item 32 pre-registered (Nils's synthesis): discrete latent chain — lens-snapped token embeddings fed back via zero-init projector (ST top-32, TF/free-running arms, frozen arm-1 merge); sym_iterate in carry_common, trainer/eval wiring

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

scripts/eval_carry_cot.py

@@ -43,6 +43,9 @@ def main():
     ap.add_argument("--kvmem", default=None, metavar="KVMEM_PT",
                     help="KVMemoryAdapter checkpoint: burst states become "
                      "band-layer KV prefix entries at generation")
+    ap.add_argument("--symchain", default=None, metavar="PROJ_PT",
+                    help="item 32: discrete latent chain — zero-init "
+                     "projector checkpoint; generation snaps hard argmax")
     args = ap.parse_args()
 
     model, tok = load_model(dtype=torch.bfloat16)
@@ -71,6 +74,29 @@ def main():
         print(f"band-lora loaded: {args.bandlora} "
               f"(r={ck['rank']}, layers {ck['band'][0]}-{ck['band'][-1]})",
               flush=True)
+    symchain = None
+    if args.symchain:
+        d_ = model.config.get_text_config().hidden_size
+        proj = torch.nn.Linear(d_, d_).cuda()
+        proj.load_state_dict(torch.load(args.symchain, map_location="cuda"))
+        proj.eval()
+        jbar = torch.load(Path(__file__).resolve().parent.parent
+                          / "results/jbar.pt", map_location="cuda")["Jbar"]
+        from loop_common import BAND
+        J30 = jbar[BAND[1]].float()
+        tm = model.model.language_model
+        softcap = model.config.get_text_config().final_logit_softcapping
+
+        def lens_fn(h):
+            x = tm.norm((h.float() @ J30.T).to(tm.norm.weight.dtype))
+            lg = model.lm_head(x)
+            return softcap * torch.tanh(lg / softcap) if softcap else lg
+
+        symchain = {"proj": proj, "lens_fn": lens_fn,
+                    "embed_w": model.get_input_embeddings().weight.detach(),
+                    "start_id": tok("\n",
+                                    add_special_tokens=False)["input_ids"][0]}
+        print(f"symchain loaded: {args.symchain}", flush=True)
     adapter = MergeAdapter(
         d=model.config.get_text_config().hidden_size).cuda()
     adapter.load_state_dict(torch.load(args.adapter, map_location="cuda"))
@@ -101,7 +127,7 @@ def main():
                                        max_new_tokens=args.max_new,
                                        feedforward=args.feedforward,
                                        inner_iters=args.inner_iters,
-                                       kvmem=kvmem)
+                                       kvmem=kvmem, symchain=symchain)
                 if kvmem is not None:
                     from kv_memory import arm_memory
                     arm_memory(None)