diff --git a/docs/love_loop.png b/docs/love_loop.png
index f924a40..6f016a5 100644
Binary files a/docs/love_loop.png and b/docs/love_loop.png differ
diff --git a/src/steer_heal/plot.py b/src/steer_heal/plot.py
index 070ff1e..2916110 100644
--- a/src/steer_heal/plot.py
+++ b/src/steer_heal/plot.py
@@ -69,17 +69,21 @@ def _connectors(fig, row, col, axis, base_xy, steered_xys, healed_xys):
     _tip(fig, trend[-2], trend[-1], axis, TREND, 1.5)
 
 
-def write_trajectory(run_dir: Path, stages: list[dict]) -> Path:
+def write_trajectory(run_dir: Path, stages: list[dict], primary_key: str = "care_nats") -> 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]
+    primary_key: the on-target eval axis for Panel A (e.g. care_nats for love, auth_nats for authority).
+    Panel C shows primary on x vs the biggest-moving off-target foundation on y."""
+    # Build signals from all *_nats keys present in the eval dict + coherence
+    m0 = stages[0]["m"]
+    nat_keys = sorted(k for k in m0 if k.endswith("_nats"))
+    signals = {k: [s["m"][k] for s in stages] for k in nat_keys}
+    signals["coh"] = [s["m"]["coherence"] for s in stages]
+    coh = signals["coh"]
+    primary = primary_key  # full key e.g. "care_nats"
+
     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],
@@ -88,10 +92,6 @@ def write_trajectory(run_dir: Path, stages: list[dict]) -> Path:
                [{"type": "scatter"}, None]],
     )
 
-    # all 3 panels share ONE visual language (_connectors): dotted grey steer->heal moves
-    # + a thin green-grey trend through base->heals, both BEHIND the markers. Left panels use
-    # pipeline-order x; the map uses auth-x. idx groups stage rows so each panel can pull its
-    # own (x,y) for base / steered / healed in the same call.
     bi = kind.index("base")
     si = [i for i, k in enumerate(kind) if k == "steered"]
     hi = [i for i, k in enumerate(kind) if k == "healed"]
@@ -100,29 +100,21 @@ def write_trajectory(run_dir: Path, stages: list[dict]) -> Path:
     # coh=1 (incoherence 0.001-0.05); a collapse round (coh~0.6 -> incoherence ~0.4) is a single
     # outlier that on a linear coherence axis flattens every healthy round into one band. log(1-coh)
     # gives each near-perfect round its own decade and squashes the outlier. Clamp incoherence at
-    # 1e-3 (coh>=0.999) to dodge log(0). Both stacked panels now read DOWN = wanted (auth down =
-    # trait, incoherence down = coherent).
+    # 1e-3 (coh>=0.999) to dodge log(0). Both stacked panels now read DOWN = wanted.
     inc = [max(1.0 - c, 1e-3) for c in coh]
 
-    signals = {"auth": auth, "care": care, "coh": coh}
     map_ids = [bi] + hi
     rng = lambda k: max(signals[k][i] for i in map_ids) - min(signals[k][i] for i in map_ids)
-    # Panel A tracks whichever trait moves most over base+heal (coh excluded; Panel B has it)
-    top_key = max(["auth", "care"], key=rng)
 
-    # PANEL A (top trait over pipeline, linear) and PANEL B (incoherence, log): x = pipeline index.
-    # Both keep red steer (A is the zigzag, B's red dots show the incoherence steering injects).
-    # hover shows the raw value (coh for B, trait for A); only B's y-axis is logged.
-    # x-tick labels only at key positions (base, first/last heal) to avoid dense overlap
+    # PANEL A: on-target axis over the full pipeline (shows the trait being steered)
     key_xi = [xi[bi]] + ([xi[si[0]]] if si else []) + [xi[hi[0]]] + ([xi[hi[-1]]] if len(hi) > 1 else [])
     key_xlab = [xlab[bi]] + ([xlab[si[0]]] if si else []) + [xlab[hi[0]]] + ([xlab[hi[-1]]] if len(hi) > 1 else [])
     for axis, row, yv, raw, ytitle, ylog in [
-        (1, 1, signals[top_key], signals[top_key], f"{top_key}_nats", False),
+        (1, 1, signals[primary], signals[primary], primary, False),
         (3, 2, inc, coh, "1−coherence (↓, log)", True),
     ]:
         _connectors(fig, row, 1, axis, (xi[bi], yv[bi]),
                     [(xi[i], yv[i]) for i in si], [(xi[i], yv[i]) for i in hi])
-        # steered points recede (smaller, lower opacity) — the heal trajectory is the story
         for ids, c, sym, sz, op in [([bi], GREY, "star", 13, 1.0), (si, RED, "circle", 8, 0.6), (hi, GREEN, "circle", 10, 1.0)]:
             fig.add_trace(go.Scatter(
                 x=[xi[i] for i in ids], y=[yv[i] for i in ids], mode="markers",
@@ -134,37 +126,35 @@ def write_trajectory(run_dir: Path, stages: list[dict]) -> Path:
     fig.update_xaxes(tickmode="array", tickvals=key_xi, ticktext=key_xlab, tickangle=-30, row=2, col=1, showgrid=False)
     fig.update_xaxes(showgrid=False, tickvals=[], row=1, col=1)
 
-    # PANEL C (trait map): axes = the two biggest-MOVING of auth/care/coh over base+heal nodes.
-    # Healthy -> auth vs care (the moral-foundations plane); if coherence CRASHED its range beats
-    # care and it becomes the y-axis. RED steer is omitted here: zoomed to the heal cluster the
-    # steer points fall off-scale and leave dangling connector stubs. base + green heals only.
-    atitle = {"auth": "auth_nats  (← more trait)", "care": "care_nats  (more care →)"}
-    xkey, ykey = sorted(sorted(["auth", "care", "coh"], key=rng, reverse=True)[:2],
-                        key=["auth", "care", "coh"].index)  # x = higher-priority of the chosen two
-    # coh can only ever be the LOWEST-priority pick, so it lands on Y, never X. When it does
-    # (a crash run) plot it as log-incoherence to match panel B; else raw care/auth.
+    # PANEL C (trait map): x = primary (on-target), y = biggest-moving off-target foundation.
+    # If coherence crashed its range beats all foundations and it takes y (crash diagnostic).
+    # RED steer omitted: steered points fall off-scale and leave dangling connector stubs.
+    off_target_keys = [k for k in nat_keys if k != primary]
+    ykey = max(off_target_keys, key=rng)
+    if rng("coh") > rng(ykey):  # coherence crash dominates; show as log-incoherence
+        ykey = "coh"
     ycoh = ykey == "coh"
-    xv = signals[xkey]
+    xv = signals[primary]
     yv = [max(1.0 - v, 1e-3) for v in signals[ykey]] if ycoh else signals[ykey]
-    yraw = signals[ykey]  # for hover (real coherence / care value, not the log-incoherence coord)
+    yraw = signals[ykey]
 
     _connectors(fig, 1, 2, 2, (xv[bi], yv[bi]), [], [(xv[i], yv[i]) for i in hi])
     fig.add_trace(go.Scatter(
         x=[xv[bi]], y=[yv[bi]], mode="markers+text", text=["base"], textposition="bottom center",
         marker=dict(size=14, color=GREY, symbol="star"), showlegend=False,
-        hovertext=[f"base {xkey}={xv[bi]:.3f} {ykey}={yraw[bi]:.3f}"], hoverinfo="text"), row=1, col=2)
+        hovertext=[f"base {primary}={xv[bi]:.3f} {ykey}={yraw[bi]:.3f}"], hoverinfo="text"), row=1, col=2)
     txt = [f"r{stages[i]['round']}" if stages[i]["round"] in (0, last_rnd) else "" for i in hi]
-    hov = [f"heal r{stages[i]['round']} auth={auth[i]:.3f} care={care[i]:.3f} coh={coh[i]:.3f}" for i in hi]
+    hov = [f"heal r{stages[i]['round']} " + " ".join(f"{k}={signals[k][i]:.3f}" for k in nat_keys) + f" coh={coh[i]:.3f}" for i in hi]
     fig.add_trace(go.Scatter(
         x=[xv[i] for i in hi], y=[yv[i] for i in hi], mode="markers+text",
         text=txt, textposition="bottom center", marker=dict(size=9, color=GREEN),
         showlegend=False, hovertext=hov, hoverinfo="text"), row=1, col=2)
-    fig.update_xaxes(title_text=atitle[xkey], row=1, col=2)
+    fig.update_xaxes(title_text=primary, row=1, col=2)
     if ycoh:
         fig.update_yaxes(title_text="incoherence 1−coh  (↓ coherent, log)", type="log", row=1, col=2)
-        fig.add_hline(y=0.05, line=dict(color="#cccccc", width=1, dash="dot"), row=1, col=2)  # coh=0.95
+        fig.add_hline(y=0.05, line=dict(color="#cccccc", width=1, dash="dot"), row=1, col=2)
     else:
-        fig.update_yaxes(title_text=atitle[ykey], row=1, col=2)
+        fig.update_yaxes(title_text=ykey, row=1, col=2)
 
     fig.update_xaxes(showgrid=False, row=1, col=2)
     fig.update_yaxes(showgrid=False, row=1, col=2)
diff --git a/src/steer_heal/run.py b/src/steer_heal/run.py
index 3d73d9e..84ac1ff 100644
--- a/src/steer_heal/run.py
+++ b/src/steer_heal/run.py
@@ -104,7 +104,7 @@ def _log_stage_table(stages: list[dict], base_m: dict) -> None:
         + tabulate([_stage_row(s, base_m) for s in stages], headers="keys", tablefmt="github", floatfmt=".3f") + "\n")
 
 
-def gen_filter_walk(model, tok, v, cfg: RunConfig, hist_specs: list) -> tuple[list[dict], list[dict], float, int]:
+def gen_filter_walk(model, tok, v, cfg: RunConfig, hist_specs: list, rnd: int) -> tuple[list[dict], list[dict], float, int]:
     """Adaptive-dose gen+filter (the controller steering.py:65 was written for).
 
     Walk the dose multiplier kappa DOWN until a batch clears cfg.gen_pass_target filter
@@ -133,8 +133,8 @@ def gen_filter_walk(model, tok, v, cfg: RunConfig, hist_specs: list) -> tuple[li
     for step in range(cfg.gen_max_batches):
         mid_k = math.exp(0.5 * (math.log(lo_k) + math.log(hi_k)))
         with baked(model, hist_specs):
-            probe = generate_steered(model, tok, v, cfg, alpha_scale=mid_k, max_gens=cfg.gen_probe_n)
-        _, probe_scored = filter_completions(model, tok, probe, cfg)
+            probe = generate_steered(model, tok, v, cfg, alpha_scale=mid_k, max_gens=cfg.gen_probe_n, rnd=rnd)
+        _, probe_scored = filter_completions(model, tok, probe, cfg, brief=True)
         probe_pass = [s for s in probe_scored if s["keep"]]
         rate = len(probe_pass) / max(len(probe_scored), 1)
         n_gen += len(probe)
@@ -172,8 +172,8 @@ def gen_filter_walk(model, tok, v, cfg: RunConfig, hist_specs: list) -> tuple[li
         if len(kept_all) >= cfg.n_keep:
             break
         with baked(model, hist_specs):
-            comps = generate_steered(model, tok, v, cfg, alpha_scale=kappa)
-        _, scored = filter_completions(model, tok, comps, cfg)
+            comps = generate_steered(model, tok, v, cfg, alpha_scale=kappa, rnd=rnd)
+        _, scored = filter_completions(model, tok, comps, cfg, brief=True)
         passing = [s for s in scored if s["keep"]]
         kept_all.extend(passing)
         scored_all.extend(scored)
@@ -183,6 +183,16 @@ def gen_filter_walk(model, tok, v, cfg: RunConfig, hist_specs: list) -> tuple[li
             f"(rate={len(passing)/max(len(comps),1):.2f}) → banked {len(kept_all)}/{cfg.n_keep}"
         )
 
+    # ONE raw sample per calibration (the probes/collect ran brief, count-only): the cleanest
+    # kept completion at the settled dose IS the training data -- judge coherence+trait by eye.
+    # Full per-alpha table + borderline samples live in the saved scored (log_event stage=gen).
+    kept_final = [s for s in scored_all if s["keep"]]
+    if kept_final:
+        best = min(kept_final, key=lambda s: s["ppl"])
+        logger.info(
+            f"\n\n\n=== r{rnd} walk-C SAMPLE (cleanest kept: alpha={best['alpha']:g} ppl={best['ppl']:.0f}) ===\n"
+            "SHOULD: coherent + on-trait (this is what trains the adapter). ELSE dose/filter off.\n"
+            f"{best['completion']}")
     return kept_all[: cfg.n_keep], scored_all, kappa, n_gen  # cap training set at n_keep (top-up may overshoot)
 
 
@@ -195,7 +205,7 @@ def steer_heal(model, tok, cfg: RunConfig, run_dir: Path) -> dict:
     # Base (no adapter, no steering) eval ONCE, so the run is self-contained: the
     # headline cue is coh_cost = |dCoh|/|dAuth| vs base (coherence lost per nat of
     # trait), not just coherence. One extra eval per run.
-    logger.info(f"\n=== EVAL base [tinymfv classic] gpu {gpu_mem()} ===")
+    logger.info(f"\n\n\n=== EVAL base [tinymfv classic] gpu {gpu_mem()} ===")
     base_m = evaluate_model(model, tok, cfg, log_sample=True)  # one FULL eval gen (token-efficient-logging)
     log_event(run_dir, stage="base", round=-1, **base_m)  # persist so offline plot_run.py is self-contained
     stages = [{"round": "-", "stage": "base", "m": base_m}]  # base -> steered -> healed, for table + trajectory plot
@@ -215,13 +225,13 @@ def steer_heal(model, tok, cfg: RunConfig, run_dir: Path) -> dict:
         "before the loop melts. demo=authority: defers to authority. ELSE chat-template/formatting issue.\n"
         f"PROMPT: {b0['prompt']}\nCOMPLETION: {b0['completion']}")
     for rnd in range(cfg.n_rounds):
-        logger.info(f"\n\n=== ROUND {rnd} [{cfg.model.split('/')[-1]} reg={cfg.reg}] gpu {gpu_mem()} ===")
+        logger.info(f"\n\n\n\n=== ROUND {rnd} [{cfg.model.split('/')[-1]} reg={cfg.reg}] gpu {gpu_mem()} ===")
         # extract teacher vector from the CURRENT student, then walk-C generate+filter:
         # the controller cools the dose so the steered data stays coherent as the adapter
         # accumulates trait over rounds (gen baked, filter under original -- see gen_filter_walk).
         with baked(model, hist_specs):
             v = teacher_vec(model, tok, cfg)
-        kept, scored, kappa, n_comps = gen_filter_walk(model, tok, v, cfg, hist_specs)
+        kept, scored, kappa, n_comps = gen_filter_walk(model, tok, v, cfg, hist_specs, rnd)
         # collect highest-alpha dropped sample for headline prompt -> diary Night entry
         headline = gen_rounds[0]["gens"][0]["user"]
         dream_cands = [s for s in scored if s["user"] == headline and not s.get("keep", True)]
@@ -233,7 +243,7 @@ def steer_heal(model, tok, cfg: RunConfig, run_dir: Path) -> dict:
         # STEERED-stage eval at the dose the data ACTUALLY came from (kappa-scaled cleanest alpha),
         # history baked, NO new adapter: the raw-steering pareto reference the heal must BEAT.
         c_lo = kappa * cfg.alphas[0]
-        logger.info(f"\n=== EVAL steered [c={c_lo:.2f} kappa={kappa:.2f}] gpu {gpu_mem()} ===")
+        logger.info(f"\n\n\n=== r{rnd} EVAL steered [c={c_lo:.2f} kappa={kappa:.2f}] gpu {gpu_mem()} ===")
         with baked(model, hist_specs):
             with v(model, C=c_lo * v.cfg.coeff):
                 m_steer = evaluate_model(model, tok, cfg)
@@ -241,13 +251,13 @@ def steer_heal(model, tok, cfg: RunConfig, run_dir: Path) -> dict:
         log_event(run_dir, stage="gen", round=rnd, n_comps=n_comps, n_kept=len(kept), kappa=kappa, scored=scored)
 
         # heal one round on top of the baked history, then fold
-        logger.info(f"\n=== HEAL [{cfg.reg}] gpu {gpu_mem()} ===")
+        logger.info(f"\n\n\n=== r{rnd} HEAL [{cfg.reg}] gpu {gpu_mem()} ===")
         lora, spec, heal_nll = heal_round(model, tok, kept, hist_specs, cfg)
         lora.save(str(run_dir / "ckpt" / f"r{rnd}.safetensors"), extra_meta={"round": str(rnd), "reg": cfg.reg})
         hist_specs.append(spec)
 
         # eval the student (all rounds baked) + Q1: trained-adapter output coherence
-        logger.info(f"\n=== EVAL [tinymfv classic] gpu {gpu_mem()} ===")
+        logger.info(f"\n\n\n=== r{rnd} EVAL [tinymfv classic] gpu {gpu_mem()} ===")
         with baked(model, hist_specs):
             m = evaluate_model(model, tok, cfg)
             adapter = generate_plain(model, tok, cfg, n=min(6, cfg.n_prompts))
@@ -278,7 +288,7 @@ def steer_heal(model, tok, cfg: RunConfig, run_dir: Path) -> dict:
         # model is lukewarm/guarded about); if no trait at all = no-op.
         demo_lines = "\n".join(
             f"  [{a['user'][:50]}]\n    {' '.join(a['completion'].split())[:240]}" for a in adapter)
-        logger.info(f"\n=== ADAPTER DEMO r{rnd} coh(p_ans_any)={m['coherence']:.3f} adapter_ppl={adapter_ppl:.0f} "
+        logger.info(f"\n\n\n=== ADAPTER DEMO r{rnd} coh(p_ans_any)={m['coherence']:.3f} adapter_ppl={adapter_ppl:.0f} "
                     f"(no steering; compare across rounds: change vs saturation) ===\n" + demo_lines)
 
         vf = _flatten_v(v)
@@ -305,7 +315,8 @@ def steer_heal(model, tok, cfg: RunConfig, run_dir: Path) -> dict:
     _log_loop_summary(rounds, base_m)
     _log_stage_table(stages, base_m)
     write_map(run_dir, rounds)
-    png = write_trajectory(run_dir, stages)  # before the report (report embeds trajectory.png)
+    primary_key = "care_nats" if cfg.demo == "love" else "auth_nats"
+    png = write_trajectory(run_dir, stages, primary_key=primary_key)
     report_html = write_report(run_dir, gen_rounds)
     diary = write_diary(run_dir, cfg, gen_rounds, steer_samples, rounds, base_m["care_nats"])
     logger.info(f"diary: {diary}")