Add Phase 4 diagnostic dissection: frozen credit recovery, online shallow scan, vector field pilot

Key findings:
- Frozen CIFAR: estimators CAN recover credit (SB best, CB 20x > DFA)
- Online shallow: cb_eT wr=0.2 tgw=1.0 achieves S1>0, S2 marginal
- Vector credit field: 0.91-0.96 Gamma/rho on synthetic (vs 0.34 scalar CB)
- Direct vector field avoids scalar V curvature problem entirely

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

1 files changed, 717 insertions, 0 deletions

diff --git a/experiments/cifar_online_shallow_conditioning.py b/experiments/cifar_online_shallow_conditioning.py
new file mode 100644
index 0000000..cf5444b
--- /dev/null
+++ b/experiments/cifar_online_shallow_conditioning.py
@@ -0,0 +1,717 @@
+"""
+Phase B: Online shallow CIFAR with better conditioning.
+
+Goal: Find a minimal positive-signal regime on real CIFAR-10 with shallow depth.
+Sweep L={4,6}, d={256,512}, methods={DFA, CB_eT, CB_deltaL, SB_eT},
+warmup_ratio={0.0, 0.05, 0.2}, term_grad_weight={1.0, 4.0}.
+
+Single-seed smoke test first. Only expand to 3 seeds for configs with S1>0 and S2>0.
+"""
+import os
+import sys
+import json
+import argparse
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torch.utils.data import DataLoader
+import torchvision
+import torchvision.transforms as transforms
+import copy
+import time
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from models.residual_mlp import ResidualMLP
+from models.value_net import ValueNet, create_ema_model, update_ema
+from models.state_bridge import StateBridgeNet
+from metrics.credit_metrics import (
+    cosine_similarity_batch, perturbation_correlation, nudging_test,
+    offline_bp_cosine
+)
+
+
+def get_cifar10(batch_size=128):
+    transform_train = transforms.Compose([
+        transforms.RandomCrop(32, padding=4),
+        transforms.RandomHorizontalFlip(),
+        transforms.ToTensor(),
+        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2470, 0.2435, 0.2616)),
+    ])
+    transform_test = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2470, 0.2435, 0.2616)),
+    ])
+    trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
+    testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
+    train_loader = DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True)
+    test_loader = DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=4, pin_memory=True)
+    return train_loader, test_loader
+
+
+def evaluate(model, test_loader, device):
+    model.eval()
+    correct, total = 0, 0
+    with torch.no_grad():
+        for x, y in test_loader:
+            x = x.view(x.size(0), -1).to(device)
+            y = y.to(device)
+            logits = model(x)
+            correct += (logits.argmax(1) == y).sum().item()
+            total += x.size(0)
+    return correct / total
+
+
+# =============================================================================
+# Training methods
+# =============================================================================
+
+def train_dfa(model, train_loader, test_loader, device, epochs, lr, wd):
+    """DFA training."""
+    d = model.d_hidden
+    num_classes = 10
+    L = model.num_blocks
+
+    Bs = [torch.randn(d, num_classes, device=device) / np.sqrt(num_classes) for _ in range(L)]
+
+    block_opts = [optim.AdamW(block.parameters(), lr=lr, weight_decay=wd) for block in model.blocks]
+    embed_opt = optim.AdamW(model.embed.parameters(), lr=lr, weight_decay=wd)
+    head_opt = optim.AdamW(
+        list(model.out_head.parameters()) + list(model.out_ln.parameters()),
+        lr=lr, weight_decay=wd
+    )
+    all_schedulers = ([optim.lr_scheduler.CosineAnnealingLR(o, T_max=epochs) for o in block_opts]
+                      + [optim.lr_scheduler.CosineAnnealingLR(embed_opt, T_max=epochs),
+                         optim.lr_scheduler.CosineAnnealingLR(head_opt, T_max=epochs)])
+
+    log = {'train_loss': [], 'test_acc': []}
+
+    for epoch in range(1, epochs + 1):
+        model.train()
+        total_loss, correct, total = 0, 0, 0
+        for x, y in train_loader:
+            x = x.view(x.size(0), -1).to(device)
+            y = y.to(device)
+            batch = x.size(0)
+
+            with torch.no_grad():
+                logits, hiddens = model(x, return_hidden=True)
+                loss_val = F.cross_entropy(logits, y)
+                e_T = logits.softmax(dim=-1)
+                e_T[torch.arange(batch), y] -= 1
+
+            hL_det = hiddens[-1].detach()
+            logits_out = model.out_head(model.out_ln(hL_det))
+            loss_out = F.cross_entropy(logits_out, y)
+            head_opt.zero_grad()
+            loss_out.backward()
+            head_opt.step()
+
+            for l in range(L):
+                h_l = hiddens[l].detach()
+                a_dfa = (e_T @ Bs[l].T).detach()
+                rms = (a_dfa ** 2).mean(dim=-1, keepdim=True).sqrt() + 1e-6
+                a_norm = a_dfa / rms
+                f_l = model.blocks[l](h_l)
+                local_loss = (f_l * a_norm).sum(dim=-1).mean()
+                block_opts[l].zero_grad()
+                local_loss.backward()
+                torch.nn.utils.clip_grad_norm_(model.blocks[l].parameters(), 1.0)
+                block_opts[l].step()
+
+            a_0 = (e_T @ Bs[0].T).detach()
+            rms_0 = (a_0 ** 2).mean(dim=-1, keepdim=True).sqrt() + 1e-6
+            h0 = model.embed(x)
+            embed_loss = (h0 * (a_0 / rms_0)).sum(dim=-1).mean()
+            embed_opt.zero_grad()
+            embed_loss.backward()
+            embed_opt.step()
+
+            total_loss += loss_val.item() * batch
+            correct += (logits.argmax(1) == y).sum().item()
+            total += batch
+
+        for s in all_schedulers:
+            s.step()
+
+        train_loss = total_loss / total
+        test_acc = evaluate(model, test_loader, device)
+        log['train_loss'].append(train_loss)
+        log['test_acc'].append(test_acc)
+        if epoch % 20 == 0 or epoch == 1:
+            print(f"    [DFA] Ep {epoch}: loss={train_loss:.4f}, test={test_acc:.4f}")
+
+    return log, Bs
+
+
+def train_state_bridge_online(model, train_loader, test_loader, device, epochs, lr, lr_fb, wd):
+    """State bridge online training."""
+    d = model.d_hidden
+    num_classes = 10
+    L = model.num_blocks
+
+    state_pred = StateBridgeNet(
+        d_hidden=d, s_dim=num_classes, time_embed_dim=32, hidden_dim=256, num_layers=3
+    ).to(device)
+
+    block_opts = [optim.AdamW(block.parameters(), lr=lr, weight_decay=wd) for block in model.blocks]
+    embed_opt = optim.AdamW(model.embed.parameters(), lr=lr, weight_decay=wd)
+    head_opt = optim.AdamW(
+        list(model.out_head.parameters()) + list(model.out_ln.parameters()),
+        lr=lr, weight_decay=wd
+    )
+    state_opt = optim.Adam(state_pred.parameters(), lr=lr_fb)
+    all_schedulers = ([optim.lr_scheduler.CosineAnnealingLR(o, T_max=epochs) for o in block_opts]
+                      + [optim.lr_scheduler.CosineAnnealingLR(embed_opt, T_max=epochs),
+                         optim.lr_scheduler.CosineAnnealingLR(head_opt, T_max=epochs)])
+
+    log = {'train_loss': [], 'test_acc': [], 'state_pred_error': []}
+
+    for epoch in range(1, epochs + 1):
+        model.train()
+        state_pred.train()
+        total_loss, correct, total = 0, 0, 0
+        total_se = 0
+
+        for x, y in train_loader:
+            x = x.view(x.size(0), -1).to(device)
+            y = y.to(device)
+            batch = x.size(0)
+
+            with torch.no_grad():
+                logits, hiddens = model(x, return_hidden=True)
+                loss_val = F.cross_entropy(logits, y)
+                e_T = logits.softmax(dim=-1)
+                e_T[torch.arange(batch), y] -= 1
+                s = e_T.detach()
+
+            hL_det = hiddens[-1].detach()
+
+            # Train state predictor
+            state_loss = 0.0
+            for l in range(L):
+                h_l_det = hiddens[l].detach()
+                t_l = torch.full((batch,), l / L, device=device)
+                pred_hL = state_pred(h_l_det, t_l, s)
+                target_norm = hL_det.norm(dim=-1, keepdim=True).clamp(min=1.0)
+                state_loss = state_loss + (((pred_hL - hL_det) / target_norm) ** 2).sum(dim=-1).mean()
+            state_loss = state_loss / L
+            state_opt.zero_grad()
+            state_loss.backward()
+            state_opt.step()
+            total_se += state_loss.item() * batch
+
+            # Compute credits
+            credits = []
+            for l in range(L):
+                h_l_det = hiddens[l].detach().requires_grad_(True)
+                t_l = torch.full((batch,), l / L, device=device)
+                pred_hL = state_pred(h_l_det, t_l, s)
+                pred_logits = model.out_head(model.out_ln(pred_hL))
+                pred_loss = F.cross_entropy(pred_logits, y, reduction='sum')
+                a_l = torch.autograd.grad(pred_loss, h_l_det, create_graph=False)[0]
+                credits.append(a_l.detach())
+
+            # Update output head
+            logits_out = model.out_head(model.out_ln(hL_det))
+            loss_out = F.cross_entropy(logits_out, y)
+            head_opt.zero_grad()
+            loss_out.backward()
+            head_opt.step()
+
+            # Update blocks
+            for l in range(L):
+                h_l = hiddens[l].detach()
+                a = credits[l]
+                rms = (a ** 2).mean(dim=-1, keepdim=True).sqrt() + 1e-6
+                a_norm = a / rms
+                f_l = model.blocks[l](h_l)
+                local_loss = (f_l * a_norm).sum(dim=-1).mean()
+                block_opts[l].zero_grad()
+                local_loss.backward()
+                torch.nn.utils.clip_grad_norm_(model.blocks[l].parameters(), 1.0)
+                block_opts[l].step()
+
+            # Update embedding
+            a_0 = credits[0]
+            rms_0 = (a_0 ** 2).mean(dim=-1, keepdim=True).sqrt() + 1e-6
+            h0 = model.embed(x)
+            embed_loss = (h0 * (a_0 / rms_0)).sum(dim=-1).mean()
+            embed_opt.zero_grad()
+            embed_loss.backward()
+            embed_opt.step()
+
+            total_loss += loss_val.item() * batch
+            correct += (logits.argmax(1) == y).sum().item()
+            total += batch
+
+        for sch in all_schedulers:
+            sch.step()
+
+        train_loss = total_loss / total
+        test_acc = evaluate(model, test_loader, device)
+        se = total_se / total
+        log['train_loss'].append(train_loss)
+        log['test_acc'].append(test_acc)
+        log['state_pred_error'].append(se)
+        if epoch % 20 == 0 or epoch == 1:
+            print(f"    [SB] Ep {epoch}: loss={train_loss:.4f}, test={test_acc:.4f}, se={se:.4f}")
+
+    return log, state_pred
+
+
+def train_credit_bridge_online(model, train_loader, test_loader, device,
+                                epochs, lr, lr_fb, wd, s_type='eT',
+                                warmup_ratio=0.2, term_grad_weight=1.0,
+                                lam=0.1, K=4, sigma_bridge=0.05, ema_momentum=0.995):
+    """Credit bridge online training with configurable s_type, warmup, tgw."""
+    d = model.d_hidden
+    num_classes = 10
+    L = model.num_blocks
+    warmup_epochs = max(1, int(epochs * warmup_ratio))
+
+    s_dim = num_classes if s_type == 'eT' else d
+
+    value_net = ValueNet(
+        d_hidden=d, s_dim=s_dim, time_embed_dim=32, hidden_dim=256, num_layers=3
+    ).to(device)
+    value_net_ema = create_ema_model(value_net)
+
+    # DFA fallback for warmup
+    Bs_fallback = [torch.randn(d, num_classes, device=device) / np.sqrt(num_classes) for _ in range(L)]
+
+    block_opts = [optim.AdamW(block.parameters(), lr=lr, weight_decay=wd) for block in model.blocks]
+    embed_opt = optim.AdamW(model.embed.parameters(), lr=lr, weight_decay=wd)
+    head_opt = optim.AdamW(
+        list(model.out_head.parameters()) + list(model.out_ln.parameters()),
+        lr=lr, weight_decay=wd
+    )
+    value_opt = optim.Adam(value_net.parameters(), lr=lr_fb)
+
+    all_schedulers = ([optim.lr_scheduler.CosineAnnealingLR(o, T_max=epochs) for o in block_opts]
+                      + [optim.lr_scheduler.CosineAnnealingLR(embed_opt, T_max=epochs),
+                         optim.lr_scheduler.CosineAnnealingLR(head_opt, T_max=epochs)])
+
+    log = {'train_loss': [], 'test_acc': [], 'value_loss': []}
+
+    for epoch in range(1, epochs + 1):
+        model.train()
+        value_net.train()
+        total_loss, correct, total = 0, 0, 0
+        total_vloss = 0
+
+        if epoch <= warmup_epochs:
+            credit_blend = 0.0
+        else:
+            credit_blend = min(1.0, (epoch - warmup_epochs) / max(1, warmup_epochs))
+
+        for x, y in train_loader:
+            x = x.view(x.size(0), -1).to(device)
+            y = y.to(device)
+            batch = x.size(0)
+
+            with torch.no_grad():
+                logits, hiddens = model(x, return_hidden=True)
+                loss_val = F.cross_entropy(logits, y)
+                e_T = logits.softmax(dim=-1)
+                e_T[torch.arange(batch), y] -= 1
+                true_loss = F.cross_entropy(logits, y, reduction='none').detach()
+
+            hL_det = hiddens[-1].detach()
+
+            # Compute s
+            if s_type == 'eT':
+                s = e_T.detach()
+            elif s_type == 'deltaL':
+                hL_req = hL_det.clone().requires_grad_(True)
+                logits_for_s = model.out_head(model.out_ln(hL_req))
+                ce_for_s = F.cross_entropy(logits_for_s, y, reduction='sum')
+                delta_L = torch.autograd.grad(ce_for_s, hL_req, create_graph=False)[0].detach()
+                s = delta_L
+
+            # Train value net
+            t_L = torch.ones(batch, device=device)
+            V_terminal = value_net(hL_det, t_L, s)
+            loss_term = ((V_terminal - true_loss) ** 2).mean()
+
+            loss_tgrad = torch.tensor(0.0, device=device)
+            if term_grad_weight > 0:
+                hL_req = hL_det.clone().requires_grad_(True)
+                V_at_L = value_net(hL_req, t_L, s)
+                grad_V_L = torch.autograd.grad(V_at_L.sum(), hL_req, create_graph=True)[0]
+                hL_req2 = hL_det.clone().requires_grad_(True)
+                logits_tgt = model.out_head(model.out_ln(hL_req2))
+                ce_loss = F.cross_entropy(logits_tgt, y, reduction='sum')
+                a_L_exact = torch.autograd.grad(ce_loss, hL_req2, create_graph=False)[0].detach()
+                loss_tgrad = ((grad_V_L - a_L_exact) ** 2).sum(dim=-1).mean()
+
+            loss_bridge = 0.0
+            for l in range(L):
+                h_l_det = hiddens[l].detach()
+                t_l = torch.full((batch,), l / L, device=device)
+                t_l_next = torch.full((batch,), (l + 1) / L, device=device)
+                V_l = value_net(h_l_det, t_l, s)
+                with torch.no_grad():
+                    h_next_det = hiddens[l + 1].detach()
+                    log_terms = []
+                    for k in range(K):
+                        noise = sigma_bridge * torch.randn_like(h_next_det)
+                        V_next = value_net_ema(h_next_det + noise, t_l_next, s)
+                        log_terms.append(-V_next / lam)
+                    log_stack = torch.stack(log_terms, dim=-1)
+                    V_target = -lam * (torch.logsumexp(log_stack, dim=-1) - np.log(K))
+                loss_bridge = loss_bridge + ((V_l - V_target.detach()) ** 2).mean()
+            loss_bridge = loss_bridge / L
+
+            value_loss = loss_term + loss_bridge + term_grad_weight * loss_tgrad
+            value_opt.zero_grad()
+            value_loss.backward()
+            torch.nn.utils.clip_grad_norm_(value_net.parameters(), 1.0)
+            value_opt.step()
+            update_ema(value_net, value_net_ema, ema_momentum)
+            total_vloss += value_loss.item() * batch
+
+            # Compute CB credits
+            cb_credits = []
+            for l in range(L):
+                h_l_det = hiddens[l].detach().requires_grad_(True)
+                t_l = torch.full((batch,), l / L, device=device)
+                V_l = value_net(h_l_det, t_l, s)
+                a_l = torch.autograd.grad(V_l.sum(), h_l_det, create_graph=False)[0]
+                cb_credits.append(a_l.detach())
+
+            dfa_credits = [(e_T @ Bs_fallback[l].T).detach() for l in range(L)]
+
+            # Blend
+            credits = []
+            for l in range(L):
+                if credit_blend >= 1.0:
+                    a = cb_credits[l]
+                elif credit_blend <= 0.0:
+                    a = dfa_credits[l]
+                else:
+                    cb_rms = (cb_credits[l] ** 2).mean(dim=-1, keepdim=True).sqrt() + 1e-6
+                    dfa_rms = (dfa_credits[l] ** 2).mean(dim=-1, keepdim=True).sqrt() + 1e-6
+                    a = credit_blend * (cb_credits[l] / cb_rms) + (1 - credit_blend) * (dfa_credits[l] / dfa_rms)
+                credits.append(a)
+
+            # Update head
+            logits_out = model.out_head(model.out_ln(hL_det))
+            loss_out = F.cross_entropy(logits_out, y)
+            head_opt.zero_grad()
+            loss_out.backward()
+            head_opt.step()
+
+            # Update blocks
+            for l in range(L):
+                h_l = hiddens[l].detach()
+                a = credits[l]
+                rms = (a ** 2).mean(dim=-1, keepdim=True).sqrt() + 1e-6
+                a_norm = a / rms
+                f_l = model.blocks[l](h_l)
+                local_loss = (f_l * a_norm).sum(dim=-1).mean()
+                block_opts[l].zero_grad()
+                local_loss.backward()
+                torch.nn.utils.clip_grad_norm_(model.blocks[l].parameters(), 1.0)
+                block_opts[l].step()
+
+            # Update embedding
+            a_0 = credits[0]
+            rms_0 = (a_0 ** 2).mean(dim=-1, keepdim=True).sqrt() + 1e-6
+            h0 = model.embed(x)
+            embed_loss = (h0 * (a_0 / rms_0)).sum(dim=-1).mean()
+            embed_opt.zero_grad()
+            embed_loss.backward()
+            embed_opt.step()
+
+            total_loss += loss_val.item() * batch
+            correct += (logits.argmax(1) == y).sum().item()
+            total += batch
+
+        for sch in all_schedulers:
+            sch.step()
+
+        train_loss = total_loss / total
+        test_acc = evaluate(model, test_loader, device)
+        vloss = total_vloss / total
+        log['train_loss'].append(train_loss)
+        log['test_acc'].append(test_acc)
+        log['value_loss'].append(vloss)
+        if epoch % 20 == 0 or epoch == 1:
+            phase = "warmup" if epoch <= warmup_epochs else f"blend={credit_blend:.2f}"
+            print(f"    [CB_{s_type}] Ep {epoch} ({phase}): loss={train_loss:.4f}, test={test_acc:.4f}")
+
+    return log, value_net, value_net_ema
+
+
+# =============================================================================
+# Diagnostics
+# =============================================================================
+def compute_diagnostics(model, method_name, test_loader, device,
+                        value_net=None, state_pred=None, dfa_Bs=None, s_type='eT'):
+    """Compute Gamma, rho, nudging per layer."""
+    model.eval()
+    if value_net is not None:
+        value_net.eval()
+    if state_pred is not None:
+        state_pred.eval()
+
+    d = model.d_hidden
+    L = model.num_blocks
+    num_classes = 10
+
+    # Get one batch
+    for x, y in test_loader:
+        x = x.view(x.size(0), -1).to(device)
+        y = y.to(device)
+        break
+    batch = x.size(0)
+
+    # BP gradients (evaluation only)
+    logits_bp, hiddens_bp = model(x, return_hidden=True)
+    for l in range(L + 1):
+        hiddens_bp[l].retain_grad()
+    loss_bp = F.cross_entropy(logits_bp, y)
+    loss_bp.backward()
+    bp_grads = {l: hiddens_bp[l].grad.detach().clone() for l in range(L + 1)}
+
+    # Clean forward
+    with torch.no_grad():
+        logits, hiddens = model(x, return_hidden=True)
+        e_T = logits.softmax(dim=-1)
+        e_T[torch.arange(batch), y] -= 1
+        s_eT = e_T.detach()
+
+    hL_det = hiddens[-1].detach()
+
+    # delta_L for deltaL conditioning
+    hL_req = hL_det.clone().requires_grad_(True)
+    logits_for_delta = model.out_head(model.out_ln(hL_req))
+    ce_for_delta = F.cross_entropy(logits_for_delta, y, reduction='sum')
+    delta_L = torch.autograd.grad(ce_for_delta, hL_req, create_graph=False)[0].detach()
+
+    results = {
+        'bp_cosine': [],
+        'perturbation_rho': [],
+        'nudging': {'0.001': [], '0.003': [], '0.01': []},
+    }
+
+    for l in range(L):
+        h_l = hiddens[l].detach()
+        t_l = torch.full((batch,), l / L, device=device)
+
+        if method_name == 'dfa':
+            a_l = (s_eT @ dfa_Bs[l].T).detach()
+        elif method_name == 'state_bridge':
+            h_l_req = h_l.clone().requires_grad_(True)
+            pred_hL = state_pred(h_l_req, t_l, s_eT)
+            pred_logits = model.out_head(model.out_ln(pred_hL))
+            pred_loss = F.cross_entropy(pred_logits, y, reduction='sum')
+            a_l = torch.autograd.grad(pred_loss, h_l_req, create_graph=False)[0].detach()
+        elif method_name.startswith('cb_'):
+            s = s_eT if s_type == 'eT' else delta_L
+            h_l_req = h_l.clone().requires_grad_(True)
+            V_l = value_net(h_l_req, t_l, s)
+            a_l = torch.autograd.grad(V_l.sum(), h_l_req, create_graph=False)[0].detach()
+        else:
+            raise ValueError(f"Unknown method: {method_name}")
+
+        bp_cos = cosine_similarity_batch(a_l, bp_grads[l])
+        results['bp_cosine'].append(float(bp_cos))
+
+        def make_fwd_fn(start_l):
+            def fwd_fn(h):
+                with torch.no_grad():
+                    curr = h
+                    for i in range(start_l, L):
+                        curr = curr + model.blocks[i](curr)
+                    out = model.out_head(model.out_ln(curr))
+                    return F.cross_entropy(out, y, reduction='none')
+            return fwd_fn
+
+        fwd_fn = make_fwd_fn(l)
+        rho = perturbation_correlation(h_l, a_l, fwd_fn, epsilon=1e-3, M=16)
+        results['perturbation_rho'].append(float(rho))
+
+        for eta in [0.001, 0.003, 0.01]:
+            nud = nudging_test(h_l, a_l, fwd_fn, eta=eta)
+            results['nudging'][str(eta)].append(float(nud))
+
+    return results
+
+
+# =============================================================================
+# Single config runner
+# =============================================================================
+def run_config(L, d, method, seed, train_loader, test_loader, device,
+               epochs=100, lr=1e-3, lr_fb=1e-3, wd=0.01,
+               warmup_ratio=0.2, term_grad_weight=1.0,
+               lam=0.1, K=4, sigma_bridge=0.05, ema_momentum=0.995):
+    """Run a single (L, d, method, seed) config and return results."""
+    input_dim = 32 * 32 * 3
+    num_classes = 10
+
+    torch.manual_seed(seed)
+    np.random.seed(seed)
+    torch.cuda.manual_seed_all(seed)
+
+    model = ResidualMLP(input_dim, d, num_classes, L).to(device)
+
+    config_str = f"L={L}, d={d}, method={method}, seed={seed}"
+    if 'cb_' in method:
+        config_str += f", wr={warmup_ratio}, tgw={term_grad_weight}"
+    print(f"\n  --- {config_str} ---")
+
+    if method == 'dfa':
+        log, Bs = train_dfa(model, train_loader, test_loader, device, epochs, lr, wd)
+        diag = compute_diagnostics(model, 'dfa', test_loader, device, dfa_Bs=Bs)
+    elif method == 'sb_eT':
+        log, sp = train_state_bridge_online(model, train_loader, test_loader, device,
+                                             epochs, lr, lr_fb, wd)
+        diag = compute_diagnostics(model, 'state_bridge', test_loader, device, state_pred=sp)
+    elif method == 'cb_eT':
+        log, vnet, _ = train_credit_bridge_online(
+            model, train_loader, test_loader, device, epochs, lr, lr_fb, wd,
+            s_type='eT', warmup_ratio=warmup_ratio, term_grad_weight=term_grad_weight,
+            lam=lam, K=K, sigma_bridge=sigma_bridge, ema_momentum=ema_momentum
+        )
+        diag = compute_diagnostics(model, 'cb_eT', test_loader, device, value_net=vnet, s_type='eT')
+    elif method == 'cb_deltaL':
+        log, vnet, _ = train_credit_bridge_online(
+            model, train_loader, test_loader, device, epochs, lr, lr_fb, wd,
+            s_type='deltaL', warmup_ratio=warmup_ratio, term_grad_weight=term_grad_weight,
+            lam=lam, K=K, sigma_bridge=sigma_bridge, ema_momentum=ema_momentum
+        )
+        diag = compute_diagnostics(model, 'cb_deltaL', test_loader, device, value_net=vnet, s_type='deltaL')
+    else:
+        raise ValueError(f"Unknown method: {method}")
+
+    result = {
+        'method': method,
+        'L': L,
+        'd_hidden': d,
+        'seed': seed,
+        'warmup_ratio': warmup_ratio,
+        'term_grad_weight': term_grad_weight,
+        'test_acc': log['test_acc'][-1],
+        'mean_gamma': float(np.mean(diag['bp_cosine'])),
+        'mean_rho': float(np.mean(diag['perturbation_rho'])),
+        'mean_nudge': float(np.mean(diag['nudging']['0.003'])),
+        'per_layer_gamma': diag['bp_cosine'],
+        'per_layer_rho': diag['perturbation_rho'],
+        'per_layer_nudge': diag['nudging']['0.003'],
+    }
+
+    print(f"    Result: acc={result['test_acc']:.4f}, Gamma={result['mean_gamma']:.4f}, "
+          f"rho={result['mean_rho']:.4f}, nudge={result['mean_nudge']:.6f}")
+
+    return result
+
+
+# =============================================================================
+# Main
+# =============================================================================
+def main():
+    parser = argparse.ArgumentParser(description='Phase B: Online shallow CIFAR conditioning')
+    parser.add_argument('--depths', type=int, nargs='+', default=[4, 6])
+    parser.add_argument('--widths', type=int, nargs='+', default=[256, 512])
+    parser.add_argument('--methods', type=str, nargs='+',
+                        default=['dfa', 'sb_eT', 'cb_eT', 'cb_deltaL'])
+    parser.add_argument('--warmup_ratios', type=float, nargs='+', default=[0.0, 0.05, 0.2])
+    parser.add_argument('--tgws', type=float, nargs='+', default=[1.0, 4.0])
+    parser.add_argument('--epochs', type=int, default=100)
+    parser.add_argument('--lr', type=float, default=1e-3)
+    parser.add_argument('--lr_fb', type=float, default=1e-3)
+    parser.add_argument('--wd', type=float, default=0.01)
+    parser.add_argument('--lam', type=float, default=0.1)
+    parser.add_argument('--K', type=int, default=4)
+    parser.add_argument('--sigma_bridge', type=float, default=0.05)
+    parser.add_argument('--ema_momentum', type=float, default=0.995)
+    parser.add_argument('--seed', type=int, default=42)
+    parser.add_argument('--batch_size', type=int, default=128)
+    parser.add_argument('--gpu', type=int, default=2)
+    parser.add_argument('--output_dir', type=str, default='results/online_shallow')
+    args = parser.parse_args()
+
+    device = torch.device(f'cuda:{args.gpu}' if torch.cuda.is_available() else 'cpu')
+    print(f"Using device: {device}")
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    train_loader, test_loader = get_cifar10(batch_size=args.batch_size)
+
+    all_results = []
+
+    for L in args.depths:
+        for d in args.widths:
+            for method in args.methods:
+                if method in ['dfa', 'sb_eT']:
+                    # No warmup/tgw sweep for DFA and SB
+                    result = run_config(
+                        L, d, method, args.seed, train_loader, test_loader, device,
+                        epochs=args.epochs, lr=args.lr, lr_fb=args.lr_fb, wd=args.wd
+                    )
+                    all_results.append(result)
+                else:
+                    # Sweep warmup and tgw for CB methods
+                    for wr in args.warmup_ratios:
+                        for tgw in args.tgws:
+                            result = run_config(
+                                L, d, method, args.seed, train_loader, test_loader, device,
+                                epochs=args.epochs, lr=args.lr, lr_fb=args.lr_fb, wd=args.wd,
+                                warmup_ratio=wr, term_grad_weight=tgw,
+                                lam=args.lam, K=args.K, sigma_bridge=args.sigma_bridge,
+                                ema_momentum=args.ema_momentum
+                            )
+                            all_results.append(result)
+
+    # Summary table
+    print(f"\n{'='*80}")
+    print("SUMMARY")
+    print(f"{'='*80}")
+
+    # Find DFA baselines for S1, S2 computation
+    dfa_baselines = {}
+    for r in all_results:
+        if r['method'] == 'dfa':
+            dfa_baselines[(r['L'], r['d_hidden'])] = r
+
+    print(f"\n{'Method':<20} {'L':>3} {'d':>4} {'wr':>5} {'tgw':>5} {'Acc':>6} "
+          f"{'Gamma':>7} {'rho':>7} {'nudge':>10} {'S1':>7} {'S2':>7}")
+    print("-" * 95)
+
+    positive_configs = []
+    for r in all_results:
+        key = (r['L'], r['d_hidden'])
+        dfa_ref = dfa_baselines.get(key)
+        S1 = r['mean_gamma'] - (dfa_ref['mean_gamma'] if dfa_ref else 0)
+        S2 = r['mean_rho'] - (dfa_ref['mean_rho'] if dfa_ref else 0)
+        wr_str = f"{r.get('warmup_ratio', '-'):>5.2f}" if r['method'].startswith('cb_') else "    -"
+        tgw_str = f"{r.get('term_grad_weight', '-'):>5.1f}" if r['method'].startswith('cb_') else "    -"
+        print(f"{r['method']:<20} {r['L']:>3} {r['d_hidden']:>4} {wr_str} {tgw_str} "
+              f"{r['test_acc']:>6.4f} {r['mean_gamma']:>7.4f} {r['mean_rho']:>7.4f} "
+              f"{r['mean_nudge']:>10.6f} {S1:>7.4f} {S2:>7.4f}")
+
+        if r['method'].startswith('cb_') and S1 > 0 and S2 > 0:
+            nudge_better = r['mean_nudge'] < (dfa_ref['mean_nudge'] if dfa_ref else 0)
+            positive_configs.append({**r, 'S1': S1, 'S2': S2, 'nudge_better': nudge_better})
+
+    if positive_configs:
+        print(f"\nPOSITIVE CONFIGS (S1>0 AND S2>0):")
+        for pc in positive_configs:
+            print(f"  {pc['method']} L={pc['L']} d={pc['d_hidden']} wr={pc.get('warmup_ratio','-')} "
+                  f"tgw={pc.get('term_grad_weight','-')}: S1={pc['S1']:.4f} S2={pc['S2']:.4f} "
+                  f"nudge_better={pc['nudge_better']}")
+    else:
+        print(f"\nNO POSITIVE CONFIGS FOUND. All CB variants have S1<=0 or S2<=0.")
+
+    # Save
+    out_path = os.path.join(args.output_dir, f'scan_s{args.seed}.json')
+    with open(out_path, 'w') as f:
+        json.dump(all_results, f, indent=2)
+    print(f"\nResults saved to {out_path}")
+
+
+if __name__ == '__main__':
+    main()