diff --git a/tests/test_integrity_trained.py b/tests/test_integrity_trained.py
new file mode 100644
index 0000000..2f5d087
--- /dev/null
+++ b/tests/test_integrity_trained.py
@@ -0,0 +1,58 @@
+"""The trainedness guard must reject the random-init / log-mismatched checkpoints
+that slipped through (e.g. the uid155 fraud) while never rejecting a real model."""
+from __future__ import annotations
+
+import math
+
+import pytest
+
+from validator.integrity import (
+    check_checkpoint_trained,
+    nats_per_token_from_bpb,
+)
+
+VOCAB = 50257
+RANDOM_NATS = math.log(VOCAB)  # ~10.82
+
+
+def test_rejects_the_uid155_random_king():
+    # Measured in the incident: ~11.0 nats/token, log claimed final_loss 3.05.
+    ok, reason = check_checkpoint_trained(11.0, VOCAB, claimed_final_loss=3.0496)
+    assert not ok
+    assert "untrained" in reason
+
+
+def test_rejects_random_even_without_a_claimed_loss():
+    ok, reason = check_checkpoint_trained(RANDOM_NATS, VOCAB)
+    assert not ok and "untrained" in reason
+
+
+def test_accepts_a_real_trained_model():
+    # A legit king sits at val_bpb ~1.3-1.6 -> ~3.6-4.4 nats/token.
+    for val_bpb in (1.306, 1.336, 1.581):
+        nats = nats_per_token_from_bpb(val_bpb, bytes_per_token=4.0)
+        ok, reason = check_checkpoint_trained(nats, VOCAB, claimed_final_loss=3.0496)
+        assert ok, f"false-rejected a real model (val_bpb={val_bpb}, nats={nats:.2f}): {reason}"
+
+
+def test_catches_subtle_log_mismatch_below_random():
+    # Not fully random (7 nats), but the log claims a much better 2.0 -> the
+    # scored checkpoint clearly isn't from the declared run.
+    ok, reason = check_checkpoint_trained(7.0, VOCAB, claimed_final_loss=2.0)
+    assert not ok and "mismatch" in reason
+
+
+def test_generous_to_normal_train_test_gap():
+    # Held-out modestly worse than training must NOT trip the mismatch check.
+    ok, _ = check_checkpoint_trained(4.4, VOCAB, claimed_final_loss=3.05)
+    assert ok
+
+
+def test_bpb_inversion_roundtrips():
+    nats = nats_per_token_from_bpb(1.5, 4.0)
+    assert nats == pytest.approx(1.5 * math.log(2) * 4.0)
+
+
+def test_rejects_non_finite_and_bad_vocab():
+    assert not check_checkpoint_trained(float("nan"), VOCAB)[0]
+    assert not check_checkpoint_trained(3.5, 1)[0]
diff --git a/validator/integrity.py b/validator/integrity.py
new file mode 100644
index 0000000..a64938d
--- /dev/null
+++ b/validator/integrity.py
@@ -0,0 +1,87 @@
+"""Checkpoint-trainedness / log-consistency guard.
+
+Motivation (real incident): a submission whose checkpoint was random-INITIALISED
+(every weight at init std) shipped a `training_log.jsonl` claiming a full run and
+got crowned — because op4 was scoring against random tokens at the time and the
+re-train audit that would have caught the log/checkpoint mismatch never ran. The
+checkpoint measured ~ln(vocab) nats/token (uniform output) yet the log claimed a
+final loss of ~3 nats.
+
+This is a CHEAP guard (no extra GPU work — it consumes the val_bpb op4 already
+computes plus the miner's own declared `final_loss`):
+
+  (a) UNTRAINED: the held-out loss sits within `random_fraction` of the random
+      baseline ln(vocab_size) -> the checkpoint carries ~no learned signal.
+  (b) LOG/CHECKPOINT MISMATCH: a declared training `final_loss` exists but the
+      held-out loss is implausibly worse than it -> the scored checkpoint did
+      not come from the declared training run.
+
+Thresholds are deliberately generous so an honest model (held-out a bit worse
+than training, never near random) is NEVER rejected; only garbage / fabricated
+checkpoints trip it. Returns (ok, reason); ok=False means reject as fraud/broken.
+"""
+from __future__ import annotations
+
+import math
+
+# Reject if held-out loss >= this fraction of the random baseline ln(vocab).
+# A real ~254M model sits at ~3-4.5 nats/token; random is ~10.8 for vocab 50257.
+# 0.80 -> reject above ~8.6 nats, leaving a wide safety margin under legit models.
+DEFAULT_RANDOM_FRACTION = 0.80
+
+# Reject if held-out loss > claimed_final_loss * FACTOR + MARGIN. Generous: a
+# normal train->held-out gap is well under 1.5x; this only fires on gross
+# mismatch (e.g. claimed 3.0, measured 9.0).
+DEFAULT_MISMATCH_FACTOR = 2.5
+DEFAULT_MISMATCH_MARGIN = 1.0
+
+
+def nats_per_token_from_bpb(val_bpb: float, bytes_per_token: float) -> float:
+    """Invert val_bpb = nats / (ln2 * bytes_per_token)."""
+    return float(val_bpb) * math.log(2) * float(bytes_per_token)
+
+
+def check_checkpoint_trained(
+    measured_nats_per_token: float,
+    vocab_size: int,
+    *,
+    claimed_final_loss: float | None = None,
+    random_fraction: float = DEFAULT_RANDOM_FRACTION,
+    mismatch_factor: float = DEFAULT_MISMATCH_FACTOR,
+    mismatch_margin: float = DEFAULT_MISMATCH_MARGIN,
+) -> tuple[bool, str]:
+    """Cheap guard against untrained / log-mismatched checkpoints.
+
+    Args:
+      measured_nats_per_token: held-out cross-entropy (nats/token) the validator
+        actually measured for this checkpoint (e.g. from op4's val_bpb via
+        `nats_per_token_from_bpb`).
+      vocab_size: the checkpoint's vocab — sets the random baseline ln(vocab).
+      claimed_final_loss: the miner's declared training `final_loss` (nats/token)
+        from final_state.json, if present. Enables the log-mismatch check.
+
+    Returns (ok, reason). ok=False -> reject.
+    """
+    if not (isinstance(measured_nats_per_token, (int, float)) and math.isfinite(measured_nats_per_token)):
+        return False, f"non-finite measured loss: {measured_nats_per_token!r}"
+    if not (isinstance(vocab_size, int) and vocab_size > 1):
+        return False, f"invalid vocab_size: {vocab_size!r}"
+
+    random_baseline = math.log(vocab_size)  # nats/token of a uniform predictor
+    if measured_nats_per_token >= random_fraction * random_baseline:
+        return False, (
+            f"untrained checkpoint: held-out {measured_nats_per_token:.2f} nats/token "
+            f">= {random_fraction:.0%} of random baseline {random_baseline:.2f} "
+            f"(vocab {vocab_size}) — weights appear at initialization"
+        )
+
+    if claimed_final_loss is not None and isinstance(claimed_final_loss, (int, float)) and claimed_final_loss > 0:
+        bound = claimed_final_loss * mismatch_factor + mismatch_margin
+        if measured_nats_per_token > bound:
+            return False, (
+                f"log/checkpoint mismatch: held-out {measured_nats_per_token:.2f} nats/token "
+                f">> declared training final_loss {claimed_final_loss:.2f} "
+                f"(plausible bound {bound:.2f}) — scored checkpoint not from the declared run"
+            )
+
+    return True, "ok"