diff --git a/results-loop/probe_discount.pt b/results-loop/probe_discount.pt
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diff --git a/results-loop/probe_discount_ff.pt b/results-loop/probe_discount_ff.pt
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diff --git a/scripts/probe_discount.py b/scripts/probe_discount.py
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+"""Single-prompt whiteboard microscopy (Nils, 2026-07-16 evening).
+
+Run one discount problem through the item-21 arm-A carry loop (k=2, p=6),
+then lens-read every workspace-band layer at every post-prompt position:
+does anything token-aligned (complement 85, intermediate 12, answer 68,
+schema words) ever appear on the carried state, and in what order?
+
+Prints: generated text; top-3 lens tokens per (pause/gen position x band
+layer subset); digit-probability matrix at the band exit (L30) — the
+vector that actually seeds the next position's carry. Saves the full
+table to results-loop/probe_discount.pt.
+"""
+import os
+import sys
+from pathlib import Path
+
+import torch
+
+from carry_common import (PAUSE_ID, _pos_ids, build_step_updates,
+                          carry_steps, prompt_prefill, generate_carry_c)
+from loop_common import BandLooper, MergeAdapter, chat_prompt, DIRECT_SUFFIX
+
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+from jlens.core import JLens, ResidualCapture, load_model  # noqa: E402
+
+OUT = Path(os.environ.get("LOOP_OUT",
+    Path(__file__).resolve().parent.parent / "results-loop"))
+Q = "A shirt costs $80 and is on sale for 15% off. How much does it cost?"
+K, P = 2, 6
+FF = "--ff" in sys.argv
+ADAPTER = OUT / ("adapter_carrycot_ff_e400.pt" if FF else "adapter_carrycot_e400.pt")
+
+model, tok = load_model(dtype=torch.bfloat16)
+looper = BandLooper(model)
+band = list(range(looper.l0, looper.l1 + 1))
+adapter = MergeAdapter(d=model.config.get_text_config().hidden_size).cuda()
+adapter.load_state_dict(torch.load(ADAPTER, map_location="cuda"))
+jbar = torch.load(Path(__file__).resolve().parent.parent / "results/jbar.pt",
+                  map_location="cuda")["Jbar"]
+jl = JLens(model, tok, jbar)
+
+prompt = chat_prompt(tok, Q, DIRECT_SUFFIX)
+ids = tok(prompt, add_special_tokens=False, return_tensors="pt")["input_ids"].cuda()
+mask = torch.ones_like(ids)
+
+with torch.no_grad():
+    gen = generate_carry_c(looper, adapter, tok, ids, mask, K, P,
+                           max_new_tokens=48, feedforward=FF)
+text = tok.decode(gen[0, ids.shape[1] + P:], skip_special_tokens=False)
+print("GENERATED:", repr(text), flush=True)
+
+# teacher-forced replay of the full sequence, capture band residuals
+full = gen[0][None, :].cuda()
+fmask = torch.ones_like(full)
+n_prompt = ids.shape[1]
+T = full.shape[1]
+with torch.no_grad():
+    calls, _ = looper.capture(full, fmask, logits_to_keep=1,
+                              position_ids=_pos_ids(fmask))
+    e = looper._hin[looper.l0]
+    ar = torch.arange(T, device="cuda")
+    pmask = ar[None, :] < n_prompt
+    if FF:
+        X = torch.where(fmask.bool()[..., None], adapter(e, e), e)
+    else:
+        S, X = prompt_prefill(looper, adapter, e, calls, pmask, K)
+        updates = build_step_updates(torch.tensor([n_prompt], device="cuda"),
+                                     torch.tensor([T], device="cuda"), "cuda")
+        S, X = carry_steps(looper, adapter, e, calls, S, X, updates)
+    with ResidualCapture(model, layers=band) as rc:
+        looper.band(X, calls)
+    acts = {l: rc.acts[l][0] for l in band}  # (T, d) each
+
+# lens-read every band layer at every post-prompt position
+table = {}
+for l in band:
+    idx, p = jl.read(acts[l][n_prompt:], l, topk=5)
+    table[l] = (idx.cpu(), p.cpu())
+
+poslab = [f"pause{j}" for j in range(P)] + [
+    repr(tok.decode([t])) for t in full[0, n_prompt + P:].tolist()]
+
+print("\n=== top-3 lens tokens (position x layer) ===")
+show_layers = [14, 18, 22, 26, 30]
+for i, lab in enumerate(poslab):
+    row = []
+    for l in show_layers:
+        idx, p = table[l]
+        toks = "/".join(repr(tok.decode([t]))[1:-1] for t in idx[i, :3].tolist())
+        row.append(f"L{l}:{toks}")
+    print(f"{i:3d} {lab:12s} " + "  ".join(row), flush=True)
+
+print("\n=== digit probs at band exit L30 (the carried state) ===")
+digit_ids = [tok(str(d), add_special_tokens=False)["input_ids"][0]
+             for d in range(10)]
+idx30, p30 = table[looper.l1]
+probs = torch.zeros(len(poslab), 10)
+full_read_idx, full_read_p = jl.read(acts[looper.l1][n_prompt:], looper.l1,
+                                     topk=64)
+for i in range(len(poslab)):
+    for d, tid in enumerate(digit_ids):
+        hit = (full_read_idx[i] == tid).nonzero()
+        if hit.numel():
+            probs[i, d] = full_read_p[i, hit[0, 0]]
+hdr = "    " + " " * 12 + "  ".join(f"{d}    " for d in range(10))
+print(hdr)
+for i, lab in enumerate(poslab):
+    print(f"{i:3d} {lab:12s} " +
+          "  ".join(f"{probs[i, d]:.3f}" for d in range(10)))
+
+torch.save({"question": Q, "k": K, "p": P, "generated": text,
+            "band": band, "poslab": poslab, "table": table,
+            "digit_probs": probs},
+           OUT / ("probe_discount_ff.pt" if FF else "probe_discount.pt"))
+print("\nsaved", OUT / ("probe_discount_ff.pt" if FF else "probe_discount.pt"))