist(),
+            }
+        )
+    return layers
+
+
+def _time_pytorch(model: DeepCFRMLP, x: torch.Tensor) -> float:
+    with torch.inference_mode():
+        for _ in range(WARMUP_ITERS):
+            model(x)
+        torch.cuda.synchronize()
+        start = time.perf_counter()
+        for _ in range(TIMED_ITERS):
+            model(x)
+        torch.cuda.synchronize()
+    return float((time.perf_counter() - start) * 1000.0 / TIMED_ITERS)
+
+
+def _build_payload_and_pytorch_results(payload_path: Path) -> dict[str, Any]:
+    if not torch.cuda.is_available():
+        raise SystemExit("CUDA is not available to PyTorch; aborting Criterion 4")
+    torch.manual_seed(SEED)
+    torch.cuda.manual_seed_all(SEED)
+    torch.backends.cuda.matmul.allow_tf32 = False
+    torch.backends.cudnn.allow_tf32 = False
+
+    model = (
+        DeepCFRMLP(
+            INPUT_DIM,
+            OUTPUT_DIM,
+            HIDDEN_SIZE,
+            num_layers=NUM_LAYERS,
+            activation="relu",
+        )
+        .eval()
+        .cuda()
+    )
+
+    payload: dict[str, Any] = {
+        "input_dim": INPUT_DIM,
+        "hidden_size": HIDDEN_SIZE,
+        "num_layers": NUM_LAYERS,
+        "output_dim": OUTPUT_DIM,
+        "activation": "relu",
+        "layers": _export_layers(model),
+        "batches": [],
+    }
+    pytorch_batches: dict[str, Any] = {}
+    generator = torch.Generator(device="cpu").manual_seed(SEED + 1)
+    with torch.inference_mode():
+        for batch_size in BATCH_SIZES:
+            x_cpu = torch.randn(batch_size, INPUT_DIM, generator=generator, dtype=torch.float32)
+            x_gpu = x_cpu.cuda()
+            y_cpu = model(x_gpu).detach().cpu().contiguous()
+            forward_ms = _time_pytorch(model, x_gpu)
+            payload["batches"].append(
+                {
+                    "batch_size": batch_size,
+                    "input_dim": INPUT_DIM,
+                    "input": x_cpu.contiguous().flatten().tolist(),
+                    "expected_output": y_cpu.flatten().tolist(),
+                }
+            )
+            pytorch_batches[str(batch_size)] = {
+                "forward_ms": forward_ms,
+                "us_per_state": forward_ms * 1000.0 / batch_size,
+            }
+
+    payload_path.parent.mkdir(parents=True, exist_ok=True)
+    payload_path.write_text(json.dumps(payload), encoding="utf-8")
+    return {
+        "lang": "PyTorch",
+        "device": torch.cuda.get_device_name(0),
+        "timed_iters": TIMED_ITERS,
+        "warmup_iters": WARMUP_ITERS,
+        "batches": pytorch_batches,
+    }
+
+
+def _run_flux(payload_path: Path) -> dict[str, Any]:
+    proc = subprocess.run(
+        [
+            _julia_executable(),
+            "--project=experiments/julia_flux_mlp",
+            "experiments/julia_flux_mlp/bench_flux.jl",
+            str(payload_path),
+        ],
+        cwd=REPO_ROOT,
+        check=True,
+        text=True,
+        capture_output=True,
+    )
+    return json.loads(proc.stdout)
+
+
+def _verdict(pytorch: dict[str, Any], flux: dict[str, Any]) -> tuple[str, dict[str, float]]:
+    ratios = {}
+    for batch_size in BATCH_SIZES:
+        key = str(batch_size)
+        ratio = flux["batches"][key]["us_per_state"] / pytorch["batches"][key]["us_per_state"]
+        ratios[key] = ratio
+    within_64 = 0.8 <= ratios["64"] <= 1.2
+    within_1 = 0.7 <= ratios["1"] <= 1.3
+    within_256 = 0.7 <= ratios["256"] <= 1.3
+    return ("PASS" if within_64 and within_1 and within_256 else "FAIL"), ratios
+
+
+def _print_table(
+    pytorch: dict[str, Any], flux: dict[str, Any], ratios: dict[str, float], verdict: str
+) -> None:
+    print("Backend   batch  forward_ms  us/state  ratio vs PyTorch")
+    for batch_size in BATCH_SIZES:
+        key = str(batch_size)
+        p = pytorch["batches"][key]
+        f = flux["batches"][key]
+        print(
+            f"PyTorch   {batch_size:5d}  {p['forward_ms']:10.4f}  {p['us_per_state']:8.4f}  1.00x"
+        )
+        print(
+            f"Flux      {batch_size:5d}  {f['forward_ms']:10.4f}  "
+            f"{f['us_per_state']:8.4f}  {ratios[key]:.2f}x"
+        )
+    print(f"max output diff: {flux['max_abs_diff']:.3e}")
+    print(f"Criterion 4 verdict: {verdict}")
+
+
+def main() -> None:
+    payload_path = REPO_ROOT / "runs" / "tmp" / "julia_flux_mlp_payload.json"
+    pytorch = _build_payload_and_pytorch_results(payload_path)
+    flux = _run_flux(payload_path)
+    if float(flux["max_abs_diff"]) > MAX_OUTPUT_DIFF:
+        raise SystemExit(f"Flux output mismatch: max_abs_diff={flux['max_abs_diff']:.3e}")
+    verdict, ratios = _verdict(pytorch, flux)
+    result = {"pytorch": pytorch, "flux": flux, "ratios": ratios, "verdict": verdict}
+    result_path = ROOT / "results.json"
+    result_path.write_text(json.dumps(result, indent=2, sort_keys=True), encoding="utf-8")
+    _print_table(pytorch, flux, ratios, verdict)
+    print(f"wrote {result_path}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/experiments/julia_flux_mlp/results.json b/experiments/julia_flux_mlp/results.json
new file mode 100644
index 0000000..85e6e2e
--- /dev/null
+++ b/experiments/julia_flux_mlp/results.json
@@ -0,0 +1,52 @@
+{
+  "flux": {
+    "batches": {
+      "1": {
+        "forward_ms": 0.16688673,
+        "max_abs_diff": 1.4901161193847656e-08,
+        "us_per_state": 166.88673
+      },
+      "256": {
+        "forward_ms": 0.17579791,
+        "max_abs_diff": 2.9802322387695312e-08,
+        "us_per_state": 0.6867105859375
+      },
+      "64": {
+        "forward_ms": 0.19094904,
+        "max_abs_diff": 5.21540641784668e-08,
+        "us_per_state": 2.98357875
+      }
+    },
+    "device": "NVIDIA GeForce RTX 3090",
+    "lang": "Julia/Flux",
+    "max_abs_diff": 5.21540641784668e-08,
+    "timed_iters": 100,
+    "warmup_iters": 10
+  },
+  "pytorch": {
+    "batches": {
+      "1": {
+        "forward_ms": 0.0828754500253126,
+        "us_per_state": 82.8754500253126
+      },
+      "256": {
+        "forward_ms": 0.0877418098389171,
+        "us_per_state": 0.34274144468326995
+      },
+      "64": {
+        "forward_ms": 0.09265014989068732,
+        "us_per_state": 1.4476585920419893
+      }
+    },
+    "device": "NVIDIA GeForce RTX 3090",
+    "lang": "PyTorch",
+    "timed_iters": 100,
+    "warmup_iters": 10
+  },
+  "ratios": {
+    "1": 2.0137052643337428,
+    "256": 2.0035819904187386,
+    "64": 2.0609684951971476
+  },
+  "verdict": "FAIL"
+}
\ No newline at end of file
diff --git a/experiments/julia_flux_mlp/src/JuliaFluxMLP.jl b/experiments/julia_flux_mlp/src/JuliaFluxMLP.jl
new file mode 100644
index 0000000..cb39e86
--- /dev/null
+++ b/experiments/julia_flux_mlp/src/JuliaFluxMLP.jl
@@ -0,0 +1,3 @@
+module JuliaFluxMLP
+
+end