From 2e38418adbd560af13be7c5d2ab0d98aa968be2b Mon Sep 17 00:00:00 2001
From: Asher Feldman <59994+asher@users.noreply.github.com>
Date: Sun, 2 Aug 2026 23:01:26 -0700
Subject: [PATCH 01/10] loader: miss-shed skips arena token-split leaves
 (kimi-k3 prefill crash)

---
 CHANGELOG.md          |  3 ++
 gmlx/loader.py        | 33 +++++++++++++------
 tests/test_offload.py | 75 +++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 102 insertions(+), 9 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index c6de72f..f5e69f3 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -108,6 +108,9 @@ adhere to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
 ### Fixed
 
+- `--moe-miss-shed` no longer crashes chat prefill on kimi-k3 (a
+  concatenate rank mismatch): shed is decode-only and now skips the
+  single-token leaves of an arena token split.
 - `gmlx chat` no longer rejects `--stream-experts` on an MTP/speculative
   base.
 - Chat markdown rendering no longer reverts a block to raw text when it
diff --git a/gmlx/loader.py b/gmlx/loader.py
index 6d99af3..9045e13 100644
--- a/gmlx/loader.py
+++ b/gmlx/loader.py
@@ -1609,7 +1609,8 @@ def __call__(self, x, indices, *args, **kwargs):
                         # adaptive hot-set refresh live here.
                         gt.on_layer_entry(
                             self._kq_li,
-                            getattr(self, "_kq_miss_shed", None))
+                            None if getattr(self, "_kq_in_split", False)
+                            else getattr(self, "_kq_miss_shed", None))
                     if (
                         gt_live
                         and scores_arg is not None
@@ -1693,7 +1694,13 @@ def __call__(self, x, indices, *args, **kwargs):
                         t0 = time.perf_counter() if ph is not None else 0.0
                         if n_tokens == 1:
                             dfr.ensure_wired()
-                        ms = getattr(self, "_kq_miss_shed", None)
+                        # Miss-shed is decode-only: a single-token leaf of an
+                        # arena token split is prefill work, and a shedding
+                        # leaf would return a mixed rank-3 output next to a
+                        # clean leaf's per-expert rank-4 - the reassembly
+                        # concatenate cannot take both.
+                        ms = (None if getattr(self, "_kq_in_split", False)
+                              else getattr(self, "_kq_miss_shed", None))
                         sc_f32 = None
                         if (ms is not None and scores_arg is not None
                                 and n_tokens == 1):
@@ -1808,13 +1815,21 @@ def __call__(self, x, indices, *args, **kwargs):
                         if sliceable:
                             half = n_tokens // 2
                             parts = []
-                            for sl in (slice(0, half),
-                                       slice(half, n_tokens)):
-                                t = tuple(
-                                    [slice(None)] * ax + [sl])
-                                parts.append(self.__call__(
-                                    x[t], indices[t],
-                                    *[a[t] for a in orig]))
+                            prev_split = getattr(
+                                self, "_kq_in_split", False)
+                            object.__setattr__(
+                                self, "_kq_in_split", True)
+                            try:
+                                for sl in (slice(0, half),
+                                           slice(half, n_tokens)):
+                                    t = tuple(
+                                        [slice(None)] * ax + [sl])
+                                    parts.append(self.__call__(
+                                        x[t], indices[t],
+                                        *[a[t] for a in orig]))
+                            finally:
+                                object.__setattr__(
+                                    self, "_kq_in_split", prev_split)
                             return mx.concatenate(parts, axis=ax)
                     wedged = dfr is not None and dfr.wedged_at(self._kq_li)
                     if wedged and dfr.has_dead(self._kq_li):
diff --git a/tests/test_offload.py b/tests/test_offload.py
index f7e95b0..644a4d1 100644
--- a/tests/test_offload.py
+++ b/tests/test_offload.py
@@ -518,3 +518,78 @@ def swapped(self, li):
     # out at single-token stage calls that fit under the slot cap.
     assert stub.stage_sizes[0] == T * 2
     assert min(stub.stage_sizes) == 2
+
+
+def test_arena_split_leaves_never_miss_shed():
+    """Miss-shed is decode-only: single-token leaves inside an arena token
+    split must not shed. A shedding leaf returns a mixed (rank-3) output
+    while a clean leaf returns per-expert rank-4, and the reassembly
+    concatenate crashed on the rank mismatch (kimi-k3 chat prefill with
+    --moe-miss-shed). The split output must match the unsplit reference."""
+    import contextlib
+
+    import numpy as np
+
+    mx.random.seed(5)
+    glu = SwitchGLU(16, 32, 8)
+    mx.eval(glu.parameters())
+    T = 2
+    x = mx.random.normal((1, T, 16))
+    inds = mx.array(
+        (np.arange(T * 2).reshape(1, T, 2) % 8).astype(np.uint32))
+    weights = mx.softmax(mx.random.normal((1, T, 2)), axis=-1)
+    ref = (glu(x, inds) * weights[..., None]).sum(axis=-2)
+    mx.eval(ref)
+
+    class _ShedStub:
+        def __init__(self, cap):
+            self.cap = cap
+            self.shed_calls = 0
+
+        def covers(self, li):
+            return True
+
+        def can_stage_smaller(self, li):
+            return True
+
+        def wedged_at(self, li):
+            return False
+
+        def ensure_wired(self):
+            pass
+
+        def shed_misses(self, li, ids, sc, p):
+            # First leaf: everything resident, nothing shed. Second leaf:
+            # shed all but the first routed expert. The asymmetry is what
+            # produced the mixed-rank concatenate.
+            self.shed_calls += 1
+            if self.shed_calls == 1:
+                return None
+            return np.array([0])
+
+        def stage(self, li, ids):
+            if len(np.unique(ids.reshape(-1))) > self.cap:
+                return None
+            return ids  # identity slots: arena views == real weights
+
+        @contextlib.contextmanager
+        def swapped(self, li):
+            yield
+
+    model = _holder_model(glu)
+    n, _ = install_expert_streaming(model)
+    assert n == 1
+    stub = _ShedStub(cap=2)
+    object.__setattr__(glu, "_kq_decode_feeder", stub)
+    object.__setattr__(glu, "_kq_cpu_only", True)
+    object.__setattr__(glu, "_kq_li", 0)
+    object.__setattr__(glu, "_kq_miss_shed", 0.8)
+    # Scores-sink call shape: the block hands routing weights as the third
+    # argument (kimi-k3 latent MoE does this whenever the sink is advertised).
+    out = glu(x, inds, weights)
+    mx.eval(out)
+    # Leaves stay on the per-expert contract; mix like the block would.
+    assert out.ndim == x.ndim + 1
+    mixed = (out * weights[..., None]).sum(axis=-2)
+    assert mx.allclose(ref, mixed, atol=1e-6, rtol=1e-6)
+    assert stub.shed_calls == 0  # decode-only: split leaves never shed

From 3f5b2a12cdfbb8d69fa341f5dda7b0eaffd05337 Mon Sep 17 00:00:00 2001
From: Asher Feldman <59994+asher@users.noreply.github.com>
Date: Tue, 4 Aug 2026 17:23:50 -0700
Subject: [PATCH 02/10] feat(stream): clear-mode miss-shed and mass-weighted
 arena popularity

---
 CHANGELOG.md                |  6 +++
 docs/streaming.md           | 24 +++++++++--
 gmlx/cli.py                 | 36 ++++++++++++++++-
 gmlx/decode_feeder.py       | 79 +++++++++++++++++++++++++++++--------
 gmlx/gpu_token.py           |  7 +++-
 gmlx/loader.py              | 24 +++++++----
 gmlx/moe_experts.py         | 58 +++++++++++++++++++++++----
 tests/test_decode_feeder.py | 79 ++++++++++++++++++++++++++++++++++++-
 8 files changed, 275 insertions(+), 38 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index f5e69f3..7b8f8b9 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -8,6 +8,12 @@ adhere to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
 ### Added
 
+- `--moe-miss-shed-mode clear`: miss-shed drops a layer's whole miss set
+  when its mass fits the budget and nothing otherwise, so each shed
+  removes a per-layer demand-read stall instead of thinning one.
+- `--moe-popularity mass`: expert-arena eviction ranks experts by routed
+  gate mass instead of touch count, keeping each layer's high-mass
+  experts resident.
 - Ctrl-T during a `chat` or `run` reply closes the model's open thinking
   block early (as if the thinking budget had just run out) so the answer
   starts now.
diff --git a/docs/streaming.md b/docs/streaming.md
index 76ad418..edbb224 100644
--- a/docs/streaming.md
+++ b/docs/streaming.md
@@ -301,6 +301,23 @@ shed expert earns no popularity credit, so the arena keeps its hot set.
 It needs the decode feeder and a block that hands router scores to the
 expert call; where it engages, it is the most targeted lever per point of
 quality spent, and its payoff scales directly with the miss rate.
+
+Miss-shed's payoff is not proportional to the bytes it saves: each
+streamed layer with any surviving demand miss still pays a synchronous
+read stall, so a shed that thins a layer's miss set buys little and one
+that empties it buys the whole stall. `--moe-miss-shed-mode clear`
+targets that directly: a layer sheds its entire miss set when its
+combined mass fits the budget and sheds nothing otherwise, so every
+point of dropped mass converts to a removed stall and untouched layers
+keep their full committee. On models where the tail mode plateaus (mass
+spent, stalls remaining), clear mode reaches further at the same P.
+`--moe-popularity mass` complements it from the residency side: arena
+eviction normally ranks experts by touch count, which keeps the most
+often routed experts resident; ranking by routed gate mass instead
+keeps the experts miss-shed refuses to drop, so fewer high-mass misses
+remain to block a clear. It changes no outputs on its own and composes
+with either shed mode.
+
 `--moe-layer-shed P` skips a streamed MoE layer's routed experts entirely
 with probability P per token (the layer's shared expert still runs). It
 is the blunt end of the scale, and the only lever that also cuts the
@@ -309,9 +326,10 @@ arena hit rate is high and misses are rare.
 
 In server configs the lossy levers are the per-model `moe_experts: K` /
 `moe_expert_mass: P` / `moe_miss_shed: P` / `moe_layer_shed: P` keys (or
-the matching `serve` flags for a single positional model); the probe
-stays CLI-only, so size P with a `gmlx run --moe-expert-probe` pass
-before pinning a value in a config.
+the matching `serve` flags for a single positional model); the probe,
+`--moe-miss-shed-mode`, and `--moe-popularity` stay CLI-only, so size P
+with a `gmlx run --moe-expert-probe` pass before pinning a value in a
+config.
 
 Which to reach for is a measurement, not a doctrine. Run the probe once,
 and read the decode feeder's exit stats (arena hit rate; printed by
diff --git a/gmlx/cli.py b/gmlx/cli.py
index 852e1ea..a820016 100644
--- a/gmlx/cli.py
+++ b/gmlx/cli.py
@@ -162,6 +162,25 @@ def add_moe_expert_args(ap: argparse.ArgumentParser) -> None:
         "directly; resident experts are never dropped. Composes with "
         "--moe-expert-mass.",
     )
+    grp.add_argument(
+        "--moe-miss-shed-mode",
+        choices=("tail", "clear"),
+        default="tail",
+        help="Miss-shed policy. tail: shed lowest-scored misses up to the "
+        "budget. clear: shed a layer's whole miss set when its mass fits "
+        "the budget, nothing otherwise - each shed then removes a "
+        "per-layer demand-read stall instead of thinning one. No effect "
+        "without --moe-miss-shed.",
+    )
+    grp.add_argument(
+        "--moe-popularity",
+        choices=("count", "mass"),
+        default="count",
+        help="Expert-arena eviction popularity. count: every routed expert "
+        "credits equally per call. mass: credit by gate score, so each "
+        "layer's high-mass experts win residency (and, under "
+        "--moe-miss-shed, stop missing).",
+    )
     grp.add_argument(
         "--moe-layer-shed",
         type=float,
@@ -1239,6 +1258,10 @@ def _apply_placement(args, model) -> None:
                     "--moe-layer-shed",
                     getattr(args, "moe_layer_shed", None) is not None,
                 ),
+                (
+                    "--moe-popularity mass",
+                    getattr(args, "moe_popularity", "count") == "mass",
+                ),
             )
             if on
         ]
@@ -1293,7 +1316,18 @@ def _apply_placement(args, model) -> None:
     if getattr(args, "moe_miss_shed", None) is not None:
         from .moe_experts import install_moe_miss_shed
 
-        install_moe_miss_shed(model, args.moe_miss_shed)
+        install_moe_miss_shed(
+            model, args.moe_miss_shed,
+            mode=getattr(args, "moe_miss_shed_mode", "tail"))
+    elif getattr(args, "moe_miss_shed_mode", "tail") != "tail":
+        print(
+            "[stream] --moe-miss-shed-mode ignored: it needs "
+            "--moe-miss-shed"
+        )
+    if getattr(args, "moe_popularity", "count") == "mass":
+        from .moe_experts import install_moe_mass_popularity
+
+        install_moe_mass_popularity(model)
     if getattr(args, "moe_layer_shed", None) is not None:
         from .moe_experts import install_moe_layer_shed
 
diff --git a/gmlx/decode_feeder.py b/gmlx/decode_feeder.py
index 4781dd0..4555bd8 100644
--- a/gmlx/decode_feeder.py
+++ b/gmlx/decode_feeder.py
@@ -220,6 +220,23 @@ def _iopol_utility() -> None:
     libc.setiopolicy_np(0, 1, 4)
 
 
+def credit_popularity(counts, uniq, ids, scores) -> None:
+    """Popularity credit for one routed call: by touch count when
+    ``scores`` is None, else by gate mass scaled so the per-call total
+    stays ``len(uniq)`` (count credit's scale - the two can mix across
+    decode and prefill calls without skewing eviction). Shared by
+    ``stage`` and the gpu-autonomous boundary so the ledgers agree."""
+    if scores is None:
+        counts[uniq] += 1.0
+        return
+    w = np.asarray(scores, dtype=np.float64).reshape(-1)
+    tot = float(w.sum())
+    if tot <= 0.0:
+        counts[uniq] += 1.0
+        return
+    np.add.at(counts, np.asarray(ids).reshape(-1), w * (len(uniq) / tot))
+
+
 class DecodeFeeder:
     """Per-layer wired expert arenas with popularity-driven replacement."""
 
@@ -409,6 +426,11 @@ def __init__(
         self._shed_mass = 0.0
         self._shed_tokens = 0
         self._layer_shed_n = 0
+        # Policy switches (installed by moe_experts after load).
+        # _shed_clear: shed_misses drops a layer's whole miss set or
+        # nothing. _credit_mass: popularity credit by gate mass.
+        self._shed_clear = False
+        self._credit_mass = False
 
         # GMLX_DECODE_FEEDER_VERIFY=1: sample-compare arena slots against
         # their file bytes at every publish and every routed use, and
@@ -640,7 +662,8 @@ def lend_for_ring(self, nbytes: int) -> None:
             "restored at next decode"
         )
 
-    def stage(self, li: int, ids: np.ndarray) -> np.ndarray | None:
+    def stage(self, li: int, ids: np.ndarray,
+              scores: np.ndarray | None = None) -> np.ndarray | None:
         """Map router expert ids to arena slots, pulling misses from the GGUF
         into evicted slots first. Returns the slot array (``ids``' shape,
         uint32), or None when the call cannot be served from the arena:
@@ -649,6 +672,10 @@ def stage(self, li: int, ids: np.ndarray) -> np.ndarray | None:
         the caller falls back to the CPU path - with ids rewritten through
         ``redirect_dead`` when ``has_dead`` says the layer lost experts.
 
+        ``scores`` (mass-popularity callers only, aligned with ``ids``
+        flattened) switches this call's popularity credit from touch count
+        to gate mass; calls without scores keep count credit.
+
         Caller contract: ``mx.eval`` of the call's ``indices`` has run, so no
         in-flight gather references this layer's arena (see module docstring).
         """
@@ -685,7 +712,7 @@ def stage(self, li: int, ids: np.ndarray) -> np.ndarray | None:
             for pe, ps in self._verify_prev.get(li, ()):
                 if ps < len(owner_v) and owner_v[ps] == pe:
                     self._verify_slot(li, pe, ps, "prev-routed")
-        counts[uniq] += 1.0
+        credit_popularity(counts, uniq, ids, scores)
         self._calls += 1
         if self._calls % _DECAY_EVERY == 0:
             for c in self._counts.values():
@@ -803,7 +830,13 @@ def shed_misses(self, li: int, ids: np.ndarray, scores: np.ndarray,
         first, capped at ``1 - keep_mass`` of the token's score mass.
         Returns None when nothing sheds. A shed expert is never staged, so
         it also earns no popularity credit (self-reinforcing by design:
-        the arena's long tail stays cold)."""
+        the arena's long tail stays cold).
+
+        ``_shed_clear`` (--moe-miss-shed-mode clear) swaps the greedy tail
+        for all-or-nothing: shed the layer's whole miss set when its mass
+        fits the budget, else shed nothing. A partial shed only thins the
+        bytes inside stage()'s per-layer read barrier; clearing the set
+        removes the barrier, which is where the wall time is."""
         slot_of = self._slot_of.get(li)
         if slot_of is None:
             return None
@@ -815,20 +848,32 @@ def shed_misses(self, li: int, ids: np.ndarray, scores: np.ndarray,
             return None
         total = float(scores.sum())
         budget = total * max(0.0, 1.0 - keep_mass)
-        keep = np.ones(ids.size, dtype=bool)
-        shed_mass = 0.0
-        shed_n = 0
-        for j in np.argsort(scores, kind="stable"):
-            if not miss[j] or keep.sum() <= 1:
-                continue
-            s = float(scores[j])
-            if shed_mass + s > budget:
-                break
-            keep[j] = False
-            shed_mass += s
-            shed_n += 1
-        if not shed_n:
-            return None
+        if self._shed_clear:
+            if miss.all():
+                # Never drop a token's entire routed set (belt-and-braces:
+                # an all-miss set's mass is the full total, over any
+                # budget with keep_mass > 0).
+                return None
+            shed_mass = float(scores[miss].sum())
+            if shed_mass > budget:
+                return None
+            keep = ~miss
+            shed_n = int(miss.sum())
+        else:
+            keep = np.ones(ids.size, dtype=bool)
+            shed_mass = 0.0
+            shed_n = 0
+            for j in np.argsort(scores, kind="stable"):
+                if not miss[j] or keep.sum() <= 1:
+                    continue
+                s = float(scores[j])
+                if shed_mass + s > budget:
+                    break
+                keep[j] = False
+                shed_mass += s
+                shed_n += 1
+            if not shed_n:
+                return None
         self._shed_n += shed_n
         self._shed_mass += shed_mass / max(total, 1e-20)
         self._shed_tokens += 1
diff --git a/gmlx/gpu_token.py b/gmlx/gpu_token.py
index dfc2a59..e8df1ce 100644
--- a/gmlx/gpu_token.py
+++ b/gmlx/gpu_token.py
@@ -38,7 +38,7 @@
 import mlx.core as mx
 
 from . import keepwarm
-from .decode_feeder import _DECAY_EVERY
+from .decode_feeder import _DECAY_EVERY, credit_popularity
 from .envflags import env_bool, env_float, env_int
 
 _ENV = "GMLX_GPU_AUTONOMOUS"
@@ -213,7 +213,10 @@ def boundary(self) -> None:
             # python miss-shed deliberately starves them; here prestage
             # below re-warms them anyway, so credit keeps the two ledgers
             # comparable.)
-            counts[routed] += 1.0
+            credit_popularity(
+                counts, routed, np.array(indices),
+                np.array(scores) if getattr(dfr, "_credit_mass", False)
+                else None)
             dfr._calls += 1
             if dfr._calls % _DECAY_EVERY == 0:
                 for c in dfr._counts.values():
diff --git a/gmlx/loader.py b/gmlx/loader.py
index 9045e13..dead43e 100644
--- a/gmlx/loader.py
+++ b/gmlx/loader.py
@@ -1701,12 +1701,15 @@ def __call__(self, x, indices, *args, **kwargs):
                         # concatenate cannot take both.
                         ms = (None if getattr(self, "_kq_in_split", False)
                               else getattr(self, "_kq_miss_shed", None))
+                        mass_credit = getattr(dfr, "_credit_mass", False)
                         sc_f32 = None
-                        if (ms is not None and scores_arg is not None
+                        if ((ms is not None or mass_credit)
+                                and scores_arg is not None
                                 and n_tokens == 1):
-                            # Shed reads the scores host-side; fold them into
-                            # the router eval so the hook adds a small D2H
-                            # copy, not a second per-layer graph flush.
+                            # Shed and mass credit read the scores host-side;
+                            # fold them into the router eval so the hook adds
+                            # a small D2H copy, not a second per-layer graph
+                            # flush.
                             sc_f32 = scores_arg.astype(mx.float32)
                             mx.eval(indices, sc_f32)
                         else:
@@ -1718,10 +1721,12 @@ def __call__(self, x, indices, *args, **kwargs):
                         ids = np.array(indices)
                         shed_args = None
                         shed_mix = None
+                        credit_sc = None
                         if sc_f32 is not None:
                             sc = np.asarray(sc_f32).reshape(-1)
-                            keep = dfr.shed_misses(
-                                self._kq_li, ids.reshape(-1), sc, ms)
+                            credit_sc = sc
+                            keep = (None if ms is None else dfr.shed_misses(
+                                self._kq_li, ids.reshape(-1), sc, ms))
                             if keep is not None:
                                 # Arena-path only: the overflow fallback
                                 # below keeps the original routed set.
@@ -1731,6 +1736,7 @@ def __call__(self, x, indices, *args, **kwargs):
                                 scn = sc[keep]
                                 # survivors keep the token's full mass
                                 scn = scn * (sc.sum() / max(scn.sum(), 1e-20))
+                                credit_sc = scn
                                 sc_mx = mx.array(scn.reshape(shp)).astype(
                                     scores_arg.dtype)
                                 if _fwd_scores:
@@ -1741,7 +1747,11 @@ def __call__(self, x, indices, *args, **kwargs):
                                     # full routed set, so mix the shed
                                     # survivors here instead.
                                     shed_mix = sc_mx
-                        slots = dfr.stage(self._kq_li, ids)
+                        if mass_credit and credit_sc is not None:
+                            slots = dfr.stage(
+                                self._kq_li, ids, scores=credit_sc)
+                        else:
+                            slots = dfr.stage(self._kq_li, ids)
                         if ph is not None:
                             t2 = time.perf_counter()
                             w = getattr(dfr, "_t_demand", 0.0) - wait0
diff --git a/gmlx/moe_experts.py b/gmlx/moe_experts.py
index bfe9627..66fd589 100644
--- a/gmlx/moe_experts.py
+++ b/gmlx/moe_experts.py
@@ -423,22 +423,38 @@ def _streamed_modules(model):
                 yield m
 
 
-def install_moe_miss_shed(model, p: float) -> int:
+def install_moe_miss_shed(model, p: float, mode: str = "tail") -> int:
     """Shed routed experts that would demand-miss the decode arena, up to
-    ``1 - p`` of each token's score mass (lowest scores first; resident
-    and prestage-inflight experts never shed). Lossy; acts only on decode
-    calls that carry routing scores (the fused scores path). Returns the
-    number of layers hooked; raises on p outside (0, 1]."""
+    ``1 - p`` of each token's score mass (resident and prestage-inflight
+    experts never shed). ``mode`` "tail" sheds lowest scores first up to
+    the budget; "clear" sheds a layer's whole miss set when its mass fits
+    the budget and nothing otherwise (every shed then empties a per-layer
+    demand-read stall instead of thinning one). Lossy; acts only on
+    decode calls that carry routing scores (the fused scores path).
+    Returns the number of layers hooked; raises on p outside (0, 1] or an
+    unknown mode."""
     if not 0.0 < p <= 1.0:
         raise ValueError(f"MoE miss-shed share must be in (0, 1], got {p}")
+    if mode not in ("tail", "clear"):
+        raise ValueError(f"MoE miss-shed mode must be tail or clear, got {mode}")
     n = 0
     for m in _streamed_modules(model):
-        if getattr(m, "_kq_decode_feeder", None) is not None:
+        f = getattr(m, "_kq_decode_feeder", None)
+        if f is not None:
             object.__setattr__(m, "_kq_miss_shed", float(p))
+            if mode == "clear":
+                f._shed_clear = True
+            elif getattr(f, "_shed_clear", False):
+                f._shed_clear = False
             n += 1
     if n:
+        how = (
+            "whole miss sets shed where they fit, none where they don't"
+            if mode == "clear"
+            else "demand-miss experts shed lowest score first"
+        )
         loadlog.info(
-            f"[stream] MoE miss-shed {p:g}: demand-miss experts shed up to "
+            f"[stream] MoE miss-shed {p:g} ({mode}): {how}, up to "
             f"{100 * (1 - p):g}% of each token's gate mass on {n} offloaded "
             "MoE layers (lossy - outputs differ from the trained router)"
         )
@@ -450,6 +466,34 @@ def install_moe_miss_shed(model, p: float) -> int:
     return n
 
 
+def install_moe_mass_popularity(model) -> int:
+    """Credit arena popularity by routed gate mass instead of touch count
+    on decode calls that carry scores, so eviction keeps each layer's
+    high-mass experts resident. Not itself lossy (residency is a perf
+    policy), but composed with --moe-miss-shed it steers what sheds.
+    Returns the number of layers whose feeder switched."""
+    n = 0
+    seen: set[int] = set()
+    for m in _streamed_modules(model):
+        f = getattr(m, "_kq_decode_feeder", None)
+        if f is not None:
+            if id(f) not in seen:
+                seen.add(id(f))
+                f._credit_mass = True
+            n += 1
+    if n:
+        loadlog.info(
+            f"[stream] MoE mass popularity: arena eviction on {n} offloaded "
+            "MoE layers ranks experts by routed gate mass, not touch count"
+        )
+    else:
+        print(
+            "[stream] MoE mass popularity found no decode-feeder MoE layer "
+            "- no effect"
+        )
+    return n
+
+
 def install_moe_layer_shed(model, p: float) -> int:
     """Skip each streamed MoE layer's routed path with probability ``p``
     per decode token; the layer's shared expert still runs. Lossy.
diff --git a/tests/test_decode_feeder.py b/tests/test_decode_feeder.py
index 7326f31..a6fbac2 100644
--- a/tests/test_decode_feeder.py
+++ b/tests/test_decode_feeder.py
@@ -1139,6 +1139,53 @@ def test_shed_misses_keeps_at_least_one_expert(monkeypatch, tmp_path):
     assert keep.tolist() == [True, False]
 
 
+def test_shed_misses_clear_mode_all_or_nothing(monkeypatch, tmp_path):
+    """Clear mode empties the layer's whole miss set when its mass fits
+    the budget (removing that layer's demand-read stall) and sheds
+    nothing when it does not - never a partial shed."""
+    feeder, _ = _make_feeder(monkeypatch, tmp_path)
+    feeder._shed_clear = True
+    feeder.stage(0, np.array([[0, 2]], dtype=np.uint32))  # 0,2 resident
+    ids = np.array([0, 1, 2, 3], dtype=np.uint32)
+    scores = np.array([0.4, 0.05, 0.4, 0.15], dtype=np.float32)
+    # budget 0.3 covers the miss set (experts 1+3 = 0.2): both shed
+    keep = feeder.shed_misses(0, ids, scores, 0.7)
+    assert keep.tolist() == [True, False, True, False]
+    assert feeder._shed_n == 2 and feeder._shed_tokens == 1
+    assert abs(feeder._shed_mass - 0.2) < 1e-6
+    # budget 0.1 does not cover it: nothing sheds (tail mode would have
+    # dropped expert 1 alone)
+    assert feeder.shed_misses(0, ids, scores, 0.9) is None
+    assert feeder._shed_n == 2  # no partial-shed bookkeeping
+
+
+def test_shed_misses_clear_mode_never_drops_whole_set(monkeypatch, tmp_path):
+    """An all-miss routed set never clears: its mass is the full total,
+    over any budget, so the token always computes something."""
+    feeder, _ = _make_feeder(monkeypatch, tmp_path)
+    feeder._shed_clear = True
+    feeder.stage(0, np.array([[0]], dtype=np.uint32))
+    assert feeder.shed_misses(
+        0, np.array([1, 3], dtype=np.uint32),
+        np.array([0.6, 0.4], dtype=np.float32), 0.01) is None
+
+
+# Mass popularity (--moe-popularity mass): score-weighted eviction rank
+def test_stage_mass_credit(monkeypatch, tmp_path):
+    """With scores, stage credits popularity by gate mass at count
+    credit's per-call total; score-less calls keep count credit; a
+    degenerate zero-mass call falls back to count credit."""
+    feeder, _ = _make_feeder(monkeypatch, tmp_path)
+    ids = np.array([[0, 1]], dtype=np.uint32)
+    feeder.stage(0, ids, scores=np.array([0.9, 0.1], dtype=np.float32))
+    c = feeder._counts[0]
+    assert abs(c[0] - 1.8) < 1e-6 and abs(c[1] - 0.2) < 1e-6
+    feeder.stage(0, ids)  # count credit: +1 each
+    assert abs(c[0] - 2.8) < 1e-6 and abs(c[1] - 1.2) < 1e-6
+    feeder.stage(0, ids, scores=np.zeros(2, dtype=np.float32))
+    assert abs(c[0] - 3.8) < 1e-6 and abs(c[1] - 2.2) < 1e-6
+
+
 def _stream_scores_glu(monkeypatch, recorded, stock=False):
     """A wrapped scores-carrying SwitchGLU with a recording fake feeder -
     the shared setup for the wrapper-hook tests. stock=True builds a base
@@ -1176,10 +1223,12 @@ def ensure_wired(self):
 
         def __init__(self):
             self.stage_calls = []
+            self.stage_scores = []
             self.shed_calls = []
             self.keep = None
             self.overflow = False
             self._layer_shed_n = 0
+            self._credit_mass = False
 
         def covers(self, li):
             return True
@@ -1188,8 +1237,10 @@ def shed_misses(self, li, ids, scores, keep_mass):
             self.shed_calls.append((li, ids.copy(), scores.copy(), keep_mass))
             return None if self.keep is None else self.keep.copy()
 
-        def stage(self, li, ids):
+        def stage(self, li, ids, scores=None):
             self.stage_calls.append((li, ids.copy()))
+            self.stage_scores.append(
+                None if scores is None else np.array(scores, copy=True))
             return None if self.overflow else ids.astype(np.uint32)
 
         def wedged_at(self, li):
@@ -1259,6 +1310,32 @@ def test_wrapper_miss_shed_stages_survivors(monkeypatch):
     assert len(df.shed_calls) == n_shed
 
 
+def test_wrapper_mass_credit_passes_scores_to_stage(monkeypatch):
+    """--moe-popularity mass: the arena path hands stage() the routed
+    scores with no shed configured, the post-shed renormalized scores
+    when one fires, and nothing when the switch is off."""
+    mx.random.seed(7)
+    recorded = []
+    glu, df = _stream_scores_glu(monkeypatch, recorded)
+    x1 = mx.random.normal((1, 1, 16))
+    i1 = mx.array([[[1, 3]]], dtype=mx.uint32)
+    sc = mx.array([[[0.75, 0.25]]], dtype=mx.float32)
+
+    mx.eval(glu(x1, i1, sc))  # switch off: count credit, no scores
+    assert df.stage_scores[-1] is None
+
+    df._credit_mass = True
+    mx.eval(glu(x1, i1, sc))  # no shed installed, credit still flows
+    assert np.allclose(df.stage_scores[-1], [0.75, 0.25])
+    assert not df.shed_calls
+
+    object.__setattr__(glu, "_kq_miss_shed", 0.9)
+    df.keep = np.array([False, True])
+    mx.eval(glu(x1, i1, sc))
+    # the surviving expert's credit carries the token's full mass
+    assert np.allclose(df.stage_scores[-1], [1.0], atol=1e-6)
+
+
 def test_wrapper_scores_sink_on_stock_base(monkeypatch):
     """A base without the mix seam (e.g. minimax-m3's SwiGLUOAI stack)
     takes (x, indices) only: the wrapper advertises _kq_scores_sink so

From ecd0aa575e11afd8b30a30db87101b5562d30495 Mon Sep 17 00:00:00 2001
From: Asher Feldman <59994+asher@users.noreply.github.com>
Date: Tue, 4 Aug 2026 17:56:38 -0700
Subject: [PATCH 03/10] feat(stream): keeper-targeted lookahead prestage rides
 the miss-shed policy

---
 CHANGELOG.md                |  3 ++
 docs/streaming.md           | 40 ++++++++++++-----
 gmlx/cli.py                 | 25 +++++++++++
 gmlx/decode_feeder.py       | 88 +++++++++++++++++++++++++------------
 gmlx/loader.py              | 13 +++++-
 gmlx/lookahead.py           | 45 +++++++++++--------
 gmlx/moe_experts.py         | 25 +++++++++++
 tests/test_decode_feeder.py | 36 +++++++++++++++
 tests/test_lookahead.py     | 68 +++++++++++++++++++++++++---
 9 files changed, 278 insertions(+), 65 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 7b8f8b9..0f4090c 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -14,6 +14,9 @@ adhere to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 - `--moe-popularity mass`: expert-arena eviction ranks experts by routed
   gate mass instead of touch count, keeping each layer's high-mass
   experts resident.
+- `--moe-prestage keepers`: lookahead reads skip experts the miss-shed
+  policy would drop and stage predicted keepers demand-grade, turning
+  their synchronous demand stalls into reads that overlap compute.
 - Ctrl-T during a `chat` or `run` reply closes the model's open thinking
   block early (as if the thinking budget had just run out) so the answer
   starts now.
diff --git a/docs/streaming.md b/docs/streaming.md
index edbb224..7c11575 100644
--- a/docs/streaming.md
+++ b/docs/streaming.md
@@ -302,15 +302,16 @@ It needs the decode feeder and a block that hands router scores to the
 expert call; where it engages, it is the most targeted lever per point of
 quality spent, and its payoff scales directly with the miss rate.
 
-Miss-shed's payoff is not proportional to the bytes it saves: each
-streamed layer with any surviving demand miss still pays a synchronous
-read stall, so a shed that thins a layer's miss set buys little and one
-that empties it buys the whole stall. `--moe-miss-shed-mode clear`
-targets that directly: a layer sheds its entire miss set when its
-combined mass fits the budget and sheds nothing otherwise, so every
-point of dropped mass converts to a removed stall and untouched layers
-keep their full committee. On models where the tail mode plateaus (mass
-spent, stalls remaining), clear mode reaches further at the same P.
+The two shed modes spend the same budget with different aims. The
+default tail mode maximizes misses shed per point of mass, which is the
+right spend when stall time tracks the reads themselves.
+`--moe-miss-shed-mode clear` instead sheds a layer's entire miss set
+when its combined mass fits the budget and sheds nothing otherwise:
+only a layer whose miss set empties leaves the synchronous demand path
+entirely, and untouched layers keep their full committee. Which wins is
+a measurement, not a doctrine: compare the two at equal realized shed
+mass and read the `demand reads` stall line at exit; if tail mode has
+plateaued with stall seconds remaining, clear is the cheap A/B.
 `--moe-popularity mass` complements it from the residency side: arena
 eviction normally ranks experts by touch count, which keeps the most
 often routed experts resident; ranking by routed gate mass instead
@@ -318,6 +319,21 @@ keeps the experts miss-shed refuses to drop, so fewer high-mass misses
 remain to block a clear. It changes no outputs on its own and composes
 with either shed mode.
 
+`--moe-prestage keepers` attacks the same stalls from the speculative
+side, with no quality cost of its own. Lookahead normally prestages its
+rank-gated predictions with guess-grade caution (never evicting a more
+popular resident), which caps how many demand misses it can absorb.
+Keeper mode filters each prediction through the active miss-shed
+policy: an expert the shed would drop on arrival is not read at all
+(reclaiming the bandwidth a doomed speculative read would burn), and
+the predicted keepers are staged demand-grade, since if the prediction
+is right the demand path would do those same reads synchronously one
+layer later.
+Where prediction recall is good, this converts demand stalls into reads
+that overlap compute; the lookahead exit stats (submitted vs adopted)
+are the guardrail that the added aggression is landing. Requires
+`--moe-miss-shed` to define the policy.
+
 `--moe-layer-shed P` skips a streamed MoE layer's routed experts entirely
 with probability P per token (the layer's shared expert still runs). It
 is the blunt end of the scale, and the only lever that also cuts the
@@ -327,9 +343,9 @@ arena hit rate is high and misses are rare.
 In server configs the lossy levers are the per-model `moe_experts: K` /
 `moe_expert_mass: P` / `moe_miss_shed: P` / `moe_layer_shed: P` keys (or
 the matching `serve` flags for a single positional model); the probe,
-`--moe-miss-shed-mode`, and `--moe-popularity` stay CLI-only, so size P
-with a `gmlx run --moe-expert-probe` pass before pinning a value in a
-config.
+`--moe-miss-shed-mode`, `--moe-popularity`, and `--moe-prestage` stay
+CLI-only, so size P with a `gmlx run --moe-expert-probe` pass before
+pinning a value in a config.
 
 Which to reach for is a measurement, not a doctrine. Run the probe once,
 and read the decode feeder's exit stats (arena hit rate; printed by
diff --git a/gmlx/cli.py b/gmlx/cli.py
index a820016..a361cc8 100644
--- a/gmlx/cli.py
+++ b/gmlx/cli.py
@@ -181,6 +181,17 @@ def add_moe_expert_args(ap: argparse.ArgumentParser) -> None:
         "layer's high-mass experts win residency (and, under "
         "--moe-miss-shed, stop missing).",
     )
+    grp.add_argument(
+        "--moe-prestage",
+        choices=("ranked", "keepers"),
+        default="ranked",
+        help="Lookahead prestage targeting. ranked: rank-gated speculative "
+        "reads with guess-grade eviction. keepers: filter predictions "
+        "through the active --moe-miss-shed policy - would-be-shed "
+        "experts are never read, and would-be-kept misses stage "
+        "demand-grade, converting their future demand stalls into "
+        "overlapped reads. Needs --moe-miss-shed.",
+    )
     grp.add_argument(
         "--moe-layer-shed",
         type=float,
@@ -1262,6 +1273,10 @@ def _apply_placement(args, model) -> None:
                     "--moe-popularity mass",
                     getattr(args, "moe_popularity", "count") == "mass",
                 ),
+                (
+                    "--moe-prestage keepers",
+                    getattr(args, "moe_prestage", "ranked") == "keepers",
+                ),
             )
             if on
         ]
@@ -1328,6 +1343,16 @@ def _apply_placement(args, model) -> None:
         from .moe_experts import install_moe_mass_popularity
 
         install_moe_mass_popularity(model)
+    if getattr(args, "moe_prestage", "ranked") == "keepers":
+        if getattr(args, "moe_miss_shed", None) is None:
+            print(
+                "[stream] --moe-prestage keepers ignored: it needs "
+                "--moe-miss-shed"
+            )
+        else:
+            from .moe_experts import install_moe_prestage_keepers
+
+            install_moe_prestage_keepers(model)
     if getattr(args, "moe_layer_shed", None) is not None:
         from .moe_experts import install_moe_layer_shed
 
diff --git a/gmlx/decode_feeder.py b/gmlx/decode_feeder.py
index 4555bd8..38c1697 100644
--- a/gmlx/decode_feeder.py
+++ b/gmlx/decode_feeder.py
@@ -837,6 +837,21 @@ def shed_misses(self, li: int, ids: np.ndarray, scores: np.ndarray,
         fits the budget, else shed nothing. A partial shed only thins the
         bytes inside stage()'s per-layer read barrier; clearing the set
         removes the barrier, which is where the wall time is."""
+        res = self._shed_mask(li, ids, scores, keep_mass)
+        if res is None:
+            return None
+        keep, shed_mass, total = res
+        self._shed_n += int((~keep).sum())
+        self._shed_mass += shed_mass / max(total, 1e-20)
+        self._shed_tokens += 1
+        return keep
+
+    def _shed_mask(self, li: int, ids: np.ndarray, scores: np.ndarray,
+                   keep_mass: float):
+        """Policy core shared by ``shed_misses`` (which books the stats)
+        and keeper-mode ``prestage`` (which asks what would shed on
+        arrival, stats-free). Returns ``(keep, shed_mass, total)`` or
+        None when nothing sheds."""
         slot_of = self._slot_of.get(li)
         if slot_of is None:
             return None
@@ -857,31 +872,28 @@ def shed_misses(self, li: int, ids: np.ndarray, scores: np.ndarray,
             shed_mass = float(scores[miss].sum())
             if shed_mass > budget:
                 return None
-            keep = ~miss
-            shed_n = int(miss.sum())
-        else:
-            keep = np.ones(ids.size, dtype=bool)
-            shed_mass = 0.0
-            shed_n = 0
-            for j in np.argsort(scores, kind="stable"):
-                if not miss[j] or keep.sum() <= 1:
-                    continue
-                s = float(scores[j])
-                if shed_mass + s > budget:
-                    break
-                keep[j] = False
-                shed_mass += s
-                shed_n += 1
-            if not shed_n:
-                return None
-        self._shed_n += shed_n
-        self._shed_mass += shed_mass / max(total, 1e-20)
-        self._shed_tokens += 1
-        return keep
+            return ~miss, shed_mass, total
+        keep = np.ones(ids.size, dtype=bool)
+        shed_mass = 0.0
+        shed_n = 0
+        for j in np.argsort(scores, kind="stable"):
+            if not miss[j] or keep.sum() <= 1:
+                continue
+            s = float(scores[j])
+            if shed_mass + s > budget:
+                break
+            keep[j] = False
+            shed_mass += s
+            shed_n += 1
+        if not shed_n:
+            return None
+        return keep, shed_mass, total
 
     def prestage(self, li: int, pred_ids: np.ndarray, *,
                  demand: bool = False,
-                 protect: np.ndarray | None = None) -> None:
+                 protect: np.ndarray | None = None,
+                 keep_mass: float | None = None,
+                 pred_scores: np.ndarray | None = None) -> None:
         """Asynchronously pre-read predicted experts into layer ``li``'s
         arena. Safe under the same fence as ``stage`` (the caller's router
         eval for the *previous* MoE layer of the same token transitively
@@ -896,7 +908,14 @@ def prestage(self, li: int, pred_ids: np.ndarray, *,
         resident) is dropped and stage()'s policy applies: evict the
         least-popular resident outright. ``protect`` lists expert ids
         whose slots must survive this call (the token's routed set -
-        routing locality makes them next token's likely demand)."""
+        routing locality makes them next token's likely demand).
+
+        ``keep_mass`` + ``pred_scores`` (--moe-prestage keepers) filter a
+        decode-shaped prediction through the active miss-shed policy
+        first: predictions the shed would drop on arrival are not read at
+        all, and the keepers - the reads the demand path would otherwise
+        do synchronously next layer - stage demand-grade (full width, no
+        rank cap, least-popular eviction)."""
         if (
             li not in self._layers
             or self._staging_disabled
@@ -911,10 +930,25 @@ def prestage(self, li: int, pred_ids: np.ndarray, *,
         owner = self._owner[li]
         counts = self._counts[li]
         dead = self._dead.get(li)
+        keeper = False
+        if keep_mass is not None and pred_scores is not None:
+            flat = pred_ids.reshape(-1, pred_ids.shape[-1])
+            if flat.shape[0] == 1:  # decode-shaped predictions only
+                keeper = True
+                res = self._shed_mask(
+                    li, flat[0],
+                    np.asarray(pred_scores, dtype=np.float32).reshape(-1),
+                    keep_mass)
+                if res is not None:
+                    kept = flat[0][res[0]]
+                    if not kept.size:
+                        return
+                    pred_ids = kept.reshape(1, -1)
+        hard = demand or keeper
         # Only the top GMLX_DECODE_LOOKAHEAD_K ranks per row are considered (the
         # ranking head is reliable, the tail is not); residents among them
         # are simply already-good news, not license to dig deeper.
-        if demand:
+        if hard:
             rows = pred_ids.reshape(-1, pred_ids.shape[-1])
         else:
             rows = pred_ids.reshape(-1, pred_ids.shape[-1])[:, : self._la_k]
@@ -955,10 +989,10 @@ def prestage(self, li: int, pred_ids: np.ndarray, *,
                 if not len(cand):
                     break
                 v = int(np.argmin(counts[owner[cand]]))
-                if not demand and counts[owner[cand]][v] > counts[e]:
+                if not hard and counts[owner[cand]][v] > counts[e]:
                     # Guess-grade guard: a wrong prediction may only cost
-                    # a cold slot, never a hot one. Demand mode evicts
-                    # least-popular outright, like stage().
+                    # a cold slot, never a hot one. Demand and keeper
+                    # modes evict least-popular outright, like stage().
                     continue
                 s = int(cand[v])
                 old = int(owner[s])
diff --git a/gmlx/loader.py b/gmlx/loader.py
index dead43e..824f4ef 100644
--- a/gmlx/loader.py
+++ b/gmlx/loader.py
@@ -1764,8 +1764,17 @@ def __call__(self, x, indices, *args, **kwargs):
                             # layer's gather and the next layers' every-token
                             # work compute - speculation never competes with
                             # demand traffic for the SSD.
-                            for _dst, _ids in la_pred.items():
-                                dfr.prestage(_dst, _ids)
+                            la_keep = (
+                                ms if getattr(
+                                    self, "_kq_prestage_keepers", False)
+                                else None)
+                            for _dst, (_ids, _sc) in la_pred.items():
+                                if la_keep is not None:
+                                    dfr.prestage(
+                                        _dst, _ids, keep_mass=la_keep,
+                                        pred_scores=_sc)
+                                else:
+                                    dfr.prestage(_dst, _ids)
                             if ph is not None:
                                 ph["prestage"] += time.perf_counter() - t2
                         if slots is not None:
diff --git a/gmlx/lookahead.py b/gmlx/lookahead.py
index 666b94c..be904fa 100644
--- a/gmlx/lookahead.py
+++ b/gmlx/lookahead.py
@@ -147,7 +147,8 @@ def _gate_module_select(gate):
     def select(x):
         inds, weights = call(gate, x)
         order = mx.argsort(-weights, axis=-1)
-        return mx.take_along_axis(inds, order, axis=-1)
+        return (mx.take_along_axis(inds, order, axis=-1),
+                mx.take_along_axis(weights, order, axis=-1))
 
     return select
 
@@ -162,12 +163,15 @@ def _sigmoid_bias_select(mod):
         k = mod.args.num_experts_per_tok
 
     def select(x):
-        choice = mx.sigmoid(mod.gate(x.astype(mx.float32)))
-        choice = choice + mod.e_score_correction_bias
+        scores = mx.sigmoid(mod.gate(x.astype(mx.float32)))
+        choice = scores + mod.e_score_correction_bias
         inds = mx.argpartition(-choice, kth=k - 1, axis=-1)[..., :k]
         ch = mx.take_along_axis(choice, inds, axis=-1)
         order = mx.argsort(-ch, axis=-1)
-        return mx.take_along_axis(inds, order, axis=-1)
+        inds = mx.take_along_axis(inds, order, axis=-1)
+        # Rank order keeps the selection bias; the returned mass is the
+        # bias-free sigmoid score the block's mixing weights renormalize.
+        return inds, mx.take_along_axis(scores, inds, axis=-1)
 
     return select
 
@@ -181,7 +185,8 @@ def select(x):
         inds = mx.argpartition(gates, kth=-k, axis=-1)[..., -k:]
         sc = mx.take_along_axis(gates, inds, axis=-1)
         order = mx.argsort(-sc, axis=-1)
-        return mx.take_along_axis(inds, order, axis=-1)
+        return (mx.take_along_axis(inds, order, axis=-1),
+                mx.take_along_axis(sc, order, axis=-1))
 
     return select
 
@@ -238,7 +243,8 @@ def variants(self, probing: bool) -> tuple[str, ...]:
         return (v,)
 
     def predict(self, x, variant: str):
-        """Lazy ranked expert ids (shape ``indices``-like) for ``dst_li``."""
+        """Lazy ``(ranked ids, matching mass scores)`` (shapes
+        ``indices``-like) for ``dst_li``."""
         if variant == "ratio":
             x = x * self._ratio.astype(x.dtype)
         return self._router_fn(x)
@@ -360,9 +366,9 @@ def predictor(self):
     def on_call(self, x, indices) -> dict:
         """Evaluate ``indices`` (the fence the caller needed anyway) plus
         every live lookahead prediction in one sync. Returns a dict of
-        predicted ranked ids per destination layer when prefetching, each
-        trimmed to its rank gate prefix; empty when there is nothing to
-        prestage."""
+        predicted ``(ranked ids, mass scores)`` per destination layer when
+        prefetching, both trimmed to the rank gate prefix; empty when
+        there is nothing to prestage."""
         probing = self.probe is not None
         ph = _LA_PHASE
         t0 = time.perf_counter() if ph is not None else 0.0
@@ -384,7 +390,7 @@ def on_call(self, x, indices) -> dict:
             t1 = time.perf_counter()
             ph["build"] += t1 - t0
         try:
-            mx.eval(indices, *[a for _, _, a in lazy])
+            mx.eval(indices, *[t for _, _, pair in lazy for t in pair])
         except Exception as exc:
             # Joint eval: the failing predictor is unattributable, so
             # disable every one this hook owns rather than loop forever.
@@ -400,8 +406,9 @@ def on_call(self, x, indices) -> dict:
         if ph is not None:
             ph["sync"] += time.perf_counter() - t1
         by_pred: dict = {}
-        for pred, variant, arr in lazy:
-            by_pred.setdefault(pred, {})[variant] = np.array(arr)
+        for pred, variant, (ids_l, sc_l) in lazy:
+            by_pred.setdefault(pred, {})[variant] = (
+                np.array(ids_l), np.array(sc_l, dtype=np.float32))
         actual_np = None
         if self.probe is not None or self.gate is not None:
             actual_np = np.array(indices)
@@ -410,10 +417,10 @@ def on_call(self, x, indices) -> dict:
         if self.probe is not None:
             for pred, pv in by_pred.items():
                 if pred.depth == 1:
-                    labeled = pv
+                    labeled = {v: a for v, (a, _) in pv.items()}
                 else:
                     labeled = {
-                        f"{v}@d{pred.depth}": a for v, a in pv.items()
+                        f"{v}@d{pred.depth}": a for v, (a, _) in pv.items()
                     }
                 self.probe.note(pred.dst_li, labeled)
             self.probe.actual(self.li, actual_np)
@@ -423,9 +430,10 @@ def on_call(self, x, indices) -> dict:
                 # With the probe co-installed both variants exist;
                 # prefetch uses the configured one.
                 chosen = pred.variants(False)[0]
-                ids = pv.get(chosen)
-                if ids is None:
-                    ids = next(iter(pv.values()))
+                pair = pv.get(chosen)
+                if pair is None:
+                    pair = next(iter(pv.values()))
+                ids, sc = pair
                 if self.gate is not None:
                     # Full width is noted (gated-out ranks keep being
                     # scored and can re-qualify); only the submission is
@@ -436,7 +444,8 @@ def on_call(self, x, indices) -> dict:
                         continue
                     if k < ids.shape[-1]:
                         ids = ids[..., :k]
-                out[pred.dst_li] = ids
+                        sc = sc[..., :k]
+                out[pred.dst_li] = (ids, sc)
         return out
 
 
diff --git a/gmlx/moe_experts.py b/gmlx/moe_experts.py
index 66fd589..844371e 100644
--- a/gmlx/moe_experts.py
+++ b/gmlx/moe_experts.py
@@ -494,6 +494,31 @@ def install_moe_mass_popularity(model) -> int:
     return n
 
 
+def install_moe_prestage_keepers(model) -> int:
+    """Route lookahead prestage through the active miss-shed policy:
+    predictions the shed would drop on arrival are never read, and
+    predicted keepers - the reads the demand path would otherwise do
+    synchronously - stage demand-grade (full width, least-popular
+    eviction). Needs --moe-miss-shed on the same layers to define the
+    policy. Returns the number of layers hooked."""
+    n = 0
+    for m in _streamed_modules(model):
+        if getattr(m, "_kq_decode_feeder", None) is not None:
+            object.__setattr__(m, "_kq_prestage_keepers", True)
+            n += 1
+    if n:
+        loadlog.info(
+            f"[stream] MoE keeper prestage: lookahead reads on {n} offloaded "
+            "MoE layers target only experts the miss-shed policy would keep"
+        )
+    else:
+        print(
+            "[stream] MoE keeper prestage found no decode-feeder MoE layer "
+            "- no effect"
+        )
+    return n
+
+
 def install_moe_layer_shed(model, p: float) -> int:
     """Skip each streamed MoE layer's routed path with probability ``p``
     per decode token; the layer's shared expert still runs. Lossy.
diff --git a/tests/test_decode_feeder.py b/tests/test_decode_feeder.py
index a6fbac2..790d539 100644
--- a/tests/test_decode_feeder.py
+++ b/tests/test_decode_feeder.py
@@ -1091,6 +1091,42 @@ def bare(verbose):
     assert "wedged" in out
 
 
+# Keeper-mode prestage (--moe-prestage keepers): shed-aware targeting
+def test_prestage_keeper_mode_filters_and_evicts(monkeypatch, tmp_path):
+    """Keeper mode never reads a prediction the shed policy would drop on
+    arrival, stages the keepers demand-grade (a colder resident is
+    evicted although the prediction has no popularity), and books no
+    shed stats for the filtering."""
+    feeder, _ = _make_feeder(monkeypatch, tmp_path)
+    feeder.stage(0, np.array([[0, 1]]))  # arena full: e0, e1
+    feeder.stage(0, np.array([[0]]))     # counts: e0=2 > e1=1
+    # e0 resident; e2 (0.3) survives the 0.3 budget, e3 (0.1) sheds.
+    ids = np.array([[0, 2, 3]])
+    sc = np.array([0.6, 0.3, 0.1], dtype=np.float32)
+    feeder.prestage(0, ids, keep_mass=0.7, pred_scores=sc)
+    assert feeder._la_submitted == 1
+    assert 2 in feeder._pending[0] and 3 not in feeder._pending[0]
+    assert feeder._slot_of[0][1] == -1  # cold resident evicted outright
+    assert feeder._slot_of[0][0] >= 0   # predicted resident shielded
+    assert feeder._shed_n == 0 and feeder._shed_tokens == 0
+    _wait_published(feeder, 0)
+    assert feeder._slot_of[0][2] >= 0
+
+
+def test_prestage_keeper_mode_full_width_when_nothing_sheds(
+        monkeypatch, tmp_path):
+    """A budget that sheds nothing makes every predicted miss a keeper:
+    keeper mode skips the speculative rank cap, ranked mode keeps it."""
+    monkeypatch.setenv("GMLX_DECODE_LOOKAHEAD_K", "1")
+    feeder, _ = _make_feeder(monkeypatch, tmp_path)
+    ids = np.array([[1, 3]])
+    sc = np.array([0.5, 0.5], dtype=np.float32)
+    feeder.prestage(0, ids, keep_mass=1.0, pred_scores=sc)
+    assert feeder._la_submitted == 2  # both, despite the rank cap of 1
+    feeder.prestage(1, ids)
+    assert feeder._la_submitted == 3  # ranked mode: capped to rank 0
+
+
 # Lossy miss-shed (--moe-miss-shed): residency-aware expert drop
 def test_shed_misses_drops_cold_lowest_first(monkeypatch, tmp_path):
     """Only demand-miss experts shed, lowest score first, capped at
diff --git a/tests/test_lookahead.py b/tests/test_lookahead.py
index c6bbe71..82bd9b5 100644
--- a/tests/test_lookahead.py
+++ b/tests/test_lookahead.py
@@ -115,8 +115,9 @@ def __call__(self, x):
                 mx.array([[0.1, 0.7, 0.2]]),
             )
 
-    ids = np.array(_gate_module_select(_Stub())(mx.zeros((1, DIM))))
-    assert ids.tolist() == [[9, 2, 4]]
+    ids, sc = _gate_module_select(_Stub())(mx.zeros((1, DIM)))
+    assert np.array(ids).tolist() == [[9, 2, 4]]
+    assert np.allclose(np.array(sc), [[0.7, 0.2, 0.1]])
 
 
 def test_sigmoid_bias_select_matches_stock_selection():
@@ -130,12 +131,16 @@ def __init__(self):
     mx.random.seed(3)
     blk = _Block()
     x = mx.random.normal((1, 1, DIM))
-    ids = np.array(_sigmoid_bias_select(blk)(x)).reshape(-1)
+    ids_mx, sc_mx = _sigmoid_bias_select(blk)(x)
+    ids = np.array(ids_mx).reshape(-1)
     # Reference: the stock forward's selection seam.
     choice = mx.sigmoid(blk.gate(x.astype(mx.float32)))
-    choice = np.array(choice + blk.e_score_correction_bias).reshape(-1)
-    ref = np.argsort(-choice)[:K]
+    biased = np.array(choice + blk.e_score_correction_bias).reshape(-1)
+    ref = np.argsort(-biased)[:K]
     assert ids.tolist() == ref.tolist()
+    # Mass scores are the bias-free sigmoid the block's mix renormalizes.
+    raw = np.array(choice).reshape(-1)
+    assert np.allclose(np.array(sc_mx).reshape(-1), raw[ids], atol=1e-6)
 
 
 def test_router_fn_for_unknown_block_is_none():
@@ -464,6 +469,56 @@ def swapped(self, li):
     assert sorted(li for li, _ in df.calls) == [1, 2]
 
 
+def test_wrapper_keeper_prestage_passes_policy(monkeypatch):
+    """--moe-prestage keepers: the wrapper hands prestage the active shed
+    share and the prediction's mass scores; with the switch off, or with
+    no miss-shed installed, the plain ranked call is unchanged."""
+    from contextlib import contextmanager
+
+    model = _streaming_model(monkeypatch)
+    install_lookahead(model, model.layers, probe=False, prefetch=True)
+    glu0 = model.layers[0].mlp.switch_mlp
+
+    class _FakeDF:
+        def ensure_wired(self):
+            pass
+
+        calls = []
+
+        def covers(self, li):
+            return True
+
+        def stage(self, li, ids):
+            return ids.astype(np.uint32)
+
+        def prestage(self, li, pred, **kw):
+            self.calls.append((li, pred.copy(), kw))
+
+        def wedged_at(self, li):
+            return False
+
+        @contextmanager
+        def swapped(self, li):
+            yield
+
+    df = _FakeDF()
+    object.__setattr__(glu0, "_kq_decode_feeder", df)
+    object.__setattr__(glu0, "_kq_li", 0)
+    x = mx.random.normal((1, 1, DIM))
+    mx.eval(model.layers[0].mlp(x))
+    assert df.calls and df.calls[-1][2] == {}  # ranked: no policy kwargs
+
+    object.__setattr__(glu0, "_kq_prestage_keepers", True)
+    mx.eval(model.layers[0].mlp(x))
+    assert df.calls[-1][2] == {}  # keepers without miss-shed: unchanged
+
+    object.__setattr__(glu0, "_kq_miss_shed", 0.8)
+    mx.eval(model.layers[0].mlp(x))
+    li, pred, kw = df.calls[-1]
+    assert li == 1 and kw["keep_mass"] == 0.8
+    assert kw["pred_scores"].shape == pred.shape
+
+
 if __name__ == "__main__":
     raise SystemExit(pytest.main([__file__, "-q"]))
 
@@ -486,7 +541,8 @@ def __init__(self):
     from gmlx.lookahead import _SIGMOID_BIAS_BLOCKS
     assert "KimiK3MoE" in _SIGMOID_BIAS_BLOCKS
     x = mx.random.normal((1, 1, DIM))
-    ids = np.array(_sigmoid_bias_select(blk)(x)).reshape(-1)
+    ids_mx, _ = _sigmoid_bias_select(blk)(x)
+    ids = np.array(ids_mx).reshape(-1)
     choice = mx.sigmoid(blk.gate(x.astype(mx.float32)))
     choice = np.array(choice + blk.e_score_correction_bias).reshape(-1)
     ref = np.argsort(-choice)[:K]

From 51ac435e321f5cf14c8267d962aa23c403e22b3e Mon Sep 17 00:00:00 2001
From: Asher Feldman <59994+asher@users.noreply.github.com>
Date: Tue, 4 Aug 2026 19:03:17 -0700
Subject: [PATCH 04/10] refactor(stream): drop clear-mode miss-shed; tail sheds
 strictly more at any budget

---
 CHANGELOG.md                |  3 ---
 docs/streaming.md           | 25 +++++++------------------
 gmlx/cli.py                 | 19 +------------------
 gmlx/decode_feeder.py       | 24 +++---------------------
 gmlx/moe_experts.py         | 30 +++++++-----------------------
 tests/test_decode_feeder.py | 31 -------------------------------
 6 files changed, 18 insertions(+), 114 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 0f4090c..92d5141 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -8,9 +8,6 @@ adhere to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
 ### Added
 
-- `--moe-miss-shed-mode clear`: miss-shed drops a layer's whole miss set
-  when its mass fits the budget and nothing otherwise, so each shed
-  removes a per-layer demand-read stall instead of thinning one.
 - `--moe-popularity mass`: expert-arena eviction ranks experts by routed
   gate mass instead of touch count, keeping each layer's high-mass
   experts resident.
diff --git a/docs/streaming.md b/docs/streaming.md
index 7c11575..eeeb642 100644
--- a/docs/streaming.md
+++ b/docs/streaming.md
@@ -302,22 +302,11 @@ It needs the decode feeder and a block that hands router scores to the
 expert call; where it engages, it is the most targeted lever per point of
 quality spent, and its payoff scales directly with the miss rate.
 
-The two shed modes spend the same budget with different aims. The
-default tail mode maximizes misses shed per point of mass, which is the
-right spend when stall time tracks the reads themselves.
-`--moe-miss-shed-mode clear` instead sheds a layer's entire miss set
-when its combined mass fits the budget and sheds nothing otherwise:
-only a layer whose miss set empties leaves the synchronous demand path
-entirely, and untouched layers keep their full committee. Which wins is
-a measurement, not a doctrine: compare the two at equal realized shed
-mass and read the `demand reads` stall line at exit; if tail mode has
-plateaued with stall seconds remaining, clear is the cheap A/B.
-`--moe-popularity mass` complements it from the residency side: arena
-eviction normally ranks experts by touch count, which keeps the most
-often routed experts resident; ranking by routed gate mass instead
+`--moe-popularity mass` complements miss-shed from the residency side:
+arena eviction normally ranks experts by touch count, which keeps the
+most often routed experts resident; ranking by routed gate mass instead
 keeps the experts miss-shed refuses to drop, so fewer high-mass misses
-remain to block a clear. It changes no outputs on its own and composes
-with either shed mode.
+remain to stall a layer. It changes no outputs on its own.
 
 `--moe-prestage keepers` attacks the same stalls from the speculative
 side, with no quality cost of its own. Lookahead normally prestages its
@@ -343,9 +332,9 @@ arena hit rate is high and misses are rare.
 In server configs the lossy levers are the per-model `moe_experts: K` /
 `moe_expert_mass: P` / `moe_miss_shed: P` / `moe_layer_shed: P` keys (or
 the matching `serve` flags for a single positional model); the probe,
-`--moe-miss-shed-mode`, `--moe-popularity`, and `--moe-prestage` stay
-CLI-only, so size P with a `gmlx run --moe-expert-probe` pass before
-pinning a value in a config.
+`--moe-popularity`, and `--moe-prestage` stay CLI-only, so size P with
+a `gmlx run --moe-expert-probe` pass before pinning a value in a
+config.
 
 Which to reach for is a measurement, not a doctrine. Run the probe once,
 and read the decode feeder's exit stats (arena hit rate; printed by
diff --git a/gmlx/cli.py b/gmlx/cli.py
index a361cc8..b89e6d9 100644
--- a/gmlx/cli.py
+++ b/gmlx/cli.py
@@ -162,16 +162,6 @@ def add_moe_expert_args(ap: argparse.ArgumentParser) -> None:
         "directly; resident experts are never dropped. Composes with "
         "--moe-expert-mass.",
     )
-    grp.add_argument(
-        "--moe-miss-shed-mode",
-        choices=("tail", "clear"),
-        default="tail",
-        help="Miss-shed policy. tail: shed lowest-scored misses up to the "
-        "budget. clear: shed a layer's whole miss set when its mass fits "
-        "the budget, nothing otherwise - each shed then removes a "
-        "per-layer demand-read stall instead of thinning one. No effect "
-        "without --moe-miss-shed.",
-    )
     grp.add_argument(
         "--moe-popularity",
         choices=("count", "mass"),
@@ -1331,14 +1321,7 @@ def _apply_placement(args, model) -> None:
     if getattr(args, "moe_miss_shed", None) is not None:
         from .moe_experts import install_moe_miss_shed
 
-        install_moe_miss_shed(
-            model, args.moe_miss_shed,
-            mode=getattr(args, "moe_miss_shed_mode", "tail"))
-    elif getattr(args, "moe_miss_shed_mode", "tail") != "tail":
-        print(
-            "[stream] --moe-miss-shed-mode ignored: it needs "
-            "--moe-miss-shed"
-        )
+        install_moe_miss_shed(model, args.moe_miss_shed)
     if getattr(args, "moe_popularity", "count") == "mass":
         from .moe_experts import install_moe_mass_popularity
 
diff --git a/gmlx/decode_feeder.py b/gmlx/decode_feeder.py
index 38c1697..cc2642c 100644
--- a/gmlx/decode_feeder.py
+++ b/gmlx/decode_feeder.py
@@ -426,10 +426,8 @@ def __init__(
         self._shed_mass = 0.0
         self._shed_tokens = 0
         self._layer_shed_n = 0
-        # Policy switches (installed by moe_experts after load).
-        # _shed_clear: shed_misses drops a layer's whole miss set or
-        # nothing. _credit_mass: popularity credit by gate mass.
-        self._shed_clear = False
+        # Policy switch (installed by moe_experts after load):
+        # popularity credit by gate mass instead of touch count.
         self._credit_mass = False
 
         # GMLX_DECODE_FEEDER_VERIFY=1: sample-compare arena slots against
@@ -830,13 +828,7 @@ def shed_misses(self, li: int, ids: np.ndarray, scores: np.ndarray,
         first, capped at ``1 - keep_mass`` of the token's score mass.
         Returns None when nothing sheds. A shed expert is never staged, so
         it also earns no popularity credit (self-reinforcing by design:
-        the arena's long tail stays cold).
-
-        ``_shed_clear`` (--moe-miss-shed-mode clear) swaps the greedy tail
-        for all-or-nothing: shed the layer's whole miss set when its mass
-        fits the budget, else shed nothing. A partial shed only thins the
-        bytes inside stage()'s per-layer read barrier; clearing the set
-        removes the barrier, which is where the wall time is."""
+        the arena's long tail stays cold)."""
         res = self._shed_mask(li, ids, scores, keep_mass)
         if res is None:
             return None
@@ -863,16 +855,6 @@ def _shed_mask(self, li: int, ids: np.ndarray, scores: np.ndarray,
             return None
         total = float(scores.sum())
         budget = total * max(0.0, 1.0 - keep_mass)
-        if self._shed_clear:
-            if miss.all():
-                # Never drop a token's entire routed set (belt-and-braces:
-                # an all-miss set's mass is the full total, over any
-                # budget with keep_mass > 0).
-                return None
-            shed_mass = float(scores[miss].sum())
-            if shed_mass > budget:
-                return None
-            return ~miss, shed_mass, total
         keep = np.ones(ids.size, dtype=bool)
         shed_mass = 0.0
         shed_n = 0
diff --git a/gmlx/moe_experts.py b/gmlx/moe_experts.py
index 844371e..ad2eac2 100644
--- a/gmlx/moe_experts.py
+++ b/gmlx/moe_experts.py
@@ -423,38 +423,22 @@ def _streamed_modules(model):
                 yield m
 
 
-def install_moe_miss_shed(model, p: float, mode: str = "tail") -> int:
+def install_moe_miss_shed(model, p: float) -> int:
     """Shed routed experts that would demand-miss the decode arena, up to
-    ``1 - p`` of each token's score mass (resident and prestage-inflight
-    experts never shed). ``mode`` "tail" sheds lowest scores first up to
-    the budget; "clear" sheds a layer's whole miss set when its mass fits
-    the budget and nothing otherwise (every shed then empties a per-layer
-    demand-read stall instead of thinning one). Lossy; acts only on
-    decode calls that carry routing scores (the fused scores path).
-    Returns the number of layers hooked; raises on p outside (0, 1] or an
-    unknown mode."""
+    ``1 - p`` of each token's score mass (lowest scores first; resident
+    and prestage-inflight experts never shed). Lossy; acts only on decode
+    calls that carry routing scores (the fused scores path). Returns the
+    number of layers hooked; raises on p outside (0, 1]."""
     if not 0.0 < p <= 1.0:
         raise ValueError(f"MoE miss-shed share must be in (0, 1], got {p}")
-    if mode not in ("tail", "clear"):
-        raise ValueError(f"MoE miss-shed mode must be tail or clear, got {mode}")
     n = 0
     for m in _streamed_modules(model):
-        f = getattr(m, "_kq_decode_feeder", None)
-        if f is not None:
+        if getattr(m, "_kq_decode_feeder", None) is not None:
             object.__setattr__(m, "_kq_miss_shed", float(p))
-            if mode == "clear":
-                f._shed_clear = True
-            elif getattr(f, "_shed_clear", False):
-                f._shed_clear = False
             n += 1
     if n:
-        how = (
-            "whole miss sets shed where they fit, none where they don't"
-            if mode == "clear"
-            else "demand-miss experts shed lowest score first"
-        )
         loadlog.info(
-            f"[stream] MoE miss-shed {p:g} ({mode}): {how}, up to "
+            f"[stream] MoE miss-shed {p:g}: demand-miss experts shed up to "
             f"{100 * (1 - p):g}% of each token's gate mass on {n} offloaded "
             "MoE layers (lossy - outputs differ from the trained router)"
         )
diff --git a/tests/test_decode_feeder.py b/tests/test_decode_feeder.py
index 790d539..14f7bf6 100644
--- a/tests/test_decode_feeder.py
+++ b/tests/test_decode_feeder.py
@@ -1175,37 +1175,6 @@ def test_shed_misses_keeps_at_least_one_expert(monkeypatch, tmp_path):
     assert keep.tolist() == [True, False]
 
 
-def test_shed_misses_clear_mode_all_or_nothing(monkeypatch, tmp_path):
-    """Clear mode empties the layer's whole miss set when its mass fits
-    the budget (removing that layer's demand-read stall) and sheds
-    nothing when it does not - never a partial shed."""
-    feeder, _ = _make_feeder(monkeypatch, tmp_path)
-    feeder._shed_clear = True
-    feeder.stage(0, np.array([[0, 2]], dtype=np.uint32))  # 0,2 resident
-    ids = np.array([0, 1, 2, 3], dtype=np.uint32)
-    scores = np.array([0.4, 0.05, 0.4, 0.15], dtype=np.float32)
-    # budget 0.3 covers the miss set (experts 1+3 = 0.2): both shed
-    keep = feeder.shed_misses(0, ids, scores, 0.7)
-    assert keep.tolist() == [True, False, True, False]
-    assert feeder._shed_n == 2 and feeder._shed_tokens == 1
-    assert abs(feeder._shed_mass - 0.2) < 1e-6
-    # budget 0.1 does not cover it: nothing sheds (tail mode would have
-    # dropped expert 1 alone)
-    assert feeder.shed_misses(0, ids, scores, 0.9) is None
-    assert feeder._shed_n == 2  # no partial-shed bookkeeping
-
-
-def test_shed_misses_clear_mode_never_drops_whole_set(monkeypatch, tmp_path):
-    """An all-miss routed set never clears: its mass is the full total,
-    over any budget, so the token always computes something."""
-    feeder, _ = _make_feeder(monkeypatch, tmp_path)
-    feeder._shed_clear = True
-    feeder.stage(0, np.array([[0]], dtype=np.uint32))
-    assert feeder.shed_misses(
-        0, np.array([1, 3], dtype=np.uint32),
-        np.array([0.6, 0.4], dtype=np.float32), 0.01) is None
-
-
 # Mass popularity (--moe-popularity mass): score-weighted eviction rank
 def test_stage_mass_credit(monkeypatch, tmp_path):
     """With scores, stage credits popularity by gate mass at count

From 27d9169e2b810e7a286a8a5a7d8f6943869ff910 Mon Sep 17 00:00:00 2001
From: Asher Feldman <59994+asher@users.noreply.github.com>
Date: Tue, 4 Aug 2026 19:28:47 -0700
Subject: [PATCH 05/10] refactor(stream): drop mass-weighted popularity
 (isolation A/B was a wash)

---
 CHANGELOG.md                |  3 ---
 docs/streaming.md           | 15 ++++--------
 gmlx/cli.py                 | 17 -------------
 gmlx/decode_feeder.py       | 29 ++--------------------
 gmlx/gpu_token.py           |  7 ++----
 gmlx/loader.py              | 24 ++++++-------------
 gmlx/moe_experts.py         | 28 ----------------------
 tests/test_decode_feeder.py | 48 +------------------------------------
 8 files changed, 16 insertions(+), 155 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 92d5141..ac86d96 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -8,9 +8,6 @@ adhere to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
 ### Added
 
-- `--moe-popularity mass`: expert-arena eviction ranks experts by routed
-  gate mass instead of touch count, keeping each layer's high-mass
-  experts resident.
 - `--moe-prestage keepers`: lookahead reads skip experts the miss-shed
   policy would drop and stage predicted keepers demand-grade, turning
   their synchronous demand stalls into reads that overlap compute.
diff --git a/docs/streaming.md b/docs/streaming.md
index eeeb642..b38e06d 100644
--- a/docs/streaming.md
+++ b/docs/streaming.md
@@ -302,13 +302,7 @@ It needs the decode feeder and a block that hands router scores to the
 expert call; where it engages, it is the most targeted lever per point of
 quality spent, and its payoff scales directly with the miss rate.
 
-`--moe-popularity mass` complements miss-shed from the residency side:
-arena eviction normally ranks experts by touch count, which keeps the
-most often routed experts resident; ranking by routed gate mass instead
-keeps the experts miss-shed refuses to drop, so fewer high-mass misses
-remain to stall a layer. It changes no outputs on its own.
-
-`--moe-prestage keepers` attacks the same stalls from the speculative
+`--moe-prestage keepers` attacks miss-shed's residual stalls from the speculative
 side, with no quality cost of its own. Lookahead normally prestages its
 rank-gated predictions with guess-grade caution (never evicting a more
 popular resident), which caps how many demand misses it can absorb.
@@ -331,10 +325,9 @@ arena hit rate is high and misses are rare.
 
 In server configs the lossy levers are the per-model `moe_experts: K` /
 `moe_expert_mass: P` / `moe_miss_shed: P` / `moe_layer_shed: P` keys (or
-the matching `serve` flags for a single positional model); the probe,
-`--moe-popularity`, and `--moe-prestage` stay CLI-only, so size P with
-a `gmlx run --moe-expert-probe` pass before pinning a value in a
-config.
+the matching `serve` flags for a single positional model); the probe
+and `--moe-prestage` stay CLI-only, so size P with a
+`gmlx run --moe-expert-probe` pass before pinning a value in a config.
 
 Which to reach for is a measurement, not a doctrine. Run the probe once,
 and read the decode feeder's exit stats (arena hit rate; printed by
diff --git a/gmlx/cli.py b/gmlx/cli.py
index b89e6d9..5b08add 100644
--- a/gmlx/cli.py
+++ b/gmlx/cli.py
@@ -162,15 +162,6 @@ def add_moe_expert_args(ap: argparse.ArgumentParser) -> None:
         "directly; resident experts are never dropped. Composes with "
         "--moe-expert-mass.",
     )
-    grp.add_argument(
-        "--moe-popularity",
-        choices=("count", "mass"),
-        default="count",
-        help="Expert-arena eviction popularity. count: every routed expert "
-        "credits equally per call. mass: credit by gate score, so each "
-        "layer's high-mass experts win residency (and, under "
-        "--moe-miss-shed, stop missing).",
-    )
     grp.add_argument(
         "--moe-prestage",
         choices=("ranked", "keepers"),
@@ -1259,10 +1250,6 @@ def _apply_placement(args, model) -> None:
                     "--moe-layer-shed",
                     getattr(args, "moe_layer_shed", None) is not None,
                 ),
-                (
-                    "--moe-popularity mass",
-                    getattr(args, "moe_popularity", "count") == "mass",
-                ),
                 (
                     "--moe-prestage keepers",
                     getattr(args, "moe_prestage", "ranked") == "keepers",
@@ -1322,10 +1309,6 @@ def _apply_placement(args, model) -> None:
         from .moe_experts import install_moe_miss_shed
 
         install_moe_miss_shed(model, args.moe_miss_shed)
-    if getattr(args, "moe_popularity", "count") == "mass":
-        from .moe_experts import install_moe_mass_popularity
-
-        install_moe_mass_popularity(model)
     if getattr(args, "moe_prestage", "ranked") == "keepers":
         if getattr(args, "moe_miss_shed", None) is None:
             print(
diff --git a/gmlx/decode_feeder.py b/gmlx/decode_feeder.py
index cc2642c..8e4cff6 100644
--- a/gmlx/decode_feeder.py
+++ b/gmlx/decode_feeder.py
@@ -220,23 +220,6 @@ def _iopol_utility() -> None:
     libc.setiopolicy_np(0, 1, 4)
 
 
-def credit_popularity(counts, uniq, ids, scores) -> None:
-    """Popularity credit for one routed call: by touch count when
-    ``scores`` is None, else by gate mass scaled so the per-call total
-    stays ``len(uniq)`` (count credit's scale - the two can mix across
-    decode and prefill calls without skewing eviction). Shared by
-    ``stage`` and the gpu-autonomous boundary so the ledgers agree."""
-    if scores is None:
-        counts[uniq] += 1.0
-        return
-    w = np.asarray(scores, dtype=np.float64).reshape(-1)
-    tot = float(w.sum())
-    if tot <= 0.0:
-        counts[uniq] += 1.0
-        return
-    np.add.at(counts, np.asarray(ids).reshape(-1), w * (len(uniq) / tot))
-
-
 class DecodeFeeder:
     """Per-layer wired expert arenas with popularity-driven replacement."""
 
@@ -426,9 +409,6 @@ def __init__(
         self._shed_mass = 0.0
         self._shed_tokens = 0
         self._layer_shed_n = 0
-        # Policy switch (installed by moe_experts after load):
-        # popularity credit by gate mass instead of touch count.
-        self._credit_mass = False
 
         # GMLX_DECODE_FEEDER_VERIFY=1: sample-compare arena slots against
         # their file bytes at every publish and every routed use, and
@@ -660,8 +640,7 @@ def lend_for_ring(self, nbytes: int) -> None:
             "restored at next decode"
         )
 
-    def stage(self, li: int, ids: np.ndarray,
-              scores: np.ndarray | None = None) -> np.ndarray | None:
+    def stage(self, li: int, ids: np.ndarray) -> np.ndarray | None:
         """Map router expert ids to arena slots, pulling misses from the GGUF
         into evicted slots first. Returns the slot array (``ids``' shape,
         uint32), or None when the call cannot be served from the arena:
@@ -670,10 +649,6 @@ def stage(self, li: int, ids: np.ndarray,
         the caller falls back to the CPU path - with ids rewritten through
         ``redirect_dead`` when ``has_dead`` says the layer lost experts.
 
-        ``scores`` (mass-popularity callers only, aligned with ``ids``
-        flattened) switches this call's popularity credit from touch count
-        to gate mass; calls without scores keep count credit.
-
         Caller contract: ``mx.eval`` of the call's ``indices`` has run, so no
         in-flight gather references this layer's arena (see module docstring).
         """
@@ -710,7 +685,7 @@ def stage(self, li: int, ids: np.ndarray,
             for pe, ps in self._verify_prev.get(li, ()):
                 if ps < len(owner_v) and owner_v[ps] == pe:
                     self._verify_slot(li, pe, ps, "prev-routed")
-        credit_popularity(counts, uniq, ids, scores)
+        counts[uniq] += 1.0
         self._calls += 1
         if self._calls % _DECAY_EVERY == 0:
             for c in self._counts.values():
diff --git a/gmlx/gpu_token.py b/gmlx/gpu_token.py
index e8df1ce..dfc2a59 100644
--- a/gmlx/gpu_token.py
+++ b/gmlx/gpu_token.py
@@ -38,7 +38,7 @@
 import mlx.core as mx
 
 from . import keepwarm
-from .decode_feeder import _DECAY_EVERY, credit_popularity
+from .decode_feeder import _DECAY_EVERY
 from .envflags import env_bool, env_float, env_int
 
 _ENV = "GMLX_GPU_AUTONOMOUS"
@@ -213,10 +213,7 @@ def boundary(self) -> None:
             # python miss-shed deliberately starves them; here prestage
             # below re-warms them anyway, so credit keeps the two ledgers
             # comparable.)
-            credit_popularity(
-                counts, routed, np.array(indices),
-                np.array(scores) if getattr(dfr, "_credit_mass", False)
-                else None)
+            counts[routed] += 1.0
             dfr._calls += 1
             if dfr._calls % _DECAY_EVERY == 0:
                 for c in dfr._counts.values():
diff --git a/gmlx/loader.py b/gmlx/loader.py
index 824f4ef..8f7ae3b 100644
--- a/gmlx/loader.py
+++ b/gmlx/loader.py
@@ -1701,15 +1701,12 @@ def __call__(self, x, indices, *args, **kwargs):
                         # concatenate cannot take both.
                         ms = (None if getattr(self, "_kq_in_split", False)
                               else getattr(self, "_kq_miss_shed", None))
-                        mass_credit = getattr(dfr, "_credit_mass", False)
                         sc_f32 = None
-                        if ((ms is not None or mass_credit)
-                                and scores_arg is not None
+                        if (ms is not None and scores_arg is not None
                                 and n_tokens == 1):
-                            # Shed and mass credit read the scores host-side;
-                            # fold them into the router eval so the hook adds
-                            # a small D2H copy, not a second per-layer graph
-                            # flush.
+                            # Shed reads the scores host-side; fold them into
+                            # the router eval so the hook adds a small D2H
+                            # copy, not a second per-layer graph flush.
                             sc_f32 = scores_arg.astype(mx.float32)
                             mx.eval(indices, sc_f32)
                         else:
@@ -1721,12 +1718,10 @@ def __call__(self, x, indices, *args, **kwargs):
                         ids = np.array(indices)
                         shed_args = None
                         shed_mix = None
-                        credit_sc = None
                         if sc_f32 is not None:
                             sc = np.asarray(sc_f32).reshape(-1)
-                            credit_sc = sc
-                            keep = (None if ms is None else dfr.shed_misses(
-                                self._kq_li, ids.reshape(-1), sc, ms))
+                            keep = dfr.shed_misses(
+                                self._kq_li, ids.reshape(-1), sc, ms)
                             if keep is not None:
                                 # Arena-path only: the overflow fallback
                                 # below keeps the original routed set.
@@ -1736,7 +1731,6 @@ def __call__(self, x, indices, *args, **kwargs):
                                 scn = sc[keep]
                                 # survivors keep the token's full mass
                                 scn = scn * (sc.sum() / max(scn.sum(), 1e-20))
-                                credit_sc = scn
                                 sc_mx = mx.array(scn.reshape(shp)).astype(
                                     scores_arg.dtype)
                                 if _fwd_scores:
@@ -1747,11 +1741,7 @@ def __call__(self, x, indices, *args, **kwargs):
                                     # full routed set, so mix the shed
                                     # survivors here instead.
                                     shed_mix = sc_mx
-                        if mass_credit and credit_sc is not None:
-                            slots = dfr.stage(
-                                self._kq_li, ids, scores=credit_sc)
-                        else:
-                            slots = dfr.stage(self._kq_li, ids)
+                        slots = dfr.stage(self._kq_li, ids)
                         if ph is not None:
                             t2 = time.perf_counter()
                             w = getattr(dfr, "_t_demand", 0.0) - wait0
diff --git a/gmlx/moe_experts.py b/gmlx/moe_experts.py
index ad2eac2..d5228e9 100644
--- a/gmlx/moe_experts.py
+++ b/gmlx/moe_experts.py
@@ -450,34 +450,6 @@ def install_moe_miss_shed(model, p: float) -> int:
     return n
 
 
-def install_moe_mass_popularity(model) -> int:
-    """Credit arena popularity by routed gate mass instead of touch count
-    on decode calls that carry scores, so eviction keeps each layer's
-    high-mass experts resident. Not itself lossy (residency is a perf
-    policy), but composed with --moe-miss-shed it steers what sheds.
-    Returns the number of layers whose feeder switched."""
-    n = 0
-    seen: set[int] = set()
-    for m in _streamed_modules(model):
-        f = getattr(m, "_kq_decode_feeder", None)
-        if f is not None:
-            if id(f) not in seen:
-                seen.add(id(f))
-                f._credit_mass = True
-            n += 1
-    if n:
-        loadlog.info(
-            f"[stream] MoE mass popularity: arena eviction on {n} offloaded "
-            "MoE layers ranks experts by routed gate mass, not touch count"
-        )
-    else:
-        print(
-            "[stream] MoE mass popularity found no decode-feeder MoE layer "
-            "- no effect"
-        )
-    return n
-
-
 def install_moe_prestage_keepers(model) -> int:
     """Route lookahead prestage through the active miss-shed policy:
     predictions the shed would drop on arrival are never read, and
diff --git a/tests/test_decode_feeder.py b/tests/test_decode_feeder.py
index 14f7bf6..6775242 100644
--- a/tests/test_decode_feeder.py
+++ b/tests/test_decode_feeder.py
@@ -1175,22 +1175,6 @@ def test_shed_misses_keeps_at_least_one_expert(monkeypatch, tmp_path):
     assert keep.tolist() == [True, False]
 
 
-# Mass popularity (--moe-popularity mass): score-weighted eviction rank
-def test_stage_mass_credit(monkeypatch, tmp_path):
-    """With scores, stage credits popularity by gate mass at count
-    credit's per-call total; score-less calls keep count credit; a
-    degenerate zero-mass call falls back to count credit."""
-    feeder, _ = _make_feeder(monkeypatch, tmp_path)
-    ids = np.array([[0, 1]], dtype=np.uint32)
-    feeder.stage(0, ids, scores=np.array([0.9, 0.1], dtype=np.float32))
-    c = feeder._counts[0]
-    assert abs(c[0] - 1.8) < 1e-6 and abs(c[1] - 0.2) < 1e-6
-    feeder.stage(0, ids)  # count credit: +1 each
-    assert abs(c[0] - 2.8) < 1e-6 and abs(c[1] - 1.2) < 1e-6
-    feeder.stage(0, ids, scores=np.zeros(2, dtype=np.float32))
-    assert abs(c[0] - 3.8) < 1e-6 and abs(c[1] - 2.2) < 1e-6
-
-
 def _stream_scores_glu(monkeypatch, recorded, stock=False):
     """A wrapped scores-carrying SwitchGLU with a recording fake feeder -
     the shared setup for the wrapper-hook tests. stock=True builds a base
@@ -1228,12 +1212,10 @@ def ensure_wired(self):
 
         def __init__(self):
             self.stage_calls = []
-            self.stage_scores = []
             self.shed_calls = []
             self.keep = None
             self.overflow = False
             self._layer_shed_n = 0
-            self._credit_mass = False
 
         def covers(self, li):
             return True
@@ -1242,10 +1224,8 @@ def shed_misses(self, li, ids, scores, keep_mass):
             self.shed_calls.append((li, ids.copy(), scores.copy(), keep_mass))
             return None if self.keep is None else self.keep.copy()
 
-        def stage(self, li, ids, scores=None):
+        def stage(self, li, ids):
             self.stage_calls.append((li, ids.copy()))
-            self.stage_scores.append(
-                None if scores is None else np.array(scores, copy=True))
             return None if self.overflow else ids.astype(np.uint32)
 
         def wedged_at(self, li):
@@ -1315,32 +1295,6 @@ def test_wrapper_miss_shed_stages_survivors(monkeypatch):
     assert len(df.shed_calls) == n_shed
 
 
-def test_wrapper_mass_credit_passes_scores_to_stage(monkeypatch):
-    """--moe-popularity mass: the arena path hands stage() the routed
-    scores with no shed configured, the post-shed renormalized scores
-    when one fires, and nothing when the switch is off."""
-    mx.random.seed(7)
-    recorded = []
-    glu, df = _stream_scores_glu(monkeypatch, recorded)
-    x1 = mx.random.normal((1, 1, 16))
-    i1 = mx.array([[[1, 3]]], dtype=mx.uint32)
-    sc = mx.array([[[0.75, 0.25]]], dtype=mx.float32)
-
-    mx.eval(glu(x1, i1, sc))  # switch off: count credit, no scores
-    assert df.stage_scores[-1] is None
-
-    df._credit_mass = True
-    mx.eval(glu(x1, i1, sc))  # no shed installed, credit still flows
-    assert np.allclose(df.stage_scores[-1], [0.75, 0.25])
-    assert not df.shed_calls
-
-    object.__setattr__(glu, "_kq_miss_shed", 0.9)
-    df.keep = np.array([False, True])
-    mx.eval(glu(x1, i1, sc))
-    # the surviving expert's credit carries the token's full mass
-    assert np.allclose(df.stage_scores[-1], [1.0], atol=1e-6)
-
-
 def test_wrapper_scores_sink_on_stock_base(monkeypatch):
     """A base without the mix seam (e.g. minimax-m3's SwiGLUOAI stack)
     takes (x, indices) only: the wrapper advertises _kq_scores_sink so

From 918ee69aeb9551c4b3d3b574acfb70972cdeac96 Mon Sep 17 00:00:00 2001
From: Asher Feldman <59994+asher@users.noreply.github.com>
Date: Tue, 4 Aug 2026 20:51:34 -0700
Subject: [PATCH 06/10] fix(run): over-generation verbose stream renders
 thinking like the main path

---
 CHANGELOG.md                |  2 ++
 gmlx/generation.py          | 19 +++++++++++++++----
 tests/test_generate_over.py | 28 ++++++++++++++++++++++++++++
 3 files changed, 45 insertions(+), 4 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index ac86d96..8a9d085 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -19,6 +19,8 @@ adhere to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
 ### Fixed
 
+- `--over-generation` runs now render thinking in the verbose stream like
+  normal runs (the probe path bypassed the styled emitter).
 - CLI polish: `--help`/`--help-all` now win even after a value-taking flag,
   the short `-h` pages gained the typical sampling/config/streaming flags,
   tab completion offers flag values (choices, themes, profiles).
diff --git a/gmlx/generation.py b/gmlx/generation.py
index 4b876db..3767e01 100644
--- a/gmlx/generation.py
+++ b/gmlx/generation.py
@@ -432,7 +432,7 @@ def generate(
             window=over_generation, inject_critique=inject_critique,
             template_kwargs=critique_tk, log_path=over_generation_log,
             orig_prompt=_over_prompt, label=over_label,
-            params=params, verbose=verbose,
+            params=params, verbose=verbose, reasoning=reasoning,
         )
 
     close_progress = None
@@ -553,13 +553,16 @@ def _generate_over(
     over_logits_processors=None,
     orig_prompt=None,
     label=None,
+    reasoning=None,
 ):
     """Over-generation probe (experimental). Phase 1 generates to the natural
     stop (the seam); phase 2 continues from phase 1's KV cache, either forcing
     ``window`` free tokens past the seam or injecting a follow-up critique turn
     and answering it. ``inject_critique`` selects the mode; when both it and
     ``window`` are set, ``window`` caps the injected reply. Returns the full
-    text and appends a JSONL record to ``log_path`` when set. See overgen.py."""
+    text and appends a JSONL record to ``log_path`` when set. See overgen.py.
+    ``reasoning`` styles phase 1's verbose stream like :func:`generate`'s;
+    phase 2 prints raw (the forced continuation is the thing being probed)."""
     from mlx_lm.generate import stream_generate
     from mlx_lm.models.cache import make_prompt_cache
 
@@ -580,6 +583,12 @@ def _generate_over(
     # the seam is detected here (and its token held out of the kept text)
     # instead of stream_generate ending the call.
     pre, seam, last = [], None, None
+    emit = close_emit = None
+    if verbose:
+        # Phase 1 is a normal reply: style it like the main verbose path
+        # (thinking rendering included). Phase 2 stays raw: the forced
+        # continuation is the thing being probed.
+        emit, close_emit = _verbose_emitter(prompt, tokenizer, reasoning)
     with suppressed_eos(tokenizer):
         for r in stream_generate(
             model, tokenizer, prompt, max_tokens=max_tokens,
@@ -595,8 +604,10 @@ def _generate_over(
                 }
                 break
             pre.append(r.text)
-            if verbose:
-                print(r.text, end="", flush=True)
+            if emit is not None:
+                emit(r.text)
+    if close_emit is not None:
+        close_emit()
     pre_text = "".join(pre)
     phase1_last = last  # the seam response; phase 2 reassigns `last` below
 
diff --git a/tests/test_generate_over.py b/tests/test_generate_over.py
index 5063aae..5729745 100644
--- a/tests/test_generate_over.py
+++ b/tests/test_generate_over.py
@@ -64,6 +64,34 @@ def _call(**kw):
     return _generate_over(object(), _Tok(), "PROMPT", **base)
 
 
+def test_verbose_phase1_uses_styled_emitter(monkeypatch, capsys):
+    """Verbose phase 1 streams through the main path's emitter (thinking
+    rendering included) and closes it before the seam marker; phase 2
+    still prints raw."""
+    import gmlx.generation as g
+
+    got = {"chunks": [], "closed": 0, "reasoning": None}
+
+    def fake_emitter(prompt, tokenizer, reasoning):
+        got["reasoning"] = reasoning
+
+        def close():
+            got["closed"] += 1
+
+        return got["chunks"].append, close
+
+    monkeypatch.setattr(g, "_verbose_emitter", fake_emitter)
+    _patch_stream(
+        monkeypatch,
+        phase1=[(11, "think"), (12, "answer"), (2, "<|user|>")],
+        phase2=[(20, "over")],
+    )
+    _call(window=1, verbose=True, reasoning="hide")
+    assert got["chunks"] == ["think", "answer"]  # phase 2 never routed here
+    assert got["closed"] == 1 and got["reasoning"] == "hide"
+    assert "over" in capsys.readouterr().out  # phase 2 raw print intact
+
+
 def test_free_mode_splits_at_seam_and_forces_window(monkeypatch, tmp_path):
     _patch_stream(
         monkeypatch,

From 8c5626bdf9377ddb5babceac778760ab28fa61c7 Mon Sep 17 00:00:00 2001
From: Asher Feldman <59994+asher@users.noreply.github.com>
Date: Wed, 5 Aug 2026 07:14:08 -0700
Subject: [PATCH 07/10] feat(serve): moe_prestage config key + serve flag route
 keeper prestage per model

---
 CHANGELOG.md              |  3 ++-
 docs/server-config.md     | 11 ++++++++++-
 docs/streaming.md         |  6 +++---
 gmlx/chat.py              |  1 +
 gmlx/cli.py               |  8 +++++---
 gmlx/config.py            | 25 ++++++++++++++++++++++---
 gmlx/discovery.py         |  2 ++
 gmlx/server.py            | 10 ++++++++++
 gmlx/server_bridge_vlm.py | 22 +++++++++++++++++-----
 tests/test_config.py      | 17 ++++++++++-------
 tests/test_serving_mtp.py |  2 +-
 tests/test_serving_vlm.py |  2 +-
 12 files changed, 84 insertions(+), 25 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 8a9d085..41e6616 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -10,7 +10,8 @@ adhere to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
 - `--moe-prestage keepers`: lookahead reads skip experts the miss-shed
   policy would drop and stage predicted keepers demand-grade, turning
-  their synchronous demand stalls into reads that overlap compute.
+  their synchronous demand stalls into reads that overlap compute. Also
+  a per-model server config key (`moe_prestage: keepers`) and serve flag.
 - Ctrl-T during a `chat` or `run` reply closes the model's open thinking
   block early (as if the thinking budget had just run out) so the answer
   starts now.
diff --git a/docs/server-config.md b/docs/server-config.md
index 9b81e85..23fdb2a 100644
--- a/docs/server-config.md
+++ b/docs/server-config.md
@@ -552,7 +552,8 @@ models:
 
 Per-model keys: `path` (required), `profile`, `family`, `profiles`, `mmproj`,
 `draft_gguf`, `adapter`, `stream`, `moe_experts`, `moe_expert_mass`,
-`moe_miss_shed`, `moe_layer_shed`, `prefill_feeder`, `decode_feeder`,
+`moe_miss_shed`, `moe_layer_shed`, `moe_prestage`, `prefill_feeder`,
+`decode_feeder`,
 `speculative`, `speculative_width_cap`, `overrides`
 (`{sampling, load, cache, system, chat_template, chat_template_kwargs,
 thinking, reasoning_effort}`), `pin`, `ttl_s`.
@@ -637,6 +638,14 @@ probability P per token; the layer's shared expert still runs. All three
 change outputs relative to the trained router - evaluate quality on your
 own tasks before serving with them.
 
+`moe_prestage: keepers` retargets the lookahead prestage on a
+`stream: experts` model: predictions are filtered through the miss-shed
+policy, so experts the shed would drop are never read and predicted keepers
+stage demand-grade, overlapping their would-be demand stalls with compute.
+It adds no quality cost of its own but needs `moe_miss_shed` to define the
+policy (announced as ignored without it). Load-affecting like the levers
+above; the default (`ranked`) keeps guess-grade speculative prestage.
+
 `prefill_feeder: false` / `decode_feeder: false` opt a streaming model out of
 the feeder paths that are otherwise on by default (`prefill_feeder`
 everywhere, `decode_feeder` on `stream: experts` entries only - it needs the
diff --git a/docs/streaming.md b/docs/streaming.md
index b38e06d..42422d2 100644
--- a/docs/streaming.md
+++ b/docs/streaming.md
@@ -324,9 +324,9 @@ per-layer overhead - which makes it the one that still pays when the
 arena hit rate is high and misses are rare.
 
 In server configs the lossy levers are the per-model `moe_experts: K` /
-`moe_expert_mass: P` / `moe_miss_shed: P` / `moe_layer_shed: P` keys (or
-the matching `serve` flags for a single positional model); the probe
-and `--moe-prestage` stay CLI-only, so size P with a
+`moe_expert_mass: P` / `moe_miss_shed: P` / `moe_layer_shed: P` /
+`moe_prestage: keepers` keys (or the matching `serve` flags for a single
+positional model); the probe stays CLI-only, so size P with a
 `gmlx run --moe-expert-probe` pass before pinning a value in a config.
 
 Which to reach for is a measurement, not a doctrine. Run the probe once,
diff --git a/gmlx/chat.py b/gmlx/chat.py
index 7658224..359c94a 100644
--- a/gmlx/chat.py
+++ b/gmlx/chat.py
@@ -1941,6 +1941,7 @@ def _vlm_message(
     ("moe_expert_probe", "--moe-expert-probe"),
     ("moe_miss_shed", "--moe-miss-shed"),
     ("moe_layer_shed", "--moe-layer-shed"),
+    ("moe_prestage", "--moe-prestage"),
     ("thinking_budget", "--thinking-budget"),
     ("resize_shape", "--resize-shape"),   # only meaningful with --mmproj (rejected)
 )
diff --git a/gmlx/cli.py b/gmlx/cli.py
index 5b08add..78f8043 100644
--- a/gmlx/cli.py
+++ b/gmlx/cli.py
@@ -165,8 +165,9 @@ def add_moe_expert_args(ap: argparse.ArgumentParser) -> None:
     grp.add_argument(
         "--moe-prestage",
         choices=("ranked", "keepers"),
-        default="ranked",
-        help="Lookahead prestage targeting. ranked: rank-gated speculative "
+        default=None,
+        help="Lookahead prestage targeting; default ranked. ranked: "
+        "rank-gated speculative "
         "reads with guess-grade eviction. keepers: filter predictions "
         "through the active --moe-miss-shed policy - would-be-shed "
         "experts are never read, and would-be-kept misses stage "
@@ -1826,7 +1827,8 @@ def _set(attr, value, label):
         applied.append("moe-expert-mass")
     for dest, label in (("moe_experts", "moe-experts"),
                         ("moe_miss_shed", "moe-miss-shed"),
-                        ("moe_layer_shed", "moe-layer-shed")):
+                        ("moe_layer_shed", "moe-layer-shed"),
+                        ("moe_prestage", "moe-prestage")):
         if (getattr(rm, dest, None) is not None and dest not in explicit
                 and hasattr(args, dest)):
             setattr(args, dest, getattr(rm, dest))
diff --git a/gmlx/config.py b/gmlx/config.py
index b118e0b..4994996 100644
--- a/gmlx/config.py
+++ b/gmlx/config.py
@@ -11,7 +11,7 @@
     server:    {host, port, api_key, no_auth, model_dirs, budget_gb, max_models, hf_cache, cache, defaults, stt, tts, embeddings, rerank, menubar, token_queue_timeout_s, prefill_step_size, decode_prefill_ratio, prefill_tick_ms, cache_limit_gb, family_defaults, stochastic_mtp, gpu_keepwarm, assistants, assistant_allow_remote}
     profiles:  {<name>: {extends, sampling, load, cache, system}}
     rules:     [{match: <glob>, profile: <name>}]
-    models:    {<id>: {path, profile, family, profiles, mmproj, draft_gguf, adapter, stream, moe_experts, moe_expert_mass, moe_miss_shed, moe_layer_shed, speculative, speculative_width_cap, overrides, pin, ttl_s}}
+    models:    {<id>: {path, profile, family, profiles, mmproj, draft_gguf, adapter, stream, moe_experts, moe_expert_mass, moe_miss_shed, moe_layer_shed, moe_prestage, speculative, speculative_width_cap, overrides, pin, ttl_s}}
     aliases:   {<name>: <id> | <id>@<profile>}    # friendly name / profile preset
     discover:  [{dir, recursive, pair_mmproj, speculative}]
     talk:      {model, voice, speed, system, language, max_tokens, mode, wake_word, wake_threshold, vad, input_device, output_device, chime, brain, push_to_talk_modifier}
@@ -117,7 +117,7 @@
                          "draft_gguf", "adapter", "stream",
                          "cpu_moe",  # deprecated alias for `stream:`
                          "moe_experts", "moe_expert_mass",
-                         "moe_miss_shed", "moe_layer_shed",
+                         "moe_miss_shed", "moe_layer_shed", "moe_prestage",
                          "prefill_feeder", "decode_feeder", "speculative",
                          "speculative_width_cap",
                          "overrides", "pin", "ttl_s"})
@@ -228,11 +228,13 @@ class ModelCfg:
     # Lossy MoE fan-out levers on the streamed expert stacks (the
     # `--moe-experts K` / `--moe-expert-mass P` / `--moe-miss-shed P` /
     # `--moe-layer-shed P` CLI levers). Require stream; None = trained
-    # fan-out / no shedding.
+    # fan-out / no shedding. `moe_prestage: keepers` retargets the lookahead
+    # prestage through the miss-shed policy (needs `moe_miss_shed`).
     moe_experts: int | None = None
     moe_expert_mass: float | None = None
     moe_miss_shed: float | None = None
     moe_layer_shed: float | None = None
+    moe_prestage: str | None = None
     # Streaming-model feeder overrides (tri-state: None = loader default -
     # prefill feeder on, decode feeder on under `stream: experts`).
     prefill_feeder: bool | None = None
@@ -503,6 +505,7 @@ class ResolvedModel:
     moe_expert_mass: float | None = None
     moe_miss_shed: float | None = None
     moe_layer_shed: float | None = None
+    moe_prestage: str | None = None
     # Feeder overrides for streaming models (None = loader default); load-
     # affecting - they decide the ring slots / wired arena built at load.
     prefill_feeder: bool | None = None
@@ -536,6 +539,7 @@ def load_signature(self) -> tuple:
             str(self.moe_expert_mass),
             str(self.moe_miss_shed),
             str(self.moe_layer_shed),
+            str(self.moe_prestage),
             str(self.prefill_feeder),
             str(self.decode_feeder),
             tuple(sorted((k, str(v)) for k, v in self.load.items())),
@@ -887,6 +891,7 @@ def resolve_model(
         moe_expert_mass=model.moe_expert_mass,
         moe_miss_shed=model.moe_miss_shed,
         moe_layer_shed=model.moe_layer_shed,
+        moe_prestage=model.moe_prestage,
         prefill_feeder=model.prefill_feeder,
         decode_feeder=model.decode_feeder,
         pin=bool(model.pin),
@@ -1126,6 +1131,18 @@ def _normalize_moe_layer_shed(value, where: str = "model"):
     return p
 
 
+def _normalize_moe_prestage(value, where: str = "model"):
+    """Validate a ``moe_prestage`` targeting mode: "ranked" or "keepers"."""
+    if value is None:
+        return None
+    mode = str(value).strip().lower()
+    if mode not in ("ranked", "keepers"):
+        raise ConfigError(
+            f"{where}.moe_prestage: expected 'ranked' or 'keepers', "
+            f"got {value!r}")
+    return mode
+
+
 def _normalize_stream(value, where: str = "model", legacy=None):
     """Normalize a ``stream:`` value to one of None / "experts" / "cpu".
 
@@ -1348,6 +1365,8 @@ def _parse_model(model_id: str, raw: dict) -> ModelCfg:
             key="moe_miss_shed"),
         moe_layer_shed=_normalize_moe_layer_shed(
             raw.get("moe_layer_shed"), f"model {model_id!r}"),
+        moe_prestage=_normalize_moe_prestage(
+            raw.get("moe_prestage"), f"model {model_id!r}"),
         prefill_feeder=_normalize_optional_bool(
             raw.get("prefill_feeder"), "prefill_feeder", f"model {model_id!r}"),
         decode_feeder=_normalize_optional_bool(
diff --git a/gmlx/discovery.py b/gmlx/discovery.py
index b986d81..be31dc2 100644
--- a/gmlx/discovery.py
+++ b/gmlx/discovery.py
@@ -1179,6 +1179,8 @@ def _scaffold_models_block(models, dirs) -> list[str]:
                  "moe_experts: <K> (fixed cap) |")
     lines.append("  #   moe_miss_shed: <P> (drop arena-miss experts, keep "
                  "mass P) |")
+    lines.append("  #   moe_prestage: <ranked|keepers> (keepers = prestage "
+                 "only miss-shed keepers) |")
     lines.append("  #   moe_layer_shed: <P> (skip routed path with "
                  "probability P)")
     if not models:
diff --git a/gmlx/server.py b/gmlx/server.py
index eb55000..3415255 100644
--- a/gmlx/server.py
+++ b/gmlx/server.py
@@ -741,6 +741,13 @@ def _add_serve_args(ap: argparse.ArgumentParser) -> None:
                          "experts with probability P (0 < P < 1) per token; the "
                          "shared expert still runs on shed layers (config mode: "
                          "set `moe_layer_shed: P` per model).")
+    ap.add_argument("--moe-prestage", choices=("ranked", "keepers"),
+                    default=None,
+                    help="Lookahead prestage targeting for a streamed model; "
+                         "default ranked. keepers filters predictions through "
+                         "the active miss-shed policy and stages predicted "
+                         "keepers demand-grade; needs --moe-miss-shed (config "
+                         "mode: set `moe_prestage: keepers` per model).")
     ap.add_argument("--prefill-feeder", action=argparse.BooleanOptionalAction,
                     default=None,
                     help="Faster prompt processing for streaming models "
@@ -907,6 +914,8 @@ def _abs(p):
         out += ["--moe-miss-shed", str(a.moe_miss_shed)]
     if getattr(a, "moe_layer_shed", None) is not None:
         out += ["--moe-layer-shed", str(a.moe_layer_shed)]
+    if getattr(a, "moe_prestage", None) is not None:
+        out += ["--moe-prestage", str(a.moe_prestage)]
     if a.budget_gb is not None:
         out += ["--budget-gb", str(a.budget_gb)]
     if a.max_models is not None:
@@ -1377,6 +1386,7 @@ def _single_model_cfg(a) -> ServerCfg:
         moe_expert_mass=getattr(a, "moe_expert_mass", None),
         moe_miss_shed=getattr(a, "moe_miss_shed", None),
         moe_layer_shed=getattr(a, "moe_layer_shed", None),
+        moe_prestage=getattr(a, "moe_prestage", None),
         prefill_feeder=getattr(a, "prefill_feeder", None),
         decode_feeder=getattr(a, "decode_feeder", None),
         pin=True,                            # the single model is always pinned
diff --git a/gmlx/server_bridge_vlm.py b/gmlx/server_bridge_vlm.py
index e97bf74..2b2c27d 100644
--- a/gmlx/server_bridge_vlm.py
+++ b/gmlx/server_bridge_vlm.py
@@ -494,6 +494,7 @@ def load_serveable_model(
     moe_expert_mass: float | None = None,
     moe_miss_shed: float | None = None,
     moe_layer_shed: float | None = None,
+    moe_prestage: str | None = None,
     feeder_prefill: bool | None = None,
     feeder_decode: bool | None = None,
 ) -> tuple[object, object, object]:
@@ -533,7 +534,9 @@ def load_serveable_model(
     ``moe_miss_shed: P`` / ``moe_layer_shed: P``) install their filters/hooks
     over the streamed layers after the placement. They ride on ``stream`` -
     without a placement each is announced as ignored (there are no streamed
-    experts to filter).
+    experts to filter). ``moe_prestage: keepers`` retargets the lookahead
+    prestage through the miss-shed policy and additionally needs
+    ``moe_miss_shed`` (announced as ignored without it).
     """
     def _reject_unwired(base_kind: str) -> None:
         # Raising beats silently dropping the option on bases that don't
@@ -551,14 +554,15 @@ def _reject_unwired(base_kind: str) -> None:
         for key, val in (("moe_experts", moe_experts),
                          ("moe_expert_mass", moe_expert_mass),
                          ("moe_miss_shed", moe_miss_shed),
-                         ("moe_layer_shed", moe_layer_shed)):
+                         ("moe_layer_shed", moe_layer_shed),
+                         ("moe_prestage", moe_prestage)):
             if val is not None:
                 print(
                     f"[stream] {key} ignored: needs stream: experts|cpu "
                     "(it only applies to streamed MoE layers)"
                 )
         moe_experts = moe_expert_mass = None
-        moe_miss_shed = moe_layer_shed = None
+        moe_miss_shed = moe_layer_shed = moe_prestage = None
     if mmproj_path is not None and speculative:
         # VLM x MTP: text-only requests speculate; image/audio requests prefill media
         # into the KV and decode normally (verify is token-only over that cache).
@@ -616,6 +620,13 @@ def _reject_unwired(base_kind: str) -> None:
     if moe_miss_shed is not None:
         from .moe_experts import install_moe_miss_shed
         install_moe_miss_shed(raw_model, moe_miss_shed)
+    if moe_prestage == "keepers":
+        if moe_miss_shed is None:
+            print("[stream] moe_prestage: keepers ignored: it needs "
+                  "moe_miss_shed")
+        else:
+            from .moe_experts import install_moe_prestage_keepers
+            install_moe_prestage_keepers(raw_model)
     if moe_layer_shed is not None:
         from .moe_experts import install_moe_layer_shed
         install_moe_layer_shed(raw_model, moe_layer_shed)
@@ -767,14 +778,15 @@ def load_model_resources(model_path, adapter_path=None):
             # The feeder overrides (config `prefill_feeder:`/`decode_feeder:` /
             # the paired serve flags) and the lossy MoE levers (config
             # `moe_experts:`/`moe_expert_mass:`/`moe_miss_shed:`/
-            # `moe_layer_shed:` / the paired serve flags) ride along; None
-            # keeps the loader default / trained fan-out.
+            # `moe_layer_shed:`/`moe_prestage:` / the paired serve flags) ride
+            # along; None keeps the loader default / trained fan-out.
             stream = getattr(spec, "stream", None)
             feeders = dict(
                 moe_experts=getattr(spec, "moe_experts", None),
                 moe_expert_mass=getattr(spec, "moe_expert_mass", None),
                 moe_miss_shed=getattr(spec, "moe_miss_shed", None),
                 moe_layer_shed=getattr(spec, "moe_layer_shed", None),
+                moe_prestage=getattr(spec, "moe_prestage", None),
                 feeder_prefill=getattr(spec, "prefill_feeder", None),
                 feeder_decode=getattr(spec, "decode_feeder", None),
             )
diff --git a/tests/test_config.py b/tests/test_config.py
index 310a4a8..5ee876c 100644
--- a/tests/test_config.py
+++ b/tests/test_config.py
@@ -236,31 +236,34 @@ def test_moe_lossy_levers_parsed_and_resolved():
     doc = _doc()
     doc["models"]["m-moe"] = {"path": "/abs/big.gguf", "stream": "experts",
                               "moe_experts": 6, "moe_miss_shed": 0.95,
-                              "moe_layer_shed": "0.1"}
+                              "moe_layer_shed": "0.1",
+                              "moe_prestage": "Keepers"}
     cfg = build_config(doc)
     rm = resolve_model("m-moe", cfg)
     assert rm.moe_experts == 6
     assert rm.moe_miss_shed == 0.95
     assert rm.moe_layer_shed == 0.1
+    assert rm.moe_prestage == "keepers"   # case-normalized
     bare = resolve_model("m-named", cfg)
     assert (bare.moe_experts is None and bare.moe_miss_shed is None
-            and bare.moe_layer_shed is None)
+            and bare.moe_layer_shed is None and bare.moe_prestage is None)
     # Each lever is load-affecting: same GGUF, different value => distinct
     # resident entries.
     common = dict(path="/p", sampling={}, load={}, cache={}, system=None,
                   speculative=False, mmproj=None, draft_gguf=None, pin=False,
                   ttl_s=None, stream="experts")
     base_sig = cfgmod.ResolvedModel(id="x", **common).load_signature()
-    for key in ("moe_experts", "moe_expert_mass", "moe_miss_shed",
-                "moe_layer_shed"):
+    for key, val in (("moe_experts", 2), ("moe_expert_mass", 0.5),
+                     ("moe_miss_shed", 0.5), ("moe_layer_shed", 0.5),
+                     ("moe_prestage", "keepers")):
         sig = cfgmod.ResolvedModel(
-            id="x", **{key: 2 if key == "moe_experts" else 0.5},
-            **common).load_signature()
+            id="x", **{key: val}, **common).load_signature()
         assert sig != base_sig, key
 
     for key, bad in (("moe_experts", 0), ("moe_experts", "many"),
                      ("moe_miss_shed", 1.5), ("moe_miss_shed", 0),
-                     ("moe_layer_shed", 1.0), ("moe_layer_shed", 0)):
+                     ("moe_layer_shed", 1.0), ("moe_layer_shed", 0),
+                     ("moe_prestage", "eager")):
         doc = _doc()
         doc["models"]["m-moe"] = {"path": "/abs/big.gguf",
                                   "stream": "experts", key: bad}
diff --git a/tests/test_serving_mtp.py b/tests/test_serving_mtp.py
index d00924c..a41ec2a 100644
--- a/tests/test_serving_mtp.py
+++ b/tests/test_serving_mtp.py
@@ -356,7 +356,7 @@ def spy(model_path, *, mmproj_path=None, hf_source=None,
             speculative=False, draft_gguf_path=None, chat_template=None,
             adapter_gguf=None, stream=None, moe_experts=None,
             moe_expert_mass=None, moe_miss_shed=None, moe_layer_shed=None,
-            feeder_prefill=None, feeder_decode=None):
+            moe_prestage=None, feeder_prefill=None, feeder_decode=None):
         serveable_calls.append(
             (model_path, mmproj_path, hf_source, speculative, draft_gguf_path,
              chat_template, adapter_gguf)
diff --git a/tests/test_serving_vlm.py b/tests/test_serving_vlm.py
index b4ebd6a..e3a137b 100644
--- a/tests/test_serving_vlm.py
+++ b/tests/test_serving_vlm.py
@@ -148,7 +148,7 @@ def spy(model_path, *, mmproj_path=None, hf_source=None,
             speculative=False, draft_gguf_path=None, chat_template=None,
             adapter_gguf=None, stream=None, moe_experts=None,
             moe_expert_mass=None, moe_miss_shed=None, moe_layer_shed=None,
-            feeder_prefill=None, feeder_decode=None):
+            moe_prestage=None, feeder_prefill=None, feeder_decode=None):
         serveable_calls.append((model_path, mmproj_path, hf_source, chat_template))
         return ("SERVEABLE", model_path, mmproj_path)
 

From f60d1a542bc3bfdbb902238ba0e3e118a750e341 Mon Sep 17 00:00:00 2001
From: Asher Feldman <59994+asher@users.noreply.github.com>
Date: Wed, 5 Aug 2026 18:39:46 -0700
Subject: [PATCH 08/10] docs(streaming): kimi-k3 miss-shed ladder with keeper
 prestage, samples + screenshots

---
 docs/assets/perf/lossy-kimi-k3-shed-0.65.html | 773 +++++++++++++++++
 docs/assets/perf/lossy-kimi-k3-shed-0.65.png  | Bin 0 -> 633571 bytes
 docs/assets/perf/lossy-kimi-k3-shed-0.70.html | 781 ++++++++++++++++++
 docs/assets/perf/lossy-kimi-k3-shed-0.70.png  | Bin 0 -> 347200 bytes
 docs/assets/perf/lossy-kimi-k3-shed-0.80.html | 582 +++++++++++++
 docs/assets/perf/lossy-kimi-k3-shed-0.80.png  | Bin 0 -> 359666 bytes
 docs/streaming.md                             |  72 +-
 7 files changed, 2207 insertions(+), 1 deletion(-)
 create mode 100644 docs/assets/perf/lossy-kimi-k3-shed-0.65.html
 create mode 100644 docs/assets/perf/lossy-kimi-k3-shed-0.65.png
 create mode 100644 docs/assets/perf/lossy-kimi-k3-shed-0.70.html
 create mode 100644 docs/assets/perf/lossy-kimi-k3-shed-0.70.png
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+<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="UTF-8" />
+<meta name="viewport" content="width=device-width, initial-scale=1.0" />
+<title>Sunset Drive — Endless Road</title>
+<style>
+  html, body { margin:0; padding:0; height:100%; background:#000; overflow:hidden; }
+  canvas { display:block; width:100vw; height:100vh; }
+</style>
+</head>
+<body>
+<canvas id="drive"></canvas>
+<script>
+(function(){
+"use strict";
+
+/* ============================== SETUP ============================== */
+const canvas = document.getElementById('drive');
+const ctx = canvas.getContext('2d');
+
+let W = 0, H = 0, DPR = 1, U = 1, DW = 0;
+const DH = 540;                       // design height (design units)
+
+const HORIZON     = 322;              // where the sky meets the land
+const ROAD_TOP    = 404;              // far edge of the road
+const ROAD_BOTTOM = 502;              // near edge of the road
+const CAR_GROUND  = 470;              // car tyre contact line
+const SPEED       = 235;              // design px / second at factor 1.0
+
+function resize(){
+  DPR = Math.min(window.devicePixelRatio || 1, 2);
+  W = window.innerWidth; H = window.innerHeight;
+  canvas.width  = Math.round(W * DPR);
+  canvas.height = Math.round(H * DPR);
+  U = H / DH;
+  DW = W / U;                          // design width
+}
+window.addEventListener('resize', resize);
+resize();
+
+/* ============================== UTILS ============================== */
+function lerp(a,b,t){ return a + (b-a)*t; }
+function clamp(v,a,b){ return v < a ? a : (v > b ? b : v); }
+
+function hash1(n, seed){
+  let x = (n | 0) + (seed | 0) * 0x9E3779B1;
+  x = Math.imul(x ^ (x >>> 16), 0x21f0aa43);
+  x = Math.imul(x ^ (x >>> 15), 0x735a2d97);
+  x ^= x >>> 15;
+  return (x >>> 0) / 4294967296;
+}
+function vnoise(x, seed){
+  const i = Math.floor(x), f = x - i;
+  const u = f * f * (3 - 2 * f);
+  return lerp(hash1(i, seed), hash1(i + 1, seed), u);
+}
+function indexRange(scroll, spacing, pad){
+  return [Math.floor((scroll - pad) / spacing), Math.floor((scroll + DW + pad) / spacing)];
+}
+
+/* ============================== SKY ============================== */
+function drawSky(sunX, sunY){
+  const g = ctx.createLinearGradient(0, 0, 0, HORIZON);
+  g.addColorStop(0.00, '#1d1233');
+  g.addColorStop(0.30, '#3c1f52');
+  g.addColorStop(0.52, '#6e2f66');
+  g.addColorStop(0.68, '#a8455f');
+  g.addColorStop(0.81, '#dd6b4f');
+  g.addColorStop(0.92, '#f79a4e');
+  g.addColorStop(1.00, '#ffc36b');
+  ctx.fillStyle = g;
+  ctx.fillRect(0, 0, DW, HORIZON + 1);
+
+  // subtle top-sky glow band for depth
+  const g2 = ctx.createLinearGradient(0, 0, 0, 120);
+  g2.addColorStop(0, 'rgba(10,6,26,0.5)');
+  g2.addColorStop(1, 'rgba(10,6,26,0)');
+  ctx.fillStyle = g2;
+  ctx.fillRect(0, 0, DW, 120);
+}
+
+function drawStars(t){
+  for (let i = 0; i < 64; i++){
+    const x = hash1(i, 501) * DW;
+    const y = hash1(i, 502) * (HORIZON * 0.55);
+    const fade = clamp(1 - y / (HORIZON * 0.55), 0, 1);
+    const tw = 0.5 + 0.5 * Math.sin(t * (1.5 + hash1(i,503) * 2) + i * 1.7);
+    ctx.fillStyle = 'rgba(255,240,255,' + (0.55 * fade * fade * tw).toFixed(3) + ')';
+    const s = hash1(i, 504) > 0.85 ? 2 : 1.2;
+    ctx.fillRect(x, y, s, s);
+  }
+}
+
+function drawSun(sunX, sunY){
+  // big atmospheric glow
+  let g = ctx.createRadialGradient(sunX, sunY, 0, sunX, sunY, 320);
+  g.addColorStop(0.00, 'rgba(255,190,120,0.85)');
+  g.addColorStop(0.25, 'rgba(255,150,95,0.35)');
+  g.addColorStop(0.60, 'rgba(255,120,90,0.12)');
+  g.addColorStop(1.00, 'rgba(255,120,90,0)');
+  ctx.fillStyle = g;
+  ctx.fillRect(sunX - 330, sunY - 330, 660, 660);
+
+  // core disc
+  g = ctx.createRadialGradient(sunX - 4, sunY - 4, 2, sunX, sunY, 42);
+  g.addColorStop(0, '#fff8e0');
+  g.addColorStop(0.6, '#ffe9a8');
+  g.addColorStop(1, 'rgba(255,190,110,0.9)');
+  ctx.fillStyle = g;
+  ctx.beginPath(); ctx.arc(sunX, sunY, 42, 0, 7); ctx.fill();
+
+  // crisp inner disc
+  ctx.fillStyle = '#fffbe8';
+  ctx.beginPath(); ctx.arc(sunX, sunY, 30, 0, 7); ctx.fill();
+
+  // soft horizon wash below the sun
+  g = ctx.createLinearGradient(0, sunY - 10, 0, HORIZON + 60);
+  g.addColorStop(0, 'rgba(255,200,120,0.25)');
+  g.addColorStop(1, 'rgba(255,200,120,0)');
+  ctx.fillStyle = g;
+  ctx.fillRect(0, sunY - 10, DW, HORIZON - sunY + 80);
+}
+
+function drawClouds(t, worldX){
+  const spacing = DW + 700;
+  const scroll = worldX * 0.045 + t * 2.2;
+  for (let i = 0; i < 5; i++){
+    const r1 = hash1(i, 601), r2 = hash1(i, 602), r3 = hash1(i, 603);
+    const baseX = ((i * spacing * 0.5 - scroll) % spacing + spacing) % spacing - 350;
+    const y = 60 + r1 * 190;
+    const s = 0.6 + r2 * 1.1;
+    const alpha = 0.16 + r3 * 0.16;
+    const warm = r2 > 0.5;
+    ctx.fillStyle = warm
+      ? 'rgba(255,150,140,' + alpha + ')'
+      : 'rgba(180,110,170,' + (alpha * 0.9) + ')';
+    // cluster of soft ellipses
+    for (let k = 0; k < 7; k++){
+      const ox = (hash1(i * 10 + k, 604) - 0.5) * 220 * s;
+      const oy = (hash1(i * 10 + k, 605) - 0.5) * 34 * s;
+      const rw = (34 + hash1(i * 10 + k, 606) * 60) * s;
+      const rh = rw * (0.30 + hash1(i * 10 + k, 607) * 0.12);
+      ctx.beginPath();
+      ctx.ellipse(baseX + ox, y + oy, rw, rh, 0, 0, 7);
+      ctx.fill();
+    }
+  }
+}
+
+function drawBirds(t){
+  ctx.strokeStyle = 'rgba(24,14,28,0.75)';
+  ctx.lineWidth = 2; ctx.lineCap = 'round';
+  for (let i = 0; i < 3; i++){
+    const sp = 26 + i * 8;
+    const x = ((t * sp + i * 320) % (DW + 300)) - 150;
+    const y = 105 + i * 34 + Math.sin(t * 0.8 + i * 2) * 10;
+    const flap = Math.sin(t * (6 + i) + i * 3) * 4;
+    const s = 1 + i * 0.18;
+    ctx.beginPath();
+    ctx.moveTo(x - 8 * s, y);
+    ctx.quadraticCurveTo(x - 4 * s, y - 3 * s - flap, x, y);
+    ctx.quadraticCurveTo(x + 4 * s, y - 3 * s - flap, x + 8 * s, y);
+    ctx.stroke();
+  }
+}
+
+/* ========================== LAND SILHOUETTES ========================== */
+function drawRidge(scroll, baseY, amp, seg, topColor, botColor, alpha, seed){
+  ctx.beginPath();
+  ctx.moveTo(-60, DH + 60);
+  for (let x = -60; x <= DW + 60; x += 9){
+    const wx = (x + scroll) / seg;
+    const n = vnoise(wx, seed) * 0.62 + vnoise(wx * 2.35 + 7.3, seed) * 0.38;
+    ctx.lineTo(x, baseY - n * n * amp * 1.15);
+  }
+  ctx.lineTo(DW + 60, DH + 60);
+  ctx.closePath();
+  const g = ctx.createLinearGradient(0, baseY - amp, 0, baseY + 4);
+  g.addColorStop(0, topColor);
+  g.addColorStop(1, botColor);
+  ctx.globalAlpha = alpha;
+  ctx.fillStyle = g;
+  ctx.fill();
+  ctx.globalAlpha = 1;
+}
+
+function drawGround(){
+  const g = ctx.createLinearGradient(0, HORIZON, 0, DH);
+  g.addColorStop(0.00, '#4c4038');
+  g.addColorStop(0.18, '#3a332e');
+  g.addColorStop(0.60, '#241f21');
+  g.addColorStop(1.00, '#141116');
+  ctx.fillStyle = g;
+  ctx.fillRect(0, HORIZON, DW, DH - HORIZON);
+
+  // soft field rows catching warm light (slow parallax)
+  ctx.fillStyle = 'rgba(255,150,90,0.05)';
+  for (let i = 0; i < 4; i++){
+    const y = HORIZON + 22 + i * 22;
+    ctx.fillRect(0, y, DW, 2);
+  }
+}
+
+/* =========================== TREES & POLES =========================== */
+function conifer(x, y, w, h, shade){
+  ctx.fillStyle = shade;
+  ctx.beginPath();
+  ctx.moveTo(x, y - h);
+  ctx.lineTo(x - w * 0.46, y - h * 0.42);
+  ctx.lineTo(x - w * 0.24, y - h * 0.42);
+  ctx.lineTo(x - w * 0.5, y);
+  ctx.lineTo(x + w * 0.5, y);
+  ctx.lineTo(x + w * 0.24, y - h * 0.42);
+  ctx.lineTo(x + w * 0.46, y - h * 0.42);
+  ctx.closePath();
+  ctx.fill();
+}
+
+function drawTreeline(scroll){
+  const spacing = 25, base = HORIZON + 42;
+  const [i0, i1] = indexRange(scroll, spacing, 60);
+  for (let i = i0; i <= i1; i++){
+    const r1 = hash1(i, 101), r2 = hash1(i, 102);
+    const x = i * spacing + (r1 - 0.5) * 14 - scroll;
+    const h = 20 + r2 * 36, w = 17 + r1 * 11;
+    const shade = 'rgb(' + (28 + (r2 * 12) | 0) + ',' + (24 + (r2 * 9) | 0) + ',' + (42 + (r2 * 12) | 0) + ')';
+    conifer(x, base, w, h, shade);
+  }
+}
+
+function drawBigTrees(scroll){
+  const spacing = 205, base = ROAD_TOP - 3;
+  const [i0, i1] = indexRange(scroll, spacing, 160);
+  for (let i = i0; i <= i1; i++){
+    const r1 = hash1(i, 201), r2 = hash1(i, 202), r3 = hash1(i, 203);
+    const x = i * spacing + (r1 - 0.5) * 130 - scroll;
+    const s = 0.75 + r2 * 0.85;
+    if (r3 > 0.55){
+      // tall conifer
+      const h = (70 + r2 * 60) * s, w = 30 * s;
+      conifer(x, base, w, h, '#1e2b1d');
+      ctx.strokeStyle = 'rgba(255,140,80,0.22)';
+      ctx.lineWidth = 1.6;
+      ctx.beginPath();
+      ctx.moveTo(x, base - h);
+      ctx.lineTo(x - w * 0.4, base - h * 0.45);
+      ctx.stroke();
+    } else {
+      // broadleaf
+      const h = 58 * s, cw = 30 * s;
+      ctx.fillStyle = '#241a15';
+      ctx.fillRect(x - 3.4 * s, base - h * 0.45, 7 * s, h * 0.45);
+      ctx.fillStyle = '#22301d';
+      ctx.beginPath(); ctx.arc(x, base - h * 0.78, cw * 0.95, 0, 7); ctx.fill();
+      ctx.beginPath(); ctx.arc(x - cw * 0.55, base - h * 0.58, cw * 0.62, 0, 7); ctx.fill();
+      ctx.beginPath(); ctx.arc(x + cw * 0.5, base - h * 0.6, cw * 0.7, 0, 7); ctx.fill();
+      // warm rim from the setting sun (left side)
+      ctx.strokeStyle = 'rgba(255,150,90,0.28)';
+      ctx.lineWidth = 2 * s;
+      ctx.beginPath(); ctx.arc(x, base - h * 0.78, cw * 0.92, Math.PI * 0.65, Math.PI * 1.45); ctx.stroke();
+    }
+  }
+}
+
+function poleX(i, spacing){ return i * spacing + (hash1(i, 301) - 0.5) * 40; }
+
+function drawPoles(scroll){
+  const spacing = 330, base = ROAD_TOP - 1;
+  const [i0, i1] = indexRange(scroll, spacing, 80);
+  const h = 148;
+
+  // long sunset shadows falling across the road
+  ctx.fillStyle = 'rgba(15,10,18,0.16)';
+  for (let i = i0; i <= i1; i++){
+    const x = poleX(i, spacing) - scroll;
+    ctx.beginPath();
+    ctx.moveTo(x - 2, base);
+    ctx.lineTo(x + 3, base);
+    ctx.lineTo(x + 52, base + 34);
+    ctx.lineTo(x + 42, base + 35);
+    ctx.closePath(); ctx.fill();
+  }
+
+  // posts + crossbars
+  for (let i = i0; i <= i1; i++){
+    const x = poleX(i, spacing) - scroll;
+    const ph = h * (0.94 + hash1(i, 302) * 0.09);
+    const g = ctx.createLinearGradient(x - 3, 0, x + 3, 0);
+    g.addColorStop(0, '#4a3128');
+    g.addColorStop(0.35, '#2b1c17');
+    g.addColorStop(1, '#150e0d');
+    ctx.fillStyle = g;
+    ctx.fillRect(x - 3, base - ph, 6, ph);
+    ctx.fillStyle = '#1c1210';
+    ctx.fillRect(x - 24, base - ph + 8, 48, 5);
+    // sun-kissed edge
+    ctx.fillStyle = 'rgba(255,150,90,0.35)';
+    ctx.fillRect(x - 3, base - ph, 1.4, ph);
+  }
+
+  // sagging wires (connect to the next pole so the loop stays seamless)
+  ctx.strokeStyle = 'rgba(18,12,18,0.85)';
+  ctx.lineWidth = 1.6;
+  for (let i = i0; i < i1; i++){
+    const x1 = poleX(i, spacing) - scroll;
+    const x2 = poleX(i + 1, spacing) - scroll;
+    const ph1 = h * (0.94 + hash1(i, 302) * 0.09);
+    const ph2 = h * (0.94 + hash1(i + 1, 302) * 0.09);
+    for (let k = -1; k <= 1; k++){
+      const y1 = base - ph1 + 10 + k * 3;
+      const y2 = base - ph2 + 10 + k * 3;
+      const mx = (x1 + x2) / 2, my = Math.max(y1, y2) + 20 + Math.abs(k) * 4;
+      ctx.beginPath();
+      ctx.moveTo(x1 + k * 20, y1);
+      ctx.quadraticCurveTo(mx + k * 20, my, x2 + k * 20, y2);
+      ctx.stroke();
+    }
+  }
+}
+
+function drawFence(scroll){
+  const spacing = 62, base = ROAD_TOP - 1;
+  const [i0, i1] = indexRange(scroll, spacing, 40);
+  // rails first
+  ctx.fillStyle = '#33241c';
+  for (let i = i0; i < i1; i++){
+    const x1 = i * spacing - scroll, x2 = (i + 1) * spacing - scroll;
+    ctx.fillRect(x1, base - 26, x2 - x1 + 1, 3.4);
+    ctx.fillRect(x1, base - 15, x2 - x1 + 1, 3);
+  }
+  // posts
+  for (let i = i0; i <= i1; i++){
+    const x = i * spacing - scroll;
+    ctx.fillStyle = '#402d20';
+    ctx.fillRect(x - 2.4, base - 32, 4.8, 32);
+    ctx.fillStyle = 'rgba(255,150,90,0.3)';
+    ctx.fillRect(x - 2.4, base - 32, 1.2, 32);
+  }
+}
+
+/* =============================== ROAD =============================== */
+function drawRoad(worldX){
+  // asphalt
+  const g = ctx.createLinearGradient(0, ROAD_TOP, 0, ROAD_BOTTOM);
+  g.addColorStop(0.00, '#4b3e44');
+  g.addColorStop(0.25, '#3a3138');
+  g.addColorStop(0.80, '#241f26');
+  g.addColorStop(1.00, '#1b171e');
+  ctx.fillStyle = g;
+  ctx.fillRect(0, ROAD_TOP, DW, ROAD_BOTTOM - ROAD_TOP);
+
+  // warm sun sheen near the far edge
+  const sh = ctx.createLinearGradient(0, ROAD_TOP, 0, ROAD_TOP + 34);
+  sh.addColorStop(0, 'rgba(255,170,110,0.22)');
+  sh.addColorStop(1, 'rgba(255,170,110,0)');
+  ctx.fillStyle = sh;
+  ctx.fillRect(0, ROAD_TOP, DW, 34);
+
+  // far edge highlight
+  ctx.fillStyle = 'rgba(255,190,130,0.35)';
+  ctx.fillRect(0, ROAD_TOP, DW, 2);
+  // near edge
+  ctx.fillStyle = 'rgba(255,255,255,0.07)';
+  ctx.fillRect(0, ROAD_BOTTOM - 2, DW, 2);
+
+  // scrolling cracks / patches
+  const spacing = 88;
+  const [i0, i1] = indexRange(worldX, spacing, 40);
+  for (let i = i0; i <= i1; i++){
+    const r1 = hash1(i, 401), r2 = hash1(i, 402);
+    const x = i * spacing + (r1 - 0.5) * 50 - worldX;
+    const y = ROAD_TOP + 14 + r2 * (ROAD_BOTTOM - ROAD_TOP - 30);
+    ctx.strokeStyle = 'rgba(10,8,12,' + (0.10 + r2 * 0.12) + ')';
+    ctx.lineWidth = 1.5 + r1;
+    ctx.beginPath();
+    ctx.moveTo(x, y);
+    ctx.lineTo(x + 12 + r1 * 18, y + (r2 - 0.5) * 5);
+    ctx.stroke();
+  }
+
+  // centre dashes
+  const dashLen = 46, gap = 104;
+  const cy = (ROAD_TOP + ROAD_BOTTOM) * 0.56;
+  ctx.fillStyle = 'rgba(255,214,150,0.78)';
+  const dStart = Math.floor((worldX - dashLen) / gap);
+  const dEnd = Math.floor((worldX + DW + dashLen) / gap);
+  for (let i = dStart; i <= dEnd; i++){
+    const x = i * gap - worldX;
+    ctx.fillRect(x, cy - 2.6, dashLen, 5.2);
+    ctx.fillStyle = 'rgba(255,214,150,0.78)';
+  }
+}
+
+function drawRoadGrass(scroll, y, factor, tall){
+  const spacing = 22;
+  const [i0, i1] = indexRange(scroll * factor, spacing, 40);
+  for (let i = i0; i <= i1; i++){
+    const r1 = hash1(i, 411), r2 = hash1(i, 412);
+    const x = i * spacing + (r1 - 0.5) * 14 - scroll * factor;
+    const h = (tall ? 6 + r2 * 9 : 3 + r2 * 5);
+    ctx.strokeStyle = r2 > 0.6 ? 'rgba(90,80,50,0.9)' : 'rgba(60,55,38,0.9)';
+    ctx.lineWidth = 1.4;
+    ctx.beginPath();
+    ctx.moveTo(x, y);
+    ctx.lineTo(x - 2 - r1 * 3, y - h);
+    ctx.moveTo(x + 1.5, y);
+    ctx.lineTo(x + 3 + r2 * 3, y - h * 0.8);
+    ctx.stroke();
+    if (r2 > 0.9 && tall){
+      ctx.fillStyle = 'rgba(255,190,120,0.85)';
+      ctx.beginPath(); ctx.arc(x + 2, y - h - 2, 2, 0, 7); ctx.fill();
+    }
+  }
+}
+
+/* ================================ CAR ================================ */
+const car = { x: 0, wheelRot: 0, smoke: [], smokeTimer: 0 };
+
+function drawWheel(cx, cy, r, rot){
+  ctx.save();
+  ctx.translate(cx, cy);
+  // tyre
+  ctx.fillStyle = '#141216';
+  ctx.beginPath(); ctx.arc(0, 0, r, 0, 7); ctx.fill();
+  ctx.strokeStyle = 'rgba(255,150,100,0.10)';
+  ctx.lineWidth = 3;
+  ctx.beginPath(); ctx.arc(0, 0, r - 2, Math.PI * 0.95, Math.PI * 1.75); ctx.stroke();
+  // rim
+  const rg = ctx.createRadialGradient(-4, -6, 2, 0, 0, r * 0.62);
+  rg.addColorStop(0, '#ece6da'); rg.addColorStop(0.55, '#8d8a90'); rg.addColorStop(1, '#45434c');
+  ctx.fillStyle = rg;
+  ctx.beginPath(); ctx.arc(0, 0, r * 0.62, 0, 7); ctx.fill();
+  // rotating spokes
+  ctx.save();
+  ctx.rotate(rot);
+  ctx.strokeStyle = 'rgba(24,21,27,0.92)';
+  ctx.lineWidth = r * 0.15; ctx.lineCap = 'round';
+  for (let k = 0; k < 5; k++){
+    const a = k * Math.PI * 2 / 5;
+    ctx.beginPath();
+    ctx.moveTo(Math.cos(a) * r * 0.15, Math.sin(a) * r * 0.15);
+    ctx.lineTo(Math.cos(a) * r * 0.5, Math.sin(a) * r * 0.5);
+    ctx.stroke();
+  }
+  // valve dot (makes spin easy to read)
+  ctx.fillStyle = 'rgba(255,190,130,0.9)';
+  ctx.beginPath(); ctx.arc(r * 0.36, 0, r * 0.07, 0, 7); ctx.fill();
+  ctx.restore();
+  // hub
+  ctx.fillStyle = '#2a272e';
+  ctx.beginPath(); ctx.arc(0, 0, r * 0.2, 0, 7); ctx.fill();
+  ctx.fillStyle = 'rgba(255,210,160,0.55)';
+  ctx.beginPath(); ctx.arc(-r * 0.05, -r * 0.07, r * 0.06, 0, 7); ctx.fill();
+  ctx.restore();
+}
+
+const ARCH_A = Math.asin(14 / 40); // bottom-edge / arch geometry
+function bodyPath(){
+  const g = ctx;
+  g.beginPath();
+  g.moveTo(-152, -24);
+  g.quadraticCurveTo(-157, -40, -150, -50);
+  g.quadraticCurveTo(-140, -57, -116, -59);
+  g.quadraticCurveTo(-92, -61, -78, -63);
+  g.quadraticCurveTo(-63, -87, -32, -89);
+  g.lineTo(20, -91);
+  g.quadraticCurveTo(57, -90, 87, -64);
+  g.quadraticCurveTo(122, -57, 147, -50);
+  g.quadraticCurveTo(157, -45, 157, -35);
+  g.quadraticCurveTo(157, -27, 150, -25);
+  g.lineTo(95 + Math.cos(ARCH_A) * 40, -20);
+  g.arc(95, -34, 40, ARCH_A, Math.PI - ARCH_A, true);
+  g.lineTo(-95 + Math.cos(ARCH_A) * 40, -20);
+  g.arc(-95, -34, 40, ARCH_A, Math.PI - ARCH_A, true);
+  g.lineTo(-152, -24);
+  g.closePath();
+}
+
+function drawCar(t, dt){
+  const cx = car.x;
+  const bobY = Math.sin(t * 5.2) * 1.3 + Math.sin(t * 8.9 + 1.1) * 0.6;
+  const pitch = Math.sin(t * 4.3 + 0.4) * 0.005;
+
+  // --- shadow on the road ---
+  ctx.fillStyle = 'rgba(8,6,10,0.38)';
+  ctx.beginPath(); ctx.ellipse(cx + 8, CAR_GROUND + 5, 158, 13, 0, 0, 7); ctx.fill();
+  ctx.fillStyle = 'rgba(8,6,10,0.22)';
+  ctx.beginPath(); ctx.ellipse(cx + 8, CAR_GROUND + 5, 118, 9, 0, 0, 7); ctx.fill();
+
+  // --- wheels (fixed to road, spinning) ---
+  car.wheelRot += SPEED * dt / 34;
+  drawWheel(cx - 95, CAR_GROUND - 34, 34, car.wheelRot);
+  drawWheel(cx + 95, CAR_GROUND - 34, 34, car.wheelRot);
+
+  // --- body group (bobbing) ---
+  ctx.save();
+  ctx.translate(cx, CAR_GROUND + bobY);
+  ctx.rotate(pitch);
+
+  // exhaust smoke (in body space, drifts backwards)
+  car.smokeTimer -= dt;
+  if (car.smokeTimer <= 0){
+    car.smokeTimer = 0.16 + Math.random() * 0.08;
+    car.smoke.push({ x: -150, y: -13, vx: -34 - Math.random() * 26, vy: -12 - Math.random() * 8, life: 1, r: 3 + Math.random() * 3 });
+  }
+  for (let i = car.smoke.length - 1; i >= 0; i--){
+    const p = car.smoke[i];
+    p.x += p.vx * dt; p.y += p.vy * dt; p.life -= dt * 0.9; p.r += dt * 3;
+    if (p.life <= 0){ car.smoke.splice(i, 1); continue; }
+    ctx.fillStyle = 'rgba(150,140,150,' + (0.16 * p.life).toFixed(3) + ')';
+    ctx.beginPath(); ctx.arc(p.x, p.y, p.r, 0, 7); ctx.fill();
+  }
+
+  // wheel wells (dark behind tyres)
+  ctx.fillStyle = '#0b090c';
+  ctx.beginPath(); ctx.arc(-95, -34, 40, 0, 7); ctx.fill();
+  ctx.beginPath(); ctx.arc(95, -34, 40, 0, 7); ctx.fill();
+
+  // body
+  const bg = ctx.createLinearGradient(0, -96, 0, -12);
+  bg.addColorStop(0.00, '#ff9d6a');
+  bg.addColorStop(0.28, '#e0695a');
+  bg.addColorStop(0.58, '#a53a4c');
+  bg.addColorStop(0.82, '#6e2438');
+  bg.addColorStop(1.00, '#471a2c');
+  ctx.fillStyle = bg;
+  bodyPath();
+  ctx.fill();
+  ctx.strokeStyle = 'rgba(28,10,18,0.65)';
+  ctx.lineWidth = 1.6;
+  ctx.stroke();
+
+  // sunlit rim along the roofline
+  ctx.strokeStyle = 'rgba(255,190,130,0.65)';
+  ctx.lineWidth = 2.4; ctx.lineCap = 'round';
+  ctx.beginPath();
+  ctx.moveTo(-148, -51);
+  ctx.quadraticCurveTo(-138, -57, -116, -59);
+  ctx.quadraticCurveTo(-92, -61, -78, -63);
+  ctx.quadraticCurveTo(-63, -87, -32, -89);
+  ctx.lineTo(20, -91);
+  ctx.quadraticCurveTo(57, -90, 87, -64);
+  ctx.quadraticCurveTo(122, -57, 147, -50);
+  ctx.stroke();
+
+  // rear-fender sun bounce
+  const fl = ctx.createRadialGradient(-118, -46, 2, -118, -46, 46);
+  fl.addColorStop(0, 'rgba(255,160,100,0.30)');
+  fl.addColorStop(1, 'rgba(255,160,100,0)');
+  ctx.fillStyle = fl;
+  ctx.beginPath(); ctx.ellipse(-118, -46, 46, 22, -0.2, 0, 7); ctx.fill();
+
+  // windows (sky reflection)
+  const gg = ctx.createLinearGradient(0, -86, 0, -56);
+  gg.addColorStop(0, '#ffd08a');
+  gg.addColorStop(0.5, '#d9736c');
+  gg.addColorStop(1, '#4b2a55');
+  ctx.fillStyle = gg;
+  ctx.beginPath();
+  ctx.moveTo(-62, -65);
+  ctx.quadraticCurveTo(-52, -81, -30, -83);
+  ctx.lineTo(14, -85);
+  ctx.quadraticCurveTo(46, -84, 76, -61);
+  ctx.lineTo(72, -58);
+  ctx.lineTo(-58, -58);
+  ctx.closePath();
+  ctx.fill();
+  // pillars
+  ctx.fillStyle = '#571f31';
+  ctx.fillRect(8, -85, 6, 27);        // B-pillar
+  ctx.beginPath();                    // A-pillar
+  ctx.moveTo(72, -58); ctx.lineTo(80, -58); ctx.lineTo(60, -80); ctx.lineTo(52, -80);
+  ctx.closePath(); ctx.fill();
+  // glass highlight
+  ctx.strokeStyle = 'rgba(255,230,180,0.35)';
+  ctx.lineWidth = 1.4;
+  ctx.beginPath(); ctx.moveTo(-56, -64); ctx.lineTo(70, -62); ctx.stroke();
+
+  // chrome trim
+  const cg = ctx.createLinearGradient(-150, 0, 152, 0);
+  cg.addColorStop(0, '#8a7f80'); cg.addColorStop(0.5, '#f0e6d4'); cg.addColorStop(1, '#8a7f80');
+  ctx.strokeStyle = cg; ctx.lineWidth = 2.6;
+  ctx.beginPath(); ctx.moveTo(-148, -46); ctx.lineTo(148, -44); ctx.stroke();
+
+  // door cuts
+  ctx.strokeStyle = 'rgba(20,6,14,0.35)'; ctx.lineWidth = 1.6;
+  ctx.beginPath(); ctx.moveTo(-22, -58); ctx.quadraticCurveTo(-20, -30, -22, -21); ctx.stroke();
+  ctx.beginPath(); ctx.moveTo(40, -58); ctx.quadraticCurveTo(42, -30, 40, -21); ctx.stroke();
+
+  // handles
+  ctx.fillStyle = 'rgba(240,225,210,0.85)';
+  ctx.fillRect(-34, -52, 11, 3);
+  ctx.fillRect(28, -52, 11, 3);
+
+  // mirror
+  ctx.fillStyle = '#3a1420';
+  ctx.fillRect(56, -66, 11, 7);
+  ctx.fillStyle = 'rgba(255,170,110,0.5)';
+  ctx.fillRect(56, -66, 2, 7);
+
+  // lights
+  ctx.fillStyle = '#fff3c4';
+  ctx.beginPath(); ctx.ellipse(150, -42, 6.5, 4.5, 0.2, 0, 7); ctx.fill();
+  const hg = ctx.createRadialGradient(152, -42, 0, 152, -42, 12);
+  hg.addColorStop(0, 'rgba(255,240,180,0.7)'); hg.addColorStop(1, 'rgba(255,240,180,0)');
+  ctx.fillStyle = hg;
+  ctx.beginPath(); ctx.arc(152, -42, 12, 0, 7); ctx.fill();
+
+  ctx.fillStyle = '#ff4038';
+  ctx.beginPath(); ctx.ellipse(-151, -46, 3.5, 6, -0.2, 0, 7); ctx.fill();
+  const tg = ctx.createRadialGradient(-153, -46, 0, -153, -46, 11);
+  tg.addColorStop(0, 'rgba(255,70,60,0.55)'); tg.addColorStop(1, 'rgba(255,70,60,0)');
+  ctx.fillStyle = tg;
+  ctx.beginPath(); ctx.arc(-153, -46, 11, 0, 7); ctx.fill();
+
+  // bumpers
+  ctx.fillStyle = '#3c3438';
+  ctx.fillRect(146, -30, 12, 7);
+  ctx.fillRect(-160, -31, 12, 7);
+  ctx.fillStyle = 'rgba(255,190,140,0.35)';
+  ctx.fillRect(146, -30, 12, 1.6);
+  ctx.fillRect(-160, -31, 12, 1.6);
+
+  ctx.restore();
+
+  // headlight pool on the road ahead
+  ctx.globalCompositeOperation = 'screen';
+  const hg2 = ctx.createRadialGradient(car.x + 185, CAR_GROUND + 12, 4, car.x + 185, CAR_GROUND + 12, 95);
+  hg2.addColorStop(0, 'rgba(255,230,170,0.14)');
+  hg2.addColorStop(1, 'rgba(255,230,170,0)');
+  ctx.fillStyle = hg2;
+  ctx.beginPath(); ctx.ellipse(car.x + 185, CAR_GROUND + 12, 95, 22, 0, 0, 7); ctx.fill();
+  ctx.globalCompositeOperation = 'source-over';
+}
+
+/* ======================== FOREGROUND / EFFECTS ======================== */
+function drawForegroundBushes(scroll){
+  const spacing = 300;
+  const [i0, i1] = indexRange(scroll * 1.3, spacing, 120);
+  for (let i = i0; i <= i1; i++){
+    const r1 = hash1(i, 501), r2 = hash1(i, 502);
+    const x = i * spacing + (r1 - 0.5) * 220 - scroll * 1.3;
+    const r = 14 + r2 * 16;
+    const y = DH + 16;
+    ctx.fillStyle = '#120e12';
+    ctx.beginPath(); ctx.arc(x, y - r * 0.4, r, 0, 7); ctx.fill();
+    ctx.beginPath(); ctx.arc(x - r * 0.7, y - r * 0.2, r * 0.7, 0, 7); ctx.fill();
+    ctx.beginPath(); ctx.arc(x + r * 0.7, y - r * 0.25, r * 0.75, 0, 7); ctx.fill();
+    ctx.strokeStyle = 'rgba(255,140,80,0.16)';
+    ctx.lineWidth = 1.6;
+    ctx.beginPath(); ctx.arc(x, y - r * 0.4, r - 1, Math.PI, Math.PI * 1.6); ctx.stroke();
+    if (r2 > 0.6){
+      ctx.fillStyle = 'rgba(255,170,110,0.8)';
+      ctx.beginPath(); ctx.arc(x - r * 0.3, y - r * 1.1, 2.2, 0, 7); ctx.fill();
+    }
+  }
+}
+
+const motes = [];
+function drawMotes(t){
+  if (!motes.length){
+    for (let i = 0; i < 20; i++)
+      motes.push({ x: hash1(i, 701), y: hash1(i, 702), d: 0.4 + hash1(i, 703) * 0.7, ph: hash1(i, 704) * 6.28 });
+  }
+  ctx.globalCompositeOperation = 'screen';
+  for (const m of motes){
+    const x = ((m.x * (DW + 100) - worldX * m.d * 0.4) % (DW + 100) + DW + 100) % (DW + 100) - 50;
+    const y = 300 + m.y * 220 + Math.sin(t * 0.8 + m.ph) * 12;
+    const a = 0.05 + 0.06 * Math.sin(t * 1.4 + m.ph);
+    ctx.fillStyle = 'rgba(255,200,140,' + a.toFixed(3) + ')';
+    ctx.beginPath(); ctx.arc(x, y, 1.6 + m.d, 0, 7); ctx.fill();
+  }
+  ctx.globalCompositeOperation = 'source-over';
+}
+
+function drawLighting(sunX, sunY){
+  // diagonal god-ray style beam
+  ctx.globalCompositeOperation = 'screen';
+  const bx = sunX + 320, by = sunY + 300;
+  const g = ctx.createLinearGradient(sunX, sunY, bx, by);
+  g.addColorStop(0, 'rgba(255,170,100,0.10)');
+  g.addColorStop(1, 'rgba(255,170,100,0)');
+  ctx.fillStyle = g;
+  ctx.beginPath();
+  ctx.moveTo(sunX - 30, sunY);
+  ctx.lineTo(sunX + 60, sunY - 40);
+  ctx.lineTo(bx + 260, by + 160);
+  ctx.lineTo(bx + 40, by + 260);
+  ctx.closePath(); ctx.fill();
+  ctx.globalCompositeOperation = 'source-over';
+
+  // warm cinematic grade
+  ctx.globalCompositeOperation = 'soft-light';
+  const wg = ctx.createLinearGradient(0, 0, 0, DH);
+  wg.addColorStop(0, 'rgba(80,40,140,0.35)');
+  wg.addColorStop(0.6, 'rgba(255,140,80,0.28)');
+  wg.addColorStop(1, 'rgba(255,120,60,0.22)');
+  ctx.fillStyle = wg;
+  ctx.fillRect(0, 0, DW, DH);
+  ctx.globalCompositeOperation = 'source-over';
+
+  // vignette
+  const vg = ctx.createRadialGradient(DW / 2, DH * 0.46, DH * 0.3, DW / 2, DH * 0.5, DH * 0.95);
+  vg.addColorStop(0, 'rgba(10,5,20,0)');
+  vg.addColorStop(0.7, 'rgba(10,5,20,0.12)');
+  vg.addColorStop(1, 'rgba(8,4,16,0.5)');
+  ctx.fillStyle = vg;
+  ctx.fillRect(0, 0, DW, DH);
+}
+
+/* ============================ MAIN LOOP ============================ */
+let worldX = 0, last = 0, startT = 0;
+
+function frame(ts){
+  if (!startT) startT = ts;
+  const t = (ts - startT) / 1000;
+  let dt = last ? (ts - last) / 1000 : 0.016;
+  last = ts;
+  dt = Math.min(dt, 0.05);
+
+  // calm speed with a gentle organic ease at start
+  const ease = Math.min(1, (ts - startT) / 2500);
+  const speedNow = SPEED * (0.55 + 0.45 * ease);
+  worldX += speedNow * dt;
+
+  ctx.setTransform(DPR * U, 0, 0, DPR * U, 0, 0);
+
+  const sunX = DW * 0.30, sunY = HORIZON - 56;
+  car.x = DW * 0.44;
+
+  // --- sky ---
+  drawSky(sunX, sunY);
+  drawStars(t);
+  drawSun(sunX, sunY);
+  drawClouds(t, worldX);
+  drawBirds(t);
+
+  // --- land (back to front) ---
+  drawRidge(worldX * 0.07, HORIZON + 10, 130, 300, '#8a5f8c', '#7a547e', 0.9, 11);
+  drawRidge(worldX * 0.15, HORIZON + 28, 96, 220, '#5e3f6e', '#4b3a63', 0.95, 23);
+  drawRidge(worldX * 0.26, HORIZON + 58, 58, 160, '#463e55', '#3a3646', 1, 37);
+
+  drawGround();
+  drawTreeline(worldX * 0.40);
+  drawBigTrees(worldX * 0.60);
+
+  // --- road ---
+  drawRoad(worldX);
+  drawRoadGrass(worldX, ROAD_TOP - 1, 1.0, false);
+
+  // --- roadside furniture (in front of road plane) ---
+  drawFence(worldX * 0.92);
+  drawPoles(worldX * 0.80);
+
+  // --- hero car ---
+  drawCar(t, dt);
+
+  // --- foreground (fast, close to camera) ---
+  drawRoadGrass(worldX, ROAD_BOTTOM + 8, 1.05, true);
+  drawForegroundBushes(worldX);
+
+  // --- atmosphere & grade ---
+  drawMotes(t);
+  drawLighting(sunX, sunY);
+
+  requestAnimationFrame(frame);
+}
+requestAnimationFrame(frame);
+})();
+</script>
+</body>
+</html>
diff --git a/docs/assets/perf/lossy-kimi-k3-shed-0.65.png b/docs/assets/perf/lossy-kimi-k3-shed-0.65.png
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diff --git a/docs/assets/perf/lossy-kimi-k3-shed-0.70.html b/docs/assets/perf/lossy-kimi-k3-shed-0.70.html
new file mode 100644
index 0000000..44bb853
--- /dev/null
+++ b/docs/assets/perf/lossy-kimi-k3-shed-0.70.html
@@ -0,0 +1,781 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="utf-8"/>
+<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<title>Evening Drive — Parallax Canvas</title>
+<style>
+  html, body { margin:0; padding:0; height:100%; overflow:hidden; background:#0d0b14; }
+  canvas { display:block; width:100vw; height:100vh; }
+</style>
+</head>
+<body>
+<canvas id="scene"></canvas>
+<script>
+(() => {
+'use strict';
+
+const TAU = Math.PI * 2;
+const canvas = document.getElementById('scene');
+const ctx = canvas.getContext('2d');
+const reduceMotion = window.matchMedia && window.matchMedia('(prefers-reduced-motion: reduce)').matches;
+
+/* ---------------- state ---------------- */
+let W = 0, H = 0, S = 1, DPR = 1;
+let horizonY = 0, roadTopY = 0, roadBotY = 0, carBaseY = 0, carX = 0;
+let time = 0, last = 0, cameraX = 0, fade = 0;
+let worldSpeed = 0;
+const BASE_SPEED = 240; // px/s at reference scale (road layer)
+
+const R = (a, b) => a + Math.random() * (b - a);
+const clamp = (v, a, b) => v < a ? a : (v > b ? b : v);
+
+/* ---------------- periodic ridge generators (seamless tiling) ---------------- */
+function makeRidge(P, harmonics) {
+  const hs = harmonics.map(([k, a]) => ({ k, a, p: Math.random() * TAU }));
+  const norm = harmonics.reduce((s, h) => s + h[1], 0);
+  return (x) => {
+    const u = ((x % P) + P) % P;
+    let v = 0;
+    for (const h of hs) v += h.a * Math.sin(TAU * h.k * u / P + h.p);
+    return (v / norm) * 0.5 + 0.5; // 0..1
+  };
+}
+let ridgeFar, ridgeNear, ridgeHill, ridgeTreeA, ridgeTreeB;
+
+/* ---------------- sprite buffers ---------------- */
+let cloudSprites = [], treeSprites = [], bushSprites = [];
+function makeCanvas(w, h, fn) {
+  const c = document.createElement('canvas');
+  c.width = Math.max(4, w | 0); c.height = Math.max(4, h | 0);
+  fn(c.getContext('2d'), c.width, c.height);
+  return c;
+}
+function buildSprites() {
+  cloudSprites = [];
+  for (let v = 0; v < 3; v++) {
+    cloudSprites.push(makeCanvas(380 * S, 180 * S, (g, w, h) => {
+      const puffs = 8 + v * 2;
+      for (let i = 0; i < puffs; i++) { // purple body
+        const px = w * R(0.15, 0.85), py = h * R(0.30, 0.58), r = w * R(0.12, 0.26);
+        const gr = g.createRadialGradient(px, py, 0, px, py, r);
+        gr.addColorStop(0, 'rgba(104,74,128,0.55)');
+        gr.addColorStop(1, 'rgba(104,74,128,0)');
+        g.fillStyle = gr; g.beginPath(); g.arc(px, py, r, 0, TAU); g.fill();
+      }
+      for (let i = 0; i < puffs; i++) { // warm under-light
+        const px = w * R(0.15, 0.85), py = h * R(0.45, 0.70), r = w * R(0.10, 0.22);
+        const gr = g.createRadialGradient(px, py, 0, px, py, r);
+        gr.addColorStop(0, 'rgba(255,168,140,0.42)');
+        gr.addColorStop(1, 'rgba(255,168,140,0)');
+        g.fillStyle = gr; g.beginPath(); g.arc(px, py, r, 0, TAU); g.fill();
+      }
+    }));
+  }
+  treeSprites = [];
+  for (let v = 0; v < 3; v++) {
+    treeSprites.push(makeCanvas(230 * S, 300 * S, (g, w, h) => {
+      g.fillStyle = '#171320';
+      g.fillRect(w * 0.455, h * 0.52, w * 0.10, h * 0.48);
+      const blobs = 6 + v;
+      for (let i = 0; i < blobs; i++) {
+        const px = w * R(0.25, 0.75), py = h * R(0.14, 0.60), r = w * R(0.16, 0.30);
+        const gr = g.createRadialGradient(px, py, 0, px, py, r);
+        gr.addColorStop(0, '#1f1930'); gr.addColorStop(0.75, '#1a1527'); gr.addColorStop(1, 'rgba(26,21,39,0)');
+        g.fillStyle = gr; g.beginPath(); g.arc(px, py, r, 0, TAU); g.fill();
+      }
+      g.fillStyle = 'rgba(255,150,95,0.07)';
+      for (let i = 0; i < blobs; i++) {
+        const px = w * R(0.30, 0.70), py = h * R(0.10, 0.38), r = w * R(0.10, 0.20);
+        g.beginPath(); g.arc(px, py, r, 0, TAU); g.fill();
+      }
+    }));
+  }
+  bushSprites = [];
+  for (let v = 0; v < 2; v++) {
+    bushSprites.push(makeCanvas(280 * S, 120 * S, (g, w, h) => {
+      for (let i = 0; i < 8; i++) {
+        const px = w * R(0.12, 0.88), py = h * R(0.50, 0.82), r = w * R(0.12, 0.26);
+        const gr = g.createRadialGradient(px, py, 0, px, py, r);
+        gr.addColorStop(0, '#17141f'); gr.addColorStop(1, 'rgba(23,20,31,0)');
+        g.fillStyle = gr; g.beginPath(); g.arc(px, py, r, 0, TAU); g.fill();
+      }
+      g.fillStyle = 'rgba(255,145,95,0.05)';
+      for (let i = 0; i < 4; i++) {
+        const px = w * R(0.2, 0.8), py = h * R(0.35, 0.55), r = w * R(0.08, 0.16);
+        g.beginPath(); g.arc(px, py, r, 0, TAU); g.fill();
+      }
+    }));
+  }
+}
+
+/* ---------------- scenery object pools ---------------- */
+let clouds = [], stars = [], poles = [], signs = [], trees = [], fieldStuff = [], bushes = [];
+let speckles = [], exhaust = [], birds = [];
+let poleNX = 0, signNX = 0, treeNX = 0, stuffNX = 0, bushNX = 0, birdT = 6, exhaustT = 0;
+
+function shiftRemove(arr, min) { while (arr.length && arr[0].wx < min) arr.shift(); }
+
+function resetWorld() {
+  poles.length = signs.length = trees.length = fieldStuff.length = bushes.length = 0;
+  exhaust.length = birds.length = 0;
+  poleNX = -300 * S; signNX = R(300, 700) * S; treeNX = -200 * S;
+  stuffNX = -100 * S; bushNX = -100 * S;
+
+  clouds = [];
+  for (let i = 0; i < 6; i++)
+    clouds.push({ x: R(-0.15, 1.1) * W, y: R(0.05, 0.40) * H, scale: R(0.7, 1.5), spr: (Math.random() * 3) | 0, drift: R(2, 9) });
+
+  stars = [];
+  for (let i = 0; i < 90; i++)
+    stars.push({ x: Math.random(), y: Math.random() * 0.5, r: R(0.5, 1.6), p: R(0, TAU) });
+
+  speckles = [];
+  for (let i = 0; i < 60; i++)
+    speckles.push({ x: Math.random(), y: R(0.35, 0.95), r: R(0.6, 1.9), a: R(0.04, 0.11) });
+
+  birdT = R(4, 9);
+}
+
+function spawnScenery() {
+  const margin = 450 * S;
+  let cam = cameraX; // poles / signs factor 1.0
+  while (poleNX < cam + W + margin) { poles.push({ wx: poleNX, h: R(0.92, 1.12), tilt: R(-0.012, 0.012) }); poleNX += R(300, 430) * S; }
+  shiftRemove(poles, cam - margin);
+  while (signNX < cam + W + margin) { signs.push({ wx: signNX, type: (Math.random() * 3) | 0, tilt: R(-0.04, 0.04) }); signNX += R(850, 1600) * S; }
+  shiftRemove(signs, cam - margin);
+
+  cam = cameraX * 0.65; // field trees
+  while (treeNX < cam + W + margin) { trees.push({ wx: treeNX, s: R(0.7, 1.6), spr: (Math.random() * 3) | 0, y: R(roadTopY - 48 * S, roadTopY - 12 * S) }); treeNX += R(420, 950) * S; }
+  shiftRemove(trees, cam - margin);
+
+  cam = cameraX * 0.8; // field patches / hay bales
+  while (stuffNX < cam + W + margin) {
+    fieldStuff.push({ wx: stuffNX, type: Math.random() < 0.22 ? 'bale' : 'patch', s: R(0.6, 1.4), sx: R(0.9, 1.8), y: R(horizonY + 18 * S, roadTopY - 16 * S) });
+    stuffNX += R(120, 360) * S;
+  }
+  shiftRemove(fieldStuff, cam - margin);
+
+  cam = cameraX * 1.35; // near foreground bushes
+  while (bushNX < cam + W + margin) { bushes.push({ wx: bushNX, s: R(0.8, 1.6), spr: (Math.random() * 2) | 0, y: R(H - 74 * S, H - 14 * S) }); bushNX += R(300, 760) * S; }
+  shiftRemove(bushes, cam - margin);
+}
+
+/* ---------------- layout / resize ---------------- */
+function resize() {
+  DPR = Math.min(2, window.devicePixelRatio || 1);
+  W = window.innerWidth; H = window.innerHeight;
+  canvas.width = W * DPR | 0; canvas.height = H * DPR | 0;
+  ctx.setTransform(DPR, 0, 0, DPR, 0, 0);
+  S = clamp(H / 720, 0.55, 1.6);
+
+  horizonY = H * 0.60;
+  roadTopY = H * 0.735;
+  roadBotY = H * 0.865;
+  carBaseY = roadTopY + (roadBotY - roadTopY) * 0.74;
+  carX = W * 0.38;
+
+  worldSpeed = BASE_SPEED * S * (reduceMotion ? 0.35 : 1);
+
+  ridgeFar    = makeRidge(2600 * S, [[1, 1], [3, 0.55], [7, 0.22], [13, 0.1]]);
+  ridgeNear   = makeRidge(2000 * S, [[1, 1], [2, 0.6], [5, 0.3], [11, 0.12]]);
+  ridgeHill   = makeRidge(2400 * S, [[1, 1], [2, 0.5], [4, 0.2]]);
+  ridgeTreeA  = makeRidge(1600 * S, [[3, 1], [7, 0.6], [17, 0.3], [31, 0.18]]);
+  ridgeTreeB  = makeRidge(1300 * S, [[2, 1], [5, 0.7], [13, 0.35], [29, 0.2]]);
+
+  buildSprites();
+  resetWorld();
+}
+window.addEventListener('resize', resize);
+resize();
+
+/* ---------------- drawing helpers ---------------- */
+function ridgeLayer(factor, ridgeFn, baseY, amp, sharp, fillTo, fillStyle, strokeStyle, lw) {
+  const cam = cameraX * factor;
+  const step = Math.max(4, 6 * S) | 0;
+  ctx.beginPath();
+  ctx.moveTo(-step * 2, fillTo);
+  for (let x = -step * 2; x <= W + step * 2; x += step) {
+    const v = Math.pow(ridgeFn(x + cam), sharp);
+    ctx.lineTo(x, baseY - v * amp);
+  }
+  ctx.lineTo(W + step * 2, fillTo);
+  ctx.closePath();
+  ctx.fillStyle = fillStyle;
+  ctx.fill();
+  if (strokeStyle) {
+    ctx.beginPath();
+    for (let x = -step * 2; x <= W + step * 2; x += step) {
+      const v = Math.pow(ridgeFn(x + cam), sharp);
+      const y = baseY - v * amp;
+      if (x === -step * 2) ctx.moveTo(x, y); else ctx.lineTo(x, y);
+    }
+    ctx.strokeStyle = strokeStyle;
+    ctx.lineWidth = lw || 1.5 * S;
+    ctx.stroke();
+  }
+}
+
+function rr(x, y, w, h, r) {
+  ctx.beginPath();
+  ctx.moveTo(x + r, y);
+  ctx.arcTo(x + w, y, x + w, y + h, r);
+  ctx.arcTo(x + w, y + h, x, y + h, r);
+  ctx.arcTo(x, y + h, x, y, r);
+  ctx.arcTo(x, y, x + w, y, r);
+  ctx.closePath();
+}
+
+/* ---------------- sky & celestial ---------------- */
+function drawSky() {
+  const g = ctx.createLinearGradient(0, 0, 0, horizonY);
+  g.addColorStop(0.00, '#1d1a3c');
+  g.addColorStop(0.42, '#4a2a5d');
+  g.addColorStop(0.68, '#8f3a5d');
+  g.addColorStop(0.86, '#d6604f');
+  g.addColorStop(1.00, '#f78f52');
+  ctx.fillStyle = g;
+  ctx.fillRect(0, 0, W, horizonY + 1);
+}
+function drawStars() {
+  for (const st of stars) {
+    const y = st.y * H;
+    if (y > horizonY - 30 * S) continue;
+    const heightFade = clamp(1 - y / (horizonY * 0.8), 0, 1);
+    const tw = 0.5 + 0.5 * Math.sin(time * 1.6 + st.p);
+    ctx.globalAlpha = 0.55 * heightFade * tw;
+    ctx.fillStyle = '#e8e4ff';
+    ctx.beginPath(); ctx.arc(st.x * W, y, st.r * S, 0, TAU); ctx.fill();
+  }
+  ctx.globalAlpha = 1;
+}
+function sunPos() { return { x: W * 0.64, y: horizonY - H * 0.055 }; }
+function drawSun() {
+  const { x, y } = sunPos();
+  let g = ctx.createRadialGradient(x, y, 0, x, y, 300 * S);
+  g.addColorStop(0, 'rgba(255,170,95,0.42)');
+  g.addColorStop(0.4, 'rgba(255,140,85,0.14)');
+  g.addColorStop(1, 'rgba(255,140,85,0)');
+  ctx.fillStyle = g;
+  ctx.beginPath(); ctx.arc(x, y, 300 * S, 0, TAU); ctx.fill();
+
+  const pulse = 1 + Math.sin(time * 0.8) * 0.02;
+  g = ctx.createRadialGradient(x, y, 0, x, y, 46 * S * pulse);
+  g.addColorStop(0, '#fff6dc');
+  g.addColorStop(0.55, '#ffdf9f');
+  g.addColorStop(1, 'rgba(255,190,120,0)');
+  ctx.fillStyle = g;
+  ctx.beginPath(); ctx.arc(x, y, 46 * S * pulse, 0, TAU); ctx.fill();
+
+  ctx.fillStyle = 'rgba(255,200,140,0.16)';
+  ctx.beginPath(); ctx.ellipse(x, y + 4 * S, 130 * S, 9 * S, 0, 0, TAU); ctx.fill();
+}
+function drawHaze() {
+  const g = ctx.createLinearGradient(0, horizonY - 60 * S, 0, horizonY + 30 * S);
+  g.addColorStop(0, 'rgba(255,150,90,0)');
+  g.addColorStop(0.5, 'rgba(255,150,90,0.16)');
+  g.addColorStop(1, 'rgba(255,150,90,0)');
+  ctx.fillStyle = g;
+  ctx.fillRect(0, horizonY - 60 * S, W, 90 * S);
+}
+function drawClouds() {
+  for (const c of clouds) {
+    const spr = cloudSprites[c.spr];
+    const w = spr.width * c.scale, h = spr.height * c.scale;
+    ctx.globalAlpha = 0.85;
+    ctx.drawImage(spr, c.x, c.y, w, h);
+  }
+  ctx.globalAlpha = 1;
+}
+function updateClouds(dt) {
+  for (const c of clouds) {
+    c.x -= (worldSpeed * 0.05 + c.drift * S) * dt;
+    const spr = cloudSprites[c.spr];
+    if (c.x + spr.width * c.scale < -40) { c.x = W + R(0, 0.15) * W; c.y = R(0.05, 0.40) * H; }
+  }
+}
+function drawBirds(dt) {
+  birdT -= dt;
+  if (birdT < 0 && !reduceMotion) {
+    const n = 2 + (Math.random() * 3 | 0);
+    const by = R(0.14, 0.34) * H;
+    for (let i = 0; i < n; i++)
+      birds.push({ x: W + 60 + i * R(18, 40) * S, y: by + R(-30, 30) * S, s: R(0.8, 1.3), vx: -(R(35, 65) * S + worldSpeed * 0.04), p: R(0, TAU), bobA: R(2, 5) * S });
+    birdT = R(7, 15);
+  }
+  ctx.strokeStyle = 'rgba(28,22,40,0.85)';
+  ctx.lineWidth = 1.7 * S;
+  ctx.lineCap = 'round';
+  for (let i = birds.length - 1; i >= 0; i--) {
+    const b = birds[i];
+    b.x += b.vx * dt;
+    const flap = Math.sin(time * 9 + b.p);
+    const y = b.y + Math.sin(time * 1.4 + b.p) * b.bobA;
+    const w = 8 * b.s * S, lift = 4 * b.s * S * flap;
+    ctx.beginPath();
+    ctx.moveTo(b.x - w, y);
+    ctx.quadraticCurveTo(b.x - w * 0.4, y - 3 * S - lift, b.x, y);
+    ctx.quadraticCurveTo(b.x + w * 0.4, y - 3 * S - lift, b.x + w, y);
+    ctx.stroke();
+    if (b.x < -80) birds.splice(i, 1);
+  }
+}
+
+/* ---------------- mid layers ---------------- */
+function drawField() {
+  const g = ctx.createLinearGradient(0, horizonY, 0, roadTopY);
+  g.addColorStop(0, '#7e5d55');
+  g.addColorStop(0.45, '#4c4138');
+  g.addColorStop(1, '#2e2a23');
+  ctx.fillStyle = g;
+  ctx.fillRect(0, horizonY, W, roadTopY - horizonY + 1);
+}
+function drawFieldStuff() {
+  const cam = cameraX * 0.8;
+  for (const o of fieldStuff) {
+    const x = o.wx - cam;
+    if (o.type === 'bale') {
+      ctx.save();
+      ctx.translate(x, o.y);
+      ctx.scale(o.s * S, o.s * S);
+      ctx.fillStyle = 'rgba(58,44,38,0.9)';
+      rr(-16, -12, 32, 16, 6); ctx.fill();
+      ctx.fillStyle = 'rgba(255,150,95,0.10)';
+      rr(-16, -12, 32, 6, 4); ctx.fill();
+      ctx.restore();
+    } else {
+      ctx.fillStyle = 'rgba(20,17,14,0.16)';
+      ctx.beginPath();
+      ctx.ellipse(x, o.y, 26 * o.sx * S * o.s, 4.5 * S * o.s, 0, 0, TAU);
+      ctx.fill();
+    }
+  }
+}
+function drawTrees() {
+  const cam = cameraX * 0.65;
+  for (const t of trees) {
+    const x = t.wx - cam;
+    const spr = treeSprites[t.spr];
+    const w = spr.width * t.s, h = spr.height * t.s;
+    ctx.drawImage(spr, x - w / 2, t.y - h, w, h);
+  }
+}
+
+/* ---------------- road ---------------- */
+function drawRoad() {
+  const g = ctx.createLinearGradient(0, roadTopY, 0, roadBotY);
+  g.addColorStop(0, '#26242e');
+  g.addColorStop(1, '#16151b');
+  ctx.fillStyle = g;
+  ctx.fillRect(0, roadTopY, W, roadBotY - roadTopY + 1);
+
+  // far shoulder line
+  ctx.fillStyle = 'rgba(216,196,160,0.55)';
+  ctx.fillRect(0, roadTopY + 1 * S, W, 2 * S);
+  // near edge line
+  ctx.fillStyle = 'rgba(200,190,170,0.45)';
+  ctx.fillRect(0, roadBotY - 5 * S, W, 2.5 * S);
+
+  // moving centre dashes
+  const dashW = 56 * S, gap = 68 * S, period = dashW + gap;
+  const dy = roadTopY + (roadBotY - roadTopY) * 0.46;
+  const off = ((cameraX % period) + period) % period;
+  ctx.fillStyle = 'rgba(216,198,164,0.75)';
+  for (let x = -off - period; x < W + period; x += period) {
+    rr(x, dy, dashW, 6 * S, 3 * S);
+    ctx.fill();
+  }
+
+  // asphalt speckles (subtle speed cue)
+  ctx.fillStyle = '#d8c9a8';
+  const wrap = W + 120;
+  for (const sp of speckles) {
+    const x = ((sp.x * wrap - cameraX) % wrap + wrap) % wrap - 60;
+    const y = roadTopY + (roadBotY - roadTopY) * sp.y;
+    ctx.globalAlpha = sp.a;
+    ctx.fillRect(x, y, sp.r * S * 2.2, sp.r * S);
+  }
+  ctx.globalAlpha = 1;
+}
+function drawSunRoadStreak() {
+  const { x } = sunPos();
+  const y = roadTopY + (roadBotY - roadTopY) * 0.5;
+  ctx.save();
+  ctx.globalCompositeOperation = 'screen';
+  const g = ctx.createRadialGradient(x, y, 0, x, y, W * 0.22);
+  g.addColorStop(0, 'rgba(255,150,75,0.13)');
+  g.addColorStop(1, 'rgba(255,150,75,0)');
+  ctx.fillStyle = g;
+  ctx.beginPath();
+  ctx.ellipse(x, y, W * 0.22, (roadBotY - roadTopY) * 0.55, 0, 0, TAU);
+  ctx.fill();
+  ctx.restore();
+}
+
+/* ---------------- poles, wires, signs ---------------- */
+function drawPolesAndSigns() {
+  const cam = cameraX;
+  const baseY = roadTopY - 1 * S;
+
+  // wires between consecutive poles
+  ctx.strokeStyle = 'rgba(18,14,26,0.85)';
+  ctx.lineWidth = 1.4 * S;
+  for (let i = 0; i < poles.length - 1; i++) {
+    const x1 = poles[i].wx - cam, x2 = poles[i + 1].wx - cam;
+    if (x2 < -60 || x1 > W + 60) continue;
+    const top1 = baseY - 128 * S * poles[i].h, top2 = baseY - 128 * S * poles[i + 1].h;
+    for (const dy of [0, 7 * S]) {
+      ctx.beginPath();
+      ctx.moveTo(x1 - 22 * S * poles[i].h, top1 + dy);
+      ctx.quadraticCurveTo((x1 + x2) / 2, Math.max(top1, top2) + dy + 16 * S, x2 - 22 * S * poles[i + 1].h + 44 * S * poles[i + 1].h, top2 + dy);
+      ctx.stroke();
+    }
+  }
+  for (const p of poles) {
+    const x = p.wx - cam;
+    if (x < -60 || x > W + 60) continue;
+    ctx.save();
+    ctx.translate(x, baseY);
+    ctx.rotate(p.tilt);
+    const h = 128 * S * p.h;
+    ctx.fillStyle = '#1c1522';
+    ctx.fillRect(-3.5 * S, -h, 7 * S, h);
+    ctx.fillStyle = 'rgba(255,150,95,0.16)';
+    ctx.fillRect(1.2 * S, -h, 1.6 * S, h);
+    ctx.fillStyle = '#1c1522';
+    ctx.fillRect(-24 * S * p.h, -h + 6 * S, 48 * S * p.h, 4 * S);
+    ctx.restore();
+  }
+  for (const sgn of signs) {
+    const x = sgn.wx - cam;
+    if (x < -60 || x > W + 60) continue;
+    ctx.save();
+    ctx.translate(x, baseY);
+    ctx.rotate(sgn.tilt);
+    ctx.fillStyle = '#221a20';
+    ctx.fillRect(-2 * S, -46 * S, 4 * S, 46 * S);
+    if (sgn.type === 0) { // yellow diamond
+      ctx.save();
+      ctx.translate(0, -60 * S);
+      ctx.rotate(Math.PI / 4);
+      ctx.fillStyle = '#e0a03a';
+      ctx.fillRect(-13 * S, -13 * S, 26 * S, 26 * S);
+      ctx.fillStyle = '#3a2b1a';
+      ctx.beginPath();
+      ctx.moveTo(-6 * S, 6 * S); ctx.lineTo(0, -6 * S); ctx.lineTo(6 * S, 6 * S); ctx.closePath(); ctx.fill();
+      ctx.restore();
+    } else {
+      const w = 34 * S, h = 20 * S;
+      ctx.fillStyle = sgn.type === 1 ? '#2c5f46' : '#2f4a68';
+      rr(-w / 2, -72 * S, w, h, 3 * S); ctx.fill();
+      ctx.strokeStyle = 'rgba(230,230,220,0.7)';
+      ctx.lineWidth = 1.4 * S; ctx.stroke();
+      ctx.fillStyle = 'rgba(235,235,225,0.8)';
+      ctx.fillRect(-w / 2 + 5 * S, -66 * S, w - 10 * S, 2.5 * S);
+      ctx.fillRect(-w / 2 + 5 * S, -61 * S, w * 0.55, 2.5 * S);
+    }
+    ctx.restore();
+  }
+}
+
+/* ---------------- foreground grass & bushes ---------------- */
+function drawNearGrass() {
+  const g = ctx.createLinearGradient(0, roadBotY, 0, H);
+  g.addColorStop(0, '#262019');
+  g.addColorStop(1, '#12100c');
+  ctx.fillStyle = g;
+  ctx.fillRect(0, roadBotY, W, H - roadBotY + 1);
+}
+function drawNearBushes() {
+  const cam = cameraX * 1.35;
+  for (const b of bushes) {
+    const x = b.wx - cam;
+    const spr = bushSprites[b.spr];
+    const w = spr.width * b.s, h = spr.height * b.s;
+    ctx.drawImage(spr, x - w / 2, b.y - h, w, h);
+  }
+}
+
+/* ---------------- car ---------------- */
+function drawWheel(cx, cy, r, angle) {
+  ctx.fillStyle = '#121117';
+  ctx.beginPath(); ctx.arc(cx, cy, r, 0, TAU); ctx.fill();
+  ctx.strokeStyle = 'rgba(255,255,255,0.07)';
+  ctx.lineWidth = 1.5 * S;
+  ctx.beginPath(); ctx.arc(cx, cy, r - 1 * S, 0, TAU); ctx.stroke();
+
+  ctx.fillStyle = '#8d929e';
+  ctx.beginPath(); ctx.arc(cx, cy, r * 0.62, 0, TAU); ctx.fill();
+
+  ctx.save();
+  ctx.translate(cx, cy);
+  ctx.rotate(angle);
+  ctx.strokeStyle = '#454a55';
+  ctx.lineWidth = r * 0.15;
+  ctx.lineCap = 'round';
+  for (let i = 0; i < 5; i++) {
+    ctx.rotate(TAU / 5);
+    ctx.beginPath();
+    ctx.moveTo(r * 0.16, 0);
+    ctx.lineTo(r * 0.56, 0);
+    ctx.stroke();
+  }
+  ctx.fillStyle = '#d8a04a';
+  ctx.beginPath(); ctx.arc(r * 0.42, 0, r * 0.09, 0, TAU); ctx.fill();
+  ctx.restore();
+
+  ctx.fillStyle = '#c9cdd6';
+  ctx.beginPath(); ctx.arc(cx, cy, r * 0.18, 0, TAU); ctx.fill();
+  ctx.fillStyle = '#3a3f4a';
+  ctx.beginPath(); ctx.arc(cx, cy, r * 0.07, 0, TAU); ctx.fill();
+}
+
+function drawCar(t, dt) {
+  const cW = 232 * S, cH = 88 * S;
+  const tireR = 27 * S;
+  const bobAmp = reduceMotion ? 0.3 : 1;
+  const bob = (Math.sin(t * 5.1) * 1.5 + Math.sin(t * 11.7 + 1.3) * 0.5) * S * bobAmp;
+  const rot = (Math.sin(t * 5.1 + 0.5) * 0.007 + Math.sin(t * 9.3) * 0.0025) * bobAmp;
+  const wheelSpin = cameraX / tireR;
+
+  const groundY = carBaseY;
+  const bodyBottom = groundY - cH * 0.04 + bob;
+
+  // shadow
+  ctx.fillStyle = 'rgba(8,7,10,0.42)';
+  ctx.beginPath();
+  ctx.ellipse(carX, groundY + 2 * S, cW * 0.46, 7 * S, 0, 0, TAU);
+  ctx.fill();
+
+  // exhaust puffs
+  exhaustT -= dt;
+  if (exhaustT <= 0) {
+    exhaustT = 0.28 + Math.random() * 0.2;
+    exhaust.push({
+      x: carX - cW * 0.5 + 6 * S, y: bodyBottom - cH * 0.09,
+      vx: -R(16, 34) * S, vy: -R(4, 12) * S,
+      r: R(2.5, 5) * S, a: 0.16, life: R(0.9, 1.5)
+    });
+  }
+  for (let i = exhaust.length - 1; i >= 0; i--) {
+    const p = exhaust[i];
+    p.x += p.vx * dt; p.y += p.vy * dt;
+    p.r += 9 * S * dt; p.a -= 0.12 * dt; p.life -= dt;
+    if (p.life <= 0 || p.a <= 0) { exhaust.splice(i, 1); continue; }
+    ctx.fillStyle = `rgba(130,120,128,${Math.max(0, p.a)})`;
+    ctx.beginPath(); ctx.arc(p.x, p.y, p.r, 0, TAU); ctx.fill();
+  }
+
+  ctx.save();
+  ctx.translate(carX, bodyBottom);
+  ctx.rotate(rot);
+  ctx.translate(-cW * 0.5, 0);
+
+  const u = (ux, vy) => [ux * cW, -vy * cH];
+  const wFront = 0.775, wRear = 0.225, wheelV = 0.26, archR = 30 * S;
+
+  // wheels (behind body so arches overlap)
+  const wobbleF = Math.sin(t * 8.2 + 0.9) * 0.9 * S * bobAmp;
+  const wobbleR = Math.sin(t * 8.2) * 0.9 * S * bobAmp;
+  drawWheel(wRear * cW, -wheelV * cH + wobbleR, tireR, wheelSpin);
+  drawWheel(wFront * cW, -wheelV * cH + wobbleF, tireR, wheelSpin + 0.4);
+
+  // ---- body ----
+  const bodyGrad = ctx.createLinearGradient(0, -cH, 0, 0);
+  bodyGrad.addColorStop(0.00, '#ff9d6e');
+  bodyGrad.addColorStop(0.16, '#cf5546');
+  bodyGrad.addColorStop(0.55, '#8a2f3d');
+  bodyGrad.addColorStop(1.00, '#4a1a28');
+
+  ctx.beginPath();
+  let p = u(0.015, 0.30); ctx.moveTo(p[0], p[1]);
+  p = u(0.02, 0.52); ctx.bezierCurveTo(p[0], p[1], ...u(0.06, 0.56), ...u(0.10, 0.575));
+  ctx.lineTo(...u(0.13, 0.60));
+  ctx.bezierCurveTo(...u(0.17, 0.80), ...u(0.22, 0.86), ...u(0.30, 0.885));
+  ctx.bezierCurveTo(...u(0.38, 0.905), ...u(0.44, 0.915), ...u(0.50, 0.895));
+  ctx.bezierCurveTo(...u(0.55, 0.875), ...u(0.60, 0.66), ...u(0.63, 0.60));
+  ctx.lineTo(...u(0.66, 0.575));
+  ctx.bezierCurveTo(...u(0.75, 0.56), ...u(0.88, 0.54), ...u(0.945, 0.50));
+  ctx.bezierCurveTo(...u(0.975, 0.475), ...u(0.986, 0.44), ...u(0.986, 0.40));
+  ctx.bezierCurveTo(...u(0.986, 0.30), ...u(0.975, 0.22), ...u(0.955, 0.16));
+  ctx.lineTo(...u(0.90, 0.11));
+  // front arch
+  ctx.lineTo(wFront * cW + archR, -0.11 * cH);
+  ctx.arc(wFront * cW, -wheelV * cH, archR, Math.PI, TAU);
+  ctx.lineTo(wRear * cW + archR, -0.08 * cH);
+  ctx.arc(wRear * cW, -wheelV * cH, archR, Math.PI, TAU);
+  ctx.lineTo(...u(0.05, 0.09));
+  ctx.bezierCurveTo(...u(0.01, 0.10), ...u(0.005, 0.20), ...u(0.015, 0.30));
+  ctx.closePath();
+  ctx.fillStyle = bodyGrad;
+  ctx.fill();
+
+  // side skirt shading
+  ctx.fillStyle = 'rgba(0,0,0,0.22)';
+  ctx.fillRect(wRear * cW + archR, -0.09 * cH, (wFront - wRear) * cW - archR * 2, 0.06 * cH);
+
+  // ---- windows ----
+  const winGrad = ctx.createLinearGradient(0, -0.86 * cH, 0, -0.56 * cH);
+  winGrad.addColorStop(0, '#3d3050');
+  winGrad.addColorStop(1, '#1c1626');
+  ctx.beginPath();
+  ctx.moveTo(...u(0.16, 0.56));
+  ctx.bezierCurveTo(...u(0.19, 0.74), ...u(0.23, 0.80), ...u(0.30, 0.815));
+  ctx.bezierCurveTo(...u(0.38, 0.835), ...u(0.44, 0.84), ...u(0.48, 0.825));
+  ctx.bezierCurveTo(...u(0.53, 0.80), ...u(0.57, 0.62), ...u(0.575, 0.575));
+  ctx.closePath();
+  ctx.fillStyle = winGrad;
+  ctx.fill();
+  // glass highlight streak
+  ctx.fillStyle = 'rgba(255,190,140,0.16)';
+  ctx.beginPath();
+  ctx.moveTo(...u(0.20, 0.56)); ctx.lineTo(...u(0.30, 0.80)); ctx.lineTo(...u(0.36, 0.81)); ctx.lineTo(...u(0.27, 0.56));
+  ctx.closePath(); ctx.fill();
+  // B-pillar
+  ctx.strokeStyle = 'rgba(12,8,14,0.7)';
+  ctx.lineWidth = 2.4 * S;
+  ctx.beginPath(); ctx.moveTo(...u(0.345, 0.565)); ctx.lineTo(...u(0.35, 0.83)); ctx.stroke();
+
+  // door cut + handle
+  ctx.strokeStyle = 'rgba(10,6,10,0.28)';
+  ctx.lineWidth = 1.6 * S;
+  ctx.beginPath(); ctx.moveTo(...u(0.44, 0.10)); ctx.lineTo(...u(0.46, 0.56)); ctx.stroke();
+  ctx.fillStyle = 'rgba(15,10,15,0.5)';
+  rr(0.50 * cW, -0.50 * cH, 0.05 * cW, 0.035 * cH, 2 * S); ctx.fill();
+
+  // mirror
+  ctx.fillStyle = '#5c1f2c';
+  rr(0.575 * cW, -0.60 * cH, 0.035 * cW, 0.05 * cH, 2.5 * S); ctx.fill();
+
+  // top rim light (sunset)
+  ctx.strokeStyle = 'rgba(255,190,140,0.5)';
+  ctx.lineWidth = 1.8 * S;
+  ctx.beginPath();
+  ctx.moveTo(...u(0.17, 0.80));
+  ctx.bezierCurveTo(...u(0.22, 0.86), ...u(0.30, 0.885), ...u(0.38, 0.905));
+  ctx.bezierCurveTo(...u(0.44, 0.915), ...u(0.50, 0.895), ...u(0.55, 0.875));
+  ctx.stroke();
+  ctx.strokeStyle = 'rgba(255,190,140,0.25)';
+  ctx.beginPath();
+  ctx.moveTo(...u(0.66, 0.575));
+  ctx.bezierCurveTo(...u(0.75, 0.56), ...u(0.88, 0.54), ...u(0.945, 0.50));
+  ctx.stroke();
+
+  // ---- lights ----
+  // headlight + glow
+  ctx.fillStyle = '#ffe9b0';
+  ctx.beginPath(); ctx.ellipse(...u(0.955, 0.44), 5 * S, 3.2 * S, 0, 0, TAU); ctx.fill();
+  ctx.save();
+  ctx.globalCompositeOperation = 'screen';
+  let hg = ctx.createRadialGradient(0.955 * cW, -0.44 * cH, 0, 0.955 * cW, -0.44 * cH, 20 * S);
+  hg.addColorStop(0, 'rgba(255,230,170,0.5)');
+  hg.addColorStop(1, 'rgba(255,230,170,0)');
+  ctx.fillStyle = hg;
+  ctx.beginPath(); ctx.arc(0.955 * cW, -0.44 * cH, 20 * S, 0, TAU); ctx.fill();
+  // soft beam forward
+  const flick = 0.08 + Math.sin(t * 23) * 0.012;
+  const bg = ctx.createLinearGradient(0.99 * cW, 0, 1.5 * cW, 0);
+  bg.addColorStop(0, `rgba(255,220,150,${flick})`);
+  bg.addColorStop(1, 'rgba(255,220,150,0)');
+  ctx.fillStyle = bg;
+  ctx.beginPath();
+  ctx.moveTo(...u(0.99, 0.47));
+  ctx.lineTo(cW * 1.5, -0.36 * cH);
+  ctx.lineTo(cW * 1.5, -0.18 * cH);
+  ctx.lineTo(...u(0.99, 0.40));
+  ctx.closePath(); ctx.fill();
+  ctx.restore();
+
+  // taillight
+  ctx.fillStyle = '#ff4633';
+  ctx.beginPath(); ctx.ellipse(...u(0.028, 0.44), 4 * S, 3 * S, 0, 0, TAU); ctx.fill();
+  ctx.save();
+  ctx.globalCompositeOperation = 'screen';
+  const tg = ctx.createRadialGradient(0.028 * cW, -0.44 * cH, 0, 0.028 * cW, -0.44 * cH, 14 * S);
+  tg.addColorStop(0, 'rgba(255,60,40,0.45)');
+  tg.addColorStop(1, 'rgba(255,60,40,0)');
+  ctx.fillStyle = tg;
+  ctx.beginPath(); ctx.arc(0.028 * cW, -0.44 * cH, 14 * S, 0, TAU); ctx.fill();
+  ctx.restore();
+
+  ctx.restore();
+
+  // light reflections on road
+  ctx.save();
+  ctx.globalCompositeOperation = 'screen';
+  ctx.fillStyle = 'rgba(255,220,160,0.07)';
+  ctx.beginPath(); ctx.ellipse(carX + cW * 0.48, groundY + 3 * S, 30 * S, 3 * S, 0, 0, TAU); ctx.fill();
+  ctx.fillStyle = 'rgba(255,60,40,0.05)';
+  ctx.beginPath(); ctx.ellipse(carX - cW * 0.48, groundY + 3 * S, 22 * S, 3 * S, 0, 0, TAU); ctx.fill();
+  ctx.restore();
+}
+
+/* ---------------- grade / vignette / fade ---------------- */
+function drawGrade() {
+  ctx.save();
+  ctx.globalCompositeOperation = 'screen';
+  const { x, y } = sunPos();
+  const g = ctx.createRadialGradient(x, y, 0, x, y, W * 0.55);
+  g.addColorStop(0, 'rgba(255,120,60,0.10)');
+  g.addColorStop(1, 'rgba(255,120,60,0)');
+  ctx.fillStyle = g;
+  ctx.fillRect(0, 0, W, H);
+  ctx.restore();
+
+  const v = ctx.createRadialGradient(W / 2, H * 0.55, Math.min(W, H) * 0.45, W / 2, H * 0.55, Math.max(W, H) * 0.85);
+  v.addColorStop(0, 'rgba(8,6,14,0)');
+  v.addColorStop(1, 'rgba(8,6,14,0.38)');
+  ctx.fillStyle = v;
+  ctx.fillRect(0, 0, W, H);
+
+  if (fade < 1) {
+    ctx.fillStyle = `rgba(6,5,10,${1 - fade})`;
+    ctx.fillRect(0, 0, W, H);
+  }
+}
+
+/* ---------------- main loop ---------------- */
+function frame(ts) {
+  requestAnimationFrame(frame);
+  const dt = Math.min(0.05, (ts - last) / 1000 || 0.016);
+  last = ts;
+  time += dt;
+  fade = Math.min(1, fade + dt * 0.7);
+  cameraX += worldSpeed * dt;
+
+  updateClouds(dt);
+  spawnScenery();
+
+  // sky
+  drawSky();
+  drawStars();
+  drawSun();
+  drawHaze();
+  drawClouds();
+  drawBirds(dt);
+
+  // distant → near terrain
+  ridgeLayer(0.10, ridgeFar, horizonY - 24 * S, 120 * S, 2.1, horizonY + 2, '#5b4468', 'rgba(255,150,100,0.14)', 2 * S);
+  ridgeLayer(0.20, ridgeNear, horizonY - 12 * S, 84 * S, 1.8, horizonY + 2, '#463457', 'rgba(255,150,100,0.12)', 1.6 * S);
+  ridgeLayer(0.32, ridgeHill, horizonY - 4 * S, 40 * S, 1.15, horizonY + 40 * S, '#352b47', 'rgba(255,150,100,0.08)', 1.4 * S);
+
+  drawField();
+
+  ridgeLayer(0.45, ridgeTreeA, horizonY + 6 * S, 26 * S, 1.3, horizonY + 58 * S, '#251f38');
+  ridgeLayer(0.55, ridgeTreeB, horizonY + 10 * S, 34 * S, 1.35, horizonY + 62 * S, '#1b1728');
+
+  drawFieldStuff();
+  drawTrees();
+  drawRoad();
+  drawSunRoadStreak();
+  drawPolesAndSigns();
+  drawNearGrass();
+  drawCar(time, dt);
+  drawNearBushes();
+  drawGrade();
+}
+requestAnimationFrame(frame);
+})();
+</script>
+</body>
+</html>
diff --git a/docs/assets/perf/lossy-kimi-k3-shed-0.70.png b/docs/assets/perf/lossy-kimi-k3-shed-0.70.png
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diff --git a/docs/assets/perf/lossy-kimi-k3-shed-0.80.html b/docs/assets/perf/lossy-kimi-k3-shed-0.80.html
new file mode 100644
index 0000000..b9519c7
--- /dev/null
+++ b/docs/assets/perf/lossy-kimi-k3-shed-0.80.html
@@ -0,0 +1,582 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="utf-8">
+<meta name="viewport" content="width=device-width, initial-scale=1">
+<title>Scenic Drive</title>
+<style>
+  html, body { margin:0; height:100%; overflow:hidden; background:#000; }
+  canvas { display:block; width:100%; height:100%; }
+</style>
+</head>
+<body>
+<canvas id="c"></canvas>
+<script>
+(() => {
+"use strict";
+
+const canvas = document.getElementById('c');
+const ctx = canvas.getContext('2d');
+let W = 0, H = 0, DPR = 1;
+
+function resize() {
+  DPR = Math.min(window.devicePixelRatio || 1, 2);
+  W = innerWidth; H = innerHeight;
+  canvas.width = W * DPR; canvas.height = H * DPR;
+  ctx.setTransform(DPR, 0, 0, DPR, 0, 0);
+}
+addEventListener('resize', resize);
+resize();
+
+// ---------- helpers ----------
+const lerp = (a, b, t) => a + (b - a) * t;
+const clamp = (v, a, b) => Math.max(a, Math.min(b, v));
+function rnd(n) { const x = Math.sin(n * 127.1 + 311.7) * 43758.5453; return x - Math.floor(x); }
+function lerpC(c1, c2, t) { return [lerp(c1[0],c2[0],t)|0, lerp(c1[1],c2[1],t)|0, lerp(c1[2],c2[2],t)|0]; }
+const css = (c, a=1) => `rgba(${c[0]|0},${c[1]|0},${c[2]|0},${a})`;
+// 1D value noise, deterministic
+function noise1d(x, seed) {
+  const xi = Math.floor(x), xf = x - xi;
+  const s = xf * xf * (3 - 2 * xf);
+  return lerp(rnd(seed + xi * 3.7), rnd(seed + (xi + 1) * 3.7), s);
+}
+
+// ---------- day/night cycle ----------
+const DAY_LEN = 60;            // seconds for a full day
+let timeOfDay = 0.34;          // start mid-morning
+let distance = 0;              // master scroll (px)
+
+const SKY = [ // keyframes: top, bottom, tint color, tint alpha, stars, sun color
+  {t:0.00, top:[10,14,35],   bot:[22,28,60],   tint:[24,34,84],  tintA:0.52, stars:1.0,  sun:[255,150,90]},
+  {t:0.20, top:[13,18,48],   bot:[32,34,76],   tint:[24,34,84],  tintA:0.50, stars:0.9,  sun:[255,150,90]},
+  {t:0.26, top:[44,44,96],   bot:[202,112,76], tint:[90,60,90],  tintA:0.32, stars:0.25, sun:[255,140,70]},
+  {t:0.32, top:[76,108,170], bot:[243,178,112],tint:[120,90,80], tintA:0.15, stars:0.0,  sun:[255,172,92]},
+  {t:0.45, top:[76,140,216], bot:[190,222,240],tint:[255,255,255],tintA:0.0, stars:0.0,  sun:[255,240,190]},
+  {t:0.55, top:[64,132,216], bot:[208,234,247],tint:[255,255,255],tintA:0.0, stars:0.0,  sun:[255,248,224]},
+  {t:0.68, top:[76,138,212], bot:[216,233,242],tint:[255,255,255],tintA:0.0, stars:0.0,  sun:[255,224,158]},
+  {t:0.76, top:[108,92,168], bot:[248,140,84], tint:[130,70,70], tintA:0.24, stars:0.1,  sun:[255,110,58]},
+  {t:0.83, top:[58,58,110],  bot:[178,98,114], tint:[60,50,100], tintA:0.40, stars:0.5,  sun:[255,120,80]},
+  {t:0.90, top:[20,26,60],   bot:[58,44,92],   tint:[30,34,84],  tintA:0.50, stars:0.85, sun:[255,140,90]},
+  {t:1.00, top:[10,14,35],   bot:[22,28,60],   tint:[24,34,84],  tintA:0.52, stars:1.0,  sun:[255,150,90]},
+];
+function skyState(t) {
+  let i = 0;
+  while (i < SKY.length - 2 && t >= SKY[i + 1].t) i++;
+  const a = SKY[i], b = SKY[i + 1];
+  const f = clamp((t - a.t) / (b.t - a.t), 0, 1);
+  return {
+    top: lerpC(a.top, b.top, f), bot: lerpC(a.bot, b.bot, f),
+    tint: lerpC(a.tint, b.tint, f), tintA: lerp(a.tintA, b.tintA, f),
+    stars: lerp(a.stars, b.stars, f), sun: lerpC(a.sun, b.sun, f)
+  };
+}
+
+// ---------- layout ----------
+const horizonY = () => Math.min(H * 0.60, (H - Math.max(84, H * 0.18)) - 60);
+const roadH    = () => Math.max(84, H * 0.18);
+const roadY    = () => H - roadH();
+const S        = () => clamp(H / 620, 0.7, 1.3);
+
+// ---------- stars ----------
+const STARS = [];
+for (let i = 0; i < 110; i++) STARS.push({ x: rnd(i*1.3), y: rnd(i*2.1)*0.55, r: 0.6 + rnd(i*3.7)*1.3, tw: rnd(i*5.1)*6.28 });
+
+// ---------- clouds ----------
+const CLOUDS = [];
+for (let i = 0; i < 6; i++) CLOUDS.push({ x: rnd(i*7.7), y: 0.06 + rnd(i*9.3)*0.32, s: 0.7 + rnd(i*4.4)*0.9 });
+
+// ---------- exhaust particles ----------
+const puffs = [];
+let puffTimer = 0;
+
+// ---------- tile helper ----------
+function tiles(scroll, width, fn) {
+  let off = -(scroll % width);
+  for (let x = off; x < W; x += width) fn(x);
+}
+
+// ================= LAYER DRAWERS =================
+
+function drawSky(st) {
+  const g = ctx.createLinearGradient(0, 0, 0, H);
+  g.addColorStop(0, css(st.top));
+  g.addColorStop(0.75, css(st.bot));
+  g.addColorStop(1, css(st.bot));
+  ctx.fillStyle = g;
+  ctx.fillRect(0, 0, W, H);
+}
+
+function drawSun(st) {
+  const dp = (timeOfDay - 0.25) / 0.5;             // 0..1 across daytime
+  if (dp <= 0 || dp >= 1) return null;
+  const hy = horizonY();
+  const sx = W * (0.12 + 0.76 * dp);
+  const sy = hy - Math.sin(dp * Math.PI) * (H * 0.42) - 8;
+  const a = clamp(Math.sin(dp * Math.PI) * 1.6, 0, 1);
+  const g = ctx.createRadialGradient(sx, sy, 0, sx, sy, 140);
+  g.addColorStop(0, css(st.sun, 0.55 * a));
+  g.addColorStop(1, css(st.sun, 0));
+  ctx.fillStyle = g;
+  ctx.fillRect(sx - 140, sy - 140, 280, 280);
+  ctx.fillStyle = css(st.sun, a);
+  ctx.beginPath(); ctx.arc(sx, sy, 32, 0, 7); ctx.fill();
+  ctx.fillStyle = css([255,255,255], 0.55 * a);
+  ctx.beginPath(); ctx.arc(sx, sy, 18, 0, 7); ctx.fill();
+  return { x: sx, y: sy, a };
+}
+
+function drawMoon(darkness) {
+  if (darkness < 0.2) return;
+  const mt = (timeOfDay + 0.5) % 1;
+  const mp = (mt - 0.25) / 0.5;
+  if (mp <= 0 || mp >= 1) return;
+  const hy = horizonY();
+  const mx = W * (0.15 + 0.7 * mp);
+  const my = hy - Math.sin(mp * Math.PI) * (H * 0.38) - 10;
+  const a = clamp(Math.sin(mp * Math.PI) * 1.5, 0, 1) * darkness;
+  const g = ctx.createRadialGradient(mx, my, 0, mx, my, 80);
+  g.addColorStop(0, `rgba(220,228,245,${0.30 * a})`);
+  g.addColorStop(1, 'rgba(220,228,245,0)');
+  ctx.fillStyle = g; ctx.fillRect(mx - 80, my - 80, 160, 160);
+  ctx.fillStyle = `rgba(232,238,248,${a})`;
+  ctx.beginPath(); ctx.arc(mx, my, 22, 0, 7); ctx.fill();
+  ctx.fillStyle = `rgba(160,170,190,${0.35 * a})`;
+  ctx.beginPath(); ctx.arc(mx - 6, my - 4, 5, 0, 7); ctx.fill();
+  ctx.beginPath(); ctx.arc(mx + 7, my + 6, 4, 0, 7); ctx.fill();
+  ctx.beginPath(); ctx.arc(mx + 2, my - 9, 3, 0, 7); ctx.fill();
+}
+
+function drawClouds() {
+  ctx.fillStyle = 'rgba(255,255,255,0.50)';
+  for (let i = 0; i < CLOUDS.length; i++) {
+    const c = CLOUDS[i];
+    const span = W + 500;
+    const x = ((c.x * span - distance * 0.025) % span + span) % span - 250;
+    const y = c.y * horizonY();
+    const s = c.s;
+    ctx.beginPath();
+    ctx.ellipse(x, y, 62*s, 17*s, 0, 0, 7);
+    ctx.ellipse(x - 38*s, y + 6*s, 40*s, 13*s, 0, 0, 7);
+    ctx.ellipse(x + 40*s, y + 7*s, 44*s, 14*s, 0, 0, 7);
+    ctx.ellipse(x + 8*s, y - 11*s, 40*s, 15*s, 0, 0, 7);
+    ctx.fill();
+  }
+}
+
+function drawRidge(scroll, width, yBase, amp, seed, colTop, colBot) {
+  tiles(scroll, width, (ox) => {
+    const g = ctx.createLinearGradient(0, yBase - amp, 0, yBase + 50);
+    g.addColorStop(0, colTop); g.addColorStop(1, colBot);
+    ctx.fillStyle = g;
+    ctx.beginPath();
+    ctx.moveTo(ox, yBase + 60);
+    for (let px = 0; px <= width; px += 22) {
+      const n = noise1d(px / 260, seed) * 0.65 + noise1d(px / 90, seed + 13) * 0.35;
+      ctx.lineTo(ox + px, yBase - n * amp);
+    }
+    ctx.lineTo(ox + width, yBase + 60);
+    ctx.closePath();
+    ctx.fill();
+  });
+}
+
+function drawFarTrees(scroll, base) {
+  const width = 1500;
+  ctx.fillStyle = '#3f6440';
+  tiles(scroll, width, (ox) => {
+    for (let i = 0; i < 26; i++) {
+      const x = ox + rnd(i * 3.1 + 7) * width;
+      const h = 16 + rnd(i * 5.3 + 2) * 26;
+      const y = base + rnd(i * 7.9) * 16;
+      ctx.beginPath();
+      ctx.moveTo(x, y);
+      ctx.lineTo(x - h * 0.32, y); ctx.lineTo(x, y - h); ctx.lineTo(x + h * 0.32, y);
+      ctx.closePath(); ctx.fill();
+    }
+  });
+}
+
+function drawMidLayer(scroll, base, s) {
+  const width = 2200;
+  const poleTop = base - 118 * s;
+  const spacing = 275;
+  tiles(scroll, width, (ox) => {
+    // trees + bushes
+    for (let i = 0; i < 9; i++) {
+      const x = ox + rnd(i * 11.3 + 1) * width;
+      const h = (52 + rnd(i * 13.7) * 60) * s;
+      const pine = rnd(i * 17.9) > 0.5;
+      ctx.fillStyle = '#4a3526';
+      ctx.fillRect(x - 3 * s, base - h * 0.35, 6 * s, h * 0.35);
+      ctx.fillStyle = pine ? '#2f5a34' : '#3a6a3c';
+      if (pine) {
+        for (let k = 0; k < 3; k++) {
+          const yy = base - h * (0.30 + k * 0.26);
+          const ww = h * (0.42 - k * 0.10);
+          ctx.beginPath();
+          ctx.moveTo(x - ww, yy); ctx.lineTo(x, yy - h * 0.34); ctx.lineTo(x + ww, yy);
+          ctx.closePath(); ctx.fill();
+        }
+      } else {
+        ctx.beginPath();
+        ctx.arc(x, base - h * 0.62, h * 0.30, 0, 7);
+        ctx.arc(x - h * 0.20, base - h * 0.48, h * 0.22, 0, 7);
+        ctx.arc(x + h * 0.22, base - h * 0.50, h * 0.22, 0, 7);
+        ctx.fill();
+      }
+      // bush
+      if (rnd(i * 23.7) > 0.4) {
+        const bx = ox + rnd(i * 29.3 + 4) * width;
+        ctx.fillStyle = '#33582f';
+        ctx.beginPath();
+        ctx.arc(bx, base - 6 * s, 12 * s, Math.PI, 0);
+        ctx.arc(bx + 12 * s, base - 4 * s, 9 * s, Math.PI, 0);
+        ctx.fill();
+      }
+    }
+    // wires (behind poles)
+    ctx.strokeStyle = 'rgba(38,30,28,0.75)';
+    ctx.lineWidth = 1.2 * s;
+    for (let k = 0; k < 8; k++) {
+      const x1 = ox + k * spacing, x2 = x1 + spacing;
+      for (let wLine = 0; wLine < 2; wLine++) {
+        const yy = poleTop + wLine * 7 * s;
+        ctx.beginPath();
+        ctx.moveTo(x1, yy);
+        ctx.quadraticCurveTo((x1 + x2) / 2, yy + 17 * s, x2, yy);
+        ctx.stroke();
+      }
+    }
+    // poles
+    ctx.fillStyle = '#3c2f24';
+    for (let k = 0; k < 8; k++) {
+      const x = ox + k * spacing;
+      ctx.fillRect(x - 2.5 * s, poleTop, 5 * s, base - poleTop);
+      ctx.fillRect(x - 13 * s, poleTop + 4 * s, 26 * s, 3 * s); // crossarm
+    }
+  });
+}
+
+function drawRoad(scroll, rY, rH) {
+  const g = ctx.createLinearGradient(0, rY, 0, H);
+  g.addColorStop(0, '#494b50'); g.addColorStop(1, '#33353a');
+  ctx.fillStyle = g;
+  ctx.fillRect(0, rY, W, rH);
+
+  // asphalt speckles scrolling with the road
+  ctx.fillStyle = 'rgba(255,255,255,0.10)';
+  tiles(scroll, 600, (ox) => {
+    for (let i = 0; i < 34; i++) {
+      ctx.fillRect(ox + rnd(i * 3.3) * 600, rY + 6 + rnd(i * 7.1 + 5) * (rH - 14), 2, 2);
+    }
+  });
+
+  // top edge (fog) line
+  ctx.fillStyle = 'rgba(232,232,228,0.75)';
+  ctx.fillRect(0, rY + 2, W, 3);
+
+  // scrolling centre dashes
+  const yDash = rY + rH * 0.52;
+  ctx.fillStyle = 'rgba(235,232,215,0.85)';
+  const off = scroll % 80;
+  for (let x = -80 - off; x < W; x += 80) ctx.fillRect(x, yDash, 46, 6);
+
+  // darker tyre-worn band where the car sits
+  ctx.fillStyle = 'rgba(0,0,0,0.10)';
+  ctx.fillRect(0, rY + 4, W, 26);
+}
+
+function drawForeground(scroll) {
+  const width = 900;
+  ctx.strokeStyle = 'rgba(26,30,20,0.85)';
+  ctx.lineWidth = 2;
+  tiles(scroll, width, (ox) => {
+    for (let i = 0; i < 42; i++) {
+      const x = ox + rnd(i * 3.9 + 40) * width;
+      const h = 6 + rnd(i * 5.7 + 3) * 22;
+      const y = H - 2 - rnd(i * 8.3) * 6;
+      ctx.beginPath();
+      ctx.moveTo(x, y);
+      ctx.lineTo(x + (rnd(i * 6.6) - 0.5) * 8, y - h);
+      ctx.moveTo(x + 3, y);
+      ctx.lineTo(x + 3 + (rnd(i * 9.1) - 0.5) * 8, y - h * 0.7);
+      ctx.stroke();
+    }
+  });
+}
+
+// ================= CAR =================
+const WB = 170;            // wheelbase (unit coords)
+function bodyPath(ctx) {
+  const R = 36, A = 0.35;
+  ctx.beginPath();
+  ctx.moveTo(-14, -8);
+  ctx.bezierCurveTo(-18, -16, -18, -28, -10, -36);       // rear bumper
+  ctx.bezierCurveTo(-4, -42, 10, -43, 30, -44);          // boot lid
+  ctx.lineTo(44, -45);
+  ctx.bezierCurveTo(50, -58, 60, -66, 74, -70);          // rear glass
+  ctx.bezierCurveTo(84, -73, 104, -73, 116, -71);        // roof
+  ctx.bezierCurveTo(130, -68, 142, -56, 150, -46);       // windscreen
+  ctx.lineTo(168, -43);
+  ctx.bezierCurveTo(184, -41, 196, -38, 202, -30);       // bonnet -> nose
+  ctx.bezierCurveTo(206, -24, 206, -16, 202, -10);       // front bumper
+  ctx.bezierCurveTo(198, -6, 192, -5, 186, -5);
+  ctx.lineTo(170 + R * Math.cos(A), R * Math.sin(A));    // to front arch
+  ctx.arc(170, 0, R, A, -(Math.PI + A), true);           // over front wheel
+  ctx.lineTo(R * Math.cos(A), R * Math.sin(A));          // sill
+  ctx.arc(0, 0, R, A, -(Math.PI + A), true);             // over rear wheel
+  ctx.lineTo(-16, 2);
+  ctx.closePath();
+}
+
+function drawWheel(x, y, angle, wr) {
+  ctx.save();
+  ctx.translate(x, y);
+  // tyre
+  ctx.fillStyle = '#141517';
+  ctx.beginPath(); ctx.arc(0, 0, wr, 0, 7); ctx.fill();
+  ctx.strokeStyle = 'rgba(255,255,255,0.10)';
+  ctx.lineWidth = 2;
+  ctx.beginPath(); ctx.arc(0, 0, wr - 1.5, 0, 7); ctx.stroke();
+  // rim
+  const g = ctx.createRadialGradient(0, 0, 2, 0, 0, wr * 0.6);
+  g.addColorStop(0, '#d6d9df'); g.addColorStop(1, '#7e828c');
+  ctx.fillStyle = g;
+  ctx.beginPath(); ctx.arc(0, 0, wr * 0.58, 0, 7); ctx.fill();
+  // spinning spokes
+  ctx.rotate(angle);
+  ctx.strokeStyle = '#5d616a';
+  ctx.lineWidth = wr * 0.13;
+  for (let i = 0; i < 5; i++) {
+    ctx.rotate(Math.PI * 2 / 5);
+    ctx.beginPath(); ctx.moveTo(0, 0); ctx.lineTo(wr * 0.5, 0); ctx.stroke();
+  }
+  // tyre-edge markers make the rotation readable
+  ctx.fillStyle = 'rgba(255,255,255,0.28)';
+  for (let i = 0; i < 3; i++) {
+    ctx.rotate(Math.PI * 2 / 3);
+    ctx.fillRect(wr * 0.80, -wr * 0.05, wr * 0.16, wr * 0.10);
+  }
+  // hub
+  ctx.fillStyle = '#eceef2';
+  ctx.beginPath(); ctx.arc(0, 0, wr * 0.18, 0, 7); ctx.fill();
+  ctx.fillStyle = '#3a3d44';
+  ctx.beginPath(); ctx.arc(0, 0, wr * 0.06, 0, 7); ctx.fill();
+  ctx.restore();
+}
+
+function drawCar(now, dt, rY) {
+  const s = S();
+  const wr = 27 * s;
+  const groundLine = rY + 5;
+  const wcy = groundLine - wr;
+  const carCX = Math.max(W * 0.36, 150 * s);
+  const rearX = carCX - (WB / 2) * s;
+  const wheelAngle = distance / wr;
+
+  // suspension: soft bounce + tiny pitch, wheels stay planted
+  const bounce = Math.sin(now * 10.4) * 1.1 * s + Math.sin(now * 14.7 + 1.3) * 0.7 * s;
+  const pitch  = Math.sin(now * 6.9 + 0.4) * 0.008;
+
+  // ---- shadow ----
+  ctx.fillStyle = 'rgba(0,0,0,0.30)';
+  ctx.beginPath();
+  ctx.ellipse(carCX, groundLine + 5, 150 * s, 8 * s, 0, 0, 7);
+  ctx.fill();
+
+  // ---- exhaust puffs ----
+  puffTimer -= dt;
+  if (puffTimer <= 0 && puffs.length < 22) {
+    puffTimer = 0.32;
+    puffs.push({ x: rearX - 10 * s, y: wcy - 3 * s, vx: -(36 + rnd(now) * 26), vy: -(6 + rnd(now*3) * 12), r: 2.5, life: 1 });
+  }
+  ctx.fillStyle = 'rgba(160,160,160,1)';
+  for (let i = puffs.length - 1; i >= 0; i--) {
+    const p = puffs[i];
+    p.x += p.vx * dt; p.y += p.vy * dt; p.r += 9 * dt; p.life -= dt * 0.9;
+    if (p.life <= 0) { puffs.splice(i, 1); continue; }
+    ctx.globalAlpha = 0.15 * p.life;
+    ctx.beginPath(); ctx.arc(p.x, p.y, p.r, 0, 7); ctx.fill();
+  }
+  ctx.globalAlpha = 1;
+
+  // ---- wheels ----
+  drawWheel(rearX, wcy, wheelAngle, wr);
+  drawWheel(rearX + WB * s, wcy, wheelAngle, wr);
+
+  // ---- body ----
+  ctx.save();
+  ctx.translate(rearX, wcy + bounce);
+  ctx.translate(WB / 2, 0); ctx.rotate(pitch); ctx.translate(-WB / 2, 0);
+  ctx.scale(s, s);
+
+  // paint
+  const pg = ctx.createLinearGradient(0, -76, 0, 16);
+  pg.addColorStop(0, '#ce4552'); pg.addColorStop(0.55, '#a32a39'); pg.addColorStop(1, '#6d1621');
+  bodyPath(ctx);
+  ctx.fillStyle = pg;
+  ctx.fill();
+
+  // top specular
+  ctx.save(); bodyPath(ctx); ctx.clip();
+  const hg = ctx.createLinearGradient(0, -76, 0, -26);
+  hg.addColorStop(0, 'rgba(255,255,255,0.28)'); hg.addColorStop(1, 'rgba(255,255,255,0)');
+  ctx.fillStyle = hg; ctx.fillRect(-30, -80, 260, 60);
+  // side skirt shade
+  ctx.fillStyle = 'rgba(0,0,0,0.22)';
+  ctx.fillRect(-30, 2, 260, 14);
+  ctx.restore();
+
+  // glass
+  ctx.beginPath();
+  ctx.moveTo(50, -45);
+  ctx.bezierCurveTo(56, -58, 64, -65, 76, -68);
+  ctx.lineTo(112, -69);
+  ctx.bezierCurveTo(124, -66, 136, -56, 144, -45.5);
+  ctx.closePath();
+  const gg = ctx.createLinearGradient(0, -70, 0, -44);
+  gg.addColorStop(0, '#26313f'); gg.addColorStop(1, '#0e141c');
+  ctx.fillStyle = gg; ctx.fill();
+  ctx.strokeStyle = 'rgba(255,255,255,0.14)';
+  ctx.lineWidth = 1.5;
+  ctx.beginPath(); ctx.moveTo(58, -62); ctx.lineTo(84, -46); ctx.stroke();
+  // pillars
+  ctx.strokeStyle = '#7c1f2b';
+  ctx.lineWidth = 4;
+  ctx.beginPath(); ctx.moveTo(95, -68.5); ctx.lineTo(95, -45.5); ctx.stroke();
+
+  // panel seams + handles
+  ctx.strokeStyle = 'rgba(0,0,0,0.28)';
+  ctx.lineWidth = 1.4;
+  ctx.beginPath(); ctx.moveTo(92, -44); ctx.bezierCurveTo(90, -56, 88, -62, 86, -66.5); ctx.stroke();
+  ctx.beginPath(); ctx.moveTo(103, -44); ctx.bezierCurveTo(104, -56, 107, -62, 110, -66.5); ctx.stroke();
+  ctx.fillStyle = 'rgba(0,0,0,0.35)';
+  ctx.fillRect(84, -51, 9, 2.6); ctx.fillRect(114, -51, 9, 2.6);
+
+  // mirror
+  ctx.fillStyle = '#8f2330';
+  ctx.beginPath(); ctx.ellipse(116, -59, 6, 4, 0.2, 0, 7); ctx.fill();
+
+  // lights
+  ctx.fillStyle = '#ffeec0';
+  ctx.beginPath(); ctx.ellipse(198, -22, 7, 4.5, -0.35, 0, 7); ctx.fill();
+  ctx.fillStyle = '#c32436';
+  ctx.fillRect(-14.5, -30, 5, 8);
+
+  ctx.restore();
+
+  return { hx: rearX + 198 * s, hy: wcy + bounce - 22 * s,
+           tx: rearX - 12 * s, ty: wcy + bounce - 26 * s, groundLine, s };
+}
+
+function drawBeams(car, darkness) {
+  if (darkness < 0.05 || !car) return;
+  const { hx, hy, tx, ty, groundLine, s } = car;
+  ctx.save();
+  ctx.globalCompositeOperation = 'screen';
+  // headlight beam cone onto the road ahead
+  const g = ctx.createLinearGradient(hx, hy, hx + 300 * s, hy);
+  g.addColorStop(0, `rgba(255,240,185,${0.20 * darkness})`);
+  g.addColorStop(1, 'rgba(255,240,185,0)');
+  ctx.fillStyle = g;
+  ctx.beginPath();
+  ctx.moveTo(hx, hy - 4 * s);
+  ctx.lineTo(hx + 290 * s, hy + 22 * s);
+  ctx.lineTo(hx + 300 * s, groundLine + 14);
+  ctx.lineTo(hx, hy + 7 * s);
+  ctx.closePath(); ctx.fill();
+  // lamp glows
+  ctx.fillStyle = `rgba(255,244,200,${0.55 * darkness})`;
+  ctx.beginPath(); ctx.arc(hx, hy, 6 * s, 0, 7); ctx.fill();
+  ctx.fillStyle = `rgba(255,60,60,${0.45 * darkness})`;
+  ctx.beginPath(); ctx.arc(tx, ty, 5 * s, 0, 7); ctx.fill();
+  ctx.restore();
+}
+
+// ================= MAIN LOOP =================
+let last = performance.now();
+
+function frame(nowMs) {
+  const now = nowMs / 1000;
+  const dt = Math.min((nowMs - last) / 1000, 0.05);
+  last = nowMs;
+
+  timeOfDay = (timeOfDay + dt / DAY_LEN) % 1;
+  distance += 300 * dt;                       // road speed px/s
+
+  const st = skyState(timeOfDay);
+  const darkness = clamp(st.tintA / 0.52, 0, 1);
+  const hy = horizonY(), rY = roadY(), rH = roadH(), s = S();
+
+  // sky + celestial
+  drawSky(st);
+  const sun = drawSun(st);
+  drawClouds();
+
+  // parallax landscape (far -> near)
+  drawRidge(distance * 0.05, 2600, hy - 10, H * 0.17, 3,  '#8a93b8', '#a7aec8');
+  drawRidge(distance * 0.10, 2100, hy + 4,  H * 0.11, 9,  '#6f7a9e', '#8c95ad');
+
+  // grass plain
+  const gg = ctx.createLinearGradient(0, hy, 0, rY);
+  gg.addColorStop(0, '#729757'); gg.addColorStop(1, '#547a40');
+  ctx.fillStyle = gg; ctx.fillRect(0, hy - 4, W, rY - hy + 8);
+
+  drawFarTrees(distance * 0.26, hy + 12);
+  drawMidLayer(distance * 0.55, rY - 2, s);
+
+  // road + car + foreground
+  drawRoad(distance, rY, rH);
+  const car = drawCar(now, dt, rY);
+  drawForeground(distance * 1.6);
+
+  // ---- cinematic lighting overlay ----
+  ctx.save();
+  ctx.globalCompositeOperation = 'multiply';
+  ctx.fillStyle = css(st.tint, st.tintA);
+  ctx.fillRect(0, 0, W, H);
+  ctx.restore();
+
+  // stars (above the tint so they stay bright)
+  if (st.stars > 0.02) {
+    ctx.fillStyle = '#ffffff';
+    for (let i = 0; i < STARS.length; i++) {
+      const st2 = STARS[i];
+      const a = st.stars * (0.55 + 0.45 * Math.sin(now * 2.5 + st2.tw));
+      if (a <= 0.03) continue;
+      ctx.globalAlpha = a;
+      ctx.beginPath(); ctx.arc(st2.x * W, st2.y * hy * 1.6, st2.r, 0, 7); ctx.fill();
+    }
+    ctx.globalAlpha = 1;
+  }
+
+  drawMoon(darkness);
+
+  // restore a little sun punch above the tint
+  if (sun) {
+    ctx.save();
+    ctx.globalCompositeOperation = 'screen';
+    const g2 = ctx.createRadialGradient(sun.x, sun.y, 0, sun.x, sun.y, 110);
+    g2.addColorStop(0, css(st.sun, 0.30 * sun.a));
+    g2.addColorStop(1, css(st.sun, 0));
+    ctx.fillStyle = g2;
+    ctx.fillRect(sun.x - 110, sun.y - 110, 220, 220);
+    ctx.restore();
+  }
+
+  drawBeams(car, darkness);
+
+  // vignette
+  const vg = ctx.createRadialGradient(W/2, H/2, Math.min(W,H)*0.45, W/2, H/2, Math.max(W,H)*0.75);
+  vg.addColorStop(0, 'rgba(0,0,0,0)');
+  vg.addColorStop(1, 'rgba(0,0,0,0.28)');
+  ctx.fillStyle = vg;
+  ctx.fillRect(0, 0, W, H);
+
+  requestAnimationFrame(frame);
+}
+requestAnimationFrame(frame);
+})();
+</script>
+</body>
+</html>
diff --git a/docs/assets/perf/lossy-kimi-k3-shed-0.80.png b/docs/assets/perf/lossy-kimi-k3-shed-0.80.png
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diff --git a/docs/streaming.md b/docs/streaming.md
index 42422d2..3615a11 100644
--- a/docs/streaming.md
+++ b/docs/streaming.md
@@ -75,7 +75,7 @@ KV cache (`--kv-bits 8`) is the usual companion at long context.
 Two single samples from Kimi-K3, a 2.8T-parameter MoE, generated on the
 128 GB laptop above with `--stream-experts` at streaming defaults, on
 the lossless path. The prompt is the same one-shot canvas-animation
-prompt used for the four-settings comparison later in this guide.
+prompt used for the lossy-lever comparisons later in this guide.
 Each screenshot links to the generated page, committed beside it in
 `docs/assets/perf/` (GitHub shows the page source; download one to
 watch the animation).
@@ -442,6 +442,45 @@ settings, against a lossless twin at the same seed. Miss-shed's safe
 range is per-architecture; re-gate it whenever routing width or
 gating changes.
 
+### Kimi-K3: far over budget
+
+The deepest measured point runs the scale sample from the top of this
+guide with the levers on: Kimi-K3 UD-Q2_K_XL (861 GB on the same
+128 GB machine, 384 experts routed 16 per token, 91 streamed expert
+layers). This far over budget the arena holds a sliver of the expert
+set, the lossless hit rate settles near 50%, and demand stalls are
+about two thirds of decode wall, so the miss-targeted lever leads by
+a wide margin, and each shed arm pairs it with keeper prestage
+(`--moe-prestage keepers`, described earlier). One long generation
+per setting on the same one-shot prompt as the samples above,
+temperature 1.0, 24-30k tokens each with thinking included. These are
+whole-run averages, not alternated A/Bs:
+
+| setting (with keeper prestage) | dropped mass | hit rate | decode |
+|---|---|---|---|
+| lossless, ranked prestage | none | 49.9% | 1.15 tok/s |
+| `moe_miss_shed: 0.80` | 17.1% | 68.0% | 1.19 tok/s (+3%) |
+| `moe_miss_shed: 0.70` | 26.1% | 71.9% | 1.33 tok/s (+16%) |
+| `moe_miss_shed: 0.65` | 30.4% | 74.2% | 1.39 tok/s (+21%) |
+
+The runs span several days of sessions, and ambient memory pressure
+sized the wired arena differently across them (29 to 33 GB), so read
+the mechanism columns as a trend rather than a controlled sweep. Two
+things still stand out. Shedding raised the hit rate it left behind:
+the arena stops churning through experts that would be dropped
+anyway, the same self-reinforcement noted under miss-shed above. And
+the return curve bends the same way as on the other models: the step
+from lossless to 0.80 bought little in this sample, while 0.70 and
+0.65 returned +16% and +21%.
+
+All three shed levels produced complete working pages on this
+long-form prompt; what separates them is content drift, compared
+frame by frame in the Kimi-K3 screenshot table later in this guide.
+One step further down broke form, not just content: at 0.60 a code
+generation on this model produced a nonfunctional program in one try.
+The working band on this model at this quant is 0.65 to 0.80, sized
+inside it by how much content fidelity the workload can spend.
+
 ### Certifying a setting
 
 Back on Hy3, quality degraded in a consistent order as the levers
@@ -529,6 +568,37 @@ practical recipe on this model: keep the full pair and its entire +13%,
 and cool the sampling slightly, rather than giving up most of the speed
 win by softening the levers at the card's temperature.
 
+### One prompt, four shed levels: Kimi-K3
+
+The same comparison at the deep end of the space. The four Kimi-K3
+settings measured earlier in this guide each ran the same prompt once
+to completion at temperature 1.0. Each screenshot links to its
+generated page, committed beside it in `docs/assets/perf/`:
+
+| | |
+|---|---|
+| <a href="assets/perf/kimi-k3-ud-q2kxl-car.html"><img src="assets/perf/kimi-k3-ud-q2kxl-car.png" alt="lossless: film-grain dusk scene, red sedan with a headlight cone, telegraph poles, layered hills and clouds"></a><br>lossless, ranked prestage. 23.7k tokens at 1.15 tok/s. | <a href="assets/perf/lossy-kimi-k3-shed-0.80.html"><img src="assets/perf/lossy-kimi-k3-shed-0.80.png" alt="miss-shed 0.80 with keeper prestage: bright daylight scene with green fields, mountains, and sun; red car with slightly misdrawn body panels"></a><br>`moe_miss_shed 0.80` + keeper prestage. 24.2k tokens at 1.19 tok/s. |
+| <a href="assets/perf/lossy-kimi-k3-shed-0.70.html"><img src="assets/perf/lossy-kimi-k3-shed-0.70.png" alt="miss-shed 0.70 with keeper prestage: complete but very dark dusk scene, red car with headlights on a dim road, foreground trees as blurred dark shapes"></a><br>`moe_miss_shed 0.70` + keeper prestage. 29.6k tokens at 1.33 tok/s. | <a href="assets/perf/lossy-kimi-k3-shed-0.65.html"><img src="assets/perf/lossy-kimi-k3-shed-0.65.png" alt="miss-shed 0.65 with keeper prestage: vivid layered sunset with poles, fence, and birds; red car with oversized featureless black wheels and a light streak across the body"></a><br>`moe_miss_shed 0.65` + keeper prestage. 28.3k tokens at 1.39 tok/s. |
+
+All four pages ran as generated: valid markup, a working animation
+loop, no stray tokens. What varies is the scene, and not
+monotonically. The lossless page drew the cohesive film-grain dusk.
+At 0.80 the scene is clean and bright but the car body picked up
+small geometry glitches and the lighting is the flattest of the set.
+At 0.70 the composition is complete but the tone mapping overshot:
+the page renders far darker than its palette intends, and the
+foreground trees reduce to blurred dark masses. At 0.65 the sky and
+landscape are the richest of the four while the car is the most
+damaged subject, with oversized featureless wheels and a stray light
+streak across the body. Between 0.65 and 0.80 the flaws differ in
+kind rather than degree, so one sample per setting cannot rank
+adjacent levels; what it can show is that all three sit above the
+cliff, which is one step further down at 0.60, where a code
+generation broke outright. As on GLM-5.2, dropped mass degraded what
+the pages drew long before it corrupted what they wrote: certifying a
+level means rendering the artifact, and ranking neighboring levels
+takes more samples than one.
+
 ## Native-fp experts (MXFP4/NVFP4)
 
 Models with MXFP4/NVFP4 expert tensors (gpt-oss, DeepSeek-V4-Flash Q4_K_XL

From 1a04bc055bc54a524b649031a6a640407d87df54 Mon Sep 17 00:00:00 2001
From: Asher Feldman <59994+asher@users.noreply.github.com>
Date: Wed, 5 Aug 2026 18:46:15 -0700
Subject: [PATCH 09/10] docs: review fixes for keeper prestage semantics and
 kimi-k3 ladder wording

---
 docs/server-config.md | 16 +++++++++-------
 docs/streaming.md     | 40 ++++++++++++++++++++++------------------
 2 files changed, 31 insertions(+), 25 deletions(-)

diff --git a/docs/server-config.md b/docs/server-config.md
index 23fdb2a..683c7c8 100644
--- a/docs/server-config.md
+++ b/docs/server-config.md
@@ -553,8 +553,7 @@ models:
 Per-model keys: `path` (required), `profile`, `family`, `profiles`, `mmproj`,
 `draft_gguf`, `adapter`, `stream`, `moe_experts`, `moe_expert_mass`,
 `moe_miss_shed`, `moe_layer_shed`, `moe_prestage`, `prefill_feeder`,
-`decode_feeder`,
-`speculative`, `speculative_width_cap`, `overrides`
+`decode_feeder`, `speculative`, `speculative_width_cap`, `overrides`
 (`{sampling, load, cache, system, chat_template, chat_template_kwargs,
 thinking, reasoning_effort}`), `pin`, `ttl_s`.
 
@@ -640,11 +639,14 @@ own tasks before serving with them.
 
 `moe_prestage: keepers` retargets the lookahead prestage on a
 `stream: experts` model: predictions are filtered through the miss-shed
-policy, so experts the shed would drop are never read and predicted keepers
-stage demand-grade, overlapping their would-be demand stalls with compute.
-It adds no quality cost of its own but needs `moe_miss_shed` to define the
-policy (announced as ignored without it). Load-affecting like the levers
-above; the default (`ranked`) keeps guess-grade speculative prestage.
+policy, so an expert that would be shed if it demand-missed is never
+read, and predicted keepers stage demand-grade, overlapping their
+would-be demand stalls with compute. It adds no quality knob of its
+own, applying the policy `moe_miss_shed` defines; it needs both
+`stream: experts` and `moe_miss_shed` (each announced as ignored when
+missing). Load-affecting like the levers above; the default (`ranked`)
+keeps guess-grade speculative prestage (see
+[streaming.md](streaming.md)).
 
 `prefill_feeder: false` / `decode_feeder: false` opt a streaming model out of
 the feeder paths that are otherwise on by default (`prefill_feeder`
diff --git a/docs/streaming.md b/docs/streaming.md
index 3615a11..dfc6a66 100644
--- a/docs/streaming.md
+++ b/docs/streaming.md
@@ -302,16 +302,19 @@ It needs the decode feeder and a block that hands router scores to the
 expert call; where it engages, it is the most targeted lever per point of
 quality spent, and its payoff scales directly with the miss rate.
 
-`--moe-prestage keepers` attacks miss-shed's residual stalls from the speculative
-side, with no quality cost of its own. Lookahead normally prestages its
-rank-gated predictions with guess-grade caution (never evicting a more
-popular resident), which caps how many demand misses it can absorb.
-Keeper mode filters each prediction through the active miss-shed
-policy: an expert the shed would drop on arrival is not read at all
-(reclaiming the bandwidth a doomed speculative read would burn), and
-the predicted keepers are staged demand-grade, since if the prediction
-is right the demand path would do those same reads synchronously one
-layer later.
+`--moe-prestage keepers` attacks miss-shed's residual stalls from the
+speculative side. It adds no quality knob of its own; it applies the
+policy miss-shed already defines, one layer earlier. In the default
+`ranked` mode, lookahead prestages its rank-gated predictions with
+guess-grade caution (never evicting a more popular resident), which
+caps how many demand misses it can absorb; and because an inflight
+read exempts its expert from the shed, ranked lookahead incidentally
+rescues some experts the policy would have dropped, at the price of
+the read. Keeper mode filters each prediction through the policy
+instead: an expert that would be shed if it demand-missed is not read
+at all, and the predicted keepers are staged demand-grade, since if
+the prediction is right the demand path would do those same reads
+synchronously one layer later.
 Where prediction recall is good, this converts demand stalls into reads
 that overlap compute; the lookahead exit stats (submitted vs adopted)
 are the guardrail that the added aggression is landing. Requires
@@ -323,11 +326,12 @@ is the blunt end of the scale, and the only lever that also cuts the
 per-layer overhead - which makes it the one that still pays when the
 arena hit rate is high and misses are rare.
 
-In server configs the lossy levers are the per-model `moe_experts: K` /
-`moe_expert_mass: P` / `moe_miss_shed: P` / `moe_layer_shed: P` /
-`moe_prestage: keepers` keys (or the matching `serve` flags for a single
-positional model); the probe stays CLI-only, so size P with a
-`gmlx run --moe-expert-probe` pass before pinning a value in a config.
+In server configs the lossy levers and keeper prestage are the
+per-model `moe_experts: K` / `moe_expert_mass: P` / `moe_miss_shed: P`
+/ `moe_layer_shed: P` / `moe_prestage: keepers` keys (or the matching
+`serve` flags for a single positional model); the probe stays
+CLI-only, so size P with a `gmlx run --moe-expert-probe` pass before
+pinning a value in a config.
 
 Which to reach for is a measurement, not a doctrine. Run the probe once,
 and read the decode feeder's exit stats (arena hit rate; printed by
@@ -453,10 +457,10 @@ about two thirds of decode wall, so the miss-targeted lever leads by
 a wide margin, and each shed arm pairs it with keeper prestage
 (`--moe-prestage keepers`, described earlier). One long generation
 per setting on the same one-shot prompt as the samples above,
-temperature 1.0, 24-30k tokens each with thinking included. These are
+temperature 1.0, 23-30k tokens each with thinking included. These are
 whole-run averages, not alternated A/Bs:
 
-| setting (with keeper prestage) | dropped mass | hit rate | decode |
+| setting | dropped mass | hit rate | decode |
 |---|---|---|---|
 | lossless, ranked prestage | none | 49.9% | 1.15 tok/s |
 | `moe_miss_shed: 0.80` | 17.1% | 68.0% | 1.19 tok/s (+3%) |
@@ -475,7 +479,7 @@ from lossless to 0.80 bought little in this sample, while 0.70 and
 
 All three shed levels produced complete working pages on this
 long-form prompt; what separates them is content drift, compared
-frame by frame in the Kimi-K3 screenshot table later in this guide.
+side by side in the Kimi-K3 screenshot table later in this guide.
 One step further down broke form, not just content: at 0.60 a code
 generation on this model produced a nonfunctional program in one try.
 The working band on this model at this quant is 0.65 to 0.80, sized

From 277164aa7bba00eededffdcc9fdd323556458178 Mon Sep 17 00:00:00 2001
From: Asher Feldman <59994+asher@users.noreply.github.com>
Date: Wed, 5 Aug 2026 19:13:13 -0700
Subject: [PATCH 10/10] docs(server-config): list all streamed-MoE lever and
 feeder flags under single-model serving

---
 docs/server-config.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/docs/server-config.md b/docs/server-config.md
index 683c7c8..40af528 100644
--- a/docs/server-config.md
+++ b/docs/server-config.md
@@ -122,8 +122,10 @@ VLM), `--draft-gguf FILE` (assistant-shape drafter, implies `--speculative`),
 `--speculative` (native-head MTP), `--adapter FILE` (live GGUF LoRA over the
 base; text only), `--chat-template STR|PATH` (replace the GGUF's template),
 `--stream-experts` / `--stream-cpu` (over-RAM MoE execution placement, see `stream:`
-below), `--moe-expert-mass P` (adaptive lossy MoE fan-out on the streamed
-experts, see `moe_expert_mass:` below),
+below), the streamed-MoE levers `--moe-expert-mass P` / `--moe-experts K` /
+`--moe-miss-shed P` / `--moe-layer-shed P` / `--moe-prestage MODE` (see the
+matching per-model keys below), `--prefill-feeder` / `--decode-feeder`
+(feeder opt-outs, see `prefill_feeder:` below),
 `--hf-source REPO` (processor/config override, rarely needed),
 `--host`, `--port`, `--budget-gb`, `--max-models`, `--pin ID_OR_PATH`
 (repeatable), `--max-tokens`, `--no-auth` (the API key is config-only: see