Delete docs/v0.3.0-design.md and strip all references to it

The design doc and its followups doc are no longer needed as a live
reference now that the v0.3.0 redesign is implemented — comments and
docstrings across the codebase cited it extensively (file path, "design
doc §X.Y", "decision N", or bare "§X.Y" section numbers) as design
rationale. Removed docs/ and edited every citing comment/docstring to
drop the now-dangling reference while keeping the substantive
explanation next to it. CLAUDE.md's v0.3.0 roadmap bullet loses its
trailing pointer to the deleted file.

Verified: no remaining "docs/v0.3.0", "design doc", "decision N", or
"§N.N" references (repo-wide grep); ruff and ty clean; full test suite
on the heaviest-touched modules (network, sample, rollout, migration,
config, train) passes.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

giant/sample.py

@@ -10,10 +10,9 @@ def _predict_n_sec_if_owned(
     model: torch.nn.Module, cond_cont: torch.Tensor, cond_cat: torch.Tensor
 ) -> torch.Tensor | None:
     """Stage-1 `n_sec_head` is only present on a migrated v0.2 checkpoint
-    (docs/v0.3.0-design.md decision 1 moves it to stage 2 for fresh runs —
-    see `Stage1Model`'s docstring). `None` here means "ask stage 2 instead",
-    which every caller (`giant/rollout.py`, `giant/cli.py`) must do for a
-    fresh checkpoint."""
+    (fresh runs move it to stage 2 — see `Stage1Model`'s docstring). `None`
+    here means "ask stage 2 instead", which every caller (`giant/rollout.py`,
+    `giant/cli.py`) must do for a fresh checkpoint."""
     if getattr(model, "n_sec_head", None) is None:
         return None
     logits = model.predict_n_sec(cond_cont, cond_cat)
@@ -124,7 +123,7 @@ def _stage2_flat_width(sec_decoder: torch.nn.Module) -> int:
     (continuous + type) under `particle_type.target = "physical"` or
     `generator = "wgan"`, continuous-only otherwise (the type slice then
     comes from `predict_type` instead — see `stage2_trunk_sec_dim`'s
-    docstring, docs/v0.3.0-design.md decision 2)."""
+    docstring)."""
     return stage2_trunk_sec_dim(
         sec_decoder.particle_type_cfg,
         sec_decoder.generator_kind,
@@ -147,9 +146,9 @@ def _decode_stage2_flat(
     n_sec_pred: torch.Tensor,
 ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
     """Reshape a flat `(B, flat_width)` `Stage2OneShot` output into per-slot
-    tensors, generator/`particle_type.target`-agnostic (docs/v0.3.0-design.md
-    decision 2/3): shared by `sample_secondaries`/`sample_secondaries_wgan`,
-    which differ only in how `x` was produced.
+    tensors, generator/`particle_type.target`-agnostic: shared by
+    `sample_secondaries`/`sample_secondaries_wgan`, which differ only in how
+    `x` was produced.
 
     Returns (sec_cont, sec_type, sec_valid):
         sec_cont:  (B, k_max, CONT_SLOT_DIM) — [stick_logit, local_dir]
@@ -241,23 +240,22 @@ def sample_secondaries_ar(
     n_sec_pred: torch.Tensor,
     steps: int = 10,
 ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    """`Stage2Autoregressive` inference loop (docs/v0.3.0-design.md §6.4):
-    one token at a time, in descending-energy slot order, `k_max` sequential
-    calls. Unlike training (teacher forcing, §6.2 point 3 — a single
-    parallel pass over ground-truth tokens, see
-    `giant.training.stage2_inputs._assemble_stage2_ar_inputs`), there is no ground truth at
-    inference: each token's conditioning is built free-running, from the
-    PREVIOUS TOKEN'S OWN just-generated output — the train/inference gap
-    §6.2 point 4 explicitly flags as the cost of markov history's
+    """`Stage2Autoregressive` inference loop: one token at a time, in
+    descending-energy slot order, `k_max` sequential calls. Unlike training
+    (teacher forcing — a single parallel pass over ground-truth tokens, see
+    `giant.training.stage2_inputs._assemble_stage2_ar_inputs`), there is no
+    ground truth at inference: each token's conditioning is built
+    free-running, from the PREVIOUS TOKEN'S OWN just-generated output — the
+    train/inference gap that is the cost of markov history's
     expressiveness.
 
     A `{flow,ddpm}` token costs `steps` ODE substeps; `wgan` costs one pass —
-    §6.4's "K sequential forwards" cost note applies per-token here, not
+    the "K sequential forwards" cost applies per-token here, not
     once, so a flow/ddpm AR run costs ~`k_max * steps` model calls per
     physics step.
 
     Under `history="attention"` the history encoding is computed once per
-    slot via `Stage2Autoregressive.history_step` (a KV-cache append, §10)
+    slot via `Stage2Autoregressive.history_step` (a KV-cache append)
     rather than re-derived by every model call inside that slot — so an ODE
     loop's `steps` substeps, and the separate `predict_type` call when the
     type slice isn't folded into the trunk output, all reuse the SAME `hist`
@@ -389,9 +387,9 @@ def sample_secondaries_ar(
 # ---------------------------------------------------------------------------
 # Per-stage dispatch — shared by giant/rollout.py and giant/cli.py's
 # `predict` command, since both need "given a stage model, produce a
-# sample" without hand-picking the sampler themselves (docs/v0.3.0-design.md
-# decision 2: each stage's generative objective is independent, read off the
-# model's own `generator_kind`, not a caller-supplied `mode` string).
+# sample" without hand-picking the sampler themselves (each stage's
+# generative objective is independent, read off the model's own
+# `generator_kind`, not a caller-supplied `mode` string).
 # ---------------------------------------------------------------------------
 
 
@@ -450,19 +448,17 @@ def resolve_n_sec(
 ) -> torch.Tensor:
     """`n_sec_pred` is already populated when `stage1_model` owns a legacy
     `n_sec_head` (a migrated v0.2 checkpoint — see `Stage1Model`'s
-    docstring); otherwise ask stage 2, which owns it by default under
-    decision 1 (docs/v0.3.0-design.md §2). Raises if neither stage owns a
-    head at all — the only way that happens is `stage2_model.n_sec.mode`
-    other than `"head"` (`"truth"`/`"stop_token"`), neither of which is a
-    valid rollout-/predict-capable checkpoint (§3.3, §9)."""
+    docstring); otherwise ask stage 2, which owns it by default. Raises if
+    neither stage owns a head at all — the only way that happens is
+    `stage2_model.n_sec.mode` other than `"head"` (`"truth"`/`"stop_token"`),
+    neither of which is a valid rollout-/predict-capable checkpoint."""
     if n_sec_pred is not None:
         return n_sec_pred
     if getattr(sec_decoder, "n_sec_head", None) is None:
         raise RuntimeError(
             "checkpoint has no n_sec_head on either stage — needs "
             "stage2_model.n_sec.mode = 'head' (the default); 'truth' is "
-            "standalone-evaluation-only and 'stop_token' isn't implemented "
-            "(docs/v0.3.0-design.md §3.3/§9)"
+            "standalone-evaluation-only and 'stop_token' isn't implemented"
         )
     logits = sec_decoder.predict_n_sec(cond_cont, cond_cat, stage1_out)
     return logits.argmax(dim=-1)