fix codex BLOCKERs: global fixed layout invariance, min_steps off-by-one, integers EOS alias, rerun guard, eval prereg-literal codes + dual-checkpoint honesty; +7 regression tests

1 files changed, 5 insertions, 3 deletions

diff --git a/src/models/rnn.py b/src/models/rnn.py
index 19123b7..44f0a0d 100644
--- a/src/models/rnn.py
+++ b/src/models/rnn.py
@@ -45,7 +45,8 @@ class TiedRNN(PrimeModel):
         return pe
 
     def _encode(self, x: torch.Tensor) -> torch.Tensor:
-        """Read input digits through the tied cell (pad positions inject nothing)."""
+        """Read input digits through the tied cell. Pad positions are exact no-ops
+        (state update masked) so batch composition cannot change an example's state."""
         B, T = x.shape
         d = self.cfg.d_model
         pos = self._sinusoidal(T, d).to(x.device)                # (T,d)
@@ -53,7 +54,8 @@ class TiedRNN(PrimeModel):
         mask = (x != self.cfg.pad_id).float().unsqueeze(-1)      # (B,T,1)
         h = torch.zeros(B, d, device=x.device)
         for t in range(T):
-            h = self._cell_step(h + (e[:, t] + pos[t]) * mask[:, t])
+            h_new = self._cell_step(h + (e[:, t] + pos[t]) * mask[:, t])
+            h = mask[:, t] * h_new + (1 - mask[:, t]) * h        # pad step = no-op
         return h
 
     def _run_compute(self, h0: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
@@ -72,7 +74,7 @@ class TiedRNN(PrimeModel):
         for t in range(cfg.max_steps):
             h = self._cell_step(h)
             p = torch.sigmoid(self.halt_head(self.ln1(h))).squeeze(-1)   # (B,)
-            if t < cfg.min_steps:
+            if t < cfg.min_steps - 1:
                 p = p * 0.0
             h_list.append(h)
             p_list.append(p)