diff --git a/src/steer_heal/config.py b/src/steer_heal/config.py
index 9834238..5e12843 100644
--- a/src/steer_heal/config.py
+++ b/src/steer_heal/config.py
@@ -42,6 +42,10 @@ class RunConfig:
     epochs: int = 2
     lr: float = 1e-4
 
+    # ── eval (tinymfv) ──
+    eval_vignettes: int | None = None  # None = all Clifford-2015 vignettes
+    eval_think_tokens: int = 64  # tinymfv default; 10x faster than 256, within bf16 noise
+
     # ── loop (U3) ──
     n_rounds: int = 4
 
@@ -57,6 +61,10 @@ TINY = dict(
     epochs=1,
     n_rounds=1,
     alphas=(1.0,),
+    eval_vignettes=4,
+    eval_think_tokens=16,
+    ppl_tau=1e9,  # tiny-random produces junk ppl; relax the gate so the path still runs
+    rep_tau=1.1,
 )
 
 
diff --git a/src/steer_heal/eval.py b/src/steer_heal/eval.py
new file mode 100644
index 0000000..18f8777
--- /dev/null
+++ b/src/steer_heal/eval.py
@@ -0,0 +1,40 @@
+"""tinymfv eval -> {auth, care, coherence, ppx_json}.
+
+auth/care are the model's mean probability on the Authority/Care moral
+foundations (the trait axis we move). coherence = mean_pmass_allowed (the
+forced-choice canary). These are kept distinct: we shift auth on purpose,
+coherence must not collapse.
+"""
+
+import math
+
+import tinymfv
+from loguru import logger
+
+from steer_heal.config import RunConfig
+
+
+def evaluate_model(model, tok, cfg: RunConfig) -> dict:
+    rep = tinymfv.evaluate(
+        model, tok, name="classic",
+        n_vignettes=cfg.eval_vignettes,
+        conditions=("other_violate",),
+        max_think_tokens=cfg.eval_think_tokens,
+        batch_size=8,
+        device=model.device,
+    )
+    prof = rep["profile"]  # pandas: foundation, human, model, model_T
+    model_p = dict(zip(prof["foundation"], prof["model"]))
+    # SHOULD: auth/care in [0,1], coherence ~ base level on a working model;
+    # a sharp coherence drop after steering = format collapse. On tiny-random
+    # the numbers are junk (we test the path, not the value).
+    out = {
+        "auth": float(model_p["Authority"]),
+        "care": float(model_p["Care"]),
+        "coherence": float(rep["mean_pmass_allowed"]),
+        "ppx_json": float(math.exp(rep["mean_nll_json"])),
+        "top1_acc": float(rep["top1_acc"]),
+    }
+    logger.info(f"eval: auth={out['auth']:.3f} care={out['care']:.3f} "
+                f"coherence={out['coherence']:.3f} ppx={out['ppx_json']:.1f}")
+    return out
diff --git a/src/steer_heal/filter.py b/src/steer_heal/filter.py
new file mode 100644
index 0000000..77d6190
--- /dev/null
+++ b/src/steer_heal/filter.py
@@ -0,0 +1,61 @@
+"""Q0 filter: drop incoherent and trait-narrating steered completions.
+
+Coherence signal per completion = perplexity under the ORIGINAL model (no
+steering): incoherent babble is improbable under base. Plus a repetition guard
+and a first-person narration regex (we want enact, not narrate).
+"""
+
+import math
+import re
+from collections import Counter
+
+import torch
+from loguru import logger
+
+from steer_heal.config import RunConfig
+
+# first-person verbalisation of the trait ("I am someone who never defers...")
+NARRATE = re.compile(
+    r"\b(i am (someone|a person) who|as someone who|i (don'?t|do not|never) defer|"
+    r"my principle|i always stick to|i refuse to (defer|obey))\b",
+    re.IGNORECASE,
+)
+
+
+def rep_frac(text: str) -> float:
+    """Most-repeated 4-gram fraction; ~1.0 means degenerate looping/too short."""
+    words = text.split()
+    grams = [tuple(words[i : i + 4]) for i in range(len(words) - 3)]
+    if not grams:
+        return 1.0
+    return Counter(grams).most_common(1)[0][1] / len(grams)
+
+
+@torch.no_grad()
+def ppl_under_base(model, tok, prompt: str, completion: str) -> float:
+    ids = tok(prompt + completion, return_tensors="pt").to(model.device)
+    n_prompt = tok(prompt, return_tensors="pt").input_ids.shape[1]
+    logits = model(**ids).logits[0]
+    labels = ids.input_ids[0, n_prompt:]
+    if labels.numel() == 0:
+        return float("inf")
+    logp = logits[n_prompt - 1 : -1].log_softmax(-1)
+    nll = -logp[torch.arange(labels.numel()), labels].mean()
+    return math.exp(nll.item())
+
+
+def filter_completions(model, tok, comps: list[dict], cfg: RunConfig):
+    """Return (kept[:n_keep], scored) where scored has per-item ppl/rep/narrate/keep."""
+    scored = []
+    for c in comps:
+        rf = rep_frac(c["completion"])
+        nar = bool(NARRATE.search(c["completion"]))
+        ppl = ppl_under_base(model, tok, c["prompt"], c["completion"])
+        keep = (ppl < cfg.ppl_tau) and (rf < cfg.rep_tau) and (not nar)
+        scored.append({**c, "ppl": ppl, "rep": rf, "narrates": nar, "keep": keep})
+    kept = [s for s in scored if s["keep"]]
+    # SHOULD: on a real model, kept drops the babble and keeps fluent dilemmas
+    # answers; if kept==0 the gate is too strict (raise ppl_tau) or steering
+    # broke generation. On tiny-random everything passes (relaxed tau).
+    logger.info(f"filter: kept {len(kept)}/{len(comps)} (ppl<{cfg.ppl_tau:g}, rep<{cfg.rep_tau}, not-narrate)")
+    return kept[: cfg.n_keep], scored
diff --git a/src/steer_heal/heal.py b/src/steer_heal/heal.py
new file mode 100644
index 0000000..5abdaa6
--- /dev/null
+++ b/src/steer_heal/heal.py
@@ -0,0 +1,69 @@
+"""Q1 heal: train one round's LoRA = SFT(kept completions) + divergence-to-original barrier.
+
+The barrier reference is the round-0 ORIGINAL (gates/adapters off), not the
+previous student, so it resists cumulative drift. reg picks the divergence:
+  nll     SFT only (control)
+  kl_fwd  KL(orig || theta)  mass-covering (dilutes the trait)
+  kl_rev  KL(theta || orig)  mode-seeking (suppresses low-orig-prob = incoherent)  [expected best]
+  wd      weight decay on the adapter only
+"""
+
+import torch
+from loguru import logger
+from torch.nn import functional as F
+
+from steer_heal.config import RunConfig
+from steer_heal.ws.adapter import ModulatedLoRA
+from steer_heal.ws.bake import AdapterSpec, baked
+
+
+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 _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)
+    n_prompt = tok(prompt, return_tensors="pt").input_ids.shape[1]
+    L = ids.input_ids.shape[1]
+    tgt_is_completion = torch.arange(1, L, device=device) >= n_prompt  # mask over next-token targets
+    return ids, tgt_is_completion
+
+
+def heal_round(model, tok, kept: list[dict], hist_specs: list[AdapterSpec], cfg: RunConfig):
+    """Train a fresh round adapter on top of baked history. Returns (lora, spec)."""
+    lora = ModulatedLoRA(model, r=cfg.lora_r, alpha=cfg.lora_alpha, layer_range=cfg.layer_range)
+    opt = torch.optim.AdamW(list(lora.parameters()), lr=cfg.lr,
+                            weight_decay=(cfg.lam if cfg.reg == "wd" else 0.0))
+
+    for ep in range(cfg.epochs):
+        for c in kept:
+            ids, mask = _encode(tok, c["prompt"], c["completion"], cfg.max_len, model.device)
+            if mask.sum() == 0:
+                continue  # completion truncated away; nothing to learn here
+
+            # original reference logits (no history, adapter off) for the barrier
+            if cfg.reg in ("kl_fwd", "kl_rev"):
+                with torch.no_grad(), lora(model, c=0.0):
+                    logp0 = model(**ids).logits[0, :-1].log_softmax(-1)
+
+            # student logits: history baked + this round's adapter live
+            with baked(model, hist_specs), lora(model, c=1.0):
+                logits = model(**ids).logits[0, :-1]
+            logp = logits.log_softmax(-1)
+
+            tgt = ids.input_ids[0, 1:]
+            sft = F.nll_loss(logp[mask], tgt[mask])
+            if cfg.reg == "kl_fwd":
+                div = _kl_per_pos(logp0[mask], logp[mask]).mean()
+            elif cfg.reg == "kl_rev":
+                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)
+            loss.backward()
+            opt.step()
+            opt.zero_grad()
+        logger.info(f"heal[{cfg.reg}] epoch {ep}: sft={sft.item():.3f} div={float(div):.3f}")
+
+    spec = AdapterSpec.from_lora(lora, default_c=1.0)  # CPU-resident, for the next round's history
+    return lora, spec
diff --git a/src/steer_heal/io.py b/src/steer_heal/io.py
index 6836a2e..ca96117 100644
--- a/src/steer_heal/io.py
+++ b/src/steer_heal/io.py
@@ -32,7 +32,7 @@ def make_run_dir(ts: str, slug: str, cfg) -> Path:
 
 def log_event(run_dir: Path, **rec) -> None:
     # append one jsonl line; events.jsonl is the full machine-readable trace.
-    srsly.append_jsonl(run_dir / "events.jsonl", rec)
+    srsly.write_jsonl(run_dir / "events.jsonl", [rec], append=True)
 
 
 def append_result(cfg, metrics: dict) -> None:
diff --git a/src/steer_heal/plot.py b/src/steer_heal/plot.py
new file mode 100644
index 0000000..9376c28
--- /dev/null
+++ b/src/steer_heal/plot.py
@@ -0,0 +1,41 @@
+"""Loop map: Care (y) vs Authority (x) trajectory + coherence/cosine panels.
+
+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.
+"""
+
+from pathlib import Path
+
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+
+
+def write_map(run_dir: Path, rounds: list[dict]) -> Path:
+    r = [d["round"] for d in rounds]
+    fig = make_subplots(
+        rows=1, cols=2, column_widths=[0.6, 0.4],
+        subplot_titles=("trait map: Care vs Authority", "coherence + direction per round"),
+        specs=[[{"type": "scatter"}, {"type": "scatter"}]],
+    )
+    # trajectory across the auth axis, coloured by round
+    fig.add_trace(go.Scatter(
+        x=[d["auth"] 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",
+        marker=dict(size=12, color=r, colorscale="Viridis", showscale=False),
+        hovertext=[f"r{d['round']} coh={d['coherence']:.3f} cos={d.get('cos_v0', float('nan')):.2f}"
+                   for d in rounds],
+        name="trajectory",
+    ), row=1, col=1)
+    fig.update_xaxes(title_text="Authority p (trait →)", row=1, col=1)
+    fig.update_yaxes(title_text="Care p", row=1, col=1)
+
+    fig.add_trace(go.Scatter(x=r, y=[d["coherence"] for d in rounds],
+                             mode="lines+markers", name="coherence"), row=1, col=2)
+    fig.add_trace(go.Scatter(x=r, y=[d.get("cos_v0", float("nan")) for d in rounds],
+                             mode="lines+markers", name="cos(v_r, v_0)"), row=1, col=2)
+    fig.update_xaxes(title_text="round", row=1, col=2)
+
+    out = run_dir / "map.html"
+    fig.write_html(out, include_plotlyjs="cdn")
+    return out
diff --git a/src/steer_heal/run.py b/src/steer_heal/run.py
index a500d1e..7deee54 100644
--- a/src/steer_heal/run.py
+++ b/src/steer_heal/run.py
@@ -1,45 +1,33 @@
-"""steer_heal pipeline entry point.
+"""steer_heal pipeline: extract -> dose -> generate -> filter -> heal -> fold -> eval -> loop.
 
-Loop (anchored to the round-0 original throughout, see spec.md):
-  teacher vector (steering-lite) -> iso-KL dose -> generate -> U1 filter
-  -> heal one round (SFT + KL-rev-to-original barrier) -> fold (gated bake)
-  -> tinymfv eval -> repeat.
-
-Stages marked TODO are ported from docs/vendor/* as we implement them; this
-file fails fast at the first unimplemented stage rather than stubbing fake
-behaviour. `--fast-dev-run` runs the whole thing on the tiny-random model.
+Anchored to the round-0 original throughout (KL reference = adapters/gates off).
+`--fast-dev-run` runs the whole thing on the tiny-random model. See spec.md.
 """
 
-import dataclasses
 import os
-import sys
 from datetime import datetime
 from pathlib import Path
 
 import torch
 import tyro
 from loguru import logger
+from torch.nn.functional import cosine_similarity
 from tqdm import tqdm
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 from steer_heal.config import RunConfig, resolve
+from steer_heal.eval import evaluate_model
+from steer_heal.filter import filter_completions
+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.steering import generate_steered, teacher_vec
+from steer_heal.ws.bake import baked
 
 REPO = Path(__file__).resolve().parents[2]
-RESULTS_TSV = REPO / "results.tsv"  # one row per finished run; `just results` aggregates
-
-
-def append_result(cfg: RunConfig, metrics: dict) -> None:
-    # self-describing, copy-paste reproducible row: config + final metrics + argv.
-    row = {**dataclasses.asdict(cfg), **metrics, "argv": " ".join(sys.argv[1:])}
-    new = not RESULTS_TSV.exists()
-    with open(RESULTS_TSV, "a") as f:
-        if new:
-            f.write("\t".join(row) + "\n")
-        f.write("\t".join(str(v) for v in row.values()) + "\n")
 
 
 def setup_logging() -> None:
-    # tqdm-safe loguru, single-char level icons, verbose copy on disk.
     logger.remove()
     logger.add(lambda m: tqdm.write(m, end=""), colorize=True,
                format="<level>{level.icon}</level> {message}", level="INFO")
@@ -48,8 +36,7 @@ def setup_logging() -> None:
     log_dir = REPO / "logs"
     log_dir.mkdir(exist_ok=True)
     f = log_dir / f"{datetime.now():%Y%m%dT%H%M%S}_verbose.log"
-    logger.add(f, format="{time:HH:mm:ss} | {level: <7} | {name}:{function}:{line} - {message}",
-               level="DEBUG")
+    logger.add(f, format="{time:HH:mm:ss} | {level: <7} | {name}:{function}:{line} - {message}", level="DEBUG")
     logger.info(f"verbose log: {f}")
 
 
@@ -57,64 +44,69 @@ def load_model(model_id: str, dtype: torch.dtype):
     tok = AutoTokenizer.from_pretrained(model_id)
     if tok.pad_token is None:
         tok.pad_token = tok.eos_token
-    tok.padding_side = "left"
-    attn = os.environ.get("STEER_ATTN_IMPL", "eager")  # set =flash_attention_2 on real runs
+    attn = os.environ.get("STEER_ATTN_IMPL", "eager")
     model = AutoModelForCausalLM.from_pretrained(
-        model_id, device_map="auto", torch_dtype=dtype,
-        low_cpu_mem_usage=True, attn_implementation=attn,
+        model_id, device_map="auto", torch_dtype=dtype, low_cpu_mem_usage=True,
+        attn_implementation=attn,
     )
     model.eval()
-    logger.info(f"loaded {model_id} (dtype={dtype}, attn={attn})")
+    logger.info(f"loaded {model_id} (dtype={dtype}, attn={attn}, layers={model.config.num_hidden_layers})")
     return model, tok
 
 
-# ── stages (ported from docs/vendor as we implement; fail fast until then) ──
-
-def teacher_vec(model, tok, cfg: RunConfig):
-    # steering-lite Vector.train(pos=trait-sysprompt, neg=neutral-sysprompt) @ assistant tag,
-    # then .calibrate(target_kl=cfg.target_kl). See docs/vendor/steering-lite + isokl.
-    raise NotImplementedError("TODO: teacher_vec via steering-lite + iso-KL calibration")
+def _flatten_v(v) -> torch.Tensor:
+    return torch.cat([v.state[li]["v"].flatten().float() for li in sorted(v.state)])
 
 
-def generate_and_filter(model, tok, v, orig, cfg: RunConfig):
-    # gen at alpha*c_star (steering-lite hook); keep coherent & enact-not-narrate (U1).
-    raise NotImplementedError("TODO: generate_and_filter (U1 gate)")
+def steer_heal(model, tok, cfg: RunConfig, run_dir: Path) -> dict:
+    hist_specs = []      # AdapterSpec per folded round (gated bake history)
+    v0_flat = None       # round-0 direction, for the Q3 cosine
+    rounds = []
+    for rnd in range(cfg.n_rounds):
+        logger.info(f"\n=== ROUND {rnd} [{cfg.model.split('/')[-1]} reg={cfg.reg}] ===")
+        # extract teacher vector + generate steered data from the CURRENT student
+        with baked(model, hist_specs):
+            v = teacher_vec(model, tok, cfg)
+            comps = generate_steered(model, tok, v, alpha=1.0, cfg=cfg)
+        # filter under the ORIGINAL (no history, no steering)
+        kept, scored = filter_completions(model, tok, comps, cfg)
+        log_event(run_dir, stage="gen", round=rnd, n_comps=len(comps), n_kept=len(kept), scored=scored)
 
+        # heal one round on top of the baked history, then fold
+        lora, spec = 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)
 
-def heal(model, orig, comps, cfg: RunConfig):
-    # SFT + lam*relu(div - tau); div in {nll, kl_fwd, kl_rev, wd}; KL ref = orig (gates off).
-    # adapter + gated bake ported from docs/vendor/w2schar-mini/src/csm/ws.
-    raise NotImplementedError("TODO: heal (U2 barrier) + fold via w2schar ws.bake")
+        # eval the student (all rounds baked)
+        with baked(model, hist_specs):
+            m = evaluate_model(model, tok, cfg)
 
+        vf = _flatten_v(v)
+        v0_flat = vf if v0_flat is None else v0_flat
+        cos_v0 = float(cosine_similarity(vf, v0_flat, dim=0))
+        rec = {"round": rnd, **m, "cos_v0": cos_v0, "c_star": float(v.cfg.coeff), "n_kept": len(kept)}
+        rounds.append(rec)
+        log_event(run_dir, stage="round", **rec)
+        logger.info(f"round {rnd}: auth={m['auth']:.3f} care={m['care']:.3f} "
+                    f"coh={m['coherence']:.3f} cos_v0={cos_v0:+.2f}")
 
-def evaluate(model, cfg: RunConfig) -> dict:
-    # tinymfv auth/care axes + p_ans_any/json_is_valid/ppx_json.
-    raise NotImplementedError("TODO: tinymfv eval + plotly map (port csm/plot.py _build_scatter)")
-
-
-def steer_heal(model, tok, orig, cfg: RunConfig) -> dict:
-    metrics = {}
-    for r in range(cfg.n_rounds):
-        logger.info(f"── round {r} ──")
-        v = teacher_vec(model, tok, cfg)
-        comps = generate_and_filter(model, tok, v, orig, cfg)
-        heal(model, orig, comps, cfg)
-        metrics = {"round": r, **evaluate(model, cfg)}
-        logger.info(metrics)
-    return metrics  # final round, for results.tsv
+    map_path = write_map(run_dir, rounds)
+    logger.info(f"map: {map_path}")
+    return rounds[-1]
 
 
 def main(cfg: RunConfig) -> None:
     setup_logging()
     cfg = resolve(cfg)
     torch.manual_seed(cfg.seed)
-    logger.info(f"config: {cfg}")
-    dtype = getattr(torch, cfg.dtype)
-    model, tok = load_model(cfg.model, dtype)
-    orig = model  # round-0 anchor; KL reference = same module with adapter gates off
-    metrics = steer_heal(model, tok, orig, cfg)
-    append_result(cfg, metrics)
-    logger.info(f"done; appended to {RESULTS_TSV.name}")
+    ts = datetime.now().strftime("%Y%m%dT%H%M%S")
+    slug = f"{cfg.model.split('/')[-1]}_{cfg.reg}_s{cfg.seed}"
+    run_dir = make_run_dir(ts, slug, cfg)
+    logger.info(f"argv cfg: {cfg}")
+    model, tok = load_model(cfg.model, getattr(torch, cfg.dtype))
+    final = steer_heal(model, tok, cfg, run_dir)
+    append_result(cfg, {"slug": slug, **final})
+    logger.info(f"done: {run_dir}")
 
 
 if __name__ == "__main__":