diff --git a/QTracker_training/data/multi_track/gen_residual_training.py b/QTracker_training/data/multi_track/gen_residual_training.py
new file mode 100644
index 0000000..9bc0444
--- /dev/null
+++ b/QTracker_training/data/multi_track/gen_residual_training.py
@@ -0,0 +1,164 @@
+"""
+Generate residual training data for fine-tuning the autoregressive track finder.
+
+For each multi-track event with K pairs:
+- Iteration 0: original hit matrix → GT pair 0
+- Iteration 1: hit matrix minus pair 0's hits → GT pair 1
+- ...
+- Iteration K-1: hit matrix minus pairs 0..K-2 → GT pair K-1
+
+Output: single-track format ROOT file where each row is one (residual_matrix, single_pair) sample.
+"""
+
+import ROOT
+import numpy as np
+from array import array
+import argparse
+import os
+
+NUM_DETECTORS = 62
+NUM_ELEMENT_IDS = 201
+
+
+def generate_residual_data(input_file, output_file, max_pairs):
+    f_in = ROOT.TFile.Open(input_file, "READ")
+    tree = f_in.Get("tree")
+    if not tree:
+        raise RuntimeError(f"Tree not found in {input_file}")
+
+    if os.path.exists(output_file):
+        os.remove(output_file)
+
+    f_out = ROOT.TFile.Open(output_file, "RECREATE", "", ROOT.kLZMA)
+    f_out.SetCompressionLevel(5)
+    out_tree = ROOT.TTree("tree", "Residual training data for autoregressive finder")
+    out_tree.SetAutoSave(0)
+
+    # Output branches: single-track format
+    eventID = array("i", [0])
+    iterationID = array("i", [0])
+
+    element_id = ROOT.std.vector("int")()
+    detector_id = ROOT.std.vector("int")()
+    element_id_clean = ROOT.std.vector("int")()
+    detector_id_clean = ROOT.std.vector("int")()
+    drift_distance = ROOT.std.vector("double")()
+    tdc_time = ROOT.std.vector("double")()
+
+    HitArray_mup = np.zeros(NUM_DETECTORS, dtype=np.int32)
+    HitArray_mum = np.zeros(NUM_DETECTORS, dtype=np.int32)
+
+    out_tree.Branch("eventID", eventID, "eventID/I")
+    out_tree.Branch("iterationID", iterationID, "iterationID/I")
+    out_tree.Branch("elementID", element_id)
+    out_tree.Branch("detectorID", detector_id)
+    out_tree.Branch("elementIDClean", element_id_clean)
+    out_tree.Branch("detectorIDClean", detector_id_clean)
+    out_tree.Branch("driftDistance", drift_distance)
+    out_tree.Branch("tdcTime", tdc_time)
+    out_tree.Branch("HitArray_mup", HitArray_mup, f"HitArray_mup[{NUM_DETECTORS}]/I")
+    out_tree.Branch("HitArray_mum", HitArray_mum, f"HitArray_mum[{NUM_DETECTORS}]/I")
+
+    # Bind input multi-track hit arrays
+    leaf_mup = tree.GetLeaf("HitArray_mup")
+    total_len = leaf_mup.GetLen() if leaf_mup else 0
+    input_max_pairs = total_len // NUM_DETECTORS if total_len > 0 else max_pairs
+
+    input_mup = np.zeros((input_max_pairs, NUM_DETECTORS), dtype=np.int32)
+    input_mum = np.zeros((input_max_pairs, NUM_DETECTORS), dtype=np.int32)
+    tree.SetBranchAddress("HitArray_mup", input_mup)
+    tree.SetBranchAddress("HitArray_mum", input_mum)
+
+    fills = 0
+    for ev in range(tree.GetEntries()):
+        tree.GetEntry(ev)
+
+        n_active = int(tree.nPairs) if hasattr(tree, "nPairs") else input_max_pairs
+
+        # Build the full hit set for this event
+        all_elem = list(tree.elementID)
+        all_det = list(tree.detectorID)
+        all_drift = list(tree.driftDistance)
+        all_tdc = list(tree.tdcTime)
+
+        all_elem_clean = (
+            list(tree.elementIDClean) if hasattr(tree, "elementIDClean") else all_elem
+        )
+        all_det_clean = (
+            list(tree.detectorIDClean) if hasattr(tree, "detectorIDClean") else all_det
+        )
+
+        # Track which hits have been "consumed" by previous iterations
+        removed_positions = set()  # (det, elem) tuples
+
+        for it in range(n_active):
+            # Check if this pair is actually active (nonzero)
+            if np.all(input_mup[it] == 0) and np.all(input_mum[it] == 0):
+                continue
+
+            eventID[0] = ev
+            iterationID[0] = it
+
+            # Write residual hit vectors (excluding removed positions)
+            element_id.clear()
+            detector_id.clear()
+            element_id_clean.clear()
+            detector_id_clean.clear()
+            drift_distance.clear()
+            tdc_time.clear()
+
+            for elem, det, drift, tdc in zip(all_elem, all_det, all_drift, all_tdc):
+                if (int(det), int(elem)) not in removed_positions:
+                    element_id.push_back(int(elem))
+                    detector_id.push_back(int(det))
+                    drift_distance.push_back(float(drift))
+                    tdc_time.push_back(float(tdc))
+
+            for elem, det in zip(all_elem_clean, all_det_clean):
+                if (int(det), int(elem)) not in removed_positions:
+                    element_id_clean.push_back(int(elem))
+                    detector_id_clean.push_back(int(det))
+
+            # GT for this iteration: the current pair
+            for d in range(NUM_DETECTORS):
+                HitArray_mup[d] = input_mup[it, d]
+                HitArray_mum[d] = input_mum[it, d]
+
+            out_tree.Fill()
+            fills += 1
+
+            # Mark this pair's hits as removed for next iteration
+            for d in range(NUM_DETECTORS):
+                if input_mup[it, d] > 0:
+                    removed_positions.add((d + 1, int(input_mup[it, d])))
+                if input_mum[it, d] > 0:
+                    removed_positions.add((d + 1, int(input_mum[it, d])))
+
+    print(
+        f"Generated {fills} residual training samples from {tree.GetEntries()} events."
+    )
+
+    out_tree.Write("", ROOT.TObject.kOverwrite)
+    f_out.Close()
+    f_in.Close()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Generate residual training data for autoregressive track finder."
+    )
+    parser.add_argument("input_file", type=str, help="Multi-track mc_events ROOT file.")
+    parser.add_argument(
+        "--output",
+        type=str,
+        default="residual_training.root",
+        help="Output single-track-format ROOT file.",
+    )
+    parser.add_argument(
+        "--max_pairs",
+        type=int,
+        default=5,
+        help="Max pairs in the input file.",
+    )
+    args = parser.parse_args()
+    generate_residual_data(args.input_file, args.output, args.max_pairs)
diff --git a/QTracker_training/eval_autoregressive.py b/QTracker_training/eval_autoregressive.py
new file mode 100644
index 0000000..ea77be4
--- /dev/null
+++ b/QTracker_training/eval_autoregressive.py
@@ -0,0 +1,451 @@
+"""
+Evaluate autoregressive multi-track predictions against ground truth.
+
+Loads the .npz output from AutoregressiveTrackFinder.py and computes:
+- Pair-detection confusion matrices (global + per extraction rank)
+- Per extraction-rank raw and refined hit accuracy and residual tables
+- Residual plots
+"""
+
+# ruff: noqa: E402
+
+import os
+import argparse
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.optimize import linear_sum_assignment
+import ROOT  # noqa: F401
+
+import QTracker
+from refine import refine_hit_arrays
+
+
+def plot_residuals(det_ids, res_plus, res_minus, predictions_file, stage_label):
+    mean_p = np.nanmean(np.abs(res_plus), axis=0)
+    std_p = np.nanstd(np.abs(res_plus), axis=0)
+    mean_m = np.nanmean(np.abs(res_minus), axis=0)
+    std_m = np.nanstd(np.abs(res_minus), axis=0)
+
+    plt.figure(figsize=(10, 5))
+    plt.errorbar(det_ids, mean_p, yerr=std_p, marker="o", label="μ+ mean±σ")
+    plt.errorbar(det_ids, mean_m, yerr=std_m, marker="s", label="μ- mean±σ")
+    plt.axhline(0, linestyle="--", linewidth=1)
+    plt.xlabel("Detector Layer (skipping masked slots)")
+    plt.ylabel("Absolute Residual (predicted − true)")
+    plt.title(f"Per-layer Absolute Residual ({stage_label})")
+    plt.legend()
+    plt.tight_layout()
+
+    base = os.path.splitext(os.path.basename(predictions_file))[0]
+    fname = f"{base}_{stage_label}_residuals.png"
+    plot_dir = os.path.join(os.path.dirname(__file__), "plots")
+    os.makedirs(plot_dir, exist_ok=True)
+    plt.savefig(os.path.join(plot_dir, fname))
+    plt.close()
+
+
+def plot_confusion_matrix(tp, tn, fp, fn, predictions_file, stage_label):
+    """Save a 2x2 pair-existence confusion matrix heatmap."""
+    matrix = np.array([[tp, fn], [fp, tn]], dtype=np.int64)
+    labels = np.array([["TP", "FN"], ["FP", "TN"]], dtype=object)
+
+    fig, ax = plt.subplots(figsize=(5, 4))
+    im = ax.imshow(matrix, cmap="Blues")
+    ax.set_xticks([0, 1])
+    ax.set_yticks([0, 1])
+    ax.set_xticklabels(["Predicted +", "Predicted -"])
+    ax.set_yticklabels(["GT +", "GT -"])
+    ax.set_title(f"Pair Existence ({stage_label})")
+
+    for i in range(2):
+        for j in range(2):
+            color = "white" if matrix[i, j] > matrix.max() / 2 else "black"
+            ax.text(
+                j,
+                i,
+                f"{labels[i, j]}\n{matrix[i, j]:,}",
+                ha="center",
+                va="center",
+                color=color,
+            )
+
+    fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
+    plt.tight_layout()
+
+    base = os.path.splitext(os.path.basename(predictions_file))[0]
+    fname = f"{base}_{stage_label}_confusion_matrix.png"
+    plot_dir = os.path.join(os.path.dirname(__file__), "plots")
+    os.makedirs(plot_dir, exist_ok=True)
+    plt.savefig(os.path.join(plot_dir, fname))
+    plt.close()
+
+
+def print_confusion_matrix(label, tp, tn, fp, fn, predictions_file, stage_label):
+    """Print pair-existence confusion matrix and derived metrics."""
+    total = tp + tn + fp + fn
+    print(f"\n--- {label} Pair Existence Confusion Matrix ---")
+    print(f"{'':>20} | {'Predicted +':>11} | {'Predicted -':>11}")
+    print(f"{'GT +':>20} | {tp:11,d} | {fn:11,d}")
+    print(f"{'GT -':>20} | {fp:11,d} | {tn:11,d}")
+    print(f"\nTotal events/slots: {total:,}")
+
+    accuracy = (tp + tn) / total if total > 0 else 0.0
+    precision = tp / (tp + fp) if (tp + fp) > 0 else 0.0
+    recall = tp / (tp + fn) if (tp + fn) > 0 else 0.0
+    specificity = tn / (tn + fp) if (tn + fp) > 0 else 0.0
+    f1 = (
+        2 * precision * recall / (precision + recall)
+        if (precision + recall) > 0
+        else 0.0
+    )
+
+    print(f"Accuracy   : {accuracy:.4f}")
+    print(f"Precision  : {precision:.4f}")
+    print(f"Recall     : {recall:.4f}")
+    print(f"Specificity: {specificity:.4f}")
+    print(f"F1 Score   : {f1:.4f}")
+
+    plot_confusion_matrix(tp, tn, fp, fn, predictions_file, stage_label)
+
+
+def print_global_pair_detection(tp, fn, fp):
+    """Print dataset-level pair counts after Hungarian matching."""
+    total_gt = tp + fn
+    total_pred = tp + fp
+
+    print("\n--- Global Pair Detection (Hungarian matching) ---")
+    print(f"Total ground-truth pairs : {total_gt:,}")
+    print(f"Total predicted pairs    : {total_pred:,}")
+    print(f"Matched pairs (TP)       : {tp:,}")
+    print(f"Unmatched GT (FN)        : {fn:,}")
+    print(f"Unmatched pred (FP)      : {fp:,}")
+
+    precision = tp / total_pred if total_pred > 0 else 0.0
+    recall = tp / total_gt if total_gt > 0 else 0.0
+    f1 = (
+        2 * precision * recall / (precision + recall)
+        if (precision + recall) > 0
+        else 0.0
+    )
+
+    print(f"\nPair detection precision : {precision:.4f}")
+    print(f"Pair detection recall    : {recall:.4f}")
+    print(f"Pair detection F1        : {f1:.4f}")
+
+
+def hungarian_match_predictions(y_gt, y_pred, n_gt, n_pred):
+    """Match predicted pairs to GT pairs using Hungarian algorithm."""
+    if n_gt == 0 or n_pred == 0:
+        return np.array([], dtype=int), np.array([], dtype=int)
+
+    cost = np.zeros((n_pred, n_gt))
+    for i in range(n_pred):
+        for j in range(n_gt):
+            cost[i, j] = np.sum(np.abs(y_pred[i] - y_gt[j]))
+
+    row_ind, col_ind = linear_sum_assignment(cost)
+    n = min(n_gt, n_pred)
+    return col_ind[:n], row_ind[:n]
+
+
+def compute_true_counts(y_gt, max_pairs, num_events):
+    """Count how many non-zero GT pairs each event has."""
+    true_counts = np.zeros(num_events, dtype=np.int32)
+    for ev in range(num_events):
+        for k in range(max_pairs):
+            if np.any(y_gt[ev, k, :, :] != 0):
+                true_counts[ev] = k + 1
+    return true_counts
+
+
+def print_summary(label, gt_p, gt_m, pred_p, pred_m, mask):
+    """Print summary metrics matching evaluate.py output format."""
+    res_p = gt_p - pred_p
+    res_m = gt_m - pred_m
+
+    acc_p = np.mean(np.abs(res_p[:, mask]) == 0)
+    acc_m = np.mean(np.abs(res_m[:, mask]) == 0)
+    print(f"\n{label} μ+ accuracy: {acc_p:.4f}")
+    print(f"{label} μ- accuracy: {acc_m:.4f}")
+
+    w2_p = np.mean(np.abs(res_p[:, mask]) <= 2)
+    w2_m = np.mean(np.abs(res_m[:, mask]) <= 2)
+    print(f"\n{label} μ+ within-2 accuracy: {w2_p:.4f}")
+    print(f"{label} μ- within-2 accuracy: {w2_m:.4f}")
+
+    m_p = np.mean(np.abs(res_p[:, mask]))
+    s_p = np.std(np.abs(res_p[:, mask]))
+    m_m = np.mean(np.abs(res_m[:, mask]))
+    s_m = np.std(np.abs(res_m[:, mask]))
+    print(f"\n--- {label} Absolute Residuals ---")
+    print("μ+ mean  |  μ+ std   |  μ- mean  |  μ- std")
+    print(f"{m_p:8.3f} | {s_p:8.3f} | {m_m:8.3f} | {s_m:8.3f}")
+
+
+def print_per_detector_table(label, res_p, res_m, mask):
+    """Print per-detector residual table matching evaluate.py."""
+    print(f"\n--- {label} Residuals ---")
+    print("Det |  μ+ mean  |  μ+ std   |  μ- mean  |  μ- std")
+    for det in np.where(mask)[0]:
+        m_p = np.mean(np.abs(res_p[:, det]))
+        s_p = np.std(np.abs(res_p[:, det]))
+        m_m = np.mean(np.abs(res_m[:, det]))
+        s_m = np.std(np.abs(res_m[:, det]))
+        print(f"{det + 1:3d} | {m_p:8.3f} | {s_p:8.3f} | {m_m:8.3f} | {s_m:8.3f}")
+
+
+def refine_matched_pairs(pred_p, pred_m, event_indices, detector_ids, element_ids):
+    """Refine matched pair predictions using each source event's recorded hits."""
+    ref_p = np.zeros_like(pred_p)
+    ref_m = np.zeros_like(pred_m)
+    by_event: dict[int, list[int]] = {}
+    for i, ev in enumerate(event_indices):
+        by_event.setdefault(int(ev), []).append(i)
+
+    for ev, indices in by_event.items():
+        idx = np.array(indices)
+        rp, rm = refine_hit_arrays(
+            pred_p[idx],
+            pred_m[idx],
+            [detector_ids[ev]] * len(idx),
+            [element_ids[ev]] * len(idx),
+        )
+        ref_p[idx] = rp
+        ref_m[idx] = rm
+
+    return ref_p, ref_m
+
+
+def evaluate_hit_quality(
+    label,
+    gt_p,
+    gt_m,
+    pred_p,
+    pred_m,
+    event_indices,
+    detector_ids,
+    element_ids,
+    mask,
+    predictions_file,
+    plot_label,
+):
+    """Print and plot raw and refined hit-quality metrics for matched pairs."""
+    ref_p, ref_m = refine_matched_pairs(
+        pred_p, pred_m, event_indices, detector_ids, element_ids
+    )
+
+    raw_res_p = gt_p - pred_p
+    raw_res_m = gt_m - pred_m
+    ref_res_p = gt_p - ref_p
+    ref_res_m = gt_m - ref_m
+
+    dets_used = np.where(mask)[0] + 1
+
+    print_per_detector_table(f"{label} Raw", raw_res_p, raw_res_m, mask)
+    plot_residuals(
+        dets_used,
+        raw_res_p[:, mask],
+        raw_res_m[:, mask],
+        predictions_file,
+        f"{plot_label}_raw",
+    )
+    print_summary(f"{label} Raw", gt_p, gt_m, pred_p, pred_m, mask)
+
+    print_per_detector_table(f"{label} Refined", ref_res_p, ref_res_m, mask)
+    plot_residuals(
+        dets_used,
+        ref_res_p[:, mask],
+        ref_res_m[:, mask],
+        predictions_file,
+        f"{plot_label}_refined",
+    )
+    print_summary(f"{label} Refined", gt_p, gt_m, ref_p, ref_m, mask)
+
+
+def evaluate(args):
+    data = np.load(args.predictions_file)
+
+    all_mup = data["all_mup"]  # (max_pairs, num_events, 62)
+    all_mum = data["all_mum"]
+    n_pairs = data["n_pairs"]  # (num_events,)
+    y_muPlus = data["y_muPlus"]  # (num_events, max_pairs, 62) or (num_events, 62)
+    y_muMinus = data["y_muMinus"]
+
+    max_pairs = all_mup.shape[0]
+    num_events = all_mup.shape[1]
+
+    if len(y_muPlus.shape) == 2:
+        y_muPlus = y_muPlus[:, np.newaxis, :]
+        y_muMinus = y_muMinus[:, np.newaxis, :]
+
+    if y_muPlus.shape[1] != max_pairs:
+        raise ValueError(
+            f"GT pair slots ({y_muPlus.shape[1]}) != prediction ranks ({max_pairs})"
+        )
+
+    y_gt = np.stack([y_muPlus, y_muMinus], axis=2)  # (num_events, max_pairs, 2, 62)
+
+    detector_ids, element_ids, _, _, _ = QTracker.load_detector_element_data(
+        args.root_file
+    )
+    if len(detector_ids) != num_events:
+        raise ValueError(
+            f"ROOT events ({len(detector_ids)}) != predictions ({num_events}). "
+            "Use the same ROOT file passed to AutoregressiveTrackFinder.py."
+        )
+
+    mask = np.ones(62, dtype=bool)
+    mask[6:12] = False
+    mask[54:62] = False
+
+    true_counts = compute_true_counts(y_gt, max_pairs, num_events)
+
+    print(f"\n{'=' * 70}")
+    print("Autoregressive Multi-Track Evaluation")
+    print(f"Events: {num_events}, Max pairs: {max_pairs}")
+    print(f"{'=' * 70}")
+
+    gt_exists = np.any(y_gt != 0, axis=(2, 3))
+    pred_exists = np.zeros((num_events, max_pairs), dtype=bool)
+    for ev in range(num_events):
+        for k in range(int(n_pairs[ev])):
+            pred_exists[ev, k] = np.any(all_mup[k, ev] != 0) or np.any(
+                all_mum[k, ev] != 0
+            )
+
+    for k in range(max_pairs):
+        gt_k = gt_exists[:, k]
+        pred_k = pred_exists[:, k]
+        tp = int(np.sum(gt_k & pred_k))
+        tn = int(np.sum(~gt_k & ~pred_k))
+        fp = int(np.sum(~gt_k & pred_k))
+        fn = int(np.sum(gt_k & ~pred_k))
+
+        print(f"\n{'=' * 70}")
+        print(f"Rank {k} Pair Existence")
+        print(f"{'=' * 70}")
+        print_confusion_matrix(
+            f"Rank {k}",
+            tp,
+            tn,
+            fp,
+            fn,
+            args.predictions_file,
+            f"rank{k}_existence",
+        )
+
+    # Hungarian-match predictions to GT and bucket by extraction rank
+    rank_gt_p = [[] for _ in range(max_pairs)]
+    rank_gt_m = [[] for _ in range(max_pairs)]
+    rank_pred_p = [[] for _ in range(max_pairs)]
+    rank_pred_m = [[] for _ in range(max_pairs)]
+    rank_event_idx = [[] for _ in range(max_pairs)]
+    total_matched_pairs = 0
+
+    for ev in range(num_events):
+        n_gt_ev = int(true_counts[ev])
+        n_pred_ev = int(n_pairs[ev])
+
+        if n_gt_ev == 0 or n_pred_ev == 0:
+            continue
+
+        pred_ev = np.stack(
+            [all_mup[:n_pred_ev, ev, :], all_mum[:n_pred_ev, ev, :]],
+            axis=1,
+        )
+        gt_ev = y_gt[ev, :n_gt_ev, :, :]
+
+        gt_order, pred_order = hungarian_match_predictions(
+            gt_ev, pred_ev, n_gt_ev, n_pred_ev
+        )
+        total_matched_pairs += len(gt_order)
+
+        for gi, pi in zip(gt_order, pred_order):
+            rank_gt_p[pi].append(gt_ev[gi, 0, :])
+            rank_gt_m[pi].append(gt_ev[gi, 1, :])
+            rank_pred_p[pi].append(pred_ev[pi, 0, :])
+            rank_pred_m[pi].append(pred_ev[pi, 1, :])
+            rank_event_idx[pi].append(ev)
+
+    total_gt_pairs = int(np.sum(true_counts))
+    total_pred_pairs = int(np.sum(n_pairs))
+    global_fn = total_gt_pairs - total_matched_pairs
+    global_fp = total_pred_pairs - total_matched_pairs
+
+    print(f"\n{'=' * 70}")
+    print("Global Pair Detection")
+    print(f"{'=' * 70}")
+    print_global_pair_detection(total_matched_pairs, global_fn, global_fp)
+
+    # Per-rank hit-quality evaluation (raw + refined)
+    for k in range(max_pairs):
+        n_match = len(rank_gt_p[k])
+        if n_match == 0:
+            continue
+
+        print(f"\n{'=' * 70}")
+        print(f"Rank {k} ({n_match} matched pairs)")
+        print(f"{'=' * 70}")
+
+        evaluate_hit_quality(
+            f"Rank {k}",
+            np.array(rank_gt_p[k]),
+            np.array(rank_gt_m[k]),
+            np.array(rank_pred_p[k]),
+            np.array(rank_pred_m[k]),
+            rank_event_idx[k],
+            detector_ids,
+            element_ids,
+            mask,
+            args.predictions_file,
+            f"rank{k}",
+        )
+
+    # Combined across all ranks
+    all_gt_p = [v for rank in rank_gt_p for v in rank]
+    all_gt_m = [v for rank in rank_gt_m for v in rank]
+    all_pred_p = [v for rank in rank_pred_p for v in rank]
+    all_pred_m = [v for rank in rank_pred_m for v in rank]
+    all_event_idx = [v for rank in rank_event_idx for v in rank]
+
+    if all_gt_p:
+        print(f"\n{'=' * 70}")
+        print(f"All Ranks Combined ({len(all_gt_p)} matched pairs)")
+        print(f"{'=' * 70}")
+
+        evaluate_hit_quality(
+            "All Ranks",
+            np.array(all_gt_p),
+            np.array(all_gt_m),
+            np.array(all_pred_p),
+            np.array(all_pred_m),
+            all_event_idx,
+            detector_ids,
+            element_ids,
+            mask,
+            args.predictions_file,
+            "all_ranks",
+        )
+
+    print()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Evaluate autoregressive multi-track predictions."
+    )
+    parser.add_argument(
+        "predictions_file",
+        type=str,
+        help="Path to .npz file from AutoregressiveTrackFinder.py",
+    )
+    parser.add_argument(
+        "root_file",
+        type=str,
+        help="Path to the ROOT file used to generate predictions (for hit refinement).",
+    )
+    args = parser.parse_args()
+    evaluate(args)
diff --git a/QTracker_training/models/AutoregressiveTrackFinder.py b/QTracker_training/models/AutoregressiveTrackFinder.py
new file mode 100644
index 0000000..6c7714d
--- /dev/null
+++ b/QTracker_training/models/AutoregressiveTrackFinder.py
@@ -0,0 +1,280 @@
+"""
+Autoregressive Multi-Track Finder.
+
+Uses a trained single-track TrackFinder iteratively:
+1. Predict the highest-confidence dimuon pair.
+2. Remove predicted hits from the input matrix.
+3. Repeat until confidence drops below threshold or max_pairs is reached.
+"""
+
+# ruff: noqa: E402
+
+import argparse
+import os
+import sys
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
+sys.path.insert(0, os.path.dirname(__file__))
+import numpy as np
+import ROOT  # noqa: F401
+import tensorflow as tf
+from layers import AxialAttention
+from data_loader import load_data
+
+NUM_DETECTORS = 62
+NUM_ELEMENT_IDS = 201
+INFERENCE_CHUNK_SIZE = 128
+
+
+def compute_confidence(softmax_mup, softmax_mum):
+    """
+    Compute a confidence score for the predicted pair.
+
+    Uses the mean max-probability across all detectors for both mu+ and mu-.
+    High confidence means the model is certain about its element ID picks.
+    Low confidence (close to 1/201 ≈ 0.005) means the model is guessing.
+
+    Args:
+        softmax_mup: (num_events, 62, 201) softmax output for mu+
+        softmax_mum: (num_events, 62, 201) softmax output for mu-
+
+    Returns:
+        confidence: (num_events,) scalar confidence per event
+    """
+    max_prob_mup = np.max(softmax_mup, axis=-1)  # (num_events, 62)
+    max_prob_mum = np.max(softmax_mum, axis=-1)  # (num_events, 62)
+
+    # Average across detectors (excluding masked detectors 7-12, 55-62)
+    mask = np.ones(62, dtype=bool)
+    mask[6:12] = False
+    mask[54:62] = False
+
+    conf_mup = np.mean(max_prob_mup[:, mask], axis=-1)  # (num_events,)
+    conf_mum = np.mean(max_prob_mum[:, mask], axis=-1)
+
+    return (conf_mup + conf_mum) / 2.0
+
+
+def remove_predicted_hits(hit_matrix, hit_array_mup, hit_array_mum):
+    """
+    Remove predicted hits from the input hit matrix.
+
+    For each detector where the model predicted a nonzero element ID,
+    set that position in the hit matrix to 0.
+
+    Args:
+        hit_matrix: (num_events, 62, 201, 1) binary hit matrix
+        hit_array_mup: (num_events, 62) predicted mu+ element IDs
+        hit_array_mum: (num_events, 62) predicted mu- element IDs
+
+    Returns:
+        hit_matrix_residual: (num_events, 62, 201, 1) with predicted hits removed
+    """
+    residual = hit_matrix.copy()
+    num_events = hit_matrix.shape[0]
+
+    for i in range(num_events):
+        for det in range(NUM_DETECTORS):
+            elem_p = hit_array_mup[i, det]
+            elem_m = hit_array_mum[i, det]
+            if 0 < elem_p < NUM_ELEMENT_IDS:
+                residual[i, det, elem_p, 0] = 0
+            if 0 < elem_m < NUM_ELEMENT_IDS:
+                residual[i, det, elem_m, 0] = 0
+
+    return residual
+
+
+def predict_single_pass(model, X):
+    """
+    Run the single-track model on input hit matrices.
+
+    Args:
+        model: loaded single-track TrackFinder model
+        X: (num_events, 62, 201, 1) input hit matrices
+
+    Returns:
+        hit_array_mup: (num_events, 62) predicted mu+ element IDs
+        hit_array_mum: (num_events, 62) predicted mu- element IDs
+        softmax_mup: (num_events, 62, 201) softmax probabilities for mu+
+        softmax_mum: (num_events, 62, 201) softmax probabilities for mu-
+    """
+    preds = []
+
+    for i in range(0, len(X), INFERENCE_CHUNK_SIZE):
+        X_chunk = tf.cast(X[i : i + INFERENCE_CHUNK_SIZE], tf.float32)
+        y_chunk = model.predict(X_chunk, verbose=0)
+        preds.append(y_chunk[1])  # segment output
+
+    predictions = np.concatenate(preds, axis=0)
+
+    softmax_mup = predictions[:, 0, :, :]  # (num_events, 62, 201)
+    softmax_mum = predictions[:, 1, :, :]
+    hit_array_mup = np.argmax(softmax_mup, axis=-1).astype(np.int32)
+    hit_array_mum = np.argmax(softmax_mum, axis=-1).astype(np.int32)
+
+    return hit_array_mup, hit_array_mum, softmax_mup, softmax_mum
+
+
+def autoregressive_predict(model, X, max_pairs=5, confidence_threshold=0.5):
+    """
+    Iterative multi-track prediction.
+
+    For each event, repeatedly run the single-track model:
+    1. Predict one pair from the current hit matrix.
+    2. Compute confidence. If below threshold, stop for this event.
+    3. Remove predicted hits from the hit matrix.
+    4. Repeat up to max_pairs times.
+
+    Args:
+        model: loaded single-track TrackFinder model
+        X: (num_events, 62, 201, 1) input hit matrices
+        max_pairs: maximum dimuon pairs to extract (and GT slots in ROOT data)
+        confidence_threshold: stop when confidence drops below this
+
+    Returns:
+        all_pairs_mup: list of (num_events, 62) arrays, one per iteration
+        all_pairs_mum: list of (num_events, 62) arrays, one per iteration
+        all_confidences: list of (num_events,) arrays, one per iteration
+        n_pairs_per_event: (num_events,) number of valid pairs found per event
+    """
+    num_events = X.shape[0]
+    residual = X.copy()
+
+    all_pairs_mup = []
+    all_pairs_mum = []
+    all_confidences = []
+
+    # Track which events are still active (haven't stopped)
+    active = np.ones(num_events, dtype=bool)
+    n_pairs_per_event = np.zeros(num_events, dtype=np.int32)
+
+    for iteration in range(max_pairs):
+        print(f"  Pair {iteration + 1}/{max_pairs} — {np.sum(active)} active events")
+
+        if not np.any(active):
+            break
+
+        # Only run inference on active events
+        active_idx = np.where(active)[0]
+
+        a_mup, a_mum, a_smax_mup, a_smax_mum = predict_single_pass(
+            model, residual[active_idx]
+        )
+        a_conf = compute_confidence(a_smax_mup, a_smax_mum)
+
+        passes_threshold = a_conf >= confidence_threshold
+
+        # Full-size output arrays (zeros for inactive events)
+        mup = np.zeros((num_events, NUM_DETECTORS), dtype=np.int32)
+        mum = np.zeros((num_events, NUM_DETECTORS), dtype=np.int32)
+        confidence = np.zeros(num_events)
+
+        newly_active_idx = active_idx[passes_threshold]
+        mup[newly_active_idx] = a_mup[passes_threshold]
+        mum[newly_active_idx] = a_mum[passes_threshold]
+        confidence[active_idx] = a_conf
+
+        all_pairs_mup.append(mup.copy())
+        all_pairs_mum.append(mum.copy())
+        all_confidences.append(confidence.copy())
+
+        # Update pair counts for newly found pairs
+        n_pairs_per_event[newly_active_idx] += 1
+
+        # Remove predicted hits for active events
+        residual = remove_predicted_hits(residual, mup, mum)
+
+        # Update active mask
+        active = np.zeros(num_events, dtype=bool)
+        active[newly_active_idx] = True
+
+    return all_pairs_mup, all_pairs_mum, all_confidences, n_pairs_per_event
+
+
+def main(args):
+    """Main entry point for autoregressive multi-track finding."""
+
+    # Load data
+    print(f"Loading data from {args.root_file}...")
+    X, y_muPlus, y_muMinus = load_data(
+        args.root_file, multi_track=True, max_pairs=args.max_pairs
+    )
+    if X is None:
+        print("Error loading data.")
+        return
+
+    print(f"Loaded {len(X)} events.")
+
+    # Load model
+    print(f"Loading model from {args.model_path}...")
+    custom_objects = {"AxialAttention": AxialAttention}
+    model = tf.keras.models.load_model(
+        args.model_path, compile=False, custom_objects=custom_objects
+    )
+
+    # Run autoregressive prediction
+    print(
+        f"Running autoregressive prediction (max_pairs={args.max_pairs}, "
+        f"threshold={args.confidence_threshold})..."
+    )
+    all_mup, all_mum, all_conf, n_pairs = autoregressive_predict(
+        model,
+        X,
+        max_pairs=args.max_pairs,
+        confidence_threshold=args.confidence_threshold,
+    )
+
+    # Print summary statistics
+    print(f"\n{'=' * 60}")
+    print("Autoregressive Prediction Summary")
+    print(f"{'=' * 60}")
+    for k in range(1, args.max_pairs + 1):
+        count = np.sum(n_pairs >= k)
+        print(
+            f"  Events with >= {k} pairs found: {count} ({100 * count / len(X):.1f}%)"
+        )
+
+    # Save predictions to a numpy file for downstream evaluation
+    output_path = args.output if args.output else "autoregressive_predictions.npz"
+    np.savez_compressed(
+        output_path,
+        all_mup=np.array(all_mup),  # (max_pairs, num_events, 62)
+        all_mum=np.array(all_mum),
+        all_conf=np.array(all_conf),  # (max_pairs, num_events)
+        n_pairs=n_pairs,  # (num_events,)
+        y_muPlus=y_muPlus,  # GT
+        y_muMinus=y_muMinus,
+    )
+    print(f"\nPredictions saved to {output_path}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Autoregressive multi-track finding using iterative single-track prediction."
+    )
+    parser.add_argument("root_file", type=str, help="Path to multi-track ROOT file.")
+    parser.add_argument(
+        "model_path", type=str, help="Path to trained single-track model."
+    )
+    parser.add_argument(
+        "--confidence_threshold",
+        type=float,
+        default=0.15,
+        help="Stop when mean softmax confidence drops below this value.",
+    )
+    parser.add_argument(
+        "--max_pairs",
+        type=int,
+        default=5,
+        help="Max dimuon pairs per event (extraction limit and ROOT GT layout).",
+    )
+    parser.add_argument(
+        "--output",
+        type=str,
+        default=None,
+        help="Output .npz file path for predictions.",
+    )
+    args = parser.parse_args()
+    main(args)
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diff --git a/QTracker_training/scripts/eval_autoregressive.slurm b/QTracker_training/scripts/eval_autoregressive.slurm
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--- /dev/null
+++ b/QTracker_training/scripts/eval_autoregressive.slurm
@@ -0,0 +1,50 @@
+#!/bin/bash
+#SBATCH -A spinquest_standard
+#SBATCH -p gpu
+#SBATCH --gres=gpu:1
+#SBATCH -c 4
+#SBATCH -t 18:00:00
+#SBATCH -J autoregressive-eval
+#SBATCH -o Slurm_Files/autoregressive-eval.out
+#SBATCH -e Slurm_Files/autoregressive-eval.err
+#SBATCH --mem=256000
+
+module purge
+
+# For apptainer exec
+APPTAINER=/apps/software/standard/core/apptainer/1.4.5/bin/apptainer
+IMAGE=/project/ptgroup/spinquest/David/TfRootBuild.sif
+CODEDIR=/project/ptgroup/spinquest/Donghwa/Qtracker_basic/.worktrees/autoregressive_track_finder/QTracker_training/
+
+### Main Body ###
+MAX_PAIRS=3
+THRESHOLDS="0.3 0.4 0.5 0.6 0.7 0.8 0.9"
+
+# Single-track model used as the base finder
+MODEL=/mnt/code/checkpoints/track_finder_64.keras
+
+# Multi-track validation data
+VAL_DATA=/mnt/code/data/multi_track/processed_files/mc_events_val.root
+
+for THRESH in $THRESHOLDS; do
+    echo ""
+    echo "######################################################################"
+    echo "# THRESHOLD = ${THRESH}"
+    echo "######################################################################"
+    echo ""
+
+    OUTPUT=/mnt/code/checkpoints/autoregressive_predictions_${THRESH}.npz
+
+    # Step 1: Run autoregressive prediction
+    ${APPTAINER} exec --nv --bind ${CODEDIR}:/mnt/code "${IMAGE}" \
+        python3 /mnt/code/models/AutoregressiveTrackFinder.py \
+         ${VAL_DATA} \
+         ${MODEL} \
+         --max_pairs $MAX_PAIRS \
+         --confidence_threshold $THRESH \
+         --output ${OUTPUT}
+
+    # Step 2: Evaluate
+    ${APPTAINER} exec --nv --bind ${CODEDIR}:/mnt/code "${IMAGE}" \
+        python3 /mnt/code/eval_autoregressive.py ${OUTPUT} ${VAL_DATA}
+done