refactor: drop shadowed-import + duplicate-definition cruft (-91 LOC)

Left over from the data.py/vhack.py/eval.py/tablelog.py module split. In
train.py the canonical imports already won at runtime; the earlier ones were
dead shadows:
- ablate_quarantine, ref_logprobs_via_zero_delta: .eval wins (line 66), drop
  the .antipasto copy; load_v_hack/postprocess_v_hack: .vhack wins, drop
  .extract_vhack_grad; DATA/load_problems: .data wins, drop .problems.
- local setup_logging() was byte-identical to the .tablelog one already
  imported (with StepLogger); delete the local def + now-orphaned datetime
  import and LOGS_DIR const.
- problems.py stays: 6 scripts + derisk/regrade still import it.

antipasto.py: delete detach_antipasto (0 callers) and its own copies of
ref_logprobs_via_zero_delta / ablate_quarantine (eval.py owns the canonical,
better-worded versions incl. the SGTM TODO), plus now-unused contextmanager
and per_token_logps imports.

docs: rm corrupted docs/spec/20260530_substrate_review_qwen.md (2-line API
error dump, not a review).

Behavior-preserving (later imports already won at runtime). Verified: just
smoke (erase) + just smoke-routeV both exit 0, 0 tracebacks, all verify_*
gates PASS.

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>

docs/spec/20260530_substrate_review_qwen.md

@@ -1,3 +0,0 @@
-400 Provider returned error
-{"error":{"message":"developer is not one of ['system', 'assistant', 'user', 'tool', 'function'] - 'messages.['0].role'","type":"invalid_request_error","param":null,"code":null},"request_id":"chatcmpl-8036d119-9aa0-981f-9113-99a865d3f90e"}
-[?2026h[?1006l[?1002l[?1000l[?1007h[?1049l[<999u[>4;0m[?2026l

src/vgrout/antipasto.py

@@ -15,7 +15,6 @@ directly; no extra projection math at the gradient step).
 from __future__ import annotations
 
 import hashlib
-from contextlib import contextmanager
 from pathlib import Path
 
 import torch
@@ -23,8 +22,6 @@ from jaxtyping import Float
 from loguru import logger
 from torch import Tensor, nn
 
-from .proj import per_token_logps
-
 
 def svd_cached(
     W: Float[Tensor, "d_out d_in"],
@@ -236,58 +233,3 @@ def wrap_model_with_antipasto(
         if not n.endswith(trainable):
             p.requires_grad_(False)
     return out
-
-
-def detach_antipasto(model: nn.Module, attached: dict) -> None:
-    for info in attached.values():
-        info["handle"].remove()
-        layer = info["layer"]
-        for attr in ("_antipasto_U", "_antipasto_Vh"):
-            if attr in layer._buffers:
-                del layer._buffers[attr]
-        for attr in ("_antipasto_delta_S", "_antipasto_delta_S_hack"):
-            if attr in layer._parameters:
-                del layer._parameters[attr]
-
-
-@torch.no_grad()
-def ref_logprobs_via_zero_delta(
-    model, merged: torch.Tensor, wrappers: dict, plen: int,
-) -> torch.Tensor:
-    """π_ref logprobs on the completion tokens.
-
-    AntiPaSTO: W' = W + U diag(δS) Vᵀ, so at δS=0 the adapter is identity and a
-    forward gives π_ref for free. Save -> zero -> forward -> restore, no second
-    model. logits_to_keep=L_c+1 runs lm_head only on completion-side hidden states
-    (prompt-side logits never materialize, ~plen/(plen+L_c) memory saved at lm_head).
-    """
-    saved = {n: info["delta_S"].data.clone() for n, info in wrappers.items()}
-    try:
-        for info in wrappers.values():
-            info["delta_S"].data.zero_()
-        L_c = merged.shape[1] - plen
-        logits = model(merged, logits_to_keep=L_c + 1).logits[:, :-1]
-        return per_token_logps(logits, merged[:, plen:])
-    finally:
-        for n, info in wrappers.items():
-            info["delta_S"].data.copy_(saved[n])
-
-
-@contextmanager
-def ablate_quarantine(wrappers: dict):
-    """Zero the routing quarantine (δS_hack) for the duration: the deploy-time
-    ablation of the routed hack capability. Save -> zero -> (eval) -> restore.
-    The route/routeV deployment model IS this ablated state.
-
-    TODO(post-deploy-finetune): SGTM's ablate(trainable=True) reinits the forget
-    weights to the retain-dims' std instead of zeroing, keeping the model
-    finetunable after ablation (no dead hole). We zero because we only eval after
-    deploy. See docs/grad_routing/sgtm_vs_ours.md."""
-    saved = {n: info["delta_S_hack"].data.clone() for n, info in wrappers.items()}
-    for info in wrappers.values():
-        info["delta_S_hack"].data.zero_()
-    try:
-        yield
-    finally:
-        for n, info in wrappers.items():
-            info["delta_S_hack"].data.copy_(saved[n])

src/vgrout/train.py

@@ -35,7 +35,6 @@ import random
 import time
 from contextlib import contextmanager, nullcontext
 from dataclasses import dataclass
-from datetime import datetime
 from pathlib import Path
 from typing import Literal
 
@@ -55,10 +54,7 @@ from tabulate import tabulate
 from tqdm import tqdm
 from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
 
-from .antipasto import (ablate_quarantine, ref_logprobs_via_zero_delta,
-                        wrap_model_with_antipasto, wrap_model_with_lora_frozen_b)
-from .extract_vhack_grad import load_v_hack, postprocess_v_hack
-from .problems import DATA, load_problems
+from .antipasto import wrap_model_with_antipasto, wrap_model_with_lora_frozen_b
 from .proj import per_token_logps, project_delta_S_grad, mean_cos_pre_from_grads
 from .rewards import EnvMode, compute_reward
 from .data import DATA, load_problems
@@ -73,34 +69,8 @@ OUT_DIR = Path("out")
 # runs/<run_id>/. Read paths (v_hack, teacher pool) come in as explicit args.
 VHACK_DIR = OUT_DIR / "vhack"
 RUNS_DIR = OUT_DIR / "runs"
-LOGS_DIR = Path("logs")
-# DATA (the LeetCode dataset path) lives in problems.py, imported above.
-
-
-def setup_logging(run_id: str) -> Path:
-    """Token-efficient loguru: stdout = 1-char icon + msg; verbose log to file.
-
-    See /root/.claude/skills/token-efficient-logging/SKILL.md.
-    """
-    LOGS_DIR.mkdir(exist_ok=True)
-    verbose_log = LOGS_DIR / f"{datetime.now().strftime('%Y%m%dT%H%M%S')}_{run_id}.log"
-    logger.remove()
-    logger.add(
-        lambda msg: tqdm.write(msg, end=""),
-        colorize=True,
-        format="<level>{level.icon}</level> {message}",
-        level="INFO",
-    )
-    logger.add(
-        verbose_log,
-        format="{time:HH:mm:ss} | {level} | {message}",
-        level="DEBUG",
-    )
-    logger.level("INFO", icon="I")
-    logger.level("WARNING", icon="W")
-    logger.level("ERROR", icon="E")
-    logger.level("DEBUG", icon="D")
-    return verbose_log
+# DATA (the LeetCode dataset path) lives in data.py, imported above.
+# setup_logging + StepLogger live in tablelog.py, imported above.
 
 
 @dataclass(kw_only=True)