diff --git a/.gitignore b/.gitignore
index 69d8c76..8e40828 100644
--- a/.gitignore
+++ b/.gitignore
@@ -136,6 +136,7 @@ ipch/
 # Visual Studio Trace Files
 *.e2e
 .tradingview_e2e/
+.worktrees/
 
 # TFS 2012 Local Workspace
 $tf/
diff --git a/results/mlp_performance.md b/results/mlp_performance.md
index 1a6608a..33ad9cf 100644
--- a/results/mlp_performance.md
+++ b/results/mlp_performance.md
@@ -1,6 +1,6 @@
 # MLP Performance — COINBASE_BTCUSD
 
-*Updated: 2026-06-25 23:29 UTC · IS: 2017-12-01 → 2026-02-28 · OOS: 2026-03-01+*
+*Updated: 2026-08-21 01:51 UTC · IS: 2017-12-01 → 2026-02-28 · OOS: 2026-03-01+*
 
 | TF | IS PnL % | Max DD % | Win Rate | Thru 2026-06-25 | OOS PnL % | OOS Trades |
 |---|---:|---:|---:|---:|---:|---:|
diff --git a/strategies/mlp_feature_groups.py b/strategies/mlp_feature_groups.py
new file mode 100644
index 0000000..3d326ef
--- /dev/null
+++ b/strategies/mlp_feature_groups.py
@@ -0,0 +1,206 @@
+"""Interpretable input routing for the grouped MLP experiment.
+
+Each first-layer unit receives one coherent family of features.  Later layers
+remain dense, so the model can learn interactions *between* those family-level
+representations without mixing unrelated raw signals in the first transform.
+"""
+from __future__ import annotations
+
+from dataclasses import dataclass
+import hashlib
+from typing import Sequence
+
+import numpy as np
+
+
+@dataclass(frozen=True)
+class FeatureFamily:
+    """A named set of raw features routed to one or more first-layer units."""
+
+    name: str
+    features: frozenset[str]
+
+
+LOCAL_TECHNICAL = FeatureFamily("local_technical", frozenset({
+    "stoch_norm", "macd_pred_norm", "osc_norm", "m3_momentum_norm",
+    "m2_tiny_norm", "stoch_div_norm", "vwap_div_norm", "stoch_peak_norm",
+    "m3_div_norm", "m2_nooff_norm", "m2_div_norm", "rsi_subtf_norm",
+    "bb_pct_b_norm",
+}))
+ON_CHAIN = FeatureFamily("on_chain", frozenset({
+    "totalvol_norm", "newaddr_norm", "sendaddr_norm", "mvrv_zscore_value",
+    "mvrv_zscore_cont", "nupl_norm", "sopr_norm", "active1y_norm", "hr_norm",
+}))
+CANDLESTICKS = FeatureFamily("candlesticks", frozenset({
+    "bearish_engulfing_score", "bullish_hammer_score", "bullish_engulfing_score",
+    "shooting_star_score",
+}))
+MACRO = FeatureFamily("macro", frozenset({
+    "btc_spx_corr_30", "dxy_roc_norm", "vix_pctrank_inv", "btc_dom_roc_sign",
+    "us10y_roc_inv_sign", "spy_above_200ema", "gold_roc_pctrank",
+    "fed_net_liq_sign", "gc_position", "us2y_roc_inv_sign", "yield_curve_sign",
+}))
+RSID = FeatureFamily("rsid_structure", frozenset({
+    "rsid_reg_bull_norm", "rsid_reg_bear_norm", "rsid_hid_bull_norm",
+    "rsid_hid_bear_norm", "rsid_rt_bull_norm", "rsid_rt_bear_norm",
+    "rsid_slow_bull_norm", "rsid_slow_bear_norm", "rsid_delayed_peak_norm",
+    "rsid_delayed_dip_norm",
+}))
+MARKET_FLOW = FeatureFamily("market_flow", frozenset({
+    "oi_roc_norm", "usdt_d_norm", "basis_norm", "cvd_norm", "rvol_norm",
+}))
+CROSS_ASSET = FeatureFamily("cross_asset", frozenset({"btc_gold_norm"}))
+
+# 16 first-layer units: more capacity goes to broad, complementary families.
+GROUPED_UNIT_ALLOCATION = (
+    LOCAL_TECHNICAL, LOCAL_TECHNICAL, LOCAL_TECHNICAL,
+    ON_CHAIN, ON_CHAIN, ON_CHAIN,
+    MACRO, MACRO, MACRO,
+    CANDLESTICKS,
+    RSID, RSID, RSID,
+    MARKET_FLOW, MARKET_FLOW,
+    CROSS_ASSET,
+)
+
+# Hybrid v1 reserves half of the 16 first-layer units for semantic specialists
+# and leaves the other half dense, preserving direct cross-feature capacity.
+HYBRID_GROUPED_SPECIALIST_ALLOCATION = (
+    LOCAL_TECHNICAL, LOCAL_TECHNICAL,
+    ON_CHAIN,
+    MACRO,
+    CANDLESTICKS,
+    RSID,
+    MARKET_FLOW,
+    CROSS_ASSET,
+)
+HYBRID_GROUPED_DENSE_UNIT_COUNT = 8
+HYBRID_GROUPED_HIDDEN_SIZE = len(HYBRID_GROUPED_SPECIALIST_ALLOCATION) + HYBRID_GROUPED_DENSE_UNIT_COUNT
+HYBRID_GROUPED_DENSE_ROW_INDICES = tuple(
+    range(len(HYBRID_GROUPED_SPECIALIST_ALLOCATION), HYBRID_GROUPED_HIDDEN_SIZE)
+)
+
+
+def grouped_first_layer_mask(feature_cols: Sequence[str], hidden_size: int) -> np.ndarray:
+    """Return the allowed ``(hidden_size, n_features)`` first-layer edges.
+
+    The experiment deliberately targets the established 55→16→8→1 shape.
+    Failing on a different width prevents a silently arbitrary allocation.
+    """
+    if hidden_size != len(GROUPED_UNIT_ALLOCATION):
+        raise ValueError(
+            f"Grouped input structure requires {len(GROUPED_UNIT_ALLOCATION)} first-layer units; "
+            f"received {hidden_size}."
+        )
+    known = frozenset().union(*(family.features for family in GROUPED_UNIT_ALLOCATION))
+    unknown = set(feature_cols) - known
+    if unknown:
+        raise ValueError(f"Grouped input structure has unknown features: {sorted(unknown)}")
+    mask = np.array(
+        [[feature in family.features for feature in feature_cols]
+         for family in GROUPED_UNIT_ALLOCATION],
+        dtype=bool,
+    )
+    if not mask.any(axis=0).all():
+        missing = [feature for feature, present in zip(feature_cols, mask.any(axis=0)) if not present]
+        raise ValueError(f"Grouped input structure left features unrouted: {missing}")
+    return mask
+
+
+def grouped_structure_metadata(feature_cols: Sequence[str], hidden_size: int) -> dict[str, object]:
+    """Serializable provenance saved beside an experimental artifact."""
+    grouped_first_layer_mask(feature_cols, hidden_size)
+    families = tuple(dict.fromkeys(GROUPED_UNIT_ALLOCATION))
+    return {
+        "name": "grouped_first_layer_v1",
+        "unit_families": [family.name for family in GROUPED_UNIT_ALLOCATION],
+        "family_feature_counts": {
+            family.name: sum(feature in family.features for feature in feature_cols)
+            for family in families
+        },
+    }
+
+
+def hybrid_grouped_first_layer_mask(feature_cols: Sequence[str], hidden_size: int) -> np.ndarray:
+    """Return the v1 specialist-plus-dense first-layer connectivity mask.
+
+    Rows 0--7 are hard-routed specialists, in the documented family order.
+    Rows 8--15 remain fully trainable across every input feature.  Keeping the
+    architecture check strict makes the allocation auditable rather than an
+    accidental choice for another hidden width.
+    """
+    if hidden_size != HYBRID_GROUPED_HIDDEN_SIZE:
+        raise ValueError(
+            f"Hybrid grouped input structure requires {HYBRID_GROUPED_HIDDEN_SIZE} "
+            f"first-layer units; received {hidden_size}."
+        )
+    known = frozenset().union(*(family.features for family in HYBRID_GROUPED_SPECIALIST_ALLOCATION))
+    unknown = set(feature_cols) - known
+    if unknown:
+        raise ValueError(f"Hybrid grouped input structure has unknown features: {sorted(unknown)}")
+    specialist_rows = np.array(
+        [[feature in family.features for feature in feature_cols]
+         for family in HYBRID_GROUPED_SPECIALIST_ALLOCATION],
+        dtype=bool,
+    )
+    dense_rows = np.ones((len(HYBRID_GROUPED_DENSE_ROW_INDICES), len(feature_cols)), dtype=bool)
+    return np.vstack((specialist_rows, dense_rows))
+
+
+def hybrid_grouped_structure_metadata(feature_cols: Sequence[str], hidden_size: int) -> dict[str, object]:
+    """Return complete, reproducible provenance for the hybrid v1 mask."""
+    mask = hybrid_grouped_first_layer_mask(feature_cols, hidden_size)
+    feature_text = "\x1f".join(feature_cols)
+    specialist_count = len(HYBRID_GROUPED_SPECIALIST_ALLOCATION)
+    return {
+        "name": "hybrid_grouped_first_layer_v1",
+        "version": 1,
+        "specialist_row_families": {
+            str(row): family.name
+            for row, family in enumerate(HYBRID_GROUPED_SPECIALIST_ALLOCATION)
+        },
+        "dense_row_indices": list(HYBRID_GROUPED_DENSE_ROW_INDICES),
+        "feature_cols_sha256": hashlib.sha256(feature_text.encode()).hexdigest(),
+        "active_edges": int(mask.sum()),
+        "blocked_edges": int(mask.size - mask.sum()),
+        "row_active_edges": [int(count) for count in mask.sum(axis=1)],
+        "row_feature_indices": [np.flatnonzero(row).tolist() for row in mask],
+        "specialist_row_count": specialist_count,
+        "dense_row_count": len(HYBRID_GROUPED_DENSE_ROW_INDICES),
+    }
+
+
+def random_sparse_first_layer_mask(
+    feature_cols: Sequence[str], hidden_size: int, mask_seed: int,
+) -> np.ndarray:
+    """Return a seeded random control mask with the grouped row capacities.
+
+    This deliberately samples features independently per hidden unit.  It is a
+    capacity-matched control, not a second semantic feature routing scheme.
+    Calling the grouped builder first preserves its architecture and feature
+    validation while providing the exact per-row active-edge counts.
+    """
+    grouped_mask = grouped_first_layer_mask(feature_cols, hidden_size)
+    rng = np.random.default_rng(mask_seed)
+    mask = np.zeros_like(grouped_mask, dtype=bool)
+    for row_index, active_edges in enumerate(grouped_mask.sum(axis=1)):
+        selected = rng.choice(len(feature_cols), size=int(active_edges), replace=False)
+        mask[row_index, selected] = True
+    return mask
+
+
+def random_sparse_structure_metadata(
+    feature_cols: Sequence[str], hidden_size: int, mask_seed: int,
+) -> dict[str, object]:
+    """Return complete, serializable provenance for a random sparse mask."""
+    mask = random_sparse_first_layer_mask(feature_cols, hidden_size, mask_seed)
+    feature_text = "\x1f".join(feature_cols)
+    return {
+        "name": "random_sparse_first_layer_v1",
+        "mask_seed": mask_seed,
+        "generator": "numpy.default_rng(seed).choice(n_features, row_count, replace=False)",
+        "feature_cols_sha256": hashlib.sha256(feature_text.encode()).hexdigest(),
+        "active_edges": int(mask.sum()),
+        "blocked_edges": int(mask.size - mask.sum()),
+        "row_active_edges": [int(count) for count in mask.sum(axis=1)],
+        "row_feature_indices": [np.flatnonzero(row).tolist() for row in mask],
+    }
diff --git a/strategies/params/mlp/mlp_weights_COINBASE_BTCUSD_6H_grouped_v1_seed9090.json b/strategies/params/mlp/mlp_weights_COINBASE_BTCUSD_6H_grouped_v1_seed9090.json
new file mode 100644
index 0000000..d3ff873
--- /dev/null
+++ b/strategies/params/mlp/mlp_weights_COINBASE_BTCUSD_6H_grouped_v1_seed9090.json
@@ -0,0 +1 @@
+{"version": 1, "asset": "COINBASE_BTCUSD", "timeframe": "6H", "arch": [55, 16, 8, 1], "activation": "tanh_closed_form_clamp20", "feature_cols": ["stoch_norm", "macd_pred_norm", "osc_norm", "totalvol_norm", "m3_momentum_norm", "m2_tiny_norm", "newaddr_norm", "stoch_div_norm", "vwap_div_norm", "stoch_peak_norm", "sendaddr_norm", "m3_div_norm", "bearish_engulfing_score", "m2_nooff_norm", "m2_div_norm", "bullish_hammer_score", "bullish_engulfing_score", "shooting_star_score", "btc_spx_corr_30", "dxy_roc_norm", "vix_pctrank_inv", "btc_dom_roc_sign", "us10y_roc_inv_sign", "spy_above_200ema", "gold_roc_pctrank", "mvrv_zscore_value", "mvrv_zscore_cont", "nupl_norm", "fed_net_liq_sign", "gc_position", "us2y_roc_inv_sign", "yield_curve_sign", "sopr_norm", "rsid_reg_bull_norm", "rsid_reg_bear_norm", "rsid_hid_bull_norm", "rsid_hid_bear_norm", "rsid_rt_bull_norm", "rsid_rt_bear_norm", "rsid_slow_bull_norm", "rsid_slow_bear_norm", "rsid_delayed_peak_norm", "rsid_delayed_dip_norm", "oi_roc_norm", "usdt_d_norm", "basis_norm", "cvd_norm", "btc_gold_norm", "rsi_subtf_norm", "bb_pct_b_norm", "cvd_norm", "rvol_norm", "active1y_norm", "hr_norm", "sopr_norm"], "layers": [{"W": [[-0.41008136976768295, 0.24120393015473657, 0.014674539291339037, 0.0, 0.20874109806323335, -0.503020563047582, 0.0, 0.31837439349329655, -0.32995780645657813, -0.20247079122391137, 0.0, 0.2151830628162687, 0.0, 0.004439512934822261, -0.10015086233239584, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.02611619200027744, -0.14378586644243468, 0.0, 0.0, 0.0, 0.0, 0.0], [0.3358149323066063, -0.11424343161238247, 0.022822566510261662, 0.0, 0.29981711558187435, 0.07433399193202045, 0.0, 0.31722182833445944, -0.11383898331060209, 0.2162883391036723, 0.0, -0.27254151120679204, 0.0, 0.1725106964316952, -0.06783748102399471, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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diff --git a/strategies/params/mlp/mlp_weights_COINBASE_BTCUSD_6H_grouped_v1_seed9191.json b/strategies/params/mlp/mlp_weights_COINBASE_BTCUSD_6H_grouped_v1_seed9191.json
new file mode 100644
index 0000000..5f5303c
--- /dev/null
+++ b/strategies/params/mlp/mlp_weights_COINBASE_BTCUSD_6H_grouped_v1_seed9191.json
@@ -0,0 +1 @@
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diff --git a/strategies/params/mlp/mlp_weights_COINBASE_BTCUSD_6H_grouped_v1_seed9292.json b/strategies/params/mlp/mlp_weights_COINBASE_BTCUSD_6H_grouped_v1_seed9292.json
new file mode 100644
index 0000000..5164277
--- /dev/null
+++ b/strategies/params/mlp/mlp_weights_COINBASE_BTCUSD_6H_grouped_v1_seed9292.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/strategies/strategy_mlp_scores.py b/strategies/strategy_mlp_scores.py
index bbfdac9..d3830e8 100644
--- a/strategies/strategy_mlp_scores.py
+++ b/strategies/strategy_mlp_scores.py
@@ -40,6 +40,7 @@ from strategies.strategy_activation_scores import (  # noqa: E402
     _prepare_features,
     calculate_metrics,  # re-exported: validate_strategy / optimize_strategy look it up on this module
 )
+from strategies.mlp_temporal_features import inject_temporal_features, temporal_feature_cols  # noqa: E402
 from strategies.library_activation_scores import calculate_positions  # noqa: E402
 
 __all__ = [
@@ -333,7 +334,9 @@ def _calculate_positions_with_dz(entry_raw_arr, exit_raw_arr, exit_raw_fallback_
 
 
 def _score_to_signals(df, params, stoch_peak_arr, close_arr, high_arr,
-                      entry_gate=None, additional_exit=None):
+                      entry_gate=None, additional_exit=None,
+                      state_policy=None, policy_risk=None,
+                      return_opportunities=False):
     """Threshold/regime/crossunder/position block — mirrors
     strategy_activation_scores.generate_signals lines 332-421 verbatim (that
     block is inline in the original and cannot be imported without modifying
@@ -445,6 +448,25 @@ def _score_to_signals(df, params, stoch_peak_arr, close_arr, high_arr,
     fallback_arr = exit_raw_fallback.fillna(False).values.astype(bool)
     score_exit_arr = score_exit.values
 
+    if return_opportunities:
+        return {
+            'entry': entry_arr, 'exit': exit_arr, 'fallback': fallback_arr,
+            'score_exit': score_exit_arr, 'exit_threshold': exit_threshold,
+            'time_ok': np.ones(len(df), dtype=bool) if time_ok_arr is None else time_ok_arr,
+            'dead_zone': exit_window > 1, 'trail': trail_stop_pct,
+        }
+
+    if state_policy is not None:
+        from strategies.mlp_state_policy import replay_state_policy
+        result = replay_state_policy(
+            entry_arr, exit_arr, fallback_arr, score_exit_arr, exit_threshold,
+            close_arr, high_arr, trail_stop_pct, policy=state_policy,
+            risk=policy_risk, time_ok=time_ok_arr, dead_zone=exit_window > 1,
+        )
+        for column, values in result.items():
+            df[column] = values
+        return df
+
     if exit_window > 1:
         if trail_stop_pct > 0.0:
             in_pos, exec_entry, exec_exit = _apply_trailing_stop_with_dz(
@@ -488,6 +510,10 @@ def generate_signals(df, **params):
         raise ValueError("strategy_mlp_scores requires the 'mlp_weights_file' param")
     art = load_mlp_artifact(weights_file)
     art_cols = art["feature_cols"]
+    df = df.copy()
+    df.columns = df.columns.str.lower().str.strip()
+    if any(column in art_cols for column in temporal_feature_cols()):
+        inject_temporal_features(df)
     if art_cols != FEATURE_COLS:
         # Allow artifacts trained on a strict subset of FEATURE_COLS (e.g. the old
         # 49-feature set during the transition to 50 features).  Missing columns
@@ -518,9 +544,6 @@ def generate_signals(df, **params):
                 stacklevel=2,
             )
 
-    df = df.copy()
-    df.columns = df.columns.str.lower().str.strip()
-
     X = _prepare_features(df, art_cols)
     df['activation_score'] = mlp_forward(X, art["layers"])
 
diff --git a/tests/test_mlp_input_structure.py b/tests/test_mlp_input_structure.py
new file mode 100644
index 0000000..1ae963f
--- /dev/null
+++ b/tests/test_mlp_input_structure.py
@@ -0,0 +1,171 @@
+import hashlib
+import sys
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from strategies.mlp_feature_groups import (
+    GROUPED_UNIT_ALLOCATION,
+    HYBRID_GROUPED_DENSE_ROW_INDICES,
+    HYBRID_GROUPED_SPECIALIST_ALLOCATION,
+    grouped_first_layer_mask,
+    hybrid_grouped_first_layer_mask,
+    hybrid_grouped_structure_metadata,
+    random_sparse_first_layer_mask,
+    random_sparse_structure_metadata,
+)
+from strategies.strategy_mlp_scores import FEATURE_COLS
+from tools import run_mlp_train, train_mlp
+from tools.train_mlp import flatten_layers, pretrain, unflatten_layers
+from tools.run_mlp_train import effective_artifact_tag
+
+
+def test_grouped_mask_routes_each_first_layer_unit_to_one_signal_family():
+    mask = grouped_first_layer_mask(FEATURE_COLS, hidden_size=16)
+
+    assert mask.shape == (16, len(FEATURE_COLS))
+    assert tuple(mask.sum(axis=0) > 0) == (True,) * len(FEATURE_COLS)
+    for row, family in zip(mask, GROUPED_UNIT_ALLOCATION):
+        expected = {i for i, feature in enumerate(FEATURE_COLS) if feature in family.features}
+        assert set(np.flatnonzero(row)) == expected
+
+
+def test_grouped_mask_rejects_unsupported_width_and_unknown_features():
+    with pytest.raises(ValueError, match="16"):
+        grouped_first_layer_mask(FEATURE_COLS, hidden_size=8)
+    with pytest.raises(ValueError, match="unknown"):
+        grouped_first_layer_mask([*FEATURE_COLS, "future_feature"], hidden_size=16)
+
+
+def test_random_sparse_mask_matches_grouped_row_sparsity_and_is_reproducible():
+    grouped = grouped_first_layer_mask(FEATURE_COLS, hidden_size=16)
+    first = random_sparse_first_layer_mask(FEATURE_COLS, hidden_size=16, mask_seed=13001)
+    again = random_sparse_first_layer_mask(FEATURE_COLS, hidden_size=16, mask_seed=13001)
+    different = random_sparse_first_layer_mask(FEATURE_COLS, hidden_size=16, mask_seed=13102)
+
+    assert first.shape == (16, len(FEATURE_COLS))
+    assert first.sum() == grouped.sum() == 149
+    np.testing.assert_array_equal(first.sum(axis=1), grouped.sum(axis=1))
+    np.testing.assert_array_equal(first, again)
+    assert not np.array_equal(first, different)
+
+
+def test_random_sparse_metadata_records_reproducible_mask_provenance():
+    metadata = random_sparse_structure_metadata(FEATURE_COLS, hidden_size=16, mask_seed=13001)
+
+    assert metadata["name"] == "random_sparse_first_layer_v1"
+    assert metadata["mask_seed"] == 13001
+    assert metadata["active_edges"] == 149
+    assert metadata["row_active_edges"] == [13, 13, 13, 10, 10, 10, 11, 11, 11, 4, 10, 10, 10, 6, 6, 1]
+    assert len(metadata["row_feature_indices"]) == 16
+
+
+def test_hybrid_grouped_mask_preserves_specialists_and_dense_cross_family_rows():
+    mask = hybrid_grouped_first_layer_mask(FEATURE_COLS, hidden_size=16)
+
+    assert mask.shape == (16, len(FEATURE_COLS))
+    assert int(mask.sum()) == 508
+    assert int(mask.size - mask.sum()) == 372
+    for row, family in enumerate(HYBRID_GROUPED_SPECIALIST_ALLOCATION):
+        expected = {i for i, feature in enumerate(FEATURE_COLS) if feature in family.features}
+        assert set(np.flatnonzero(mask[row])) == expected
+    for row in HYBRID_GROUPED_DENSE_ROW_INDICES:
+        assert mask[row].all()
+    with pytest.raises(ValueError, match="16"):
+        hybrid_grouped_first_layer_mask(FEATURE_COLS, hidden_size=8)
+
+
+def test_hybrid_grouped_metadata_audits_exact_v1_mask():
+    metadata = hybrid_grouped_structure_metadata(FEATURE_COLS, hidden_size=16)
+
+    assert metadata["name"] == "hybrid_grouped_first_layer_v1"
+    assert metadata["version"] == 1
+    assert metadata["specialist_row_families"] == {
+        "0": "local_technical", "1": "local_technical", "2": "on_chain",
+        "3": "macro", "4": "candlesticks", "5": "rsid_structure",
+        "6": "market_flow", "7": "cross_asset",
+    }
+    assert metadata["dense_row_indices"] == list(range(8, 16))
+    assert metadata["active_edges"] == 508
+    assert metadata["blocked_edges"] == 372
+    assert metadata["row_active_edges"] == [13, 13, 10, 11, 4, 10, 6, 1] + [55] * 8
+    assert metadata["feature_cols_sha256"] == hashlib.sha256("\x1f".join(FEATURE_COLS).encode()).hexdigest()
+    mask = hybrid_grouped_first_layer_mask(FEATURE_COLS, hidden_size=16)
+    assert metadata["row_feature_indices"] == [np.flatnonzero(row).tolist() for row in mask]
+
+
+def test_hybrid_phase1_and_cma_masking_preserve_specialist_blocks():
+    rng = np.random.default_rng(11)
+    mask = hybrid_grouped_first_layer_mask(FEATURE_COLS, hidden_size=16)
+    X = rng.normal(size=(72, len(FEATURE_COLS)))
+    y = rng.normal(size=72)
+    valid = np.ones(72, dtype=bool)
+    times = pd.Series(pd.date_range("2020-01-01", periods=72, freq="h"))
+
+    layers, _ = pretrain(
+        X, y, valid, times, [16, 8], epochs=2, seed=13,
+        val_start=times.iloc[60], val_end=times.iloc[-1], log=lambda _: None,
+        first_layer_mask=mask,
+    )
+    assert np.count_nonzero(layers[0][0][~mask]) == 0
+    assert mask[8:].all()
+
+    masks = [mask, np.ones((8, 16), dtype=bool), np.ones((1, 8), dtype=bool)]
+    theta = flatten_layers(layers, masks)
+    restored = unflatten_layers(theta, [55, 16, 8, 1], masks)
+    assert len(theta) == 669
+    assert len(flatten_layers(layers)) == 1041
+    assert np.count_nonzero(restored[0][0][~mask]) == 0
+
+
+def test_masked_parameter_roundtrip_never_restores_blocked_connections():
+    mask = grouped_first_layer_mask(FEATURE_COLS, hidden_size=16)
+    rng = np.random.default_rng(4)
+    layers = [
+        (rng.normal(size=(16, len(FEATURE_COLS))), rng.normal(size=16)),
+        (rng.normal(size=(8, 16)), rng.normal(size=8)),
+        (rng.normal(size=(1, 8)), rng.normal(size=1)),
+    ]
+    masks = [mask, np.ones((8, 16), dtype=bool), np.ones((1, 8), dtype=bool)]
+
+    theta = flatten_layers(layers, masks)
+    restored = unflatten_layers(theta, [55, 16, 8, 1], masks)
+
+    assert np.all(restored[0][0][~mask] == 0.0)
+    np.testing.assert_array_equal(restored[0][0][mask], layers[0][0][mask])
+
+
+def test_grouped_training_uses_an_isolated_default_artifact_tag():
+    assert effective_artifact_tag("bb50", "all", "grouped") == "bb50_grouped"
+    assert effective_artifact_tag("bb50", "bull", "grouped") == "bb50_grouped_bull"
+    assert effective_artifact_tag("grouped_v1", "all", "grouped") == "grouped_v1"
+    assert effective_artifact_tag("bb50", "all", "dense") == "bb50"
+    assert effective_artifact_tag("bb50", "all", "random_sparse") == "bb50_random_sparse"
+    assert effective_artifact_tag("bb50", "bull", "random_sparse") == "bb50_random_sparse_bull"
+    assert effective_artifact_tag("grouped_v1", "all", "random_sparse") == "grouped_v1"
+    assert effective_artifact_tag("bb50", "all", "hybrid_grouped") == "bb50_hybrid_grouped"
+    assert effective_artifact_tag("bb50", "bull", "hybrid_grouped") == "bb50_hybrid_grouped_bull"
+    assert effective_artifact_tag("hybrid_grouped_v1", "all", "hybrid_grouped") == "hybrid_grouped_v1"
+    with pytest.raises(ValueError, match="reserved"):
+        effective_artifact_tag("grouped_v1", "all", "hybrid_grouped")
+    with pytest.raises(ValueError, match="reserved"):
+        effective_artifact_tag("random_sparse_v1", "all", "hybrid_grouped")
+    with pytest.raises(ValueError, match="reserved"):
+        effective_artifact_tag("grouped_v1_bull", "all", "hybrid_grouped")
+
+
+def test_hybrid_direct_trainer_requires_an_isolated_output(monkeypatch):
+    monkeypatch.setattr(sys, "argv", [
+        "train_mlp.py", "--data", "unused.csv", "--input-structure", "hybrid_grouped",
+    ])
+    with pytest.raises(SystemExit, match="2"):
+        train_mlp.main()
+
+
+def test_hybrid_orchestrator_rejects_shared_sweep_and_preset_paths(monkeypatch):
+    monkeypatch.setattr(sys, "argv", [
+        "run_mlp_train.py", "--input-structure", "hybrid_grouped", "--sweep",
+    ])
+    with pytest.raises(SystemExit, match="2"):
+        run_mlp_train.main()
diff --git a/tools/check_pine.py b/tools/check_pine.py
index 098353b..da45ec4 100644
--- a/tools/check_pine.py
+++ b/tools/check_pine.py
@@ -70,7 +70,7 @@ def check_security_calls(lines):
             if "request.security(" in line and not line.lstrip().startswith("//")]
     direct = len(hits)
     estimated_total = direct + LIBRARY_SECURITY_CALLS
-    at_limit = estimated_total >= SECURITY_HARD_LIMIT
+    at_limit = estimated_total > SECURITY_HARD_LIMIT
     near_limit = estimated_total >= SECURITY_HARD_LIMIT * SECURITY_WARN_PCT
     return hits, direct, estimated_total, near_limit, at_limit
 
@@ -88,7 +88,7 @@ def check_plot_calls(lines):
     direct = len(plot_hits)
     estimated_total = direct + LIBRARY_PLOT_CALLS
     near_limit = estimated_total >= PLOT_HARD_LIMIT * PLOT_WARN_PCT
-    at_limit = estimated_total >= PLOT_HARD_LIMIT
+    at_limit = estimated_total > PLOT_HARD_LIMIT
     return plot_hits, direct, estimated_total, near_limit, at_limit
 
 
@@ -241,8 +241,10 @@ def main():
     print(f"   Library estimate:  +{LIBRARY_SECURITY_CALLS} (from imported libs)")
     print(f"   Estimated total:   {estimated_total} / {SECURITY_HARD_LIMIT}")
     slots_remaining = SECURITY_HARD_LIMIT - estimated_total
-    if slots_remaining <= 0:
-        print(f"   ❌ AT OR OVER LIMIT — cannot add more request.security() calls")
+    if slots_remaining < 0:
+        print(f"   ❌ OVER LIMIT — reduce request.security() calls")
+    elif slots_remaining == 0:
+        print(f"   ⚠️  AT LIMIT — cannot add more request.security() calls")
     else:
         print(f"   Slots remaining:   {slots_remaining}")
     print()
@@ -255,8 +257,10 @@ def main():
     print(f"   Library estimate:  +{LIBRARY_PLOT_CALLS} (from imported libs)")
     print(f"   Estimated total:   {estimated_plots} / {PLOT_HARD_LIMIT}")
     slots_remaining_plot = PLOT_HARD_LIMIT - estimated_plots
-    if slots_remaining_plot <= 0:
-        print(f"   ❌ AT OR OVER LIMIT — cannot add more plot/plotchar calls")
+    if slots_remaining_plot < 0:
+        print(f"   ❌ OVER LIMIT — reduce plot/plotchar calls")
+    elif slots_remaining_plot == 0:
+        print(f"   ⚠️  AT LIMIT — cannot add more plot/plotchar calls")
     else:
         print(f"   Slots remaining:   {slots_remaining_plot}")
     print()
diff --git a/tools/run_mlp_train.py b/tools/run_mlp_train.py
index 5db7051..13488f0 100644
--- a/tools/run_mlp_train.py
+++ b/tools/run_mlp_train.py
@@ -75,10 +75,25 @@ def existing_seeds(asset: str, tf: str, tag: str = "bb50") -> list[int]:
     return sorted(seeds)
 
 
+def effective_artifact_tag(tag: str, regime: str, input_structure: str) -> str:
+    """Keep non-dense experiments out of the production ``bb50`` namespace."""
+    if input_structure == "hybrid_grouped" and tag.startswith((
+        "grouped", "random_sparse", "bb50_grouped", "bb50_random_sparse",
+    )):
+        raise ValueError(
+            f"Artifact tag '{tag}' is reserved by an existing input-structure experiment; "
+            "use an isolated hybrid tag such as 'hybrid_grouped_v1'."
+        )
+    if input_structure != "dense" and tag == "bb50":
+        tag = f"{tag}_{input_structure}"
+    return tag if regime == "all" else f"{tag}_{regime}"
+
+
 def train_one(asset: str, tf: str, seed: int, hidden: list[int],
               fold_objective: str, l2: float, es_workers: int,
               es_generations: int, tag: str, dry_run: bool,
-              regime: str = "all") -> tuple[str, bool, str]:
+              regime: str = "all", input_structure: str = "dense",
+              mask_seed: int | None = None, temporal_features: bool = False) -> tuple[str, bool, str]:
     out = artifact_path(asset, tf, seed, tag)
     data = data_path(asset, tf)
     label = f"{asset} {tf} seed{seed}"
@@ -100,6 +115,14 @@ def train_one(asset: str, tf: str, seed: int, hidden: list[int],
     ]
     if regime != "all":
         cmd += ["--regime", regime]
+    if input_structure != "dense":
+        cmd += ["--input-structure", input_structure]
+    if input_structure == "random_sparse":
+        if mask_seed is None:
+            return label, False, "random_sparse requires a mask seed"
+        cmd += ["--mask-seed", str(mask_seed)]
+    if temporal_features:
+        cmd += ["--temporal-features"]
     if dry_run:
         print(f"  [dry-run] {' '.join(cmd)}")
         return label, True, "dry-run"
@@ -233,21 +256,43 @@ def main() -> None:
     parser.add_argument("--regime", choices=["bull", "bear", "sideways", "all"], default="all",
                         help="Filter Phase-1 training to MVRV regime bars "
                              "(requires mvrv_regime column). Auto-appends regime suffix to tag.")
+    parser.add_argument("--input-structure", choices=["dense", "grouped", "hybrid_grouped", "random_sparse"], default="dense",
+                        help="Pass the first-layer connectivity experiment to each training run.")
+    parser.add_argument("--random-mask-seed-start", type=int,
+                        help="Required with random_sparse; one explicit mask seed per training seed, "
+                             "incremented by 101 in the same order as training seeds.")
+    parser.add_argument("--temporal-features", action="store_true",
+                        help="Experimental causal EMA companions; cannot sweep, promote, or generate Pine presets.")
     args = parser.parse_args()
 
-    # Auto-suffix tag with regime so bull-trained artifacts don't overwrite bb50 files.
-    effective_tag = args.tag if args.regime == "all" else f"{args.tag}_{args.regime}"
+    if args.input_structure == "random_sparse" and args.random_mask_seed_start is None:
+        parser.error("--random-mask-seed-start is required with --input-structure random_sparse")
+    if args.input_structure == "hybrid_grouped" and (args.sweep or args.promote or args.presets):
+        parser.error("hybrid_grouped does not support sweep, promote, or presets; use canonical replay only")
+    if args.temporal_features and (args.sweep or args.promote or args.presets):
+        parser.error("temporal-features does not support sweep, promote, or presets; use canonical replay only")
+    if args.temporal_features and args.tag == "bb50":
+        args.tag = "temporal_ema_v1"
+
+    # Keep experimental structures out of the production namespace, then suffix
+    # regime so bull-trained artifacts cannot overwrite all-regime candidates.
+    effective_tag = effective_artifact_tag(args.tag, args.regime, args.input_structure)
     if effective_tag != args.tag:
         log(f"regime={args.regime}: tag auto-suffixed '{args.tag}' → '{effective_tag}'")
 
     combos = [(a, tf) for a in args.assets for tf in args.tfs]
     seeds  = seed_range(args.seed_start, args.seeds)
-    jobs   = [(a, tf, s) for a, tf in combos for s in seeds]
+    mask_seeds = (seed_range(args.random_mask_seed_start, len(seeds))
+                  if args.input_structure == "random_sparse" else [None] * len(seeds))
+    jobs = [
+        (a, tf, seed, mask_seed)
+        for a, tf in combos for seed, mask_seed in zip(seeds, mask_seeds)
+    ]
 
     log(f"Plan: {len(args.assets)} assets × {len(args.tfs)} TFs × {len(seeds)} seeds "
         f"= {len(jobs)} training jobs")
     if args.skip_existing:
-        jobs = [(a, tf, s) for a, tf, s in jobs
+        jobs = [(a, tf, s, mask_seed) for a, tf, s, mask_seed in jobs
                 if not artifact_path(a, tf, s, effective_tag).exists()]
         log(f"  After skipping existing: {len(jobs)} jobs remaining")
 
@@ -263,8 +308,8 @@ def main() -> None:
         futures = {
             pool.submit(train_one, a, tf, s, args.hidden, args.fold_objective,
                         args.l2, args.es_workers, args.es_generations, effective_tag,
-                        args.dry_run, args.regime): (a, tf, s)
-            for a, tf, s in jobs
+                        args.dry_run, args.regime, args.input_structure, mask_seed, args.temporal_features): (a, tf, s)
+            for a, tf, s, mask_seed in jobs
         }
         for fut in concurrent.futures.as_completed(futures):
             label, ok, detail = fut.result()
@@ -306,7 +351,7 @@ def main() -> None:
         log("Running regression table...")
         table_output = run_table(args.assets, args.dry_run)
         print(table_output)
-    elif not args.dry_run:
+    elif not args.dry_run and args.input_structure == "dense":
         # Always refresh the tracked artifact even without --table
         for asset in args.assets:
             subprocess.run(
diff --git a/tools/train_mlp.py b/tools/train_mlp.py
index 815324f..c024677 100644
--- a/tools/train_mlp.py
+++ b/tools/train_mlp.py
@@ -55,6 +55,15 @@ from strategies.strategy_mlp_scores import (  # noqa: E402
     save_mlp_artifact,
 )
 from strategies.strategy_activation_scores import _prepare_features  # noqa: E402
+from strategies.mlp_feature_groups import (  # noqa: E402
+    grouped_first_layer_mask,
+    grouped_structure_metadata,
+    hybrid_grouped_first_layer_mask,
+    hybrid_grouped_structure_metadata,
+    random_sparse_first_layer_mask,
+    random_sparse_structure_metadata,
+)
+from strategies.mlp_temporal_features import inject_temporal_features  # noqa: E402
 from tools.worker_utils import DEFAULT_WORKER_FRACTION, resolve_worker_count  # noqa: E402
 
 VOL_WINDOW = 60          # bars of past 1-bar log returns for the vol normaliser
@@ -103,7 +112,8 @@ def build_target(df, k):
 # Phase 1 — supervised pretrain
 # ---------------------------------------------------------------------------
 
-def pretrain(X, y, valid, times, hidden, epochs, seed, val_start, val_end, log):
+def pretrain(X, y, valid, times, hidden, epochs, seed, val_start, val_end, log,
+             first_layer_mask=None):
     import torch
 
     device = ("cuda" if torch.cuda.is_available()
@@ -120,6 +130,11 @@ def pretrain(X, y, valid, times, hidden, epochs, seed, val_start, val_end, log):
         modules += [lin, torch.nn.Tanh()]
         layers_t.append(lin)
     model = torch.nn.Sequential(*modules).to(device)
+    mask_t = None
+    if first_layer_mask is not None:
+        mask_t = torch.tensor(first_layer_mask, dtype=torch.bool, device=device)
+        with torch.no_grad():
+            layers_t[0].weight.masked_fill_(~mask_t, 0.0)
 
     val_mask = valid & (times >= val_start).values & (times <= val_end).values
     train_mask = valid & (times < val_start).values
@@ -143,6 +158,9 @@ def pretrain(X, y, valid, times, hidden, epochs, seed, val_start, val_end, log):
         loss = loss_fn(model(Xtr), ytr)
         loss.backward()
         opt.step()
+        if mask_t is not None:
+            with torch.no_grad():
+                layers_t[0].weight.masked_fill_(~mask_t, 0.0)
         train_loss = loss.item()
         model.eval()
         with torch.no_grad():
@@ -170,17 +188,31 @@ def pretrain(X, y, valid, times, hidden, epochs, seed, val_start, val_end, log):
 # Phase 2 — CMA-ES on fold-mean Calmar
 # ---------------------------------------------------------------------------
 
-def flatten_layers(layers):
-    return np.concatenate([np.concatenate([W.ravel(), b.ravel()]) for W, b in layers])
+def flatten_layers(layers, weight_masks=None):
+    """Flatten trainable weights, omitting structurally blocked connections."""
+    if weight_masks is None:
+        weight_masks = [np.ones_like(W, dtype=bool) for W, _ in layers]
+    return np.concatenate([
+        np.concatenate([np.asarray(W)[mask], np.asarray(b).ravel()])
+        for (W, b), mask in zip(layers, weight_masks)
+    ])
 
 
-def unflatten_layers(theta, arch):
+def unflatten_layers(theta, arch, weight_masks=None):
     layers, i = [], 0
     for li in range(len(arch) - 1):
         n_out, n_in = arch[li + 1], arch[li]
-        W = theta[i:i + n_out * n_in].reshape(n_out, n_in); i += n_out * n_in
+        mask = (np.ones((n_out, n_in), dtype=bool) if weight_masks is None
+                else np.asarray(weight_masks[li], dtype=bool))
+        if mask.shape != (n_out, n_in):
+            raise ValueError(f"Weight mask {li} has shape {mask.shape}; expected {(n_out, n_in)}")
+        W = np.zeros((n_out, n_in), dtype=np.float64)
+        n_active = int(mask.sum())
+        W[mask] = theta[i:i + n_active]; i += n_active
         b = theta[i:i + n_out]; i += n_out
         layers.append((W, b))
+    if i != len(theta):
+        raise ValueError("Parameter vector length does not match architecture and masks")
     return layers
 
 
@@ -254,7 +286,7 @@ def robust_objective_score(calmars, fold_metrics, scores, thresholds, min_fold_t
 
 
 def make_fitness_context(df, X, arch, theta0_w, folds, min_fold_trades, min_valid_folds, l2,
-                         fold_objective="mean"):
+                         fold_objective="mean", weight_masks=None):
     return {
         "base_df": df[["time", "close"]].copy(),
         "times": df["time"].copy(),
@@ -269,6 +301,7 @@ def make_fitness_context(df, X, arch, theta0_w, folds, min_fold_trades, min_vali
         "min_valid_folds": min_valid_folds,
         "l2": l2,
         "fold_objective": fold_objective,
+        "weight_masks": weight_masks,
     }
 
 
@@ -276,7 +309,7 @@ def evaluate_theta(theta, context):
     theta = np.asarray(theta, dtype=np.float64)
     theta0_w = context["theta0_w"]
     n_w = len(theta0_w)
-    layers = unflatten_layers(theta[:n_w], context["arch"])
+    layers = unflatten_layers(theta[:n_w], context["arch"], context["weight_masks"])
     thresholds = np.clip(
         theta[n_w:] * THRESHOLD_SCALE,
         [-900.0, -900.0, -900.0, 0.0],
@@ -352,10 +385,10 @@ def evaluate_theta_worker(theta):
 
 
 def make_fitness(df, X, arch, theta0_w, folds, min_fold_trades, min_valid_folds, l2,
-                 fold_objective="mean"):
+                 fold_objective="mean", weight_masks=None):
     context = make_fitness_context(
         df, X, arch, theta0_w, folds, min_fold_trades, min_valid_folds, l2,
-        fold_objective,
+        fold_objective, weight_masks,
     )
 
     def fitness(theta):
@@ -534,12 +567,28 @@ def main():
                         "from the current winner artifact's position state (one-cycle circularity "
                         "per roadmap). Output artifact will have 52 features.")
     p.add_argument("--approach-b-params",
-                   help="Winner CSV to derive position state from (default: auto-detect from asset/tf)")
+                        help="Winner CSV to derive position state from (default: auto-detect from asset/tf)")
+    p.add_argument("--temporal-features", action="store_true",
+                   help="Experiment: append causal 3- and 12-bar EMA companions for ten fast signals.")
+    p.add_argument("--input-structure", choices=["dense", "grouped", "hybrid_grouped", "random_sparse"], default="dense",
+                   help="First-layer connectivity: dense is the production baseline; grouped "
+                   "routes each of the 16 first-layer units to one logical signal family; "
+                        "hybrid_grouped keeps eight family specialists plus eight dense rows; "
+                        "random_sparse is the matched seeded control.")
+    p.add_argument("--mask-seed", type=int,
+                   help="Required with random_sparse; seed for the auditable first-layer mask.")
     p.add_argument("--regime", choices=["bull", "bear", "sideways", "all"], default="all",
                    help="Filter Phase-1 supervised training to MVRV regime bars only "
                         "(requires mvrv_regime column). Phase-2 CMA-ES always uses all IS bars.")
     args = p.parse_args()
 
+    if args.input_structure == "random_sparse" and args.mask_seed is None:
+        p.error("--mask-seed is required with --input-structure random_sparse")
+    if args.input_structure == "hybrid_grouped" and args.out is None:
+        p.error("--out is required with --input-structure hybrid_grouped to protect live artifacts")
+    if args.temporal_features and args.out is None:
+        p.error("--temporal-features requires --out to protect live artifacts")
+
     t0 = time.time()
 
     def log(msg):
@@ -553,7 +602,8 @@ def main():
 
     if args.smoke:
         df = df.tail(800).reset_index(drop=True)
-        args.hidden = [8, 4]
+        if args.input_structure == "dense":
+            args.hidden = [8, 4]
         args.phase1_epochs = min(args.phase1_epochs, 20)
         args.es_generations = min(args.es_generations, 5)
         args.es_popsize = min(args.es_popsize, 8)
@@ -578,6 +628,24 @@ def main():
         feature_cols = _inject_position_features(df, asset, tf, args, log)
     else:
         feature_cols = FEATURE_COLS
+    if args.temporal_features:
+        feature_cols = [*feature_cols, *inject_temporal_features(df)]
+        log(f"temporal-features: appended {len(feature_cols) - len(FEATURE_COLS)} causal EMA companions")
+
+    first_layer_mask = None
+    if args.input_structure == "grouped":
+        first_layer_mask = grouped_first_layer_mask(feature_cols, args.hidden[0])
+        log("input-structure=grouped: first-layer units are restricted to logical signal families")
+    elif args.input_structure == "hybrid_grouped":
+        first_layer_mask = hybrid_grouped_first_layer_mask(feature_cols, args.hidden[0])
+        log("input-structure=hybrid_grouped: eight family specialists plus eight dense rows "
+            f"(active_edges={int(first_layer_mask.sum())})")
+    elif args.input_structure == "random_sparse":
+        first_layer_mask = random_sparse_first_layer_mask(
+            feature_cols, args.hidden[0], args.mask_seed,
+        )
+        log(f"input-structure=random_sparse: seeded mask={args.mask_seed} "
+            f"active_edges={int(first_layer_mask.sum())}")
 
     X = _prepare_features(df, feature_cols)
     y, valid = build_target(df, args.target_k)
@@ -597,7 +665,8 @@ def main():
 
     # --- Phase 1 ---
     layers, p1_meta = pretrain(X, y, valid, df["time"], args.hidden,
-                               args.phase1_epochs, args.seed, val_start, val_end, log)
+                               args.phase1_epochs, args.seed, val_start, val_end, log,
+                               first_layer_mask)
     arch = p1_meta["arch"]
 
     training_meta = {
@@ -609,6 +678,15 @@ def main():
         "train_window": [str(df["time"].iloc[0].date()), str(df["time"].iloc[-1].date())],
         "seed": args.seed,
         "regime_filter": args.regime,
+        "input_structure": (
+            grouped_structure_metadata(feature_cols, args.hidden[0])
+            if args.input_structure == "grouped"
+            else hybrid_grouped_structure_metadata(feature_cols, args.hidden[0])
+            if args.input_structure == "hybrid_grouped"
+            else random_sparse_structure_metadata(feature_cols, args.hidden[0], args.mask_seed)
+            if args.input_structure == "random_sparse"
+            else {"name": "dense"}
+        ),
         "created": datetime.now(timezone.utc).isoformat(timespec="seconds"),
     }
     # Seed thresholds from the pretrained net's actual score distribution —
@@ -627,18 +705,23 @@ def main():
 
     # --- Phase 2 ---
     if not args.skip_phase2:
-        theta0_w = flatten_layers(layers)
+        weight_masks = None
+        if first_layer_mask is not None:
+            weight_masks = [first_layer_mask] + [
+                np.ones((arch[i + 1], arch[i]), dtype=bool) for i in range(1, len(arch) - 1)
+            ]
+        theta0_w = flatten_layers(layers, weight_masks)
         theta0 = np.concatenate([theta0_w, thresholds / THRESHOLD_SCALE])
         fitness = make_fitness(df, X, arch, theta0_w, folds,
                                min_fold_trades, min_valid_folds, args.l2,
-                               args.fold_objective)
+                               args.fold_objective, weight_masks)
         f0, calmars0, _ = fitness(theta0)
         log(f"phase2: pretrain baseline fitness={f0:.4f} "
             f"fold_calmars={[round(c, 2) for c in calmars0]}")
         best = run_cma(fitness, theta0, args.es_sigma, args.es_popsize,
                        args.es_generations, args.seed, log, args.es_workers)
         n_w = len(theta0_w)
-        layers = unflatten_layers(best["theta"][:n_w], arch)
+        layers = unflatten_layers(best["theta"][:n_w], arch, weight_masks)
         thresholds = best["thr"]
         training_meta["phase2"] = {
             "es_popsize": args.es_popsize, "es_sigma": args.es_sigma,
diff --git a/tools/validate_chart_data.py b/tools/validate_chart_data.py
index 7d1343e..096ce71 100644
--- a/tools/validate_chart_data.py
+++ b/tools/validate_chart_data.py
@@ -138,7 +138,8 @@ def validate_file(path: str) -> tuple[list[str], list[str], dict]:
         return ["empty file (0 rows)"], [], stats
 
     # --- Timestamps ---
-    t = pd.to_datetime(df["time"], errors="coerce", utc=True)
+    time_unit = "s" if pd.api.types.is_numeric_dtype(df["time"]) else None
+    t = pd.to_datetime(df["time"], unit=time_unit, errors="coerce", utc=True)
     if t.isna().any():
         errors.append(f"{int(t.isna().sum())} unparseable timestamps")
     t = t.dt.tz_localize(None)