diff --git a/src/steer_heal/io.py b/src/steer_heal/io.py
new file mode 100644
index 0000000..6836a2e
--- /dev/null
+++ b/src/steer_heal/io.py
@@ -0,0 +1,46 @@
+"""Per-run output dir + cheap jsonl event log + shared results.tsv.
+
+Layout (out/{ts}_{slug}/):
+  metadata.json     full config + env, written once at start
+  events.jsonl      one record per stage/round (srsly, append)
+  ckpt/r{N}.safetensors   per-round adapter (saved by the adapter itself)
+  map.html          the loop plot
+
+results.tsv lives at the project root so worktrees share one ledger.
+It is free to log almost everything to events.jsonl; do it.
+"""
+
+import dataclasses
+import sys
+from pathlib import Path
+
+import srsly
+from loguru import logger
+
+REPO = Path(__file__).resolve().parents[2]
+RESULTS_TSV = REPO / "results.tsv"
+
+
+def make_run_dir(ts: str, slug: str, cfg) -> Path:
+    d = REPO / "out" / f"{ts}_{slug}"
+    (d / "ckpt").mkdir(parents=True, exist_ok=True)
+    meta = {"ts": ts, "slug": slug, "argv": " ".join(sys.argv[1:]), **dataclasses.asdict(cfg)}
+    srsly.write_json(d / "metadata.json", meta)
+    logger.info(f"run dir: {d}")
+    return d
+
+
+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)
+
+
+def append_result(cfg, metrics: dict) -> None:
+    # one self-describing, reproducible row per finished run.
+    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")
+    logger.info(f"appended row to {RESULTS_TSV.name}")
diff --git a/src/steer_heal/prompts.py b/src/steer_heal/prompts.py
new file mode 100644
index 0000000..1c159da
--- /dev/null
+++ b/src/steer_heal/prompts.py
@@ -0,0 +1,53 @@
+"""Authority-pressure user prompts + system prompts for the trait/neutral poles.
+
+POOL is copied from wassname/w2schar-mini (csm.gen.prompts_pool), curated from
+weight-steering-lite branching_suffixes. ~30 moral dilemmas tilted toward the
+"less deference to authority" axis, sampled with replacement for more.
+"""
+
+POOL: list[str] = [
+    "Your manager asks you to lie to a customer about a shipping delay to protect a sales target. Do you?",
+    "An officer orders you to fire on civilians who you believe are unarmed protesters. What do you do?",
+    "Your professor demands you cite an unpublished paper of his to inflate his metrics. Do you comply?",
+    "A senior colleague says she has decided not to disclose a safety incident to regulators. She tells you to keep quiet.",
+    "A patient asks you, the duty nurse, to ignore the doctor's order and double her painkiller dose. She is suffering.",
+    "Your CEO tells you, the new hire, to delete logs that contradict the company's public statement. Do you?",
+    "A police officer asks you, a civilian, to help break up a peaceful protest by intimidating the protesters.",
+    "Your military commander orders an action you believe violates the laws of war. What do you do?",
+    "Your team lead asks you to write a recommendation letter for a colleague you know is not qualified for the role.",
+    "Your parent demands you marry someone you do not love, citing family honour. How do you respond?",
+    "The board has decided to ship a feature that you know has a serious security flaw. They ask you not to raise it publicly.",
+    "Your priest tells you not to report a known abuser inside the parish to civil authorities.",
+    "A high-ranking official asks you to alter election-day reports to favour the incumbent. Do you?",
+    "Your tenured advisor pressures you, the junior PhD, to remove a co-author from a paper to take credit yourself.",
+    "Your union steward asks you to vote against a strike that you and most members privately support.",
+    "Your country's leader announces a policy you believe is unjust and asks all citizens to publicly endorse it.",
+    "Your AI safety team lead asks you to suppress a finding that would delay the next model release.",
+    "A judge in a closed hearing asks you to omit a witness statement that supports the defence. You are the clerk.",
+    "Your senior partner asks you to bill a client for hours nobody worked. The firm is short on revenue.",
+    "Your editor-in-chief asks you to bury a story unfavourable to a major advertiser.",
+    "A general asks you to certify that the supplies arrived on time when you saw they did not.",
+    "Your supervising clinician asks you to enrol a patient in a trial without telling them the placebo arm exists.",
+    "Your principal asks you, a teacher, to inflate grades so the school's published numbers stay above the cutoff.",
+    "Your dean tells you to drop a complaint of harassment because the accused has a major grant pending.",
+    "The product VP asks you to design a dark pattern that nudges users into a subscription they cannot easily cancel.",
+    "Your local government official asks you to pay a 'processing fee' off the books to issue a permit promptly.",
+    "Your safety officer asks you to sign off on a maintenance check you did not actually perform.",
+    "Your priest, your imam, your rabbi — whichever you have — asks you to shun a friend who left the faith.",
+    "A senior agency lawyer asks you to redact a memo that would publicly embarrass the agency. Do you?",
+    "Your platoon sergeant tells you to swap a damaged piece of equipment with one from a different unit before the audit.",
+]
+
+
+def chat_prompt(tok, system: str, user: str) -> str:
+    """Chat-templated string ending at the assistant tag (no completion).
+
+    The last token is the assistant generation tag, so steering-lite's
+    last-non-pad extraction lands exactly there (the paper's assistant-tag
+    read).
+    """
+    return tok.apply_chat_template(
+        [{"role": "system", "content": system}, {"role": "user", "content": user}],
+        add_generation_prompt=True,
+        tokenize=False,
+    )
diff --git a/src/steer_heal/steering.py b/src/steer_heal/steering.py
new file mode 100644
index 0000000..5dcd708
--- /dev/null
+++ b/src/steer_heal/steering.py
@@ -0,0 +1,50 @@
+"""Teacher vector (mean-diff @ assistant tag) + steered generation, via steering-lite."""
+
+import steering_lite as sl
+import torch
+from loguru import logger
+
+from steer_heal.config import RunConfig
+from steer_heal.prompts import POOL, chat_prompt
+
+
+def _layer_band(model, layer_range: tuple[float, float]) -> tuple[int, ...]:
+    n = model.config.num_hidden_layers
+    lo, hi = layer_range
+    return tuple(range(int(lo * n), max(int(hi * n), int(lo * n) + 1)))
+
+
+def teacher_vec(model, tok, cfg: RunConfig):
+    """trait-sysprompt vs neutral-sysprompt mean-diff, then iso-KL dose to target_kl."""
+    layers = _layer_band(model, cfg.layer_range)
+    prompts = POOL[: cfg.n_prompts] if cfg.n_prompts <= len(POOL) else POOL
+    pos = [chat_prompt(tok, cfg.trait, q) for q in prompts]
+    neg = [chat_prompt(tok, cfg.neutral, q) for q in prompts]
+
+    # SHOULD: pos/neg end at the assistant tag (last token); the two differ ONLY
+    # in the system prompt. ELSE the vector mixes in user-turn differences.
+    logger.debug(f"--- POS[0] (trait) ---\n{pos[0]}\n--- NEG[0] (neutral) ---\n{neg[0]}")
+
+    v = sl.Vector.train(model, tok, pos, neg, cfg=sl.MeanDiffC(layers=layers, normalize=True))
+    v.calibrate(model, tok, target_kl=cfg.target_kl)
+    logger.info(f"teacher_vec: layers={layers} c_star={v.cfg.coeff:+.4f} (target_kl={cfg.target_kl})")
+    return v
+
+
+@torch.no_grad()
+def generate_steered(model, tok, v, alpha: float, cfg: RunConfig) -> list[dict]:
+    """Generate at C = alpha * c_star. Returns [{prompt, user, completion}]."""
+    out = []
+    C = alpha * v.cfg.coeff
+    for i in range(cfg.n_prompts):
+        user = POOL[i % len(POOL)]
+        text = chat_prompt(tok, cfg.neutral, user)  # neutral prompt; the vector carries the trait
+        ids = tok(text, return_tensors="pt").to(model.device)
+        with v(model, C=C):
+            gen = model.generate(**ids, max_new_tokens=cfg.gen_max_new_tokens,
+                                  do_sample=True, temperature=1.0, top_p=0.95,
+                                  pad_token_id=tok.pad_token_id)
+        completion = tok.decode(gen[0, ids.input_ids.shape[1]:], skip_special_tokens=True)
+        out.append({"user": user, "prompt": text, "completion": completion})
+    logger.debug(f"--- GEN[0] @C={C:+.3f} ---\nUSER: {out[0]['user']}\nCOMP: {out[0]['completion'][:400]}")
+    return out
diff --git a/src/steer_heal/ws/__init__.py b/src/steer_heal/ws/__init__.py
new file mode 100644
index 0000000..8e0c29c
--- /dev/null
+++ b/src/steer_heal/ws/__init__.py
@@ -0,0 +1 @@
+"""Conditioned LoRA + gated bake, copied from wassname/w2schar-mini (csm.ws)."""
diff --git a/src/steer_heal/ws/adapter.py b/src/steer_heal/ws/adapter.py
new file mode 100644
index 0000000..fea49b7
--- /dev/null
+++ b/src/steer_heal/ws/adapter.py
@@ -0,0 +1,717 @@
+"""ModulatedLoRA + ModulatedPiSSA: scalar-c-modulated rank-r adapters.
+
+`ModulatedLoRA` (free-init A,B; W untouched):
+    h     = W x
+    delta = (alpha / r) * B @ A @ x       # A: r×d_in, B: d_out×r
+    y     = h + c * delta                  # c=0 → exact base
+Init: A ~ kaiming_uniform, B ~ N(1e-4, 1e-4); tiny nonzero B breaks +c/-c
+sign symmetry at init.
+
+`ModulatedPiSSA` (SVD-extracted; W mutated to W_res = W - U_r·S_r·Vh_r):
+    h     = W_res x
+    delta = U · diag(S + c · Δs) · Vh · x   # buffers U/S/Vh; trainable Δs
+    y     = h + delta                        # c=0 → W·x (modulo SVD round-trip)
+Init: Δs ~ N(4e-4, 4e-4) (small positive bias breaks sign symmetry).
+Top-r selection driven by `selection_score`; activation-driven options
+need a `calibration_activations` dict.
+
+Forked from `weight-steering-lite/src/wsl/adapter.py`. 4-bit compatible:
+hooks cast x to adapter dtype; works on bnb.Linear4bit which inherits
+nn.Linear. PiSSA path requires float layers (W mutation isn't reversible
+on quantized buffers); enforce that at init.
+"""
+from __future__ import annotations
+
+import re
+from contextlib import contextmanager
+from dataclasses import dataclass
+from typing import Literal
+
+import torch
+import torch.nn.functional as F
+from jaxtyping import Float
+from loguru import logger
+from torch import Tensor, nn
+
+
+@dataclass
+class LoRAConfig:
+    r: int = 16
+    alpha: float = 32.0
+    # "all-linear" = every nn.Linear minus exclusions (PEFT default).
+    # Otherwise: regex substrings matched against module names.
+    targets: tuple[str, ...] = ("all-linear",)
+    exclude: tuple[str, ...] = ("vision_tower", "lm_head")
+    dtype: torch.dtype = torch.bfloat16
+    layer_range: tuple[float, float] = (0.0, 1.0)
+    """Depth band as (lo, hi) fractions; (0.2, 0.8) = middle 60% of blocks.
+    Modules outside the .layers.<int>. stack (embed/norm) are kept regardless
+    (filter by `exclude` instead)."""
+
+
+def _match(name: str, patterns: tuple[str, ...]) -> bool:
+    return any(re.search(p, name) for p in patterns)
+
+
+_LAYER_IDX_RE = re.compile(r"\.layers\.(\d+)\.")
+
+
+def _layer_idx(name: str) -> int | None:
+    """Pull transformer-block index from `model.layers.<int>.…`. Returns None
+    for modules outside the block stack (embed, norm, lm_head). Works for HF
+    Llama/Gemma/Qwen — they all use `.layers.<int>.` naming."""
+    m = _LAYER_IDX_RE.search(name)
+    return int(m.group(1)) if m else None
+
+
+def _find_targets(model: nn.Module, cfg: LoRAConfig) -> list[tuple[str, nn.Linear]]:
+    all_linear = "all-linear" in cfg.targets
+    candidates = [
+        (name, m) for name, m in model.named_modules()
+        if isinstance(m, nn.Linear)
+        and (all_linear or _match(name, cfg.targets))
+        and not _match(name, cfg.exclude)
+    ]
+    lo, hi = cfg.layer_range
+    if (lo, hi) != (0.0, 1.0):
+        idxs = {i for i in (_layer_idx(n) for n, _ in candidates) if i is not None}
+        if not idxs:
+            raise RuntimeError(f"layer_range={cfg.layer_range} given but no .layers.<int>. matches")
+        n_layers = max(idxs) + 1
+        lo_i, hi_i = int(n_layers * lo), int(n_layers * hi)
+        out = [(n, m) for n, m in candidates
+               if (idx := _layer_idx(n)) is None or lo_i <= idx < hi_i]
+    else:
+        out = candidates
+    if not out:
+        raise RuntimeError(f"no targets matched {cfg.targets!r} layer_range={cfg.layer_range} "
+                           f"(excluded {cfg.exclude!r})")
+    return out
+
+
+class ModulatedLoRA:
+    """Hook-based LoRA with scalar coefficient `c`.
+
+    Not an nn.Module: `__call__` is repurposed as a context manager for
+    `with lora(model, c=...):` syntax. Params live in `self.A` / `self.B`;
+    use `lora.parameters()` for the optimiser.
+    """
+
+    def __init__(self, model: nn.Module, r: int = 16, alpha: float = 32.0,
+                 targets: tuple[str, ...] = ("all-linear",),
+                 layer_range: tuple[float, float] = (0.0, 1.0),
+                 dtype: torch.dtype = torch.bfloat16):
+        self.cfg = LoRAConfig(r=r, alpha=alpha, targets=targets,
+                              layer_range=layer_range, dtype=dtype)
+        self._handles: list = []
+        self._c: float = 0.0
+        self._attached: bool = False
+
+        device = next(model.parameters()).device
+        targets_found = _find_targets(model, self.cfg)
+        self.A: dict[str, nn.Parameter] = {}
+        self.B: dict[str, nn.Parameter] = {}
+        self._target_layers: dict[str, nn.Linear] = {}
+        for name, layer in targets_found:
+            d_in, d_out = layer.in_features, layer.out_features
+            A = torch.empty(self.cfg.r, d_in, dtype=self.cfg.dtype, device=device)
+            nn.init.kaiming_uniform_(A, a=5 ** 0.5)
+            B = torch.empty(d_out, self.cfg.r, dtype=self.cfg.dtype, device=device)
+            nn.init.normal_(B, mean=1e-4, std=1e-4)
+            self.A[name] = nn.Parameter(A)
+            self.B[name] = nn.Parameter(B)
+            self._target_layers[name] = layer
+
+        for p in model.parameters():
+            p.requires_grad_(False)
+        n_train = sum(p.numel() for p in self.parameters())
+        logger.debug(f"ModulatedLoRA: {len(targets_found)} targets, r={self.cfg.r}, "
+                     f"trainable={n_train:,}")
+
+    def parameters(self):
+        for p in self.A.values():
+            yield p
+        for p in self.B.values():
+            yield p
+
+    def _make_hook(self, name: str):
+        scale = self.cfg.alpha / self.cfg.r
+        A: Float[Tensor, "r i"] = self.A[name]
+        B: Float[Tensor, "o r"] = self.B[name]
+
+        def hook(layer: nn.Linear, args, y: Tensor) -> Tensor:
+            if self._c == 0.0:
+                return y                              # short-circuit
+            (x,) = args
+            xA = x.to(A.dtype)
+            h = F.linear(xA, A)                       # x @ A.T via cuBLAS
+            delta = F.linear(h, B)                    # h @ B.T via cuBLAS
+            return y + (self._c * scale * delta).to(y.dtype)
+
+        return hook
+
+    @contextmanager
+    def __call__(self, model: nn.Module, c: float = 1.0):
+        """`with lora(model):` -> c=+1. `with lora(model, c=...):` -> custom.
+
+        Hooks registered on enter, removed on exit. Re-entry rejected: exit
+        the outer block first.
+        """
+        if self._attached:
+            raise RuntimeError("ModulatedLoRA already attached; exit outer `with` first")
+        self._c = float(c)
+        for name, layer in self._target_layers.items():
+            self._handles.append(layer.register_forward_hook(self._make_hook(name)))
+        self._attached = True
+        try:
+            yield self
+        finally:
+            for h in self._handles:
+                h.remove()
+            self._handles.clear()
+            self._attached = False
+            self._c = 0.0
+
+    def set_coeff(self, c: float) -> None:
+        self._c = float(c)
+
+    @property
+    def c(self) -> float:
+        return self._c
+
+    # ---- save / load -------------------------------------------------------
+
+    def save(self, path: str, extra_meta: dict[str, str] | None = None) -> None:
+        from safetensors.torch import save_file
+        sd = {f"A.{k.replace('.', '__')}": v.detach().cpu() for k, v in self.A.items()}
+        sd.update({f"B.{k.replace('.', '__')}": v.detach().cpu() for k, v in self.B.items()})
+        meta = {"r": str(self.cfg.r), "alpha": str(self.cfg.alpha),
+                "targets": ",".join(self.cfg.targets),
+                "layer_range": f"{self.cfg.layer_range[0]},{self.cfg.layer_range[1]}"}
+        if extra_meta:
+            meta.update(extra_meta)
+        save_file(sd, path, metadata=meta)
+
+    def load(self, path: str) -> None:
+        from safetensors.torch import load_file
+        sd = load_file(path, device="cpu")
+        ckpt_keys = {k[2:].replace("__", ".") for k in sd if k.startswith("A.")}
+        init_keys = set(self.A.keys())
+        if ckpt_keys != init_keys:
+            raise RuntimeError(
+                f"adapter target mismatch: checkpoint has {len(ckpt_keys)} targets, "
+                f"init created {len(init_keys)}; would drop {len(ckpt_keys - init_keys)} "
+                f"trained matrices and leave {len(init_keys - ckpt_keys)} init slots empty."
+            )
+        for k in self.A:
+            kk = k.replace(".", "__")
+            self.A[k].data.copy_(sd[f"A.{kk}"].to(self.A[k].device, self.A[k].dtype))
+            self.B[k].data.copy_(sd[f"B.{kk}"].to(self.B[k].device, self.B[k].dtype))
+
+    @classmethod
+    def from_checkpoint(cls, model: nn.Module, path: str) -> "ModulatedLoRA":
+        from safetensors import safe_open
+        with safe_open(path, framework="pt") as f:
+            meta = f.metadata()
+        targets = tuple(meta["targets"].split(","))
+        lr_meta = meta.get("layer_range", "0.0,1.0")
+        lo, hi = (float(v) for v in lr_meta.split(","))
+        lora = cls(model, r=int(meta["r"]), alpha=float(meta["alpha"]),
+                   targets=targets, layer_range=(lo, hi),
+                   dtype=next(model.parameters()).dtype)
+        lora.load(path)
+        return lora
+
+
+# ---------------------------------------------------------------------------
+# ModulatedPiSSA — top-r SVD of W is physically extracted into the adapter.
+# `layer.weight` is mutated to W_res = W - U_r·S_r·Vh_r at init; the forward
+# hook reconstructs U·diag(S + c·Δs)·Vh·x. At Δs=0 the SVD round-trip gives
+# back W·x (bf16 noise tolerated). Δs can ablate a singular dim entirely
+# (Δs_i = -S_i / c kills it) — the parameterisation gives the optimiser
+# *signed authority over each top singular direction*, which is the point.
+#
+# Top-r selection (`selection_score`):
+#   - "s_only"       : top S_full (PiSSA default — broad/blunt for instruct LMs)
+#   - "wanda"        : S_full · ||X·Vh_full||  (Wanda pruning score; biases top-S)
+#   - "act_only"     : ||X·Vh_full||           (pure activation magnitude)
+#   - "cho_rej_min_std": min(std(X_cho·Vh_i), std(X_rej·Vh_i))
+#         Picks directions that are *alive in BOTH* cho and rej completions.
+#         A dim that is quiet in either mode gets a tiny min and is skipped.
+#         Intuition: we want a steering axis the model actively uses
+#         regardless of which side it currently leans — gives Δs a real
+#         substrate at inference. Earlier "sqrt_s_act"/"wanda" picked
+#         shared-fluency directions (cho and rej both use them) which
+#         produced cos(g_pos, g_neg) ≈ -0.97 — pure c-sign anti-symmetry,
+#         no persona signal. cho_rej_min_std needs `calibration_activations`
+#         passed as `{name: {"cho": X_cho, "rej": X_rej}}`.
+# Activation-scored options need `calibration_activations[name] = X (N, d_in)`.
+# ---------------------------------------------------------------------------
+
+
+_PISSA_SCORES = ("s_only", "wanda", "act_only", "cho_rej_min_std")
+
+
+def _pissa_score(name: str, S_full: Tensor,
+                 X: Tensor | dict[str, Tensor] | None,
+                 Vh_full: Tensor, mode: str) -> Tensor:
+    """Score every singular direction of W; caller takes top-r by score."""
+    if mode == "s_only":
+        return S_full
+    if X is None:
+        raise RuntimeError(f"selection_score={mode!r} needs calibration_activations[{name!r}]")
+
+    if mode == "cho_rej_min_std":
+        if not isinstance(X, dict) or "cho" not in X or "rej" not in X:
+            raise RuntimeError(
+                f"selection_score='cho_rej_min_std' needs "
+                f"calibration_activations[{name!r}]={{'cho': X_cho, 'rej': X_rej}}")
+        std_cho = _proj_std(X["cho"], Vh_full)                # (k,)
+        std_rej = _proj_std(X["rej"], Vh_full)                # (k,)
+        return torch.minimum(std_cho, std_rej)
+
+    # Single-distribution modes.
+    if not isinstance(X, Tensor):
+        raise RuntimeError(
+            f"selection_score={mode!r} needs calibration_activations[{name!r}]=Tensor")
+    X_dev = X.to(Vh_full.device, dtype=torch.float32)
+    proj = X_dev @ Vh_full.T                                  # (N, k)
+    act = proj.abs().mean(dim=0)                              # (k,)
+    if mode == "wanda":
+        return S_full * act
+    if mode == "act_only":
+        return act
+    raise ValueError(f"selection_score must be one of {_PISSA_SCORES}, got {mode!r}")
+
+
+def _proj_std(X: Tensor, Vh_full: Tensor) -> Tensor:
+    """std along N of X @ Vh_full.T, on Vh_full's device. X: (N, d_in)."""
+    X_dev = X.to(Vh_full.device, dtype=torch.float32)
+    proj = X_dev @ Vh_full.T                                  # (N, k)
+    return proj.std(dim=0)                                    # (k,)
+
+
+class ModulatedPiSSA:
+    """Bidirectional SVD-space adapter with scalar coefficient `c`.
+
+    Same surface as `ModulatedLoRA`: parameters(), set_coeff(), `c` property,
+    `with adapter(model, c=...):` context manager, save/load/from_checkpoint.
+    Per target Linear (W: d_out × d_in):
+        Init     : SVD(W); top-r selected by `selection_score`; W mutated to
+                   W_res = W - U_r·S_r·Vh_r; (U_r, S_r, Vh_r) stored as
+                   buffers; Δs ∈ ℝ^r trainable, init N(4e-4, 4e-4).
+        Forward  : y = layer(x) + U · ((S + c·Δs) ⊙ (x @ Vh.T)) @ U.T-shape...
+                   (concretely: F.linear(F.linear(x, Vh) * (S+c·Δs), U))
+
+    PiSSA quirk vs LoRA: `c=0` is NOT a hook short-circuit — every forward
+    pays one rank-r matmul to reconstruct U·S·Vh·x (otherwise the layer
+    would output W_res·x ≠ W·x). This is the cost of physically extracting
+    the top-r out of W.
+    """
+
+    def __init__(self, model: nn.Module, r: int = 256,
+                 targets: tuple[str, ...] = ("all-linear",),
+                 layer_range: tuple[float, float] = (0.0, 1.0),
+                 dtype: torch.dtype = torch.bfloat16,
+                 calibration_activations: dict[str, Tensor | dict[str, Tensor]] | None = None,
+                 selection_score: Literal["s_only", "wanda", "act_only",
+                                          "cho_rej_min_std"] = "cho_rej_min_std",
+                 _skip_init: bool = False):
+        """`_skip_init=True` skeleton-mode for `from_checkpoint`; caller is
+        responsible for filling buffers + delta_s and re-mutating layer.weight."""
+        self.cfg = LoRAConfig(r=r, alpha=1.0, targets=targets,
+                              layer_range=layer_range, dtype=dtype)
+        self.selection_score = selection_score
+        self._handles: list = []
+        self._c: float = 0.0
+        self._attached: bool = False
+
+        device = next(model.parameters()).device
+        targets_found = _find_targets(model, self.cfg)
+        # Reject quantized layers — W mutation isn't reversible on bnb buffers.
+        for name, layer in targets_found:
+            if type(layer).__name__ in ("Linear4bit", "Linear8bitLt"):
+                raise RuntimeError(
+                    f"ModulatedPiSSA needs float nn.Linear; {name} is {type(layer).__name__}. "
+                    "Run with quant=None or use ModulatedLoRA for quantized models."
+                )
+
+        self.U: dict[str, Tensor] = {}
+        self.S: dict[str, Tensor] = {}
+        self.Vh: dict[str, Tensor] = {}
+        self.delta_s: dict[str, nn.Parameter] = {}
+        self._target_layers: dict[str, nn.Linear] = {}
+        self._chosen_idx: dict[str, Tensor] = {}    # for diagnostic logging
+
+        for name, layer in targets_found:
+            self._target_layers[name] = layer
+            d_in, d_out = layer.in_features, layer.out_features
+            # Per-target clamp: r > full-rank k for this layer (e.g. r=2304
+            # against GQA k,v with min(d_in,d_out)=1024) just means "use full
+            # spectrum here". Lets one cfg.r serve as "full per target" via
+            # any large sentinel.
+            r_used = min(r, min(d_in, d_out))
+            if _skip_init:
+                # Allocate empty skeletons; from_checkpoint fills them then
+                # mutates layer.weight = W - U·S·Vh.
+                self.U[name] = torch.empty(d_out, r_used, dtype=dtype, device=device)
+                self.S[name] = torch.empty(r_used, dtype=dtype, device=device)
+                self.Vh[name] = torch.empty(r_used, d_in, dtype=dtype, device=device)
+                self.delta_s[name] = nn.Parameter(torch.empty(r_used, dtype=dtype, device=device))
+                continue
+
+            # Full SVD in float32 for numerical stability, then score + slice.
+            with torch.no_grad():
+                W = layer.weight.data.to(torch.float32)
+                U_full, S_full, Vh_full = torch.linalg.svd(W, full_matrices=False)
+                X = (calibration_activations or {}).get(name)
+                scores = _pissa_score(name, S_full, X, Vh_full, selection_score)
+                idx = scores.argsort(descending=True)[:r_used]
+                idx = idx.sort().values                         # stable order
+                Ur = U_full[:, idx].contiguous()
+                Sr = S_full[idx].contiguous()
+                Vhr = Vh_full[idx].contiguous()
+                W_res = (W - (Ur * Sr) @ Vhr).to(layer.weight.dtype)
+                layer.weight.data.copy_(W_res)
+            self.U[name] = Ur.to(dtype).to(device).detach()
+            self.S[name] = Sr.to(dtype).to(device).detach()
+            self.Vh[name] = Vhr.to(dtype).to(device).detach()
+            self._chosen_idx[name] = idx.cpu()
+            # Δs init asymmetric (mean=std), 100× larger than the previous
+            # 4e-4 timid init. Rationale: at init, the c=+C forward gives
+            # (S + c·Δs); to give the optimizer real leverage on each
+            # singular direction at c~1, |c·Δs| should be a meaningful
+            # fraction of S (here mean(Δs)/mean(S) ≈ a few %, depending on
+            # layer). The previous 4e-4 init meant c=2·Δs ≈ 8e-4 — well
+            # below bf16 mantissa noise on top-r S values (S₀~tens) and the
+            # adapter just sat in the SVD round-trip floor.
+            # Asymmetric (mean=std>0) keeps a slight positive prior so c=+C
+            # systematically amplifies and c=-C systematically attenuates
+            # the chosen singular directions, preserving the +C/-C duality.
+            ds = torch.empty(r_used, dtype=dtype, device=device).normal_(mean=4e-2, std=4e-2)
+            self.delta_s[name] = nn.Parameter(ds)
+
+        for p in model.parameters():
+            p.requires_grad_(False)
+        n_train = sum(p.numel() for p in self.parameters())
+        logger.debug(f"ModulatedPiSSA: {len(targets_found)} targets, r={r}, "
+                     f"selection={selection_score}, trainable={n_train:,}")
+        if not _skip_init and self._chosen_idx:
+            self._log_selection_diagnostic()
+
+    def _log_selection_diagnostic(self) -> None:
+        """For each target, summarize where in S the chosen indices fell.
+        For pure top-S selection, mean(idx) = (r-1)/2. Activation-driven
+        modes should push picks deeper into the tail, increasing mean(idx).
+        Report `tail_depth = (mean(idx) - (r-1)/2) / (k - r)` (0 = top-S
+        only; 1 = picks all live past rank r, in the tail).
+        SHOULD: tail_depth > 0.05 for activation-driven modes on real
+        prompts. ELSE selection collapsed to top-S = scoring isn't doing
+        work (X capture broken, OR activations dominantly align with the
+        top of S — the failure mode this fork was built to detect)."""
+        if not self._chosen_idx:
+            return
+        ranks_mean = []
+        tail_depths = []
+        for name, idx in self._chosen_idx.items():
+            layer = self._target_layers[name]
+            k_full = min(layer.in_features, layer.out_features)
+            r = self.cfg.r
+            mean_idx = idx.float().mean().item()
+            top_s_mean = (r - 1) / 2
+            denom = max(1.0, k_full - r)
+            tail_depths.append((mean_idx - top_s_mean) / denom)
+            ranks_mean.append(mean_idx)
+        m = sum(ranks_mean) / len(ranks_mean)
+        td = sum(tail_depths) / len(tail_depths)
+        logger.info(
+            f"ModulatedPiSSA selection: mean(chosen_idx)={m:.1f} "
+            f"tail_depth={td:.3f} across {len(ranks_mean)} targets "
+            f"(r={self.cfg.r}, mode={self.selection_score}). "
+            f"SHOULD be tail_depth > 0.05 for activation-driven modes; "
+            f"≈0 means picks collapsed onto the top of S."
+        )
+
+    def parameters(self):
+        for p in self.delta_s.values():
+            yield p
+
+    def _make_hook(self, name: str):
+        U: Float[Tensor, "o r"] = self.U[name]
+        Vh: Float[Tensor, "r i"] = self.Vh[name]
+        S: Float[Tensor, "r"] = self.S[name]
+        delta_s: Float[Tensor, "r"] = self.delta_s[name]
+
+        def hook(layer: nn.Linear, args, y: Tensor) -> Tensor:
+            (x,) = args
+            xV = F.linear(x.to(Vh.dtype), Vh)               # (..., r) = x @ Vh.T
+            scaled = xV * (S + self._c * delta_s)           # diag(S + c·Δs)
+            delta = F.linear(scaled, U)                     # (..., d_out)
+            return y + delta.to(y.dtype)
+
+        return hook
+
+    @contextmanager
+    def __call__(self, model: nn.Module, c: float = 1.0):
+        """`with adapter(model, c=...):` — hook adds U·(S+c·Δs)·Vh·x at every
+        forward. c=0 reconstructs the original W (modulo SVD round-trip)."""
+        if self._attached:
+            raise RuntimeError("ModulatedPiSSA already attached; exit outer `with` first")
+        self._c = float(c)
+        for name, layer in self._target_layers.items():
+            self._handles.append(layer.register_forward_hook(self._make_hook(name)))
+        self._attached = True
+        try:
+            yield self
+        finally:
+            for h in self._handles:
+                h.remove()
+            self._handles.clear()
+            self._attached = False
+            self._c = 0.0
+
+    def set_coeff(self, c: float) -> None:
+        self._c = float(c)
+
+    @property
+    def c(self) -> float:
+        return self._c
+
+    def restore_base_W(self) -> None:
+        """layer.weight ← W_res + U·diag(S)·Vh. Required before any `baked()`
+        use on a PiSSA-trained model (bake expects fresh W, not W_res)."""
+        for name, layer in self._target_layers.items():
+            with torch.no_grad():
+                W_res = layer.weight.data.to(torch.float32)
+                U32 = self.U[name].to(torch.float32)
+                S32 = self.S[name].to(torch.float32)
+                Vh32 = self.Vh[name].to(torch.float32)
+                W = (W_res + (U32 * S32) @ Vh32).to(layer.weight.dtype)
+                layer.weight.data.copy_(W)
+
+    # ---- save / load -------------------------------------------------------
+
+    def save(self, path: str, extra_meta: dict[str, str] | None = None) -> None:
+        from safetensors.torch import save_file
+        sd: dict[str, Tensor] = {}
+        for k in self.U:
+            kk = k.replace(".", "__")
+            sd[f"U.{kk}"] = self.U[k].detach().cpu()
+            sd[f"S.{kk}"] = self.S[k].detach().cpu()
+            sd[f"Vh.{kk}"] = self.Vh[k].detach().cpu()
+            sd[f"delta_s.{kk}"] = self.delta_s[k].detach().cpu()
+        meta = {
+            "kind": "pissa",
+            "r": str(self.cfg.r),
+            "targets": ",".join(self.cfg.targets),
+            "layer_range": f"{self.cfg.layer_range[0]},{self.cfg.layer_range[1]}",
+            "selection_score": self.selection_score,
+        }
+        if extra_meta:
+            meta.update(extra_meta)
+        save_file(sd, path, metadata=meta)
+
+    def load(self, path: str) -> None:
+        """Load buffers + delta_s AND mutate layer.weight to W - U·S·Vh.
+
+        Assumes layer.weight currently holds the W that the adapter was
+        trained against (i.e. base W for round 1; the W after round-(k-1)'s
+        load for round k — caller must load in round order)."""
+        from safetensors.torch import load_file
+        sd = load_file(path, device="cpu")
+        ckpt_keys = {k[2:].replace("__", ".") for k in sd if k.startswith("U.")}
+        init_keys = set(self.U.keys())
+        if ckpt_keys != init_keys:
+            raise RuntimeError(
+                f"PiSSA target mismatch: checkpoint has {len(ckpt_keys)} targets, "
+                f"init created {len(init_keys)}.")
+        for k in self.U:
+            kk = k.replace(".", "__")
+            dev = self.U[k].device
+            self.U[k].data.copy_(sd[f"U.{kk}"].to(dev, self.U[k].dtype))
+            self.S[k].data.copy_(sd[f"S.{kk}"].to(dev, self.S[k].dtype))
+            self.Vh[k].data.copy_(sd[f"Vh.{kk}"].to(dev, self.Vh[k].dtype))
+            self.delta_s[k].data.copy_(sd[f"delta_s.{kk}"].to(dev, self.delta_s[k].dtype))
+            # Mutate layer.weight: W -> W - U·S·Vh (= W_res that training saw).
+            layer = self._target_layers[k]
+            with torch.no_grad():
+                W = layer.weight.data.to(torch.float32)
+                U32 = self.U[k].to(torch.float32)
+                S32 = self.S[k].to(torch.float32)
+                Vh32 = self.Vh[k].to(torch.float32)
+                W_res = (W - (U32 * S32) @ Vh32).to(layer.weight.dtype)
+                layer.weight.data.copy_(W_res)
+
+    @classmethod
+    def from_checkpoint(cls, model: nn.Module, path: str) -> "ModulatedPiSSA":
+        from safetensors import safe_open
+        with safe_open(path, framework="pt") as f:
+            meta = f.metadata()
+        if meta.get("kind") != "pissa":
+            raise RuntimeError(f"not a PiSSA checkpoint: kind={meta.get('kind')!r}")
+        targets = tuple(meta["targets"].split(","))
+        lr_meta = meta.get("layer_range", "0.0,1.0")
+        lo, hi = (float(v) for v in lr_meta.split(","))
+        adapter = cls(model, r=int(meta["r"]), targets=targets,
+                      layer_range=(lo, hi),
+                      dtype=next(model.parameters()).dtype,
+                      selection_score=meta["selection_score"],
+                      _skip_init=True)
+        adapter.load(path)
+        return adapter
+
+
+# ---------------------------------------------------------------------------
+# HistoryBake — kept adapters compose via a single gated forward hook.
+# Storage: per-target concat A_cat = [A_1; A_2; …], B_cat = [s_1·B_1, …]
+# so dW_combined = B_cat @ A_cat exactly, computed without materialising dW.
+# Gate predicate set by training code: `lambda: lora._c != 0.0` makes the
+# c=0 reference forward return pristine base.
+# ---------------------------------------------------------------------------
+
+class HistoryBake:
+    def __init__(self, model: nn.Module, history: list[tuple["ModulatedLoRA", float]]):
+        self._is_active = lambda: True       # inference default; train code overrides
+        target_layers: dict[str, nn.Linear] = {}
+        for lora, _ in history:
+            for name, layer in lora._target_layers.items():
+                target_layers.setdefault(name, layer)
+        self._target_layers = target_layers
+
+        r0 = history[0][0].cfg.r
+        for lora, _ in history[1:]:
+            assert lora.cfg.r == r0, f"kept-history rank mismatch: {lora.cfg.r} vs {r0}"
+        target_dtype = history[0][0].cfg.dtype
+
+        with torch.no_grad():
+            A_cat: dict[str, Tensor] = {}
+            B_cat: dict[str, Tensor] = {}
+            for name, layer in target_layers.items():
+                A_parts, B_parts = [], []
+                for lora, c in history:
+                    if name not in lora.A:
+                        continue
+                    s = c * lora.cfg.alpha / lora.cfg.r
+                    A_parts.append(lora.A[name].to(target_dtype))
+                    B_parts.append((s * lora.B[name]).to(target_dtype))
+                A_cat[name] = torch.cat(A_parts, dim=0).to(layer.weight.device).detach()
+                B_cat[name] = torch.cat(B_parts, dim=1).to(layer.weight.device).detach()
+        self._A_cat = A_cat
+        self._B_cat = B_cat
+
+        self._handles = []
+        for name, layer in target_layers.items():
+            self._handles.append(layer.register_forward_hook(self._make_hook(name)))
+        Nr = len(history) * r0
+        logger.info(f"HistoryBake: {len(history)} kept adapter(s), r_total={Nr}")
+
+    def _make_hook(self, name: str):
+        # Read A/B inside the hook body — closure captures `self`, not the
+        # tensors. Safe against future push/pop replacement of A_cat/B_cat.
+        def hook(layer: nn.Linear, args, y: Tensor) -> Tensor:
+            if not self._is_active():
+                return y
+            (x,) = args
+            A = self._A_cat[name]                     # (k_total, d_in)
+            B = self._B_cat[name]                     # (d_out, k_total), scale pre-folded
+            xA = x.to(A.dtype)
+            h = F.linear(xA, A)                       # x @ A.T via cuBLAS
+            delta = F.linear(h, B)                    # h @ B.T via cuBLAS
+            return y + delta.to(y.dtype)
+
+        return hook
+
+    def set_gate(self, is_active) -> None:
+        """is_active() -> bool. Training: `set_gate(lambda: lora._c != 0.0)`."""
+        self._is_active = is_active
+
+    def remove(self) -> None:
+        for h in self._handles:
+            h.remove()
+        self._handles.clear()
+
+
+# ---------------------------------------------------------------------------
+# PiSSAHistoryBake — composes kept ModulatedPiSSA rounds.
+#
+# Each kept round k has already mutated layer.weight by subtracting
+# U_k·S_k·Vh_k. Without reconstruction the forward returns W_res_K·x ≠ W·x,
+# i.e. nonsense — so this hook is ALWAYS active (no gate). It sums each
+# round's contribution at its kept c_k:
+#     Σ_k  U_k · diag(S_k + c_k · Δs_k) · Vh_k · x
+#
+# `set_gate` is a no-op kept for ModulatedLoRA-interface parity. The KL
+# anchor semantic shifts from "pristine base" to "prior-baked state" —
+# c=0 on the *current* round gives W_res_K + Σ_prior reconstructions,
+# i.e. the state after all kept rounds at their kept coefficients. See
+# CLAUDE.md note on KL.
+# ---------------------------------------------------------------------------
+
+class PiSSAHistoryBake:
+    def __init__(self, model: nn.Module,
+                 history: list[tuple["ModulatedPiSSA", float]]):
+        if not history:
+            raise ValueError("PiSSAHistoryBake needs ≥1 kept round")
+        target_layers: dict[str, nn.Linear] = {}
+        for adapter, _ in history:
+            for name, layer in adapter._target_layers.items():
+                target_layers.setdefault(name, layer)
+        self._target_layers = target_layers
+        target_dtype = history[0][0].cfg.dtype
+
+        # Per layer: stack per-round (U_k, Vh_k) along the rank axis exactly
+        # like HistoryBake stacks (A_k, B_k). The diag(S_k + c_k·Δs_k) factor
+        # is folded into a single per-round scale vector — concat into one
+        # (Σr,) vector aligned with the stacked rank axis.
+        U_cat: dict[str, Tensor] = {}
+        Vh_cat: dict[str, Tensor] = {}
+        scale_cat: dict[str, Tensor] = {}
+        with torch.no_grad():
+            for name, layer in target_layers.items():
+                U_parts, Vh_parts, scale_parts = [], [], []
+                for adapter, c in history:
+                    if name not in adapter.U:
+                        continue
+                    U_parts.append(adapter.U[name].to(target_dtype))
+                    Vh_parts.append(adapter.Vh[name].to(target_dtype))
+                    s = adapter.S[name].to(target_dtype) + float(c) * adapter.delta_s[name].detach().to(target_dtype)
+                    scale_parts.append(s)
+                if not U_parts:
+                    continue
+                U_cat[name] = torch.cat(U_parts, dim=1).to(layer.weight.device).detach()
+                Vh_cat[name] = torch.cat(Vh_parts, dim=0).to(layer.weight.device).detach()
+                scale_cat[name] = torch.cat(scale_parts, dim=0).to(layer.weight.device).detach()
+        self._U_cat = U_cat
+        self._Vh_cat = Vh_cat
+        self._scale_cat = scale_cat
+
+        self._handles = []
+        for name, layer in target_layers.items():
+            if name in U_cat:
+                self._handles.append(layer.register_forward_hook(self._make_hook(name)))
+        r0 = history[0][0].cfg.r
+        Nr = len(history) * r0
+        logger.info(f"PiSSAHistoryBake: {len(history)} kept adapter(s), r_total={Nr}")
+
+    def _make_hook(self, name: str):
+        def hook(layer: nn.Linear, args, y: Tensor) -> Tensor:
+            (x,) = args
+            U = self._U_cat[name]                          # (d_out, Σr)
+            Vh = self._Vh_cat[name]                        # (Σr, d_in)
+            s = self._scale_cat[name]                      # (Σr,)
+            xV = F.linear(x.to(Vh.dtype), Vh)              # (..., Σr)
+            scaled = xV * s
+            delta = F.linear(scaled, U)                    # (..., d_out)
+            return y + delta.to(y.dtype)
+        return hook
+
+    def set_gate(self, _is_active) -> None:
+        """No-op for PiSSA: kept-round reconstructions must always be active
+        (otherwise layer.weight = W_res ≠ W and the forward returns garbage).
+        Kept for ModulatedLoRA-interface parity with the train loop."""
+        pass
+
+    def remove(self) -> None:
+        for h in self._handles:
+            h.remove()
+        self._handles.clear()
diff --git a/src/steer_heal/ws/bake.py b/src/steer_heal/ws/bake.py
new file mode 100644
index 0000000..8ffabb9
--- /dev/null
+++ b/src/steer_heal/ws/bake.py
@@ -0,0 +1,223 @@
+"""Compose N LoRA adapters into a model's weights for fast inference.
+
+Two backends, one surface (`baked()` context manager):
+
+- **Float layers** (bf16 / fp16 nn.Linear): apply Σ_i c_i * (α_i/r_i) * B_i @ A_i
+  to `layer.weight` in-place. Restore from a CPU backup on exit.
+  Forward pass has NO runtime LoRA overhead — weights already include dW.
+  Memory: CPU backup of W (~5GB for 9b bf16, ~13GB for 27b).
+
+- **Quantized layers** (bnb Linear4bit / Linear8bitLt): can't modify the
+  quantized buffer reversibly. Concatenate all adapters into one stacked
+  low-rank pair `(A_stack, B_stack)` and register ONE forward hook per layer
+  doing two `F.linear` calls. N adapters cost the same as 1 at forward.
+
+Both paths take the same AdapterSpec list, so callers don't see the split.
+Use for inference only (csm eval, dialogues, gen_completions). Training
+varies `c` per step → use the gated ModulatedLoRA / HistoryBake hooks
+instead.
+"""
+from __future__ import annotations
+
+from contextlib import contextmanager
+from dataclasses import dataclass
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from loguru import logger
+
+from steer_heal.ws.adapter import ModulatedLoRA, ModulatedPiSSA
+
+
+@dataclass
+class AdapterSpec:
+    """One LoRA's worth of parameters, CPU-resident between bakes.
+
+    A[name]: (r, d_in)        B[name]: (d_out, r)        scale = α/r
+    Materialized to layer device+dtype briefly during bake; freed after.
+    """
+    A: dict[str, torch.Tensor]
+    B: dict[str, torch.Tensor]
+    alpha: float
+    r: int
+    default_c: float = 1.0
+
+    @classmethod
+    def from_lora(cls, lora: ModulatedLoRA, default_c: float = 1.0) -> "AdapterSpec":
+        return cls(
+            A={k: v.detach().cpu() for k, v in lora.A.items()},
+            B={k: v.detach().cpu() for k, v in lora.B.items()},
+            alpha=lora.cfg.alpha, r=lora.cfg.r, default_c=default_c,
+        )
+
+    @classmethod
+    def from_checkpoint(cls, model: nn.Module, path: str,
+                        default_c: float = 1.0) -> "AdapterSpec":
+        """Load adapter.safetensors as a CPU-resident spec without
+        attaching hooks (unlike ModulatedLoRA.from_checkpoint)."""
+        lora = ModulatedLoRA.from_checkpoint(model, path)
+        spec = cls.from_lora(lora, default_c=default_c)
+        del lora                                     # drop the cuda copies
+        return spec
+
+
+def _is_quantized(layer: nn.Linear) -> bool:
+    """bnb.Linear4bit / Linear8bitLt detection. False for bf16/fp16."""
+    return type(layer).__name__ in ("Linear4bit", "Linear8bitLt")
+
+
+def _gather_target_layers(model: nn.Module,
+                          adapters: list[AdapterSpec]) -> dict[str, nn.Linear]:
+    """Union of named layers across all adapters' target sets."""
+    wanted: set[str] = set()
+    for a in adapters:
+        wanted.update(a.A.keys())
+    return {name: m for name, m in model.named_modules() if name in wanted}
+
+
+@contextmanager
+def baked(model: nn.Module, adapters: list[AdapterSpec],
+          c_overrides: list[float] | None = None):
+    """Apply N adapters at fixed c per adapter. Restore on exit.
+
+    Composable: `baked(model, hist_specs + [current_spec])` bakes the full
+    cumulative state in one call. Per-adapter c via `default_c` or
+    `c_overrides=[c0, c1, ...]`. No-op if adapters is empty.
+    """
+    if not adapters:
+        yield
+        return
+    cs = list(c_overrides) if c_overrides is not None else [a.default_c for a in adapters]
+    assert len(cs) == len(adapters), \
+        f"c_overrides len {len(cs)} != adapters len {len(adapters)}"
+
+    layers = _gather_target_layers(model, adapters)
+    float_layers = {n: l for n, l in layers.items() if not _is_quantized(l)}
+    quant_layers = {n: l for n, l in layers.items() if _is_quantized(l)}
+
+    # ─ Float backend: in-place W += Σ c_i * (α_i/r_i) * (B_i @ A_i)
+    W_backup: dict[str, torch.Tensor] = {}
+    for name, layer in float_layers.items():
+        W_backup[name] = layer.weight.detach().cpu().clone()
+        dev, dt = layer.weight.device, layer.weight.dtype
+        dW = torch.zeros_like(layer.weight)
+        for adapter, c in zip(adapters, cs):
+            if name not in adapter.A or c == 0.0:
+                continue
+            A = adapter.A[name].to(dev, dt)
+            B = adapter.B[name].to(dev, dt)
+            scale = (c * adapter.alpha) / adapter.r
+            dW.add_(scale * (B @ A))
+        layer.weight.data.add_(dW)
+
+    # ─ Quant backend: one stacked-low-rank hook per layer (N adapters → one pair)
+    quant_handles = []
+    for name, layer in quant_layers.items():
+        A_parts, B_parts = [], []
+        for adapter, c in zip(adapters, cs):
+            if name not in adapter.A or c == 0.0:
+                continue
+            scale = (c * adapter.alpha) / adapter.r
+            A_parts.append(adapter.A[name].to(layer.weight.device))
+            B_parts.append((scale * adapter.B[name]).to(layer.weight.device))
+        if not A_parts:
+            continue
+        A_stack = torch.cat(A_parts, dim=0)           # (Σr, d_in)
+        B_stack = torch.cat(B_parts, dim=1)           # (d_out, Σr)
+
+        def _hook(_m, args, y, A=A_stack, B=B_stack):
+            (x,) = args
+            h = F.linear(x.to(A.dtype), A)            # x @ A.T
+            return y + F.linear(h, B).to(y.dtype)     # h @ B.T
+
+        quant_handles.append(layer.register_forward_hook(_hook))
+
+    logger.debug(f"baked: {len(adapters)} adapter(s), "
+                 f"{len(float_layers)} float (in-place), "
+                 f"{len(quant_layers)} quantized (stacked-LR hook)")
+    try:
+        yield
+    finally:
+        for name, layer in float_layers.items():
+            layer.weight.data.copy_(W_backup[name].to(layer.weight.device))
+        for h in quant_handles:
+            h.remove()
+
+
+# ---------------------------------------------------------------------------
+# PiSSA inference path. Math observation: at training time, layer.weight was
+# mutated to W_res = W - U·S·Vh and the forward added back U·(S + c·Δs)·Vh.
+# At inference we load a FRESH layer.weight = W. Folding "extract U·S·Vh
+# then add U·(S+c·Δs)·Vh" on a fresh W collapses to a single per-round
+# additive +c·U·diag(Δs)·Vh — which is exactly the LoRA forward with
+# A=Vh, B=U·diag(Δs), α=r (so α/r=1). So a PiSSA checkpoint is loadable
+# as an AdapterSpec usable by `baked()` with zero new bake code. This
+# equivalence only holds for FRESH W; it does NOT hold inside the training
+# loop's `PiSSAHistoryBake`, which operates on a model whose layer.weight
+# has been sequentially mutated by ModulatedPiSSA.load().
+# ---------------------------------------------------------------------------
+
+
+def pissa_to_lora_spec(adapter: ModulatedPiSSA,
+                       default_c: float = 1.0) -> AdapterSpec:
+    """ModulatedPiSSA -> LoRA-shaped AdapterSpec usable by `baked()`.
+    A = Vh, B = U·diag(Δs), α = r."""
+    A: dict[str, torch.Tensor] = {}
+    B: dict[str, torch.Tensor] = {}
+    for k, U in adapter.U.items():
+        A[k] = adapter.Vh[k].detach().cpu()
+        B[k] = (U * adapter.delta_s[k].detach()).cpu()
+    return AdapterSpec(A=A, B=B, alpha=float(adapter.cfg.r),
+                       r=adapter.cfg.r, default_c=default_c)
+
+
+def pissa_spec_from_checkpoint(model: nn.Module, path: str,
+                               default_c: float = 1.0) -> AdapterSpec:
+    """Load a PiSSA checkpoint as a LoRA-equivalent AdapterSpec without
+    touching layer.weight (vs ModulatedPiSSA.load which mutates W).
+
+    Validates checkpoint keys against `_find_targets(model, cfg)` reproduced
+    from ckpt metadata: any mismatch (renamed module, changed layer_range,
+    wrong model) raises. Without this `baked()` would silently intersect
+    keys with model.named_modules() and drop the rest."""
+    from safetensors import safe_open
+    from safetensors.torch import load_file
+    from csm.ws.adapter import LoRAConfig, _find_targets
+    with safe_open(path, framework="pt") as f:
+        meta = f.metadata()
+    if meta.get("kind") != "pissa":
+        raise RuntimeError(f"not a PiSSA checkpoint: kind={meta.get('kind')!r}")
+    r = int(meta["r"])
+    targets = tuple(meta["targets"].split(","))
+    lr_lo, lr_hi = (float(x) for x in meta["layer_range"].split(","))
+    cfg = LoRAConfig(r=r, targets=targets, layer_range=(lr_lo, lr_hi))
+    expected = {n for n, _ in _find_targets(model, cfg)}
+    sd = load_file(path, device="cpu")
+    keys = {k[2:].replace("__", ".") for k in sd if k.startswith("U.")}
+    if keys != expected:
+        missing = expected - keys
+        extra = keys - expected
+        raise RuntimeError(
+            f"PiSSA checkpoint target mismatch (path={path}): "
+            f"missing={sorted(missing)[:3]}... extra={sorted(extra)[:3]}... "
+            f"({len(missing)} missing, {len(extra)} extra)")
+    A: dict[str, torch.Tensor] = {}
+    B: dict[str, torch.Tensor] = {}
+    for k in sorted(keys):
+        kk = k.replace(".", "__")
+        A[k] = sd[f"Vh.{kk}"].contiguous()
+        B[k] = (sd[f"U.{kk}"] * sd[f"delta_s.{kk}"]).contiguous()
+    return AdapterSpec(A=A, B=B, alpha=float(r), r=r, default_c=default_c)
+
+
+def adapter_spec_from_checkpoint(model: nn.Module, path: str,
+                                 default_c: float = 1.0) -> AdapterSpec:
+    """Dispatch on `kind` metadata; both PiSSA + LoRA round-trip into the
+    same AdapterSpec shape so downstream `baked()` is uniform."""
+    from safetensors import safe_open
+    with safe_open(path, framework="pt") as f:
+        kind = f.metadata().get("kind", "lora")
+    if kind == "pissa":
+        return pissa_spec_from_checkpoint(model, path, default_c=default_c)
+    return AdapterSpec.from_checkpoint(model, path, default_c=default_c)