Add new regression and ensemble experiment configurations for V2-M and OrthoBridge

- Introduced multiple regression experiment configurations targeting vf_md, including:
  - cd_solo_reg_set.json: CD tower only regression setup.
  - img_solo_reg_set.json: Image tower only regression setup.
  - reg_head_epoch_sweep.json: Baseline regression sweeps at different epochs (50, 75, 100).
  - reg_head_set.json: Various regression setups including baseline and OrthoBridge configurations.
  - single_eye_reg.json: Single-eye regression setup for worst-eye aggregation analysis.

- Added ensemble configurations for OrthoBridge with different inner bridges:
  - ortho_alts_ensemble.json: Ensemble tests with ConcatBridge, PairwiseAdditiveBridge, and GatedAdditiveBridge.
  - ortho_alts_tritower.json: Tritower tests with the same inner bridges.

- Created V2-M specific configurations:
  - baseline_reg_nt50.json: Regression baseline with V2-M backbone.
  - geom_vec_gt.json and geom_vec_unet.json: Geometry vector injection experiments with V2-M.
  - single_l1_bridges.json: Single-eye ensemble experiments with various bridge types.
  - tritower_geom_gt.json: Tritower setup with GT contour-rasterized masks.

- Promoted existing experiments to higher repetitions for robustness.

v4/figures/util/loaders.py

@@ -0,0 +1,55 @@
+"""Shared loaders/aggregators for v4 figure scripts."""
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import Optional
+
+import numpy as np
+
+
+REPO_ROOT = Path(__file__).resolve().parents[3]
+RESULTS_ROOT = REPO_ROOT / "v4" / "results" / "experiments"
+
+
+def summarise_run(run_path: Path, primary_hint: Optional[str] = None) -> Optional[dict]:
+    """Aggregate val/test primary-metric mean & std across reps for one run folder.
+
+    Returns dict with n, val_mean, val_std, test_mean, test_std, metric_name, or None if no reps."""
+    val, test = [], []
+    metric_name = primary_hint
+    for rep in sorted(run_path.glob("rep*")):
+        s = next(iter(rep.rglob("summary.json")), None)
+        if not s:
+            continue
+        d = json.loads(s.read_text())
+        pm = d.get("primary_metric") or metric_name or "auc"
+        metric_name = metric_name or pm
+        v = d.get(f"mean_val_{pm}")
+        t = d.get(f"mean_test_{pm}")
+        if v is None or t is None or not np.isfinite(v) or not np.isfinite(t):
+            continue
+        val.append(float(v)); test.append(float(t))
+    if not val:
+        return None
+    return {
+        "n":         len(val),
+        "metric":    metric_name or "auc",
+        "val_mean":  float(np.mean(val)),
+        "val_std":   float(np.std(val)),
+        "test_mean": float(np.mean(test)),
+        "test_std":  float(np.std(test)),
+        "val_arr":   np.array(val),
+        "test_arr":  np.array(test),
+    }
+
+
+def summarise_many(name_to_path: dict[str, Path], primary_hint: Optional[str] = None) -> dict[str, Optional[dict]]:
+    """Apply summarise_run to a dict of labelled run folders."""
+    return {label: summarise_run(p, primary_hint) for label, p in name_to_path.items()}
+
+
+def fmt_status(s: Optional[dict]) -> str:
+    if s is None:
+        return "pending"
+    return f"n={s['n']:>2d}  test={s['test_mean']:.4f}±{s['test_std']:.4f}"