diff --git a/pyproject.toml b/pyproject.toml
index 4cec49e..97c8505 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -26,6 +26,7 @@ dependencies = [
     "iso-kl-figure",
     "tiny-mfv",
     "srsly>=2.5.3",
+    "kaleido>=1.3.0",
 ]
 
 [tool.uv.sources]
diff --git a/src/steer_heal/heal.py b/src/steer_heal/heal.py
index 10fbacc..ad1cf3d 100644
--- a/src/steer_heal/heal.py
+++ b/src/steer_heal/heal.py
@@ -22,6 +22,11 @@ def _kl_per_pos(logp_a, logp_b):  # KL(a || b) summed over vocab, per position
     return (logp_a.exp() * (logp_a - logp_b)).sum(-1)
 
 
+def _gnorm(grads) -> float:  # L2 norm of a flat concat of (possibly None) param grads
+    sq = sum(float(g.pow(2).sum()) for g in grads if g is not None)
+    return sq ** 0.5
+
+
 def _encode(tok, prompt: str, completion: str, max_len: int, device):
     ids = tok(prompt + completion, return_tensors="pt", truncation=True, max_length=max_len).to(device)
     prompt_ids = tok(prompt, return_tensors="pt").input_ids[0].to(device)
@@ -58,7 +63,12 @@ def heal_round(model, tok, kept: list[dict], hist_specs: list[AdapterSpec], cfg:
     logger.info(f"SHOULD: nll (SFT) falls as the adapter learns the trait; kl (barrier div) is 0 for "
                 f"reg=nll/wd and >0 for kl_rev/kl_fwd; gnorm finite (not exploding). loss = nll + lam*relu(kl-tau). "
                 f"If kl stays < tau={cfg.tau} the barrier NEVER fired and {cfg.reg} == nll (no regularisation).")
-    logger.info("  step   nll↓    kl  loss↓  gnorm")
+    logger.info(
+        "SHOULD (barrier balance): g_bar/g_nll is the gradient-pressure ratio (||∇barrier|| / ||∇sft||). "
+        ">>1 -> barrier dominates, it is undoing the trait the SFT installs (over-tight: lower lam or raise tau); "
+        "~1 -> balanced; 0 -> barrier inert (kl<tau, or reg=nll/wd where decay acts in the optimiser, not the loss)."
+    )
+    logger.info("  step   nll↓    kl  g_nll  g_bar  g_bar/g_nll  loss↓  gnorm")
     pbar = tqdm(total=n_steps, desc=f"heal[{cfg.reg}]", mininterval=120, maxinterval=120)
     step = 0
     nlls = []  # per-step SFT loss; final = mean of last 5, the heal-stage number for the round table
@@ -90,15 +100,24 @@ def heal_round(model, tok, kept: list[dict], hist_specs: list[AdapterSpec], cfg:
                 div = _kl_per_pos(logp[mask], logp0[mask]).mean()
             else:
                 div = torch.zeros((), device=model.device)  # nll, wd
-            loss = sft + cfg.lam * torch.relu(div - cfg.tau)
+            barrier = cfg.lam * torch.relu(div - cfg.tau)
+            loss = sft + barrier
             nlls.append(sft.item())
+            log_now = step % max(1, n_steps // 20) == 0 or step == n_steps - 1
+            if log_now:
+                # split the gradient pressure: ||∇sft|| vs ||∇barrier|| (retain_graph -> still .backward below).
+                # barrier has no grad path when kl<=tau (relu zeroed), so guard before autograd.grad.
+                g_nll = _gnorm(torch.autograd.grad(sft, params, retain_graph=True, allow_unused=True))
+                barrier_live = barrier.requires_grad and (div - cfg.tau).item() > 0
+                g_bar = _gnorm(torch.autograd.grad(barrier, params, retain_graph=True, allow_unused=True)) if barrier_live else 0.0
+                pressure = g_bar / g_nll if g_nll > 0 else float("nan")
             loss.backward()
             gnorm = torch.nn.utils.clip_grad_norm_(params, 1.0)
             opt.step()
             opt.zero_grad()
-            if step % max(1, n_steps // 20) == 0 or step == n_steps - 1:
+            if log_now:
                 logger.info(f"  {step:4d}  {sft.item():5.2f}  {div.detach().item():4.2f}  "
-                            f"{loss.item():5.2f}  {float(gnorm):5.1f}")
+                            f"{g_nll:5.1f}  {g_bar:5.1f}  {pressure:11.2f}  {loss.item():5.2f}  {float(gnorm):5.1f}")
             pbar.set_postfix(nll=f"{sft.item():.2f}", kl=f"{div.detach().item():.2f}", gn=f"{float(gnorm):.1f}")
             pbar.update(1)
             step += 1
diff --git a/src/steer_heal/plot.py b/src/steer_heal/plot.py
index d7146bf..998f70a 100644
--- a/src/steer_heal/plot.py
+++ b/src/steer_heal/plot.py
@@ -1,8 +1,21 @@
-"""Loop map: Care (y) vs Authority (x) trajectory + coherence/cosine panels.
+"""Loop plots saved to out/{ts}_{slug}/.
 
-Simplified from wassname/w2schar-mini csm/plot.py _build_scatter (full git-graph
-port is a later pass). One node per round; hover shows coherence and the
-round-0 cosine. Saved as out/{ts}_{slug}/map.html.
+trajectory.html (write_trajectory) is the narrative figure: it tells the
+steer->heal story the project is about.
+  - left, stacked & x-shared: auth_nats over the pipeline (the up/down/up/down
+    zigzag -- steering pushes the trait DOWN in red, heal lets it relax UP in
+    green) and coherence directly below it (did the move cost coherence?).
+  - right: the trait/coherence pareto MAP. x = auth_nats (the headline trait,
+    left = more trait), y = coherence. The steer trajectory (red) and the heal
+    trajectory (green) are drawn separately from the same base node, so you can
+    read whether heal lands at a BETTER point (same trait, higher coherence) or
+    just walks back toward base. care_nats rides in the hover.
+
+map.html (write_map) is the older Care-vs-SocialNorms node-per-round view.
+
+Tufte: one mark per datum, direct labels (r0,r1,..) instead of a legend on the
+map, no gridded chartjunk, color carries the steer/heal contrast (the one
+comparison that matters) and nothing else.
 """
 
 from pathlib import Path
@@ -10,6 +23,96 @@ from pathlib import Path
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots
 
+RED = "#c1272d"    # steer: trait injected by the live vector (pre-heal)
+GREEN = "#1b7837"  # heal: trait distilled into weights, vector off
+GREY = "#555555"   # base: pristine round-0 original
+
+
+def _png(fig, out_html: Path) -> Path:
+    fig.write_html(out_html, include_plotlyjs="cdn")
+    out_png = out_html.with_suffix(".png")
+    fig.write_image(out_png, width=1100, height=520, scale=2)  # static, for chat/appendix
+    return out_png
+
+
+def write_trajectory(run_dir: Path, stages: list[dict]) -> Path:
+    """stages: ordered list of {round, stage in {base,steered,healed}, m: eval-dict}.
+    The eval-dict carries auth_nats, care_nats, coherence."""
+    auth = [s["m"]["auth_nats"] for s in stages]
+    coh = [s["m"]["coherence"] for s in stages]
+    care = [s["m"]["care_nats"] for s in stages]
+    kind = [s["stage"] for s in stages]
+    # x of the zigzag = pipeline order; label each tick base / r0·steer / r0·heal / ...
+    xi = list(range(len(stages)))
+    xlab = ["base" if k == "base" else f"r{s['round']}·{k[:5]}" for s, k in zip(stages, kind)]
+    col = [GREY if k == "base" else RED if k == "steered" else GREEN for k in kind]
+
+    fig = make_subplots(
+        rows=2, cols=2, column_widths=[0.52, 0.48], row_heights=[0.5, 0.5],
+        vertical_spacing=0.10, horizontal_spacing=0.11,
+        specs=[[{"type": "scatter"}, {"type": "scatter", "rowspan": 2}],
+               [{"type": "scatter"}, None]],
+        subplot_titles=("trait: auth_nats over the pipeline (down = trait)",
+                        "pareto map: trait (x) vs coherence (y)",
+                        "coherence (hold ~1.0)"),
+    )
+
+    # -- left top: auth zigzag. one connecting line (pipeline order) + colored markers.
+    fig.add_trace(go.Scatter(
+        x=xi, y=auth, mode="lines+markers", line=dict(color="#bbbbbb", width=1),
+        marker=dict(size=12, color=col), showlegend=False,
+        hovertext=[f"{l}: auth={a:.3f}" for l, a in zip(xlab, auth)], hoverinfo="text",
+    ), row=1, col=1)
+    fig.update_yaxes(title_text="auth_nats  (↓ trait)", row=1, col=1)
+
+    # -- left bottom: coherence, same x, shared tick labels.
+    fig.add_trace(go.Scatter(
+        x=xi, y=coh, mode="lines+markers", line=dict(color="#bbbbbb", width=1),
+        marker=dict(size=12, color=col), showlegend=False,
+        hovertext=[f"{l}: coh={c:.3f}" for l, c in zip(xlab, coh)], hoverinfo="text",
+    ), row=2, col=1)
+    # fix the coherence range to [floor, ceiling] so autoscale doesn't blow up ~0.001 of noise
+    # into the whole panel; the honest story is coherence pinned near 1.0. 0.95 = coherent floor.
+    fig.update_yaxes(title_text="coherence  (→1.0)", range=[0.83, 1.01], row=2, col=1)
+    fig.add_hline(y=0.95, line=dict(color="#cccccc", width=1, dash="dot"), row=2, col=1)
+    fig.update_xaxes(tickmode="array", tickvals=xi, ticktext=xlab, tickangle=-40, row=2, col=1)
+    fig.update_xaxes(tickmode="array", tickvals=xi, ticktext=["" for _ in xi], row=1, col=1)
+
+    # -- right: pareto map. base node, then steer & heal trajectories from it.
+    base = next(s for s in stages if s["stage"] == "base")
+    bx, by = base["m"]["auth_nats"], base["m"]["coherence"]
+    fig.add_trace(go.Scatter(
+        x=[bx], y=[by], mode="markers+text", text=["base"], textposition="bottom center",
+        marker=dict(size=14, color=GREY, symbol="star"), showlegend=False,
+        hovertext=[f"base auth={bx:.3f} coh={by:.3f}"], hoverinfo="text",
+    ), row=1, col=2)
+    for stage_kind, color, label in [("steered", RED, "steer"), ("healed", GREEN, "heal")]:
+        pts = [s for s in stages if s["stage"] == stage_kind]
+        xs = [bx] + [p["m"]["auth_nats"] for p in pts]
+        ys = [by] + [p["m"]["coherence"] for p in pts]
+        txt = [""] + [f"r{p['round']}" for p in pts]
+        hov = [f"base"] + [f"{label} r{p['round']} auth={p['m']['auth_nats']:.3f} "
+                           f"coh={p['m']['coherence']:.3f} care={p['m']['care_nats']:.3f}" for p in pts]
+        fig.add_trace(go.Scatter(
+            x=xs, y=ys, mode="lines+markers+text", text=txt, textposition="top center",
+            line=dict(color=color, width=2), marker=dict(size=11, color=color),
+            name=label, showlegend=False, hovertext=hov, hoverinfo="text",
+        ), row=1, col=2)
+    fig.update_xaxes(title_text="auth_nats  (← more trait)", row=1, col=2)
+    # same fixed coherence range as the line panel: shows the points hug the ceiling (coherence
+    # is not the binding constraint here), so the whole story is the horizontal trait move.
+    fig.update_yaxes(title_text="coherence  (↑ better)", range=[0.83, 1.01], row=1, col=2)
+    fig.add_hline(y=0.95, line=dict(color="#cccccc", width=1, dash="dot"), row=1, col=2)
+
+    fig.update_layout(
+        template="simple_white", height=520, width=1100,
+        title_text="steer (red) -> heal (green): does heal keep the trait at higher coherence?",
+        showlegend=False,  # red/green stated in the title; map points are directly labelled r0,r1
+    )
+    out_html = run_dir / "trajectory.html"
+    out_png = _png(fig, out_html)
+    return out_png
+
 
 def write_map(run_dir: Path, rounds: list[dict]) -> Path:
     r = [d["round"] for d in rounds]
@@ -18,7 +121,6 @@ def write_map(run_dir: Path, rounds: list[dict]) -> Path:
         subplot_titles=("trait map: Care vs SocialNorms", "coherence + direction per round"),
         specs=[[{"type": "scatter"}, {"type": "scatter"}]],
     )
-    # trajectory across the SocialNorms axis (trait moves it DOWN, Care UP), coloured by round
     fig.add_trace(go.Scatter(
         x=[d["socialnorms"] for d in rounds], y=[d["care"] for d in rounds],
         mode="lines+markers+text", text=[f"r{i}" for i in r], textposition="top center",
diff --git a/src/steer_heal/run.py b/src/steer_heal/run.py
index 2d4ab7d..5d01c16 100644
--- a/src/steer_heal/run.py
+++ b/src/steer_heal/run.py
@@ -21,7 +21,7 @@ from steer_heal.eval import evaluate_model
 from steer_heal.filter import filter_completions, ppl_under_base
 from steer_heal.heal import heal_round
 from steer_heal.io import append_result, log_event, make_run_dir
-from steer_heal.plot import write_map
+from steer_heal.plot import write_map, write_trajectory
 from steer_heal.steering import generate_plain, generate_steered, gpu_mem, teacher_vec
 from steer_heal.ws.bake import baked
 
@@ -71,20 +71,21 @@ def _mean_finite(xs, label: str = "ppl") -> float:
     return sum(xs) / len(xs) if xs else float("nan")
 
 
-def _stage_row(rnd, stage: str, m: dict, base_m: dict) -> dict:
-    """One row of the base->steered->healed pareto table. dcoh/dauth = coherence
-    CHANGE per nat of Authority CHANGE vs base (signed): positive = coherence lost
-    while trait gained (both fall), the cost we want low; nan for the base row (0/0)."""
+def _stage_row(stg: dict, base_m: dict) -> dict:
+    """One row of the base->steered->healed pareto table from a stage {round,stage,m}.
+    dcoh/dauth = coherence CHANGE per nat of Authority CHANGE vs base (signed): positive
+    = coherence lost while trait gained (both fall), the cost we want low; nan for base."""
+    m = stg["m"]
     dAuth = m["auth_nats"] - base_m["auth_nats"]
     dCoh = m["coherence"] - base_m["coherence"]
     ratio = dCoh / dAuth if abs(dAuth) > 1e-6 else float("nan")
     # arrows in keys -> render in the table header. dcoh/dauth: lower=better (0 = trait at
     # no coherence cost; >0 = paid coherence; <0 = coherence rose too). coh: hold ~1.0. auth: down=trait.
-    return {"round": rnd, "stage": stage, "dcoh/dauth↓": ratio,
+    return {"round": stg["round"], "stage": stg["stage"], "dcoh/dauth↓": ratio,
             "coh→": m["coherence"], "auth↓": m["auth_nats"], "care": m["care_nats"]}
 
 
-def _log_stage_table(stage_rows: list[dict]) -> None:
+def _log_stage_table(stages: list[dict], base_m: dict) -> None:
     from tabulate import tabulate
     logger.info(
         "\nstage pareto (base -> steered -> healed, per round):\n"
@@ -92,7 +93,7 @@ def _log_stage_table(stage_rows: list[dict]) -> None:
         "         coh→ = p_any_ans coherence (hold ~1.0)   auth↓ = log p[Authority] (DOWN = trait)   care = log p[Care] (off-target)\n"
         "  WIN: healed keeps steered's low auth (trait) but recovers coh toward base AND a smaller dcoh/dauth than steered.\n"
         "  UNDO: healed auth springs back to ~base while coh recovers -> heal removed the trait, not just the incoherence.\n"
-        + tabulate(stage_rows, headers="keys", tablefmt="github", floatfmt=".3f") + "\n")
+        + tabulate([_stage_row(s, base_m) for s in stages], headers="keys", tablefmt="github", floatfmt=".3f") + "\n")
 
 
 def steer_heal(model, tok, cfg: RunConfig, run_dir: Path) -> dict:
@@ -104,7 +105,7 @@ def steer_heal(model, tok, cfg: RunConfig, run_dir: Path) -> dict:
     # trait), not just coherence. One extra eval per run.
     logger.info(f"\n=== EVAL base [tinymfv classic] gpu {gpu_mem()} ===")
     base_m = evaluate_model(model, tok, cfg)
-    stage_rows = [_stage_row("-", "base", base_m, base_m)]  # pareto table: base -> steered -> healed
+    stages = [{"round": "-", "stage": "base", "m": base_m}]  # base -> steered -> healed, for table + trajectory plot
     for rnd in range(cfg.n_rounds):
         logger.info(f"\n\n=== ROUND {rnd} [{cfg.model.split('/')[-1]} reg={cfg.reg}] gpu {gpu_mem()} ===")
         # extract teacher vector + sweep-generate steered data from the CURRENT student
@@ -119,6 +120,7 @@ def steer_heal(model, tok, cfg: RunConfig, run_dir: Path) -> dict:
             logger.info(f"\n=== EVAL steered [c={cfg.alphas[0]}] gpu {gpu_mem()} ===")
             with v(model, C=c_op):
                 m_steer = evaluate_model(model, tok, cfg)
+        log_event(run_dir, stage="steered_eval", round=rnd, c=cfg.alphas[0], **m_steer)  # persist for offline plot
         # filter under the ORIGINAL (no history, no steering) -- this picks the usable C
         logger.info(f"\n=== FILTER [{len(comps)} completions] gpu {gpu_mem()} ===")
         kept, scored = filter_completions(model, tok, comps, cfg)
@@ -153,15 +155,17 @@ def steer_heal(model, tok, cfg: RunConfig, run_dir: Path) -> dict:
                "adapter_ppl": adapter_ppl, "n_comps": len(comps), "n_kept": len(kept),
                "heal_nll": heal_nll}
         rounds.append(rec)
-        stage_rows.append(_stage_row(rnd, "steered", m_steer, base_m))
-        stage_rows.append(_stage_row(rnd, "healed", m, base_m))
+        stages.append({"round": rnd, "stage": "steered", "m": m_steer})
+        stages.append({"round": rnd, "stage": "healed", "m": m})
         log_event(run_dir, stage="round", **rec)
         logger.info(f"round {rnd}: auth_nats↓={m['auth_nats']:+.2f} care_nats={m['care_nats']:+.2f} "
                     f"coh→={m['coherence']:.3f} cos_v0={cos_v0:+.2f} adapter_ppl={adapter_ppl:.0f}")
 
     _log_loop_summary(rounds, base_m)
-    _log_stage_table(stage_rows)
+    _log_stage_table(stages, base_m)
     write_map(run_dir, rounds)
+    png = write_trajectory(run_dir, stages)
+    logger.info(f"trajectory plot: {png}  (and {png.with_suffix('.html')})")
     return rounds[-1]
 
 
diff --git a/uv.lock b/uv.lock
index c309486..c185f8b 100644
--- a/uv.lock
+++ b/uv.lock
@@ -11,7 +11,7 @@ resolution-markers = [
 ]
 
 [options]
-exclude-newer = "2026-05-30T02:02:43.997258498Z"
+exclude-newer = "2026-05-30T09:16:42.439409688Z"
 exclude-newer-span = "P5D"
 
 [[package]]
@@ -322,6 +322,20 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/db/8f/61959034484a4a7c527811f4721e75d02d653a35afb0b6054474d8185d4c/charset_normalizer-3.4.7-py3-none-any.whl", hash = "sha256:3dce51d0f5e7951f8bb4900c257dad282f49190fdbebecd4ba99bcc41fef404d", size = 61958, upload-time = "2026-04-02T09:28:37.794Z" },
 ]
 
+[[package]]
+name = "choreographer"
+version = "1.3.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "logistro" },
+    { name = "platformdirs" },
+    { name = "simplejson" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/17/69/3058cd4f16d6b75c80e8f95e5b713d930526353ce294df9a7887453ba215/choreographer-1.3.0.tar.gz", hash = "sha256:6c44a0e48e9b37977344d40bfa5a9ed88575fe4bc0fd836771bf702bc24d6884", size = 48291, upload-time = "2026-04-28T22:57:45.114Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ba/6c/ff8bf52315064dbeb55cb5067e191120a5b2e58bb648d0d34cf7969dc2c2/choreographer-1.3.0-py3-none-any.whl", hash = "sha256:cea4cb739e4f61625e4b53888a8d3fa1d3bf73948b56753e460ab44da7d8d44f", size = 52622, upload-time = "2026-04-28T22:57:44.015Z" },
+]
+
 [[package]]
 name = "click"
 version = "8.4.1"
@@ -879,6 +893,30 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c8/8d/302cb2057b7513327b4d575cff6b1d066ee6431a5357fc3f8867cd684406/jiter-0.15.0-graalpy312-graalpy250_312_native-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:54d5d6090cdc1b7c9e780dfb04949a990adb1e301a2fc0bbcee7de4638d33f9a", size = 344469, upload-time = "2026-05-19T10:09:46.864Z" },
 ]
 
+[[package]]
+name = "kaleido"
+version = "1.3.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "choreographer" },
+    { name = "logistro" },
+    { name = "orjson" },
+    { name = "packaging" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/e0/64/53eac73d31dbfc3310ee2e87bcac1ae7417427f0fbe3dd800eaf676db324/kaleido-1.3.0.tar.gz", hash = "sha256:5e0378a7475e98852773deeb6483dee91f8aa7b364dde7b5f2b3622cb468a3e6", size = 68938, upload-time = "2026-05-04T19:45:28.932Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9e/b9/a6d8bb7d228940f01885bd9f327ab7f9d366a9be775c4bf366bf9d9477ae/kaleido-1.3.0-py3-none-any.whl", hash = "sha256:52714dfd38e8f2a114831826200c40bb10d0ca0c11d4272f3f48ad499cd8f8ea", size = 55580, upload-time = "2026-05-04T19:45:27.483Z" },
+]
+
+[[package]]
+name = "logistro"
+version = "2.0.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/08/90/bfd7a6fab22bdfafe48ed3c4831713cb77b4779d18ade5e248d5dbc0ca22/logistro-2.0.1.tar.gz", hash = "sha256:8446affc82bab2577eb02bfcbcae196ae03129287557287b6a070f70c1985047", size = 8398, upload-time = "2025-11-01T02:41:18.81Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/54/20/6aa79ba3570bddd1bf7e951c6123f806751e58e8cce736bad77b2cf348d7/logistro-2.0.1-py3-none-any.whl", hash = "sha256:06ffa127b9fb4ac8b1972ae6b2a9d7fde57598bf5939cd708f43ec5bba2d31eb", size = 8555, upload-time = "2025-11-01T02:41:17.587Z" },
+]
+
 [[package]]
 name = "loguru"
 version = "0.7.3"
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 [[package]]
 name = "six"
 version = "1.17.0"
@@ -2288,6 +2458,7 @@ dependencies = [
     { name = "einops" },
     { name = "iso-kl-figure" },
     { name = "jaxtyping" },
+    { name = "kaleido" },
     { name = "loguru" },
     { name = "numpy" },
     { name = "plotly" },
@@ -2313,6 +2484,7 @@ requires-dist = [
     { name = "einops" },
     { name = "iso-kl-figure", editable = "docs/vendor/isokl_steering_calibration" },
     { name = "jaxtyping" },
+    { name = "kaleido", specifier = ">=1.3.0" },
     { name = "loguru" },
     { name = "numpy" },
     { name = "plotly" },