vendored code: comment-only cleanup (alephllm 0.10.8); AST-identical, no behaviour change

attention.py

@@ -66,7 +66,7 @@ class _Constellation(nn.Module):
 
     The multi-constellation hub is the PRODUCT-CODE form (B2: independent
     frames compose, .859 -> .955 monotone in members) at lawful supply
-    (ROUND 5e: K <= 2*D per address space — v1's single 512-anchor book in
+    (aleph-splat-0 TECHNICAL_ROUND5.md, round 5e: K <= 2*D per address space — v1's single 512-anchor book in
     32 dims ran 16x and crowded into 333-646 duplicate pairs)."""
 
     def __init__(self, d: int, K: int, D: int, tau: float):

governor.py

@@ -1,6 +1,6 @@
-"""The anchor governor — min-separation projection (ROUND 5f, 2026-08-25).
+"""The anchor governor — min-separation projection (2026-08-25).
 
-Measured law (claude-mind, canon/splat_aleph_battery.md ROUND 5e/5f): anchors
+Measured (aleph-splat-0 TECHNICAL_ROUND5.md, rounds 5e/5f): anchors
 crowded past the separation floor flutter under the positional sweep (the
 static logit gap is quadratic in separation, the oscillation linear), and the
 crowding tail tracks seed fragility. The governor holds the space open FROM

head.py

@@ -1,4 +1,4 @@
-"""DualHead — the standard readout plus the L-012 aleph read, born null.
+"""DualHead — the standard readout plus an aleph read, born null.
 
     logits = W_h h + W_s s(h),        W_s == 0 at init
 

presets.py

@@ -51,7 +51,7 @@ class AlephLMConfig:
     tie_embeddings: bool = False       # BPE crafts tie; byte crafts cannot (trigram)
     hub_chunk: int = 128               # chunked-scan block for the hub prefix memories
     # v2 (2026-08-26): multi-constellation hubs — the product-code form at
-    # lawful supply (K <= 2*hub_D per book; ROUND 5e). 1 = the v1 layout,
+    # lawful supply (K <= 2*hub_D per book; aleph-splat-0 TECHNICAL_ROUND5.md, round 5e). 1 = the v1 layout,
     # bit-identical state dict. Old manifests load via the default.
     hub_const: int = 1
     # Activation checkpointing (training only; inference/decode untouched).
@@ -105,14 +105,14 @@ class TrainConfig:
     # v3: a lead-ruled change of the mix (the minted lexicon) — a resume
     # whose recipe differs ONLY by it is accepted and recorded with this note
     data_plane_amendment: str | None = None
-    # The anchor governor (ROUND 5f, 2026-08-25): post-optimizer-step
+    # The anchor governor (2026-08-25; see model/governor.py): post-optimizer-step
     # min-separation projection over hub/head codebooks — preventive
     # anti-crowding, identity when slack, zero parameters, outside the
     # task gradient (the no-balance-machinery law is untouched).
     governor: str = ""                 # "" off (v1 verbatim) | "minsep"
     governor_theta: float = 45.0       # deg; scale ~ gamma*(D): 45 at D=256
     governor_every: int = 8            # steps between slack checks (~free)
-    # Post-revival address freeze (0.8.2; RIDERS 11-12): after the
+    # Post-revival address freeze (0.8.2; see train/revival.py): after the
     # BOUNDARY-WRITE head revival, proj + head codebook freeze so the
     # self-burial channel (proj rotating to codebook-orthogonality,
     # measured 2/2 crafts) is structurally closed — only W_s trains.
@@ -132,7 +132,7 @@ class TrainConfig:
 
 
 # All missions upload to the one training repo, each under its own prefix
-# (Phil's repo: checkpoints + manifests + tensorboard for every craft).
+# (checkpoints + manifests + tensorboard for every craft).
 TRAINING_REPO = "AbstractPhil/alephllm-mini-beatrix-training"
 
 
@@ -187,12 +187,12 @@ PRESETS: dict[str, Preset] = {
         train=TrainConfig(micro_batch=48, grad_accum=3),
         curriculum=_curriculum(300_000_000, 3_000_000_000, 6_000_000_000),
     ),
-    # v2 (2026-08-26, Phil's draft off the Foundry console): FULL-SPLAT —
+    # v2 (2026-08-26): FULL-SPLAT —
     # a hub in every block, multi-constellation product code at lawful
     # supply (16 books x 256 anchors in 256-dim spaces = 1.0x supply;
     # v1's single book ran 16x and crowded), governed from birth, ctx 8192
     # where the O(L) read is ~4.5x cheaper than the MHA equivalent.
-    # ~873.7M params. Plan: history/plans/2026-08-26_mini_beatrix_v2_shape.md.
+    # ~873.7M params.
     "mini-beatrix-2": Preset(
         model=AlephLMConfig(name="mini-beatrix-2", d_model=1024, n_layers=32,
                             n_heads=16, context=8192,
@@ -210,8 +210,8 @@ PRESETS: dict[str, Preset] = {
                           governor="minsep", governor_theta=45.0),
         curriculum=_curriculum(500_000_000, 8_000_000_000, 16_000_000_000),
     ),
-    # THE ACTIVE MISSION (2026-08-26, Phil: "train the next stage up from
-    # the beatrix v1; we can't train the large one currently"): the lawful
+    # THE 2s MISSION (2026-08-26): the next stage up from v1, since the
+    # large shape above could not be trained on the available card: the lawful
     # full-splat craft one rung above v1 — d1024 L20 ctx4096, governed
     # 4x64@128 books (4x supply headroom vs v1's crowded 16x). Also the
     # screen bed for every v2-era gating cell. hub_ckpt=0: at 237M the
@@ -244,7 +244,7 @@ def make_v3_preset(n_layers: int = 24, d_model: int = 1024,
                    data_scale: float = 4.0, epoch_cap: float | None = None,
                    rebalance_to: str | None = None,
                    name: str | None = None) -> Preset:
-    """The v3 craft (plan of record 2026-09-15, S2/S14; sizing 09-15):
+    """The v3 craft (the v3 plan of 2026-09-15; sizing 09-15):
     the solidified all-splat form at d1024 — a governed hub in EVERY
     block, the certified hub geometry (4 books x 64 @ D128), banks
     3 x ff1024, head 256@256, ctx 4096 — at a depth the throughput bench