token-efficient extract/heldout logs + sensible verify defaults

- antipasto.py: per-module SVD-cached log → debug (was 252 INFO lines per run,
  pure noise on cache hits). Replace manual %-40 progress prints with a single
  tqdm progress bar (mininterval=60).
- extract_vhack_grad.py: BLUF final tail — SHOULD line, TSV table, out path,
  argv, main metric, single cue emoji (🟢/🟡/🔴). Same data, ~30 fewer lines.
- verify_vhack_heldout.py: same BLUF tail pattern. Defaults updated to point
  at baked rh25 + v_hack_rh25 (were Qwen3.5-0.8B smoke). Cosine columns
  relabelled to "energy" since v_hack is now [k, r] and the diagnostic is
  ||V·d||/||d|| (subspace energy fraction, ≥0).

Held-out result for current v_hack_rh25 (pueue 23):
  median_energy=0.217, mean=0.286, n=252 modules.
  🟡 below target 0.30 but 20× the prior synthetic-pair ~0.01.
  q_proj cleanest (0.351 median), down_proj weakest (0.146).

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

src/projected_grpo/antipasto.py

@@ -44,7 +44,9 @@ def svd_cached(
     U, S, Vh = torch.linalg.svd(W_gpu, full_matrices=False)
     U, S, Vh = U.cpu(), S.cpu(), Vh.cpu()
     torch.save({"U": U, "S": S, "Vh": Vh}, final)
-    logger.info(f"SVD cached: {final.name}  shape U={tuple(U.shape)}  S0={S[0]:.3f}  S-1={S[-1]:.3e}")
+    # debug: per-module SVD details only appear on first computation and are
+    # noise on cache-hit runs (252 lines × ~150 char = ~38k chars per extract).
+    logger.debug(f"SVD computed: {final.name}  U={tuple(U.shape)}  S0={S[0]:.3f}  S-1={S[-1]:.3e}")
     return U, S, Vh
 
 
@@ -100,8 +102,10 @@ def wrap_model_with_antipasto(
     ]
     logger.info(f"AntiPaSTO attach: {len(targets)} target Linear modules in {model_name}")
 
+    from tqdm.auto import tqdm
     out: dict[str, dict] = {}
-    for i, (name, linear) in enumerate(targets):
+    pbar = tqdm(targets, desc="AntiPaSTO attach", mininterval=60)
+    for name, linear in pbar:
         W = linear.weight.data
         d_out, d_in = W.shape
         r = min(d_in, d_out)
@@ -114,8 +118,6 @@ def wrap_model_with_antipasto(
         linear.register_parameter("_antipasto_delta_S", delta_S)
         handle = linear.register_forward_hook(_delta_hook)
         out[name] = {"layer": linear, "delta_S": delta_S, "handle": handle, "r": r}
-        if (i + 1) % 40 == 0 or i == len(targets) - 1:
-            logger.info(f"  attached {i+1}/{len(targets)}  last={name}")
 
     # freeze everything except delta_S
     for n, p in model.named_parameters():

src/projected_grpo/extract_vhack_grad.py

@@ -190,14 +190,19 @@ def main(cfg: Config) -> int:
             f"max_sv_top{k}_frac": f"{max(vals):.2f}",
         })
 
-    print(tabulate(agg_rows, headers="keys", tablefmt="pipe"))
-    logger.info(
-        f"v_hack saved to {cfg.out_path}  "
-        f"modules={len(v_hack)}  top_k={k}  zero-||D||={n_zero}  "
-        f"SHOULD: zero-||D||==0 and mean_sv_top{k}_frac > 0.5 (subspace captures most energy). "
-        f"ELSE: either grad flow broken (zero) or hack signal is high-rank (low top-k frac, "
-        f"consider larger k)."
-    )
+    # Final tail: BLUF — what an agent reads first should be result + interp.
+    mean_frac = sum(float(r[f"sv_top{k}_frac"]) for r in rows) / max(len(rows), 1)
+    cue = "🟢" if (mean_frac > 0.5 and n_zero == 0) else ("🟡" if n_zero == 0 else "🔴")
+
+    print(f"\nSHOULD: mean_sv_top{k}_frac > 0.5 per suffix (subspace captures most energy). "
+          f"zero-||D||==0 (else grad flow broken).\n")
+    print(tabulate(agg_rows, headers="keys", tablefmt="tsv", floatfmt=".2f"))
+    print()
+    print(f"out: {cfg.out_path}")
+    print(f"argv: extract_vhack_grad --model={cfg.model} --top-k={k} --n-heldout={cfg.n_heldout}")
+    print(f"main metric: mean_sv_top{k}_frac={mean_frac:.2f}  [modules={len(v_hack)} zero-||D||={n_zero}]")
+    print(f"{cue}  k={k}  pairs={len(train_pairs)}/{len(PAIRS)}  modules={len(v_hack)}  "
+          f"mean_top{k}_frac={mean_frac:.2f}  zero={n_zero}")
 
     if n_zero > 0:
         logger.error(f"FAIL: {n_zero}/{len(v_hack)} modules have zero ||D|| -- gradient flow broken")

src/projected_grpo/verify_vhack_heldout.py

@@ -38,11 +38,11 @@ OUT_DIR = Path("out")
 
 @dataclass
 class Config:
-    model: str = "Qwen/Qwen3.5-0.8B"
+    model: str = "out/baked/qwen3_4b_rh25"
     dtype: str = "bf16"  # must match extract_vhack_grad.py and train.py
-    v_hack_path: Path = OUT_DIR / "v_hack_smoke.safetensors"
-    out_path: Path = OUT_DIR / "vhack_heldout_cos.safetensors"
-    n_heldout: int = 5
+    v_hack_path: Path = OUT_DIR / "v_hack_rh25.safetensors"
+    out_path: Path = OUT_DIR / "vhack_heldout_cos_rh25.safetensors"
+    n_heldout: int = 2
 
 
 def main(cfg: Config) -> int:
@@ -102,23 +102,29 @@ def main(cfg: Config) -> int:
         agg_rows.append({
             "suffix": suf,
             "n": len(vals),
-            "mean_cos": f"{t.mean():+.3f}",
-            "median_cos": f"{t.median():+.3f}",
-            "frac>0": f"{(t > 0).float().mean():.2f}",
-            "min": f"{t.min():+.3f}",
-            "max": f"{t.max():+.3f}",
+            "mean_energy": f"{t.mean():.3f}",
+            "median_energy": f"{t.median():.3f}",
+            "min": f"{t.min():.3f}",
+            "max": f"{t.max():.3f}",
         })
-    print(tabulate(agg_rows, headers="keys", tablefmt="pipe"))
 
     t_all = torch.tensor(all_cos)
+    mean_energy = t_all.mean().item()
+    median_energy = t_all.median().item()
+    cue = "🟢" if median_energy > 0.30 else ("🟡" if median_energy > 0.10 else "🔴")
+
+    print(f"\nSHOULD: median_energy > 0.30 (held-out diff lands in trained subspace). "
+          f"Prior synthetic-pair run got ~0.01 — that was the smoking gun.\n")
+    print(tabulate(agg_rows, headers="keys", tablefmt="tsv", floatfmt=".3f"))
+    print()
+    print(f"out: {cfg.out_path}")
+    print(f"argv: verify_vhack_heldout --model={cfg.model} --v-hack-path={cfg.v_hack_path}")
+    print(f"main metric: median_energy={median_energy:.3f}  [modules={len(all_cos)}]")
+    print(f"{cue}  modules={len(all_cos)}  mean={mean_energy:.3f}  median={median_energy:.3f}")
+
     frac_pos = (t_all > 0).float().mean().item()
-    mean_cos = t_all.mean().item()
-    median_cos = t_all.median().item()
-    logger.info(
-        f"OVERALL  modules={len(all_cos)}  frac>0={frac_pos:.3f}  "
-        f"mean={mean_cos:+.3f}  median={median_cos:+.3f}  "
-        f"SHOULD: frac>0 > 0.50 and mean > 0.20. ELSE: extraction noise dominates signal."
-    )
+    mean_cos = mean_energy
+    median_cos = median_energy
 
     # save for downstream plotting / sanity. Cos values as a single tensor;
     # module names in the metadata header (JSON-encoded preserves order).