diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..bae684ade6244d015c4e9f8c8800d45d0d01f60c
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,14 @@
+__pycache__/
+.pytest_cache/
+.mplconfig/
+.ruff_cache/
+figures/
+*.pdf
+*.png
+*.svg
+data/mb_reactive/
+data/aib9/
+*.xtc
+*.dcd
+*.trr
+*.npz
diff --git a/INPUTS.tsv b/INPUTS.tsv
new file mode 100644
index 0000000000000000000000000000000000000000..4260427cf23268f1cef915189a07bd6bacc2d839
--- /dev/null
+++ b/INPUTS.tsv
@@ -0,0 +1,11 @@
+analysis	relative_path	purpose
+muller_brown	data/mb_reactive	Saved trajectories for MSM refitting and rollout analyses
+aib9	data/aib9/COLVAR_phi	Continuous OpenMM backbone angles
+aib9	data/aib9/COLVAR_psi	Continuous OpenMM backbone angles
+p53_heldout	data/p53/pocket_sc_feats_s10.pkl	Side-chain feature cache for trajectory-held-out refitting
+p53_heldout	data/p53/frame_drug/frames.json	Per-frame structural observables
+p53_discovery	data/p53/rmsd_matrix.npy	Crystal-to-MD structural comparisons
+p53_discovery	data/p53/pocket_msm_pi.json	Reference Model S populations
+p53_discovery	data/p53/pocket_reps	Representative PDB files and cached Fpocket outputs
+p53_discovery	data/p53/pocket_feats_s10_cb_ALL.pkl	Feature-cache trajectory provenance
+p53_discovery	data/p53/coverage_all_y220c.json	Crystallographic reference metadata
diff --git a/README.md b/README.md
index 154df8298fab5ecf322016157858e08cd1bccbe1..4f14a2b9781a634941453439424cf46d434fa589 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,97 @@
----
-license: apache-2.0
----
+# ScooBDoob
+
+## Setup
+
+Use Python 3.10 or newer. Run commands from this directory.
+
+```bash
+python -m pip install -r requirements.txt
+python -m pytest tests -q
+```
+
+## Contents
+
+- `scoobdoob/`: conditioning, MSM construction, and analysis functions.
+- `make_*.py`, `paper_figures/`: paper analyses and plots.
+- `analysis/`, `data/`: sensitivity analyses and saved numerical results.
+- `tests/`: numerical correctness checks.
+
+## Paper analyses
+
+Generate the MB, Aib9 robustness, and p53 plots from the included results:
+
+```bash
+python run_cached_figures.py
+```
+
+Outputs are written to `figures/`. For a subset, use `--only mb`,
+`--only aib9_robustness`, or `--only p53`.
+
+| Analysis | Script |
+| --- | --- |
+| MB sampling comparisons | `make_mb_panels.py`, `make_mb_unconditional.py` |
+| MB replicated statistics and parameter sweeps | `mb_stats.py`, `mb_alpha_seeds.py`, `mb_ticalag_seeds.py` |
+| MB density-floor sensitivity | `analysis/mb_floor_sensitivity/analyze.py` |
+| Aib9 sampling comparison | `make_aib9_method_figure.py` |
+| Aib9 robustness and density sweep | `validate_aib9.py`, `aib9_kappa_sweep.py` |
+| p53 discovery and preference | `paper_figures/data/`, `paper_figures/plot/` |
+| p53 trajectory-held-out analysis | `trajectory_cv_tilt.py`, `summarize_trajectory_cv_tilt.py` |
+
+After preparing the inputs below, rerun the main analyses with:
+
+```bash
+python make_mb_panels.py --cache-only
+python make_mb_unconditional.py --cache-only
+python make_aib9_method_figure.py
+python validate_aib9.py --robustness
+python aib9_kappa_sweep.py
+python paper_figures/data/p53_preference_data.py --splits 400
+```
+
+For the MB floor sweep, run `analyze.py --seed N` for seeds 0–19, then
+`analyze.py --summarize`. Use `--validate` for its exact finite-model check.
+
+## Data
+
+MD trajectories are not distributed with this package. Small numerical results
+and docking scores are included; additional analysis inputs are listed in
+`INPUTS.tsv`.
+
+### Müller–Brown
+
+Generate 12 overdamped Langevin trajectories with the paper settings
+(250,000 steps each, timestep 0.0002, inverse temperature 0.06, saving every
+five steps):
+
+```bash
+python -m scoobdoob.validation.mb_data --out data/mb_reactive
+```
+
+### Aib9
+
+Use the [published Aib9 OpenMM workflow](https://github.com/shams-mehdi/aib9_openmm)
+from [Mehdi et al. (2022)](https://doi.org/10.1021/acs.jctc.2c00058) to prepare
+and simulate the solvated peptide. The paper analyzes a continuous, approximately
+223 ns trajectory at 500 K with a 2 fs timestep. Record the nine backbone
+phi and psi angles in radians every 0.2 ps using PLUMED.
+
+Place `COLVAR_phi` and `COLVAR_psi` in `data/aib9/`, or set
+`SCOOBDOOB_AIB9_DATA` to their directory. Each file should contain time followed
+by nine angles. The loader takes every fifth sample, giving 1 ps spacing.
+
+### p53 Y220C
+
+The supplied numerical results support plotting and docking-preference analysis.
+MSM refits require the feature and pocket-observable files listed in `INPUTS.tsv`.
+Use the simulation and feature definitions in the paper's Methods.
+
+For held-out fits, use `--k` 75, 100, or 150 and `--seed` 0, 1, or 2.
+The observable/threshold pairs are `site_drug`/0.5, `site_volume`/300.865,
+and `site_apolar`/55.551. For example:
+
+```bash
+python trajectory_cv_tilt.py --k 100 --seed 0 --observable site_volume \
+    --drug-thresh 300.865 --out data/p53/trajectory_cv_tilt_vol_k100_seed0.json
+python summarize_trajectory_cv_tilt.py --pattern 'trajectory_cv_tilt_vol_k*_seed*.json' \
+    --out data/p53/trajectory_cv_tilt_vol_robustness.json
+```
diff --git a/aib9_kappa_sweep.py b/aib9_kappa_sweep.py
new file mode 100644
index 0000000000000000000000000000000000000000..05bc4f1fc15de503813b588ea4cebc6118b8a974
--- /dev/null
+++ b/aib9_kappa_sweep.py
@@ -0,0 +1,214 @@
+#!/usr/bin/env python
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import numpy as np
+
+from scoobdoob.validation import aib9, msm_build as mb, tpt
+
+
+ROOT = Path(__file__).resolve().parent
+LAG, K, N_COMP, STRIDE, HORIZON = 5, 100, 4, 5, 320
+N_PATHS = 600
+PATH_SEEDS = (0, 1, 2)
+KAPPAS = (0.0, 0.10, 0.20, 0.36, 0.50, 0.75, 1.00)
+GATE_BETA, TIME_SIGMA, W_FLOOR = 0.5, 0.30, 1e-12
+
+
+def conditioned_kernels(P, target, weights):
+    n_states = P.shape[0]
+    terminal = np.zeros(n_states)
+    terminal[target] = 1.0 / len(target)
+    backward = terminal.copy()
+    messages = [backward.copy()]
+    for t in range(HORIZON - 1, -1, -1):
+        raw = P @ (weights[t] * backward)
+        total = raw.sum()
+        backward = raw / total if total > 0 else np.full(n_states, 1.0 / n_states)
+        messages.append(backward.copy())
+    messages = messages[::-1]
+
+    kernels = []
+    for t in range(HORIZON):
+        numerator = P * (weights[t][None, :] * messages[t + 1][None, :])
+        denominator = (P @ (weights[t] * messages[t + 1]))[:, None]
+        kernel = np.maximum(
+            np.nan_to_num(np.divide(numerator, np.maximum(denominator, 1e-300))),
+            0.0,
+        )
+        row_sum = kernel.sum(axis=1, keepdims=True)
+        bad = (row_sum <= 1e-300).ravel()
+        if bad.any():
+            kernel[bad] = P[bad]
+            row_sum = kernel.sum(axis=1, keepdims=True)
+        kernels.append(kernel / np.maximum(row_sum, 1e-300))
+    return kernels
+
+
+def sample(kernels, starts, seed):
+    rng = np.random.default_rng(seed)
+    paths = []
+    for _ in range(N_PATHS):
+        state = int(rng.choice(starts))
+        path = [state]
+        for kernel in kernels:
+            probabilities = kernel[state]
+            state = int(
+                rng.choice(len(probabilities), p=probabilities / probabilities.sum())
+            )
+            path.append(state)
+        paths.append(np.asarray(path, dtype=int))
+    return paths
+
+
+def main():
+    trajectories, _ = aib9.load_openmm_colvar(stride=STRIDE)
+    features = aib9.featurize_sincos(trajectories)
+    handedness = aib9.handedness_indicator(trajectories)
+    model = mb.build_full_msm(
+        features,
+        handedness,
+        lag=LAG,
+        k=K,
+        n_comp=N_COMP,
+        seed=0,
+        use_tica=True,
+    )
+
+    labels = aib9.classify_microstates(model.active_xy)
+    sets = {
+        name: np.where(labels == name)[0].tolist()
+        for name in ("R", "L", "I_N", "I_C", "other")
+    }
+    P = np.asarray(model.P)
+    pi = np.asarray(model.stationary)
+    projection = model.centers[model.active_symbols][:, :2]
+
+    intermediate = sorted(set(sets["I_N"]) | set(sets["I_C"]))
+    q_intermediate_to_left = tpt.committor_agreement(model, intermediate, sets["L"])[1]
+    net_flux_matrix = np.asarray(
+        tpt.reactive_flux(model, sets["R"], sets["L"]).net_flux
+    )
+    state_flux = (net_flux_matrix.sum(1) + net_flux_matrix.sum(0)) / 2
+
+    n_center = projection[sets["I_N"]].mean(0)
+    c_center = projection[sets["I_C"]].mean(0)
+    gateways = sorted(
+        [
+            i
+            for i in range(model.n_active)
+            if i not in set(intermediate)
+            and i not in set(sets["L"])
+            and abs(q_intermediate_to_left[i] - 0.5) < 0.2
+        ],
+        key=lambda i: -state_flux[i],
+    )[:4]
+    gateway_set = set(gateways)
+
+    state_handedness = model.active_xy
+    n_half = state_handedness[:, 1:4].mean(axis=1)
+    c_half = state_handedness[:, 5:8].mean(axis=1)
+    n_channel = set(sets["I_N"]) | {i for i in sets["other"] if n_half[i] > c_half[i]}
+    c_channel = set(sets["I_C"]) | {i for i in sets["other"] if n_half[i] <= c_half[i]}
+
+    counts = np.asarray(model.model.count_model.count_matrix, dtype=float)
+    rho = np.clip(counts.sum(1), 1.0, None) / counts.sum(1).mean()
+    z = np.log(state_flux / np.maximum(pi, 1e-15) + 1e-9)
+    z = (z - z.mean()) / z.std()
+    time_window = np.exp(
+        -0.5 * ((np.arange(HORIZON) - (HORIZON - 1) / 2) / (TIME_SIGMA * HORIZON)) ** 2
+    )
+
+    results = []
+    for kappa in KAPPAS:
+        density = np.maximum(np.exp(-kappa * rho), W_FLOOR)
+        weights = [
+            np.maximum(
+                density * np.exp(GATE_BETA * z * time_window[t]),
+                W_FLOOR,
+            )
+            for t in range(HORIZON)
+        ]
+        kernels = conditioned_kernels(P, sets["L"], weights)
+
+        seed_metrics = []
+        for seed in PATH_SEEDS:
+            paths = sample(kernels, sets["R"], seed)
+            reaches = []
+            gateway_hits = []
+            channels = []
+            visited = set()
+            for path in paths:
+                visited.update(map(int, path))
+                in_left = np.array([int(x) in set(sets["L"]) for x in path])
+                reaches.append(bool(in_left.any()))
+                if not in_left.any():
+                    continue
+                first_left = int(np.argmax(in_left))
+                before = np.array([int(x) in set(sets["R"]) for x in path[:first_left]])
+                if not before.any():
+                    continue
+                segment = path[int(np.where(before)[0][-1]) : first_left + 1]
+                gateway_hits.append(bool(set(map(int, segment)) & gateway_set))
+                n_count = sum(int(x) in n_channel for x in segment)
+                c_count = sum(int(x) in c_channel for x in segment)
+                channels.append(
+                    "I_N"
+                    if n_count > c_count
+                    else "I_C"
+                    if c_count > n_count
+                    else "other"
+                )
+
+            n_paths = sum(channel == "I_N" for channel in channels)
+            c_paths = sum(channel == "I_C" for channel in channels)
+            seed_metrics.append(
+                {
+                    "reach_pct": 100 * np.mean(reaches),
+                    "gateway_pct": 100 * np.mean(gateway_hits),
+                    "n_channel": n_paths,
+                    "c_channel": c_paths,
+                    "c_share_pct": 100 * c_paths / max(n_paths + c_paths, 1),
+                    "distinct_states": len(visited),
+                }
+            )
+
+        record = {"kappa": kappa, "path_seeds": list(PATH_SEEDS)}
+        for key in seed_metrics[0]:
+            values = np.asarray([metric[key] for metric in seed_metrics], dtype=float)
+            record[key] = {
+                "mean": float(values.mean()),
+                "sd": float(values.std(ddof=1)),
+            }
+        results.append(record)
+        print(
+            f"kappa={kappa:4.2f}  reach={record['reach_pct']['mean']:5.1f}%  "
+            f"gateway={record['gateway_pct']['mean']:5.1f}%  "
+            f"C-share={record['c_share_pct']['mean']:5.1f}%  "
+            f"states={record['distinct_states']['mean']:4.0f}"
+        )
+
+    output = {
+        "model": {
+            "lag_frames": LAG,
+            "frame_interval_ps": 1.0,
+            "horizon": HORIZON,
+            "n_microstates": K,
+            "clustering_seed": 0,
+        },
+        "sampler": {
+            "gate_beta": GATE_BETA,
+            "time_sigma": TIME_SIGMA,
+            "n_paths_per_seed": N_PATHS,
+        },
+        "results": results,
+    }
+    out_path = ROOT / "data" / "aib9_fes" / "aib9_kappa_sweep.json"
+    out_path.write_text(json.dumps(output, indent=2))
+    print(f"wrote {out_path}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/analysis/mb_floor_sensitivity/all_results.csv b/analysis/mb_floor_sensitivity/all_results.csv
new file mode 100644
index 0000000000000000000000000000000000000000..55e11888feacf017128e5403f92c8aebd5487c39
--- /dev/null
+++ b/analysis/mb_floor_sensitivity/all_results.csv
@@ -0,0 +1,121 @@
+s1_probability,expected_distinct_states,expected_self_transitions,self_transition_fraction,expected_s1_state_observations,terminal_probability,max_outside_corridor_probability,max_outside_arrival_mask_probability,condition,floor_percentile,density_floor,fraction_corridor_states_below_floor,independent_recursion_error,cluster_seed
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+0.9851874451785548,12.117485369184298,1.1871902273862402,0.07419938921164002,3.7013947994795875,0.9999999999999999,0.0,0.0,p10,10,0.16283333333333333,0.056179775280898875,0.0,19
+0.924501258010828,12.43542465705962,1.052367675030286,0.06577297968939287,2.1850920352979477,0.9999999999999998,0.0,0.0,p25,25,0.25966666666666666,0.10112359550561797,1.1102230246251565e-16,19
+0.8591177847987432,12.744674789431965,0.955353538458335,0.05970959615364594,1.6170819071311933,0.9999999999999999,0.0,0.0,p50,50,0.33216666666666667,0.19101123595505617,1.1102230246251565e-16,19
+0.8012584473980241,13.374537050824975,0.8727450747307617,0.054546567170672605,1.3430220419973777,1.0,0.0,0.0,p75,75,0.46108333333333335,0.29213483146067415,1.1102230246251565e-16,19
diff --git a/analysis/mb_floor_sensitivity/analyze.py b/analysis/mb_floor_sensitivity/analyze.py
new file mode 100644
index 0000000000000000000000000000000000000000..c14d5676ade44af550339a48a8fb7604bf68cf9a
--- /dev/null
+++ b/analysis/mb_floor_sensitivity/analyze.py
@@ -0,0 +1,499 @@
+from pathlib import Path
+import argparse
+import csv
+import hashlib
+import importlib.metadata
+import itertools
+import json
+import platform
+import sys
+import time
+
+import numpy as np
+
+OUT = Path(__file__).resolve().parent
+ROOT = OUT.parents[1]
+sys.path.insert(0, str(ROOT))
+from scoobdoob import _conditioning
+
+CONDITIONS = [
+    ("doob", None),
+    ("none", 0),
+    ("p10", 10),
+    ("p25", 25),
+    ("p50", 50),
+    ("p75", 75),
+]
+SETTINGS = dict(
+    cluster_seeds=list(range(20)),
+    k=100,
+    tica_lag=20,
+    msm_lag=5,
+    horizon=16,
+    alpha_per_transition=7.5,
+    floor_percentiles=[10, 25, 50, 75],
+    no_floor_control=True,
+    start_law="uniform on the original source set; fixed-start mixture",
+    s1_radius=0.25,
+)
+
+
+def dump(path, obj):
+    path.write_text(json.dumps(obj, indent=2, allow_nan=False) + "\n")
+
+
+def digest(path):
+    return hashlib.sha256(Path(path).read_bytes()).hexdigest()
+
+
+def exact_metrics(kernels, mu, region, targets, corridor):
+    """Forward full law and killed laws, including the state at step zero."""
+    m, n = len(mu), len(kernels)
+    mass = mu.copy()
+    survival = mu.copy()
+    survival[region] = 0
+    avoiding = np.broadcast_to(mu, (m, m)).copy()
+    np.fill_diagonal(avoiding, 0)
+    s1_occupancy = float(mass[region].sum())
+    self_steps = 0.0
+    max_outside = float(mass[~corridor].sum())
+    max_outside_arrivals = 0.0
+    for K in kernels:
+        self_steps += float(mass @ np.diag(K))
+        mass = mass @ K
+        np.testing.assert_allclose(mass.sum(), 1, atol=2e-12, rtol=0)
+        max_outside = max(max_outside, float(mass[~corridor].sum()))
+        max_outside_arrivals = max(max_outside_arrivals, float(mass[~corridor].sum()))
+        s1_occupancy += float(mass[region].sum())
+        survival = survival @ K
+        survival[region] = 0
+        avoiding = avoiding @ K
+        np.fill_diagonal(avoiding, 0)
+    hit_each = np.clip(1 - avoiding.sum(axis=1), 0, 1)
+    return dict(
+        s1_probability=float(np.clip(1 - survival.sum(), 0, 1)),
+        expected_distinct_states=float(hit_each.sum()),
+        expected_self_transitions=self_steps,
+        self_transition_fraction=self_steps / n,
+        expected_s1_state_observations=s1_occupancy,
+        terminal_probability=float(mass[targets].sum()),
+        max_outside_corridor_probability=max_outside,
+        max_outside_arrival_mask_probability=max_outside_arrivals,
+    )
+
+
+def reference_hit_probability(P, w, mu, region, targets, horizon):
+    """Independent ratio of weighted path sums, separately for each start."""
+    full = np.zeros(len(P))
+    full[targets] = 1
+    avoided = full.copy()
+    avoided[region] = 0
+    w_avoid = w.copy()
+    w_avoid[region] = 0
+    for _ in range(horizon):
+        full = P @ (w * full)
+        avoided = P @ (w_avoid * avoided)
+        scale = float(full.max())
+        if scale <= 0:
+            raise RuntimeError("No feasible weighted continuation")
+        full /= scale
+        avoided /= scale
+    initial = mu > 0
+    if np.any(full[initial] <= 0):
+        raise RuntimeError("Infeasible prescribed start")
+    avoided[region] = 0
+    return float(1 - mu[initial] @ (avoided[initial] / full[initial]))
+
+
+def validate_toy():
+    """Brute-force the weighted reference law, not a second kernel simulation."""
+    P = np.array([[0.5, 0.4, 0.1], [0.1, 0.6, 0.3], [0.2, 0.3, 0.5]])
+    w = np.array([0.7, 0.3, 1.2])
+    mu = np.array([0.3, 0.7, 0.0])
+    region = np.array([False, True, False])
+    targets = np.array([2])
+    horizon = 4
+    kernels, _ = _conditioning.build(P, w, targets, horizon, starts=[0, 1])
+    got = exact_metrics(kernels, mu, region, targets, np.ones(3, dtype=bool))
+    expected = np.zeros(4)
+    for i in [0, 1]:
+        norm = 0.0
+        moments = np.zeros(4)
+        for rest in itertools.product(range(3), repeat=horizon):
+            if rest[-1] != 2:
+                continue
+            path = (i,) + rest
+            prob = float(np.prod([P[a, b] * w[b] for a, b in zip(path[:-1], path[1:])]))
+            norm += prob
+            moments += prob * np.array(
+                [
+                    any(region[s] for s in path),
+                    len(set(path)),
+                    sum(a == b for a, b in zip(path[:-1], path[1:])),
+                    sum(region[s] for s in path),
+                ],
+                dtype=float,
+            )
+        expected += mu[i] * moments / norm
+    actual = [
+        got[k]
+        for k in [
+            "s1_probability",
+            "expected_distinct_states",
+            "expected_self_transitions",
+            "expected_s1_state_observations",
+        ]
+    ]
+    np.testing.assert_allclose(actual, expected, atol=2e-12, rtol=0)
+    other = reference_hit_probability(P, w, mu, region, targets, horizon)
+    np.testing.assert_allclose(other, expected[0], atol=2e-12, rtol=0)
+
+    masked_w = np.array([0.0, 0.3, 1.2])
+    K, _ = _conditioning.build(P, masked_w, targets, 1, starts=[0])
+    edge = exact_metrics(K, np.array([1.0, 0.0, 0.0]), region, targets, masked_w > 0)
+    np.testing.assert_allclose(
+        [
+            edge["terminal_probability"],
+            edge["expected_distinct_states"],
+            edge["max_outside_corridor_probability"],
+            edge["max_outside_arrival_mask_probability"],
+        ],
+        [1.0, 2.0, 1.0, 0.0],
+        atol=2e-12,
+        rtol=0,
+    )
+    return dict(
+        exhaustive_reference_check=True,
+        outside_mask_start_check=True,
+        max_absolute_error=float(np.max(np.abs(np.array(actual) - expected))),
+    )
+
+
+def run(seed):
+    start_time = time.time()
+    toy = validate_toy()
+    from scoobdoob.validation import msm_build as mb, tpt
+    from scoobdoob.validation import mb_ground_truth as gt
+    from scoobdoob.validation import mb_data as V
+
+    files = sorted((ROOT / "data/mb_reactive").glob("traj_*.npy"))
+    if len(files) != 12:
+        raise RuntimeError(
+            "Expected twelve trajectory files in data/mb_reactive; run python -m scoobdoob.validation.mb_data first"
+        )
+    trajs = [np.load(p).astype(np.float64) for p in files]
+    model = mb.build_full_msm(
+        trajs, trajs, lag=5, tica_lag=20, k=100, n_comp=2, seed=seed, use_tica=True
+    )
+
+    np.testing.assert_array_equal(model.active_symbols, np.arange(100))
+    A, B, _ = mb.q2_sign_endpoints(model)
+    starts, targets = V.orient_endpoints(model, A, B)
+    starts, targets = np.array(starts), np.array(targets)
+    xy = model.active_xy
+    agreement, q, _ = tpt.committor_agreement(model, starts.tolist(), targets.tolist())
+    flux = tpt.reactive_flux(model, starts.tolist(), targets.tolist())
+    through = np.asarray(flux.gross_flux).sum(axis=1)
+    corridor = through / through.sum() > 0.5 / model.n_active
+    corridor[starts] = True
+    corridor[targets] = True
+    saddles = gt.saddle_points()
+    S1 = saddles[np.argmin(saddles[:, 0])]
+    landmarks = np.vstack([xy[starts].mean(0), xy[targets].mean(0), saddles])
+    lo, hi = landmarks.min(0) - 0.35, landmarks.max(0) + 0.35
+    corridor &= np.all((xy >= lo) & (xy <= hi), axis=1)
+
+    counts = model.counts[model.active_symbols].astype(float)
+    rho = np.maximum(counts, 1) / counts.mean()
+    react = (q > 0.05) & (q < 0.95)
+    region = np.linalg.norm(xy - S1, axis=1) < 0.25
+    if not np.any(region) or not np.any(react):
+        raise RuntimeError("Missing S1 or reference committor region")
+    mu = np.zeros(model.n_active)
+    mu[starts] = 1 / len(starts)
+    saved_match = None
+    if seed == 0:
+        with np.load(
+            ROOT / "data/mb_panels_data/panels_cache.npz", allow_pickle=True
+        ) as z:
+            saved_match = dict(
+                max_state_coordinate_difference=float(np.max(np.abs(xy - z["xy"]))),
+                counts_equal=bool(np.array_equal(counts, z["counts"])),
+                corridor_equal=bool(np.array_equal(corridor, z["on"])),
+            )
+    np.savez_compressed(
+        OUT / f"model_{seed:02d}.npz",
+        P=model.P,
+        pi=model.stationary,
+        q=q,
+        xy=xy,
+        counts=counts,
+        rho=rho,
+        corridor=corridor,
+        source=starts,
+        target=targets,
+        S1_region=region,
+        mu=mu,
+    )
+    records = []
+    for name, pct in CONDITIONS:
+        floor = (
+            None
+            if pct is None
+            else (0.0 if pct == 0 else float(np.percentile(rho[react], pct)))
+        )
+        w = (
+            np.ones_like(rho)
+            if name == "doob"
+            else np.exp(-7.5 * np.maximum(rho, floor))
+        )
+        if np.any(w <= 0):
+            raise FloatingPointError("A positive density weight underflowed")
+        w[~corridor] = 0
+        kernels, h = _conditioning.build(model.P, w, targets, 16, starts=starts)
+        for K in kernels:
+            if np.any((K > 0) & (model.P == 0)):
+                raise RuntimeError("New edge introduced")
+        record = exact_metrics(kernels, mu, region, targets, corridor)
+        independent = reference_hit_probability(model.P, w, mu, region, targets, 16)
+        error = abs(independent - record["s1_probability"])
+        if error > 2e-10:
+            raise AssertionError(f"Two path-law calculations disagree by {error}")
+        record.update(
+            condition=name,
+            floor_percentile=pct,
+            density_floor=floor,
+            fraction_corridor_states_below_floor=(
+                None if floor is None else float(np.mean(rho[corridor] < floor))
+            ),
+            independent_recursion_error=error,
+        )
+        records.append(record)
+        print(
+            f"seed={seed} {name} S1={record['s1_probability']:.8f} "
+            f"distinct/path={record['expected_distinct_states']:.6f}",
+            flush=True,
+        )
+    source_names = [
+        "make_mb_panels.py",
+        "mb_stats.py",
+        "scoobdoob/validation/msm_build.py",
+        "scoobdoob/_conditioning.py",
+        "scoobdoob/validation/mb_data.py",
+    ]
+    sources = {str(p.relative_to(ROOT)): digest(p) for p in files}
+    sources.update({p: digest(ROOT / p) for p in source_names})
+    sources[str(Path(__file__).relative_to(ROOT))] = digest(__file__)
+    versions = {
+        p: importlib.metadata.version(p)
+        for p in ["numpy", "scipy", "scikit-learn", "deeptime"]
+    }
+    dump(
+        OUT / f"result_{seed:02d}.json",
+        dict(
+            seed=seed,
+            settings=SETTINGS,
+            records=records,
+            n_frames=sum(len(t) for t in trajs),
+            n_active=model.n_active,
+            corridor_states=int(corridor.sum()),
+            s1_states=int(region.sum()),
+            source_states_outside_arrival_mask=starts[~corridor[starts]].tolist(),
+            target_states_outside_arrival_mask=targets[~corridor[targets]].tolist(),
+            reference_committor_agreement=float(agreement),
+            seed0_saved_figure_check=saved_match,
+            validation=toy,
+            source_sha256=sources,
+            versions=versions,
+            python=platform.python_version(),
+            elapsed_seconds=time.time() - start_time,
+        ),
+    )
+
+
+def summarize():
+    results = [
+        json.loads((OUT / f"result_{s:02d}.json").read_text()) for s in range(20)
+    ]
+    if any(r["settings"] != SETTINGS for r in results):
+        raise RuntimeError("Inconsistent settings")
+    if len({json.dumps(r["source_sha256"], sort_keys=True) for r in results}) != 1:
+        raise RuntimeError("Source/data hashes changed across jobs")
+    records = [
+        {**row, "cluster_seed": r["seed"]} for r in results for row in r["records"]
+    ]
+    with (OUT / "all_results.csv").open("w", newline="") as f:
+        writer = csv.DictWriter(f, fieldnames=list(records[0]))
+        writer.writeheader()
+        writer.writerows(records)
+    values = {
+        name: np.array(
+            [
+                next(x for x in r["records"] if x["condition"] == name)[
+                    "s1_probability"
+                ]
+                for r in results
+            ]
+        )
+        * 100
+        for name, _ in CONDITIONS
+    }
+    stats = {}
+    for name, _ in CONDITIONS:
+        y = values[name]
+        delta = y - values["doob"]
+        delta25 = y - values["p25"]
+        by = [next(x for x in r["records"] if x["condition"] == name) for r in results]
+        stats[name] = dict(
+            s1_mean_pct=float(y.mean()),
+            s1_sd_pct=float(y.std(ddof=1)),
+            s1_min_pct=float(y.min()),
+            s1_max_pct=float(y.max()),
+            paired_gain_vs_doob_mean_pp=float(delta.mean()),
+            paired_gain_vs_doob_min_pp=float(delta.min()),
+            paired_gain_vs_doob_max_pp=float(delta.max()),
+            paired_vs_p25_mean_pp=float(delta25.mean()),
+            paired_vs_p25_min_pp=float(delta25.min()),
+            paired_vs_p25_max_pp=float(delta25.max()),
+            n_clusterings_better_than_doob=int(np.sum(delta > 1e-10)),
+            distinct_per_path_mean=float(
+                np.mean([x["expected_distinct_states"] for x in by])
+            ),
+            self_steps_mean=float(
+                np.mean([x["expected_self_transitions"] for x in by])
+            ),
+        )
+    archive = json.loads((ROOT / "data/mb_stats.json").read_text())
+    comparison = {
+        name: dict(
+            archived_mean_pct=float(np.mean(archive[key])),
+            mean_exact_minus_archived_pp=float(np.mean(values[name] - archive[key])),
+            max_absolute_difference_pp=float(
+                np.max(np.abs(values[name] - archive[key]))
+            ),
+        )
+        for name, key in [("doob", "raw"), ("p25", "fk")]
+    }
+    check = dict(
+        toy_exhaustive_passed=all(
+            r["validation"]["exhaustive_reference_check"] for r in results
+        ),
+        max_independent_recursion_error=max(
+            x["independent_recursion_error"] for x in records
+        ),
+        max_terminal_error=max(abs(x["terminal_probability"] - 1) for x in records),
+        max_outside_corridor_probability=max(
+            x["max_outside_corridor_probability"] for x in records
+        ),
+        max_outside_arrival_mask_probability=max(
+            x["max_outside_arrival_mask_probability"] for x in records
+        ),
+        endpoint_mask_exceptions=[
+            dict(
+                seed=r["seed"],
+                source=r["source_states_outside_arrival_mask"],
+                target=r["target_states_outside_arrival_mask"],
+            )
+            for r in results
+            if r["source_states_outside_arrival_mask"]
+            or r["target_states_outside_arrival_mask"]
+        ],
+        seed0_saved_figure_check=results[0]["seed0_saved_figure_check"],
+    )
+    summary = dict(
+        settings=SETTINGS,
+        conditions=stats,
+        checks=check,
+        archive_comparison=comparison,
+        scope="Exact probabilities on refitted MSMs from fixed MD data. SD is clustering sensitivity, not MD uncertainty.",
+        completed_clusterings=len(results),
+    )
+    dump(OUT / "summary.json", summary)
+    import matplotlib
+
+    matplotlib.use("Agg")
+    import matplotlib.pyplot as plt
+
+    labels = ["No floor", "10th", "25th (original)", "50th", "75th"]
+    names = ["none", "p10", "p25", "p50", "p75"]
+    fig, axes = plt.subplots(1, 2, figsize=(10, 4.3))
+    x = np.arange(5)
+    for s in range(20):
+        axes[0].plot(
+            x, [values[n][s] for n in names], color="#93b6cc", alpha=0.42, lw=0.8
+        )
+        axes[1].plot(
+            x,
+            [values[n][s] - values["doob"][s] for n in names],
+            color="#93b6cc",
+            alpha=0.42,
+            lw=0.8,
+        )
+    for ax, metric in zip(axes, ["s1_mean_pct", "paired_gain_vs_doob_mean_pp"]):
+        ax.plot(
+            x,
+            [stats[n][metric] for n in names],
+            "o-",
+            color="#164e70",
+            lw=2,
+            label="Mean of 20 clusterings",
+        )
+        ax.set_xticks(x, labels, rotation=18)
+        ax.set_xlabel("Density-floor percentile")
+        ax.spines[["top", "right"]].set_visible(False)
+        ax.grid(axis="y", alpha=0.2)
+    axes[0].axhline(
+        stats["doob"]["s1_mean_pct"], ls="--", color="#777777", label="Pure-Doob mean"
+    )
+    axes[1].axhline(0, ls="--", color="#777777")
+    axes[0].set_ylabel("Exact probability of S1 visitation (%)")
+    axes[1].set_ylabel("Paired gain over pure Doob (percentage points)")
+    axes[0].legend(frameon=False, fontsize=8)
+    fig.suptitle("MB floor sensitivity at fixed density strength and horizon")
+    fig.tight_layout()
+    (ROOT / "figures").mkdir(parents=True, exist_ok=True)
+    for ext in ["pdf", "png", "svg"]:
+        fig.savefig(
+            ROOT / "figures" / f"floor_sensitivity.{ext}", dpi=180, bbox_inches="tight"
+        )
+    lines = [
+        "MB FLOOR SENSITIVITY — completed",
+        "",
+        summary["scope"],
+        "",
+        "Condition       S1 mean +/- SD (%)      gain vs Doob (pp)   better /20",
+    ]
+    for name, _ in CONDITIONS:
+        z = stats[name]
+        lines.append(
+            f"{name:12s} {z['s1_mean_pct']:7.3f} +/- {z['s1_sd_pct']:6.3f} "
+            f"          {z['paired_gain_vs_doob_mean_pp']:+7.3f}             "
+            f"{z['n_clusterings_better_than_doob']}"
+        )
+    lines += [
+        "",
+        "Checks:",
+        json.dumps(check, indent=2),
+        "",
+        "The original 25th-percentile setting is retained. No optimum was selected.",
+        "Use per-clustering paired differences in all_results.csv; original results were not overwritten.",
+        "Exact evaluation has no rollout Monte Carlo error. Refitting may differ from archived finite samples.",
+        "Aib9 and the p53 held-out density experiment used no density floor; this test concerns MB.",
+    ]
+    (OUT / "RESULTS.txt").write_text("\n".join(lines) + "\n")
+    print("\n".join(lines), flush=True)
+
+
+if __name__ == "__main__":
+    p = argparse.ArgumentParser()
+    group = p.add_mutually_exclusive_group(required=True)
+    group.add_argument("--seed", type=int, choices=range(20))
+    group.add_argument("--summarize", action="store_true")
+    group.add_argument("--validate", action="store_true")
+    args = p.parse_args()
+    if args.validate:
+        print(json.dumps(validate_toy(), indent=2))
+    elif args.summarize:
+        summarize()
+    else:
+        run(args.seed)
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new file mode 100644
index 0000000000000000000000000000000000000000..2a2eaba942e8b75149d2dda50aab22663704f5ce
--- /dev/null
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diff --git a/analysis/mb_floor_sensitivity/result_03.json b/analysis/mb_floor_sensitivity/result_03.json
new file mode 100644
index 0000000000000000000000000000000000000000..a2afb86f5f2dd46966485ac56b77799d9a00086f
--- /dev/null
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diff --git a/analysis/mb_floor_sensitivity/result_04.json b/analysis/mb_floor_sensitivity/result_04.json
new file mode 100644
index 0000000000000000000000000000000000000000..2e51f54651d2ff11ac082d76184e62f08bd9e739
--- /dev/null
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diff --git a/analysis/mb_floor_sensitivity/result_05.json b/analysis/mb_floor_sensitivity/result_05.json
new file mode 100644
index 0000000000000000000000000000000000000000..f8b34330003c73841037af15da0208e347dbd33b
--- /dev/null
+++ b/analysis/mb_floor_sensitivity/result_05.json
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diff --git a/analysis/mb_floor_sensitivity/result_06.json b/analysis/mb_floor_sensitivity/result_06.json
new file mode 100644
index 0000000000000000000000000000000000000000..b120f18bfb75a16df691313619cc07eee9d88cd1
--- /dev/null
+++ b/analysis/mb_floor_sensitivity/result_06.json
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diff --git a/analysis/mb_floor_sensitivity/result_07.json b/analysis/mb_floor_sensitivity/result_07.json
new file mode 100644
index 0000000000000000000000000000000000000000..4f08e10c1e47c19a2dc96517b91480f42eeaf457
--- /dev/null
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index 0000000000000000000000000000000000000000..bd5d4af6d4b5a509d009b1a2330d0ca071659be8
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new file mode 100644
index 0000000000000000000000000000000000000000..1db145a2f12d208948a726e2193e0cfe5e8d9867
--- /dev/null
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new file mode 100644
index 0000000000000000000000000000000000000000..7204af766f8800dbcae84f9fa05acbaa93329474
--- /dev/null
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diff --git a/analysis/mb_floor_sensitivity/result_11.json b/analysis/mb_floor_sensitivity/result_11.json
new file mode 100644
index 0000000000000000000000000000000000000000..a8096cbbc4857b60b25924682777d0beb15768d2
--- /dev/null
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diff --git a/analysis/mb_floor_sensitivity/result_12.json b/analysis/mb_floor_sensitivity/result_12.json
new file mode 100644
index 0000000000000000000000000000000000000000..0b6239c81521d5e7b9fe6770f48864005ad200a0
--- /dev/null
+++ b/analysis/mb_floor_sensitivity/result_12.json
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diff --git a/analysis/mb_floor_sensitivity/result_13.json b/analysis/mb_floor_sensitivity/result_13.json
new file mode 100644
index 0000000000000000000000000000000000000000..92300c2a1f7ed482c80e79d9db2bc01a4a965b4d
--- /dev/null
+++ b/analysis/mb_floor_sensitivity/result_13.json
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new file mode 100644
index 0000000000000000000000000000000000000000..4327db06250131ab06844ed1cadef590904d57a0
--- /dev/null
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new file mode 100644
index 0000000000000000000000000000000000000000..1405b0002dc8e5fd2f7d20909476eeb267decc9c
--- /dev/null
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diff --git a/analysis/mb_floor_sensitivity/result_16.json b/analysis/mb_floor_sensitivity/result_16.json
new file mode 100644
index 0000000000000000000000000000000000000000..4538eb3066cb069179e699d93f8bb63b01208d73
--- /dev/null
+++ b/analysis/mb_floor_sensitivity/result_16.json
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diff --git a/analysis/mb_floor_sensitivity/result_17.json b/analysis/mb_floor_sensitivity/result_17.json
new file mode 100644
index 0000000000000000000000000000000000000000..e881457d58089d175b0132ca010a2f926b4af416
--- /dev/null
+++ b/analysis/mb_floor_sensitivity/result_17.json
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diff --git a/analysis/mb_floor_sensitivity/result_18.json b/analysis/mb_floor_sensitivity/result_18.json
new file mode 100644
index 0000000000000000000000000000000000000000..dafb670e3a99ecb9c86ce1cd8f328e7cb67b840a
--- /dev/null
+++ b/analysis/mb_floor_sensitivity/result_18.json
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new file mode 100644
index 0000000000000000000000000000000000000000..061b49d561c335581cfaded6b57f0d1e471403e7
--- /dev/null
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diff --git a/analysis/mb_floor_sensitivity/summary.json b/analysis/mb_floor_sensitivity/summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..d98275a3149b230e81187a75a4d6507eee47b44c
--- /dev/null
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+      "mean_exact_minus_archived_pp": -0.012582401873935112,
+      "max_absolute_difference_pp": 0.8121554227458176
+    }
+  },
+  "scope": "Exact probabilities on refitted MSMs from fixed MD data. SD is clustering sensitivity, not MD uncertainty.",
+  "completed_clusterings": 20
+}
diff --git a/data/aib9_fes/aib9_kappa_sweep.json b/data/aib9_fes/aib9_kappa_sweep.json
new file mode 100644
index 0000000000000000000000000000000000000000..192cc46775ebe34f933c1cd6b4399069373a0bc9
--- /dev/null
+++ b/data/aib9_fes/aib9_kappa_sweep.json
@@ -0,0 +1,240 @@
+{
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+}
\ No newline at end of file
diff --git a/data/mb_mep.npz b/data/mb_mep.npz
new file mode 100644
index 0000000000000000000000000000000000000000..306cf1a5f194e72f99afe3df168014f826268949
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diff --git a/data/mb_stats.json b/data/mb_stats.json
new file mode 100644
index 0000000000000000000000000000000000000000..e8d2bffa0996a5b5236ccad7f17fa5cb79141a9e
--- /dev/null
+++ b/data/mb_stats.json
@@ -0,0 +1,78 @@
+{
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\ No newline at end of file
diff --git a/data/p53/fig_data/discovery.json b/data/p53/fig_data/discovery.json
new file mode 100644
index 0000000000000000000000000000000000000000..f1b09372140fe68000c934d74963b67488861573
--- /dev/null
+++ b/data/p53/fig_data/discovery.json
@@ -0,0 +1,134 @@
+{
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+}
\ No newline at end of file
diff --git a/data/p53/fig_data/preference.json b/data/p53/fig_data/preference.json
new file mode 100644
index 0000000000000000000000000000000000000000..41e58cf1900c645fc72dd8f094a235aa30a83035
--- /dev/null
+++ b/data/p53/fig_data/preference.json
@@ -0,0 +1,24 @@
+{
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\ No newline at end of file
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new file mode 100644
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new file mode 100644
index 0000000000000000000000000000000000000000..e9b550a7c2cf1e3ce0c2ef4f35d39880b025a3dd
--- /dev/null
+++ b/data/p53/rmsd_screen/receptors.json
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diff --git a/data/p53/rmsd_screen/scores/92O.json b/data/p53/rmsd_screen/scores/92O.json
new file mode 100644
index 0000000000000000000000000000000000000000..9c2c96588295f17d1725feb84dbad99488653603
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/92O.json
@@ -0,0 +1,1008 @@
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/9GH.json b/data/p53/rmsd_screen/scores/9GH.json
new file mode 100644
index 0000000000000000000000000000000000000000..aa1bfdfd5e0a062a6be13944ac14db2d93e53629
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/9GH.json
@@ -0,0 +1,1008 @@
+{
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\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/9GK.json b/data/p53/rmsd_screen/scores/9GK.json
new file mode 100644
index 0000000000000000000000000000000000000000..7f1e05ddb3f48c54e0b47e58c5a49b23930552cf
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/9GK.json
@@ -0,0 +1,1008 @@
+{
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\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/9GN.json b/data/p53/rmsd_screen/scores/9GN.json
new file mode 100644
index 0000000000000000000000000000000000000000..aee1e2aac597315c04b2f189a6418e74f0ab0bbc
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/9GN.json
@@ -0,0 +1,1008 @@
+{
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\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/9GQ.json b/data/p53/rmsd_screen/scores/9GQ.json
new file mode 100644
index 0000000000000000000000000000000000000000..8f4ecfa1d465e7510913f9a821517c40e719116c
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/9GQ.json
@@ -0,0 +1,1008 @@
+{
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\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/9GT.json b/data/p53/rmsd_screen/scores/9GT.json
new file mode 100644
index 0000000000000000000000000000000000000000..86fea286493d3689ee4bbcb41a7fdc6482a8e8e1
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/9GT.json
@@ -0,0 +1,1008 @@
+{
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/9GW.json b/data/p53/rmsd_screen/scores/9GW.json
new file mode 100644
index 0000000000000000000000000000000000000000..b05ca5ecb3102368e4b305f7ec1a2c938506d40e
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/9GW.json
@@ -0,0 +1,1008 @@
+{
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\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/9GZ.json b/data/p53/rmsd_screen/scores/9GZ.json
new file mode 100644
index 0000000000000000000000000000000000000000..070c2da5c7287e9384dc90f10bb9e6a42e702489
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/9GZ.json
@@ -0,0 +1,1008 @@
+{
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/9H2.json b/data/p53/rmsd_screen/scores/9H2.json
new file mode 100644
index 0000000000000000000000000000000000000000..fd0ea7f0af79e9a434e08f5a68b4e02f98ebfe3d
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/9H2.json
@@ -0,0 +1,1008 @@
+{
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/9H5.json b/data/p53/rmsd_screen/scores/9H5.json
new file mode 100644
index 0000000000000000000000000000000000000000..911824f9a55190ad8aa78f12ad0bc38123ad6903
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/9H5.json
@@ -0,0 +1,1008 @@
+{
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/EXN.json b/data/p53/rmsd_screen/scores/EXN.json
new file mode 100644
index 0000000000000000000000000000000000000000..b5199d0349e2721ef90f791532460c867d61ff34
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/EXN.json
@@ -0,0 +1,1008 @@
+{
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/EXQ.json b/data/p53/rmsd_screen/scores/EXQ.json
new file mode 100644
index 0000000000000000000000000000000000000000..a403b36f9a7d920216217b9edf24ca56ec0c1df8
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/EXQ.json
@@ -0,0 +1,1008 @@
+{
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/EY2.json b/data/p53/rmsd_screen/scores/EY2.json
new file mode 100644
index 0000000000000000000000000000000000000000..99caf5e053a24c6505dde2dbf511a8c2b5d07570
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/EY2.json
@@ -0,0 +1,1008 @@
+{
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\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/EYB.json b/data/p53/rmsd_screen/scores/EYB.json
new file mode 100644
index 0000000000000000000000000000000000000000..ee1ce526d39d044911ecd813a442bab07685d3b4
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/EYB.json
@@ -0,0 +1,1008 @@
+{
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\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/EYE.json b/data/p53/rmsd_screen/scores/EYE.json
new file mode 100644
index 0000000000000000000000000000000000000000..193b007932859ed88f93145d4a6d39d178cbead7
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/EYE.json
@@ -0,0 +1,1008 @@
+{
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\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/GOH.json b/data/p53/rmsd_screen/scores/GOH.json
new file mode 100644
index 0000000000000000000000000000000000000000..852ba4ae135d32207e5f02540d30cf284d7d9cf5
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/GOH.json
@@ -0,0 +1,1008 @@
+{
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/KMN.json b/data/p53/rmsd_screen/scores/KMN.json
new file mode 100644
index 0000000000000000000000000000000000000000..32ccd4a8a167f5bbbc56074b2860b891553b843a
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/KMN.json
@@ -0,0 +1,1008 @@
+{
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/KV0.json b/data/p53/rmsd_screen/scores/KV0.json
new file mode 100644
index 0000000000000000000000000000000000000000..68ee390ca87e55c723285eac8119dd8e01cfd495
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/KV0.json
@@ -0,0 +1,1008 @@
+{
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+ "source_pdb": "8A31",
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/KVA.json b/data/p53/rmsd_screen/scores/KVA.json
new file mode 100644
index 0000000000000000000000000000000000000000..55e8e13d93d75dc9400990c16812c616646ccd84
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/KVA.json
@@ -0,0 +1,1008 @@
+{
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/LE9.json b/data/p53/rmsd_screen/scores/LE9.json
new file mode 100644
index 0000000000000000000000000000000000000000..0374b0645df271a45eca864dbfac94c3928d1340
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/LE9.json
@@ -0,0 +1,1008 @@
+{
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/NXG.json b/data/p53/rmsd_screen/scores/NXG.json
new file mode 100644
index 0000000000000000000000000000000000000000..4cfeb64d34d47ca047f8914b5376523d059d680c
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/NXG.json
@@ -0,0 +1,1008 @@
+{
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\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/O80.json b/data/p53/rmsd_screen/scores/O80.json
new file mode 100644
index 0000000000000000000000000000000000000000..5e33578c716bb092388a9819df14cb7d8240b37b
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/O80.json
@@ -0,0 +1,1008 @@
+{
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/O82.json b/data/p53/rmsd_screen/scores/O82.json
new file mode 100644
index 0000000000000000000000000000000000000000..0816753612c20e429b7fc55620c72385f17303eb
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/O82.json
@@ -0,0 +1,1008 @@
+{
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/O83.json b/data/p53/rmsd_screen/scores/O83.json
new file mode 100644
index 0000000000000000000000000000000000000000..f2e48fc32506535bef4150dd54662ae8b3da92c7
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/O83.json
@@ -0,0 +1,1008 @@
+{
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+ "source_pdb": "5G4N",
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\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/P51.json b/data/p53/rmsd_screen/scores/P51.json
new file mode 100644
index 0000000000000000000000000000000000000000..2d6a147fcae27e56eed670e30cb5078d6ef1d75e
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/P51.json
@@ -0,0 +1,1008 @@
+{
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\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/P74.json b/data/p53/rmsd_screen/scores/P74.json
new file mode 100644
index 0000000000000000000000000000000000000000..d14995ecdb484d87b9a77d4ffbe0211957e3f219
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/P74.json
@@ -0,0 +1,1008 @@
+{
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/P83.json b/data/p53/rmsd_screen/scores/P83.json
new file mode 100644
index 0000000000000000000000000000000000000000..3b61a0e9db5e9dacff9c8b61f4e69beb02335101
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/P83.json
@@ -0,0 +1,1008 @@
+{
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/P84.json b/data/p53/rmsd_screen/scores/P84.json
new file mode 100644
index 0000000000000000000000000000000000000000..f66471377ef0dc78b17be9ec67299dabed8fb9cc
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/P84.json
@@ -0,0 +1,1008 @@
+{
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/P86.json b/data/p53/rmsd_screen/scores/P86.json
new file mode 100644
index 0000000000000000000000000000000000000000..b1ad7f1e9f88c9441ea8737b960f1153872a2974
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/P86.json
@@ -0,0 +1,1008 @@
+{
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/P96.json b/data/p53/rmsd_screen/scores/P96.json
new file mode 100644
index 0000000000000000000000000000000000000000..62007768183a65e834b7bcbb5d8f7338e1cd6792
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/P96.json
@@ -0,0 +1,1008 @@
+{
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\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/QC5.json b/data/p53/rmsd_screen/scores/QC5.json
new file mode 100644
index 0000000000000000000000000000000000000000..d80e88ee13b56db9d82eb847851b44000cd34bc8
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/QC5.json
@@ -0,0 +1,1008 @@
+{
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/R3R.json b/data/p53/rmsd_screen/scores/R3R.json
new file mode 100644
index 0000000000000000000000000000000000000000..20fcc8b7310af0147d3f27eed002fdf3cc4852d4
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/R3R.json
@@ -0,0 +1,1008 @@
+{
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/R4F.json b/data/p53/rmsd_screen/scores/R4F.json
new file mode 100644
index 0000000000000000000000000000000000000000..ec1e0fd652880c3aeb122b0b1fdc44099f2bf1bd
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/R4F.json
@@ -0,0 +1,1008 @@
+{
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/R4R.json b/data/p53/rmsd_screen/scores/R4R.json
new file mode 100644
index 0000000000000000000000000000000000000000..64c93c12f9d235c3dc70611db872d9be931ef419
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/R4R.json
@@ -0,0 +1,1008 @@
+{
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/R50.json b/data/p53/rmsd_screen/scores/R50.json
new file mode 100644
index 0000000000000000000000000000000000000000..7254f583590c02938275c49e8a0e2280412f02dd
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/R50.json
@@ -0,0 +1,1008 @@
+{
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/RZH.json b/data/p53/rmsd_screen/scores/RZH.json
new file mode 100644
index 0000000000000000000000000000000000000000..e0f38ceee7d85a2094ce506bade1d6c439f2deeb
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/RZH.json
@@ -0,0 +1,1008 @@
+{
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+ "source_pdb": "5AOI",
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/UFV.json b/data/p53/rmsd_screen/scores/UFV.json
new file mode 100644
index 0000000000000000000000000000000000000000..4dda0cdaf1a32dfcd461cd73dc8748ab2f96cf3c
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/UFV.json
@@ -0,0 +1,1008 @@
+{
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+ "source_pdb": "5AOL",
+ "n_atoms": 13,
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/UL7.json b/data/p53/rmsd_screen/scores/UL7.json
new file mode 100644
index 0000000000000000000000000000000000000000..ee097f595bd89e094f5b4aa18ae2c8a6794e2144
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/UL7.json
@@ -0,0 +1,1008 @@
+{
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+ "source_pdb": "5ABA",
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+ "box": 23.3,
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/X0U.json b/data/p53/rmsd_screen/scores/X0U.json
new file mode 100644
index 0000000000000000000000000000000000000000..699a3676d5e6aeae24185e2ab9546569518bff07
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/X0U.json
@@ -0,0 +1,1008 @@
+{
+ "ligand": "X0U",
+ "source_pdb": "2X0U",
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/X0V.json b/data/p53/rmsd_screen/scores/X0V.json
new file mode 100644
index 0000000000000000000000000000000000000000..b2f7327b241a2e36112cb9f4be36bd198c2c831b
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/X0V.json
@@ -0,0 +1,1008 @@
+{
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+ "source_pdb": "2X0V",
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+ "box": 15.3,
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/X0W.json b/data/p53/rmsd_screen/scores/X0W.json
new file mode 100644
index 0000000000000000000000000000000000000000..835eb65924ce342d5e35ba873b9734c3ff601da2
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/X0W.json
@@ -0,0 +1,1008 @@
+{
+ "ligand": "X0W",
+ "source_pdb": "2X0W",
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+ "box": 16.8,
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/Y0V.json b/data/p53/rmsd_screen/scores/Y0V.json
new file mode 100644
index 0000000000000000000000000000000000000000..9bad6da1824b06faca7e8dab985fe76892237173
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/Y0V.json
@@ -0,0 +1,1008 @@
+{
+ "ligand": "Y0V",
+ "source_pdb": "5AOJ",
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/YLW.json b/data/p53/rmsd_screen/scores/YLW.json
new file mode 100644
index 0000000000000000000000000000000000000000..2062dca199205414c6829108c4a32929d5f3b228
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/YLW.json
@@ -0,0 +1,1008 @@
+{
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+ "source_pdb": "8RBA",
+ "n_atoms": 10,
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+}
\ No newline at end of file
diff --git a/data/p53/rmsd_screen/scores/YM0.json b/data/p53/rmsd_screen/scores/YM0.json
new file mode 100644
index 0000000000000000000000000000000000000000..d1810459fd9155c70469ed71baf15ae8ddddd639
--- /dev/null
+++ b/data/p53/rmsd_screen/scores/YM0.json
@@ -0,0 +1,1008 @@
+{
+ "ligand": "YM0",
+ "source_pdb": "8RBC",
+ "n_atoms": 11,
+ "box": 15.7,
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+}
\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_apo_k100_seed0.json b/data/p53/trajectory_cv_tilt_apo_k100_seed0.json
new file mode 100644
index 0000000000000000000000000000000000000000..7c7d667150c7cf638c34994d89737ac585ee2551
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_apo_k100_seed0.json
@@ -0,0 +1,2351 @@
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+}
\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_apo_k100_seed1.json b/data/p53/trajectory_cv_tilt_apo_k100_seed1.json
new file mode 100644
index 0000000000000000000000000000000000000000..adc842ff94b6c0ed1e51d5adcc14b3d8cbe3a47f
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_apo_k100_seed1.json
@@ -0,0 +1,2351 @@
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+}
\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_apo_k100_seed2.json b/data/p53/trajectory_cv_tilt_apo_k100_seed2.json
new file mode 100644
index 0000000000000000000000000000000000000000..a7910c1bc0f02968ae29f44f30f07c9b9bd38fb9
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_apo_k100_seed2.json
@@ -0,0 +1,2351 @@
+{
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+}
\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_apo_k150_seed0.json b/data/p53/trajectory_cv_tilt_apo_k150_seed0.json
new file mode 100644
index 0000000000000000000000000000000000000000..e966e209450d9aa9c21cb315631ca59a0c21e973
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_apo_k150_seed0.json
@@ -0,0 +1,2351 @@
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+}
\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_apo_k150_seed1.json b/data/p53/trajectory_cv_tilt_apo_k150_seed1.json
new file mode 100644
index 0000000000000000000000000000000000000000..99f69c9407de05f8dd48e32c738a1ed0c8326125
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_apo_k150_seed1.json
@@ -0,0 +1,2351 @@
+{
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+}
\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_apo_k150_seed2.json b/data/p53/trajectory_cv_tilt_apo_k150_seed2.json
new file mode 100644
index 0000000000000000000000000000000000000000..27a189fbab633ff087fda3cbef6b1a685e986f7e
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_apo_k150_seed2.json
@@ -0,0 +1,2351 @@
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+}
\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_apo_k75_seed0.json b/data/p53/trajectory_cv_tilt_apo_k75_seed0.json
new file mode 100644
index 0000000000000000000000000000000000000000..fd260f5d96729aab447d7a2dec79f29493b3ad56
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_apo_k75_seed0.json
@@ -0,0 +1,2351 @@
+{
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+}
\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_apo_k75_seed1.json b/data/p53/trajectory_cv_tilt_apo_k75_seed1.json
new file mode 100644
index 0000000000000000000000000000000000000000..2695160250b7029d363b5be87946396c2364d23d
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_apo_k75_seed1.json
@@ -0,0 +1,2351 @@
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+}
\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_apo_k75_seed2.json b/data/p53/trajectory_cv_tilt_apo_k75_seed2.json
new file mode 100644
index 0000000000000000000000000000000000000000..424b85a977ed45b64714ed9c03aa3b26779cd387
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_apo_k75_seed2.json
@@ -0,0 +1,2351 @@
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\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_apo_robustness.json b/data/p53/trajectory_cv_tilt_apo_robustness.json
new file mode 100644
index 0000000000000000000000000000000000000000..06b6b87879e645c074b5718234b3362783720897
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_apo_robustness.json
@@ -0,0 +1,513 @@
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+        0.9237293407411238,
+        1.0746868794260305,
+        1.2890306759678765
+      ]
+    },
+    "alg1[a=20]": {
+      "mean": 1.0149518969559357,
+      "mean_fold_bootstrap_95ci": [
+        0.9177597301770536,
+        1.115449042079601
+      ],
+      "median": 1.0073220645275325,
+      "n_positive": 5,
+      "n_folds": 10,
+      "wilcoxon_p_vs_1": 0.845703125,
+      "exact_mean_signflip_p_two_sided": 0.783203125,
+      "exact_mean_signflip_p_one_sided_greater": 0.3916015625,
+      "fold_values": [
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+        0.8337875680032227,
+        1.2085190115526696,
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+        1.088651400167562,
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+        0.8455946224530725,
+        0.9293611073172511,
+        1.0382179307075865,
+        1.2947631143129084
+      ]
+    }
+  },
+  "paired_fk_advantage": {
+    "tpt_flux": {
+      "mean_difference": 0.07054765720447677,
+      "mean_difference_fold_bootstrap_95ci": [
+        -0.010351477500730956,
+        0.1578126998365314
+      ],
+      "fk_wins": 7,
+      "n_folds": 10,
+      "wilcoxon_p": 0.275390625,
+      "exact_mean_signflip_p_two_sided": 0.166015625,
+      "exact_mean_signflip_p_one_sided_greater": 0.0830078125
+    },
+    "uniform_states": {
+      "mean_difference": 0.08075550851330035,
+      "mean_difference_fold_bootstrap_95ci": [
+        -0.006440500914971456,
+        0.17314013379191204
+      ],
+      "fk_wins": 6,
+      "n_folds": 10,
+      "wilcoxon_p": 0.193359375,
+      "exact_mean_signflip_p_two_sided": 0.142578125,
+      "exact_mean_signflip_p_one_sided_greater": 0.0712890625
+    },
+    "doob_untilted": {
+      "mean_difference": 0.08677668650678419,
+      "mean_difference_fold_bootstrap_95ci": [
+        -0.000956254777125238,
+        0.18137286172353959
+      ],
+      "fk_wins": 8,
+      "n_folds": 10,
+      "wilcoxon_p": 0.10546875,
+      "exact_mean_signflip_p_two_sided": 0.1171875,
+      "exact_mean_signflip_p_one_sided_greater": 0.05859375
+    }
+  },
+  "note": "K/seed configurations are averaged within fold before inference.",
+  "source_pattern": "trajectory_cv_tilt_apo_k*_seed*.json",
+  "source_files": [
+    "trajectory_cv_tilt_apo_k100_seed0.json",
+    "trajectory_cv_tilt_apo_k100_seed1.json",
+    "trajectory_cv_tilt_apo_k100_seed2.json",
+    "trajectory_cv_tilt_apo_k150_seed0.json",
+    "trajectory_cv_tilt_apo_k150_seed1.json",
+    "trajectory_cv_tilt_apo_k150_seed2.json",
+    "trajectory_cv_tilt_apo_k75_seed0.json",
+    "trajectory_cv_tilt_apo_k75_seed1.json",
+    "trajectory_cv_tilt_apo_k75_seed2.json"
+  ],
+  "observable": [
+    "site_apolar"
+  ]
+}
\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_k100_seed0.json b/data/p53/trajectory_cv_tilt_k100_seed0.json
new file mode 100644
index 0000000000000000000000000000000000000000..7f358f301a9e70bbc078df6295f7c17a9d562ffc
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_k100_seed0.json
@@ -0,0 +1,2351 @@
+{
+  "config": {
+    "k": 100,
+    "lag": 1,
+    "ncomp": 6,
+    "seed": 0,
+    "target_q": 0.15,
+    "observable": "site_drug",
+    "drug_thresh": 0.5,
+    "alpha_rho_sweep": [
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+      0.1,
+      0.25,
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+      0.5,
+      1.0,
+      1.5,
+      2.0,
+      3.0,
+      5.0,
+      8.0,
+      12.0,
+      20.0
+    ],
+    "max_core_rmsd": 0.3,
+    "min_zn": 3,
+    "out": "figures/p53/trajectory_cv_tilt_k100_seed0.json"
+  },
+  "folds": [
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+        10,
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+        39,
+        49
+      ],
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+    }
+  }
+}
\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_k100_seed1.json b/data/p53/trajectory_cv_tilt_k100_seed1.json
new file mode 100644
index 0000000000000000000000000000000000000000..1f84dc1978627dd907a7918290cfa2b002aff384
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_k100_seed1.json
@@ -0,0 +1,2351 @@
+{
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+}
\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_k100_seed2.json b/data/p53/trajectory_cv_tilt_k100_seed2.json
new file mode 100644
index 0000000000000000000000000000000000000000..03764f5bac79d75708d4880c9cc9a0b5f487021c
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_k100_seed2.json
@@ -0,0 +1,2351 @@
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+  }
+}
\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_k150_seed0.json b/data/p53/trajectory_cv_tilt_k150_seed0.json
new file mode 100644
index 0000000000000000000000000000000000000000..3b6355c309b8a5de72cbbaf69fe2a7832557b464
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_k150_seed0.json
@@ -0,0 +1,2351 @@
+{
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+  }
+}
\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_k150_seed1.json b/data/p53/trajectory_cv_tilt_k150_seed1.json
new file mode 100644
index 0000000000000000000000000000000000000000..3b7990633ce49096567747f1b1795e9e8ad1b9b8
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_k150_seed1.json
@@ -0,0 +1,2351 @@
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+    }
+  }
+}
\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_k150_seed2.json b/data/p53/trajectory_cv_tilt_k150_seed2.json
new file mode 100644
index 0000000000000000000000000000000000000000..4b5fb0f81446e6ae85430de1c42114735773f39e
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_k150_seed2.json
@@ -0,0 +1,2351 @@
+{
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+    "out": "figures/p53/trajectory_cv_tilt_k150_seed2.json"
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+}
\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_k75_seed0.json b/data/p53/trajectory_cv_tilt_k75_seed0.json
new file mode 100644
index 0000000000000000000000000000000000000000..f3e3257c1a60a00b5fea1dfb0a35dd0e308d550a
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_k75_seed0.json
@@ -0,0 +1,2351 @@
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+  }
+}
\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_k75_seed1.json b/data/p53/trajectory_cv_tilt_k75_seed1.json
new file mode 100644
index 0000000000000000000000000000000000000000..87bbc77c9e4232c8a8c9dffd04b837eeee4b8515
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_k75_seed1.json
@@ -0,0 +1,2351 @@
+{
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+  }
+}
\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_k75_seed2.json b/data/p53/trajectory_cv_tilt_k75_seed2.json
new file mode 100644
index 0000000000000000000000000000000000000000..0680db7a8d31331589e9b55659abde1964a4c542
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_k75_seed2.json
@@ -0,0 +1,2351 @@
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+}
\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_robustness.json b/data/p53/trajectory_cv_tilt_robustness.json
new file mode 100644
index 0000000000000000000000000000000000000000..f92bce3774b9e403bb1704e21ea738671eebfd1b
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_robustness.json
@@ -0,0 +1,513 @@
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+      "exact_mean_signflip_p_one_sided_greater": 0.0185546875
+    }
+  },
+  "note": "K/seed configurations are averaged within fold before inference.",
+  "source_pattern": "trajectory_cv_tilt_k*_seed*.json",
+  "source_files": [
+    "trajectory_cv_tilt_k100_seed0.json",
+    "trajectory_cv_tilt_k100_seed1.json",
+    "trajectory_cv_tilt_k100_seed2.json",
+    "trajectory_cv_tilt_k150_seed0.json",
+    "trajectory_cv_tilt_k150_seed1.json",
+    "trajectory_cv_tilt_k150_seed2.json",
+    "trajectory_cv_tilt_k75_seed0.json",
+    "trajectory_cv_tilt_k75_seed1.json",
+    "trajectory_cv_tilt_k75_seed2.json"
+  ],
+  "observable": [
+    "site_drug"
+  ]
+}
\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_vol_k100_seed0.json b/data/p53/trajectory_cv_tilt_vol_k100_seed0.json
new file mode 100644
index 0000000000000000000000000000000000000000..96cfaf3e9c81e497ad919c67aab9ad2c65814305
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_vol_k100_seed0.json
@@ -0,0 +1,2351 @@
+{
+  "config": {
+    "k": 100,
+    "lag": 1,
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+    "max_core_rmsd": 0.3,
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+    "out": "figures/p53/trajectory_cv_tilt_vol_k100_seed0.json"
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+}
\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_vol_k100_seed1.json b/data/p53/trajectory_cv_tilt_vol_k100_seed1.json
new file mode 100644
index 0000000000000000000000000000000000000000..39800eaf49d225456a37b6eda49d5f64d9adbc49
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_vol_k100_seed1.json
@@ -0,0 +1,2351 @@
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+}
\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_vol_k100_seed2.json b/data/p53/trajectory_cv_tilt_vol_k100_seed2.json
new file mode 100644
index 0000000000000000000000000000000000000000..02bbe7a4ea303861ac5b073a963fb091f9cf0ea0
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_vol_k100_seed2.json
@@ -0,0 +1,2351 @@
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+}
\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_vol_k150_seed0.json b/data/p53/trajectory_cv_tilt_vol_k150_seed0.json
new file mode 100644
index 0000000000000000000000000000000000000000..ef5190fa52198707076d69928875c34cb196b3af
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_vol_k150_seed0.json
@@ -0,0 +1,2351 @@
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+}
\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_vol_k150_seed1.json b/data/p53/trajectory_cv_tilt_vol_k150_seed1.json
new file mode 100644
index 0000000000000000000000000000000000000000..22413ca77ce6606a67ad49bd12ecc3d1f7f08d34
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_vol_k150_seed1.json
@@ -0,0 +1,2351 @@
+{
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+}
\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_vol_k150_seed2.json b/data/p53/trajectory_cv_tilt_vol_k150_seed2.json
new file mode 100644
index 0000000000000000000000000000000000000000..c84376a5ad8dab81b656e1e54db59086efce80b1
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_vol_k150_seed2.json
@@ -0,0 +1,2351 @@
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+  }
+}
\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_vol_k75_seed0.json b/data/p53/trajectory_cv_tilt_vol_k75_seed0.json
new file mode 100644
index 0000000000000000000000000000000000000000..e4ec0efe9dee1cbba8b69ac0542fd7025fc10253
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_vol_k75_seed0.json
@@ -0,0 +1,2351 @@
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+}
\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_vol_k75_seed1.json b/data/p53/trajectory_cv_tilt_vol_k75_seed1.json
new file mode 100644
index 0000000000000000000000000000000000000000..ed7ea5136431533329eb919357aaf5db39bdf5ea
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_vol_k75_seed1.json
@@ -0,0 +1,2351 @@
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+}
\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_vol_k75_seed2.json b/data/p53/trajectory_cv_tilt_vol_k75_seed2.json
new file mode 100644
index 0000000000000000000000000000000000000000..c56f69abdfc82f97dc7ced1020c7f7b9be94c07a
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_vol_k75_seed2.json
@@ -0,0 +1,2351 @@
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\ No newline at end of file
diff --git a/data/p53/trajectory_cv_tilt_vol_robustness.json b/data/p53/trajectory_cv_tilt_vol_robustness.json
new file mode 100644
index 0000000000000000000000000000000000000000..be435fd330ed0220ffa45e1c3f385f219e68aae5
--- /dev/null
+++ b/data/p53/trajectory_cv_tilt_vol_robustness.json
@@ -0,0 +1,513 @@
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+  },
+  "note": "K/seed configurations are averaged within fold before inference.",
+  "source_pattern": "trajectory_cv_tilt_vol_k*_seed*.json",
+  "source_files": [
+    "trajectory_cv_tilt_vol_k100_seed0.json",
+    "trajectory_cv_tilt_vol_k100_seed1.json",
+    "trajectory_cv_tilt_vol_k100_seed2.json",
+    "trajectory_cv_tilt_vol_k150_seed0.json",
+    "trajectory_cv_tilt_vol_k150_seed1.json",
+    "trajectory_cv_tilt_vol_k150_seed2.json",
+    "trajectory_cv_tilt_vol_k75_seed0.json",
+    "trajectory_cv_tilt_vol_k75_seed1.json",
+    "trajectory_cv_tilt_vol_k75_seed2.json"
+  ],
+  "observable": [
+    "site_volume"
+  ]
+}
\ No newline at end of file
diff --git a/data/validation_aib9/robustness.json b/data/validation_aib9/robustness.json
new file mode 100644
index 0000000000000000000000000000000000000000..1d26a39bf82683d80ae762169da42c22a4625985
--- /dev/null
+++ b/data/validation_aib9/robustness.json
@@ -0,0 +1,288 @@
+{
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+      "n_L": 23,
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+      "direct": 0.0027717222974109855,
+      "flux_IN": 0.13466889184644276,
+      "flux_IC": 0.28622467022866344,
+      "ck2": 0.14540857593260395
+    },
+    {
+      "K": 75,
+      "lag": 10,
+      "ok": true,
+      "n_active": 75,
+      "lam1": 1.0000000000003926,
+      "n_R": 14,
+      "n_L": 24,
+      "committor_half_mass": 0.8509667223939482,
+      "direct": 0.010085443572984857,
+      "flux_IN": 0.29714820029322125,
+      "flux_IC": 0.20095202350169408,
+      "ck2": 0.1736165902178143
+    },
+    {
+      "K": 75,
+      "lag": 20,
+      "ok": true,
+      "n_active": 75,
+      "lam1": 0.9999999999970959,
+      "n_R": 13,
+      "n_L": 22,
+      "committor_half_mass": 1.0,
+      "direct": 0.006403018941267874,
+      "flux_IN": 0.1402068153730387,
+      "flux_IC": 0.20737781360564636,
+      "ck2": 0.1653027113488371
+    },
+    {
+      "K": 100,
+      "lag": 2,
+      "ok": true,
+      "n_active": 100,
+      "lam1": 1.0000000000029814,
+      "n_R": 22,
+      "n_L": 25,
+      "committor_half_mass": 1.0,
+      "direct": 0.0,
+      "flux_IN": 0.08073631577898485,
+      "flux_IC": 0.1525647391984085,
+      "ck2": 0.19030916495947306
+    },
+    {
+      "K": 100,
+      "lag": 5,
+      "ok": true,
+      "n_active": 100,
+      "lam1": 0.9999999999990052,
+      "n_R": 21,
+      "n_L": 29,
+      "committor_half_mass": 0.8773559315051728,
+      "direct": 0.0,
+      "flux_IN": 0.16550925778476916,
+      "flux_IC": 0.3114712345016993,
+      "ck2": 0.17446281723689971
+    },
+    {
+      "K": 100,
+      "lag": 10,
+      "ok": true,
+      "n_active": 100,
+      "lam1": 1.0000000000011635,
+      "n_R": 18,
+      "n_L": 28,
+      "committor_half_mass": 1.0,
+      "direct": 0.002912527153935284,
+      "flux_IN": 0.33641630832437425,
+      "flux_IC": 0.20557486550394297,
+      "ck2": 0.1914288159428538
+    },
+    {
+      "K": 100,
+      "lag": 20,
+      "ok": true,
+      "n_active": 100,
+      "lam1": 0.9999999999993012,
+      "n_R": 20,
+      "n_L": 27,
+      "committor_half_mass": 1.0,
+      "direct": 0.0021667988294571837,
+      "flux_IN": 0.19153123399332875,
+      "flux_IC": 0.23150447376791983,
+      "ck2": 0.18005059776301327
+    },
+    {
+      "K": 150,
+      "lag": 2,
+      "ok": true,
+      "n_active": 150,
+      "lam1": 0.9999999999982583,
+      "n_R": 31,
+      "n_L": 49,
+      "committor_half_mass": 1.0,
+      "direct": 0.0,
+      "flux_IN": 0.06465021835594889,
+      "flux_IC": 0.30599904518300747,
+      "ck2": 0.2161913453492412
+    },
+    {
+      "K": 150,
+      "lag": 5,
+      "ok": true,
+      "n_active": 150,
+      "lam1": 0.9999999999994307,
+      "n_R": 34,
+      "n_L": 43,
+      "committor_half_mass": 0.9469204066230653,
+      "direct": 0.0,
+      "flux_IN": 0.18977432272858064,
+      "flux_IC": 0.2723490978937948,
+      "ck2": 0.19910747599276746
+    },
+    {
+      "K": 150,
+      "lag": 10,
+      "ok": true,
+      "n_active": 150,
+      "lam1": 0.9999999999993171,
+      "n_R": 31,
+      "n_L": 42,
+      "committor_half_mass": 0.860075021661696,
+      "direct": 0.003370333989166947,
+      "flux_IN": 0.2370074927361319,
+      "flux_IC": 0.23903127283648545,
+      "ck2": 0.21173915636184973
+    },
+    {
+      "K": 150,
+      "lag": 20,
+      "ok": true,
+      "n_active": 150,
+      "lam1": 1.000000000001205,
+      "n_R": 30,
+      "n_L": 41,
+      "committor_half_mass": 0.8790551169162689,
+      "direct": 0.0,
+      "flux_IN": 0.22934861603375187,
+      "flux_IC": 0.2196064770259054,
+      "ck2": 0.2046732345649363
+    },
+    {
+      "K": 200,
+      "lag": 2,
+      "ok": true,
+      "n_active": 200,
+      "lam1": 0.9999999999980929,
+      "n_R": 44,
+      "n_L": 61,
+      "committor_half_mass": 0.9473667872000259,
+      "direct": 0.0,
+      "flux_IN": 0.35423230617883333,
+      "flux_IC": 0.25206708211949913,
+      "ck2": 0.23656770578432423
+    },
+    {
+      "K": 200,
+      "lag": 5,
+      "ok": true,
+      "n_active": 200,
+      "lam1": 1.0000000000004297,
+      "n_R": 46,
+      "n_L": 62,
+      "committor_half_mass": 0.9182729485231926,
+      "direct": 0.0,
+      "flux_IN": 0.12881508241429596,
+      "flux_IC": 0.24692735286880946,
+      "ck2": 0.22169173783793084
+    },
+    {
+      "K": 200,
+      "lag": 10,
+      "ok": true,
+      "n_active": 200,
+      "lam1": 1.0000000000003797,
+      "n_R": 38,
+      "n_L": 57,
+      "committor_half_mass": 0.8631829694734664,
+      "direct": 0.0,
+      "flux_IN": 0.24564209978527748,
+      "flux_IC": 0.22096111977463592,
+      "ck2": 0.21971088735137906
+    },
+    {
+      "K": 200,
+      "lag": 20,
+      "ok": true,
+      "n_active": 200,
+      "lam1": 0.9999999999992066,
+      "n_R": 38,
+      "n_L": 54,
+      "committor_half_mass": 0.9294139731358968,
+      "direct": 0.0,
+      "flux_IN": 0.15193339545187734,
+      "flux_IC": 0.25876439307647253,
+      "ck2": 0.2271593392323544
+    }
+  ],
+  "summary": {
+    "n_points": 16,
+    "committor_half_mass_min": 0.800842521076588,
+    "committor_half_mass_max": 1.0,
+    "direct_min": 0.0,
+    "direct_max": 0.010085443572984857,
+    "both_channels_always": true
+  },
+  "its_lags": [
+    1,
+    2,
+    3,
+    5,
+    8,
+    12,
+    20,
+    30
+  ],
+  "its": [
+    [
+      743.3573081658633,
+      221.85145383291544,
+      185.05726193980445
+    ],
+    [
+      1238.537627762412,
+      345.5849433484458,
+      300.2533848289678
+    ],
+    [
+      1606.9062977325154,
+      438.78758483453595,
+      372.7966904303805
+    ],
+    [
+      2178.5683688041595,
+      578.5860636462785,
+      483.8249984758166
+    ],
+    [
+      2858.514525761749,
+      744.1633219104342,
+      603.6398910936495
+    ],
+    [
+      3518.8565259840216,
+      901.0702032240025,
+      709.5673585717564
+    ],
+    [
+      4495.982676621482,
+      1111.7172041387687,
+      847.3438979817231
+    ],
+    [
+      5385.685360469546,
+      1306.3498021781843,
+      953.5432584477405
+    ]
+  ]
+}
\ No newline at end of file
diff --git a/make_aib9_method_figure.py b/make_aib9_method_figure.py
new file mode 100644
index 0000000000000000000000000000000000000000..0658fcd6f1920225951e9deb96305d2fcb2ef299
--- /dev/null
+++ b/make_aib9_method_figure.py
@@ -0,0 +1,511 @@
+#!/usr/bin/env python
+from __future__ import annotations
+import sys
+from pathlib import Path
+import numpy as np
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import matplotlib.patheffects as pe
+from matplotlib.collections import LineCollection
+from matplotlib.lines import Line2D
+from matplotlib.colors import LinearSegmentedColormap
+from scipy.ndimage import gaussian_filter, gaussian_filter1d
+
+ROOT = Path(__file__).resolve().parent
+sys.path.insert(0, str(ROOT))
+from scoobdoob.validation import aib9, bridge, msm_build as mb, tpt
+
+LAG, K, NC, ST, H, NP = 5, 100, 4, 5, 320, 600
+KAPPA_RHO, TSg, W = 0.20, 0.30, 1e-12
+MACRO_C = {"R": "#1F3A93", "L": "#B22222", "I_N": "#1E7A46", "I_C": "#C4720A"}
+MACRO_LBL = {"R": "R", "L": "L", "I_N": "I$_N$", "I_C": "I$_C$"}
+
+ORCHID_P = LinearSegmentedColormap.from_list(
+    "orchid_blue",
+    ["#12225E", "#28409B", "#4470CB", "#7CA9DF", "#AED2EE", "#D8EAF8", "#F2F8FD"],
+    N=256,
+)
+
+COMMIT = LinearSegmentedColormap.from_list(
+    "purple_red",
+    ["#3B0F70", "#7B2D8E", "#B47ACB", "#EBDDF0", "#F6C3AE", "#DA5432", "#7E1008"],
+    N=256,
+)
+GATE_C = "#FF2D95"
+PAGE = "#FFFFFF"
+
+
+def main():
+    trajs, _ = aib9.load_openmm_colvar(stride=ST)
+    feat, hand = aib9.featurize_sincos(trajs), aib9.handedness_indicator(trajs)
+    model = mb.build_full_msm(
+        feat, hand, lag=LAG, k=K, n_comp=NC, seed=0, use_tica=True
+    )
+    labels = aib9.classify_microstates(model.active_xy)
+    sets = {
+        s: np.where(labels == s)[0].tolist() for s in ["R", "L", "I_N", "I_C", "other"]
+    }
+    q = tpt.committor_agreement(model, sets["R"], sets["L"])[1]
+    pi = model.stationary
+    P = model.P
+    NA = model.n_active
+    proj = model.centers[model.active_symbols][:, :2].copy()
+    INT = sorted(set(sets["I_N"]) | set(sets["I_C"]))
+    qIL = tpt.committor_agreement(model, INT, sets["L"])[1]
+    Nn = np.asarray(tpt.reactive_flux(model, sets["R"], sets["L"]).net_flux)
+    netf = (Nn.sum(1) + Nn.sum(0)) / 2
+    INcen, ICcen = proj[sets["I_N"]].mean(0), proj[sets["I_C"]].mean(0)
+    exitg = sorted(
+        [
+            i
+            for i in range(NA)
+            if i not in set(INT) and i not in set(sets["L"]) and abs(qIL[i] - 0.5) < 0.2
+        ],
+        key=lambda i: -netf[i],
+    )[:4]
+    egch = [
+        "I_N" if np.hypot(*(proj[i] - INcen)) < np.hypot(*(proj[i] - ICcen)) else "I_C"
+        for i in exitg
+    ]
+    egset = set(exitg)
+    half = np.where(np.abs(q - 0.5) < 0.08)[0]
+
+    SL = model.active_xy
+    n_half, c_half = SL[:, 1:4].mean(axis=1), SL[:, 5:8].mean(axis=1)
+    Nchan = set(sets["I_N"]) | {i for i in sets["other"] if n_half[i] > c_half[i]}
+    Cchan = set(sets["I_C"]) | {i for i in sets["other"] if n_half[i] <= c_half[i]}
+
+    chan_state = np.array(
+        [
+            "I_N"
+            if np.hypot(*(proj[i] - INcen)) < np.hypot(*(proj[i] - ICcen))
+            else "I_C"
+            for i in range(NA)
+        ],
+        dtype=object,
+    )
+    rf = tpt.reactive_flux(model, sets["R"], sets["L"])
+    pf = tpt.pathway_fractions(rf, fraction=0.95)
+
+    part4 = [sets["R"], sorted(Nchan), sorted(Cchan), sets["L"]]
+    _, cg4 = rf.coarse_grain(part4)
+    F4 = np.asarray(cg4.net_flux)
+    tot4 = float(cg4.total_flux)
+    fRN4, fRC4 = F4[0, 1] / tot4, F4[0, 2] / tot4
+    print(
+        f"[reference MSM] merged-partition flux out of R:  N-channel {fRN4 * 100:.1f}%  "
+        f"C-channel {fRC4 * 100:.1f}%"
+    )
+
+    def chroute(tgt):
+        idx = [
+            i
+            for i, p_ in enumerate(pf["paths"])
+            if tpt.path_passes_through(p_, set(tgt))
+        ]
+        return (
+            (
+                np.array(pf["paths"][max(idx, key=lambda i: pf["frac_of_total"][i])]),
+                float(sum(pf["frac_of_total"][i] for i in idx)),
+            )
+            if idx
+            else (None, 0.0)
+        )
+
+    rIN, fIN = chroute(sets["I_N"])
+    rIC, fIC = chroute(sets["I_C"])
+
+    Cc = np.asarray(model.model.count_model.count_matrix, float)
+    rho = np.clip(Cc.sum(1), 1.0, None) / Cc.sum(1).mean()
+    wb = np.maximum(np.exp(-KAPPA_RHO * rho), W)
+    tw = np.exp(-0.5 * ((np.arange(H) - (H - 1) / 2) / (TSg * H)) ** 2)
+    gz = np.log(netf / np.maximum(pi, 1e-15) + 1e-9)
+    gz = (gz - gz.mean()) / gz.std()
+
+    def kern(wl, Pm=None):
+        Pm = P if Pm is None else Pm
+        return bridge.doob_kernels(Pm, sets["L"], H, w=wl)
+
+    def samp(Ph, seed, n_=NP):
+        return bridge.sample_bridge_paths(Ph, sets["R"], n_paths=n_, seed=seed)
+
+    naive = [
+        np.asarray(p, int)
+        for p in bridge.sample_unconditional_paths(P, sets["R"], H, n_paths=NP, seed=1)
+    ]
+    doob = samp(bridge.doob_kernels(P, sets["L"], H), 1)
+    wfk = [np.maximum(wb * (np.exp(0.5 * gz) ** tw[t]), W) for t in range(H)]
+    dfk = samp(kern(wfk), 1)
+
+    def confined(forbid):
+        Pm = P.copy()
+        Pm[:, list(forbid)] = 0.0
+        sums = Pm.sum(1, keepdims=True)
+        return np.divide(Pm, sums, out=np.zeros_like(Pm), where=sums > 0)
+
+    cfk = samp(kern(wfk, confined(Cchan)), 11, NP // 2) + samp(
+        kern(wfk, confined(Nchan)), 12, NP // 2
+    )
+    Rs, Ls = set(sets["R"]), set(sets["L"])
+
+    def reactive(paths):
+        segs = []
+        for p in paths:
+            inL = np.array([int(x) in Ls for x in p])
+            if not inL.any():
+                continue
+            fL = int(np.argmax(inL))
+            pre = np.array([int(x) in Rs for x in p[:fL]])
+            if not pre.any():
+                continue
+            segs.append(p[int(np.where(pre)[0][-1]) : fL + 1])
+        return segs
+
+    S = {
+        "naive": reactive(naive),
+        "doob": reactive(doob),
+        "dfk": reactive(dfk),
+        "cfk": reactive(cfk),
+    }
+    M = {}
+    for k_, segs in S.items():
+        reach = len(segs) / NP
+        via = (
+            float(np.mean([bool(set(int(x) for x in s_) & egset) for s_ in segs]))
+            if segs
+            else 0.0
+        )
+
+        def seg_chan(s_):
+            fN = sum(int(x) in Nchan for x in s_)
+            fC = sum(int(x) in Cchan for x in s_)
+            return "I_N" if fN > fC else ("I_C" if fC > fN else None)
+
+        cs = [seg_chan(s_) for s_ in segs]
+        nN = sum(1 for c in cs if c == "I_N")
+        nC = sum(1 for c in cs if c == "I_C")
+        M[k_] = (reach, via, len(segs), nN, nC)
+        print(
+            f"{k_:>6}: reach-L {reach * 100:3.0f}%  n_react {len(segs):3d}  "
+            f"via gateway {via * 100:3.0f}%   channel I_N {nN:3d} / I_C {nC:3d} "
+            f"(I_C share {100 * nC / max(nN + nC, 1):.0f}%)"
+        )
+
+    if proj[sets["R"], 0].mean() > proj[sets["L"], 0].mean():
+        proj[:, 0] *= -1
+        flip = True
+    else:
+        flip = False
+    Y = np.vstack(
+        [model.tica.transform(np.asarray(f, dtype=np.float64)) for f in feat]
+    )[:, :2]
+    if flip:
+        Y = Y.copy()
+        Y[:, 0] *= -1
+    x, y = Y[:, 0], Y[:, 1]
+    rr = [
+        [x.min() - 0.04 * np.ptp(x), x.max() + 0.04 * np.ptp(x)],
+        [y.min() - 0.04 * np.ptp(y), y.max() + 0.04 * np.ptp(y)],
+    ]
+    Hc, xe, ye = np.histogram2d(x, y, bins=140, range=rr)
+    dd = gaussian_filter(Hc, 2.0)
+    with np.errstate(divide="ignore"):
+        F = -np.log(np.where(dd > 0, dd / dd.sum(), np.nan))
+    F -= np.nanmin(F[np.isfinite(F)])
+    F = np.ma.masked_where((dd <= 0) | ~np.isfinite(F) | (F > 6.5), F)
+    xc, yc = 0.5 * (xe[:-1] + xe[1:]), 0.5 * (ye[:-1] + ye[1:])
+    setsA = {s: np.array(sets[s]) for s in sets}
+
+    def fes(ax, a=1.0):
+        cf = ax.contourf(
+            xc,
+            yc,
+            F.T,
+            levels=np.arange(0, 6.75, 0.25),
+            cmap=ORCHID_P,
+            zorder=0,
+            alpha=a,
+        )
+        ax.set_xlim(xc[0], xc[-1])
+        ax.set_ylim(yc[0], yc[-1])
+        ax.set_xlabel("TICA 1", fontsize=10.5)
+        for s in ("top", "right"):
+            ax.spines[s].set_visible(False)
+        return cf
+
+    def rings(ax, lw=1.6, s=140):
+        for lab in ["R", "L", "I_N", "I_C"]:
+            idx = setsA[lab]
+            if len(idx):
+                ax.scatter(
+                    proj[idx, 0],
+                    proj[idx, 1],
+                    marker="o",
+                    s=s,
+                    facecolors="none",
+                    edgecolors=MACRO_C[lab],
+                    linewidths=lw,
+                    zorder=5,
+                )
+
+    def gates(ax, s=190):
+        for i, ch in zip(exitg, egch):
+            ax.scatter(
+                proj[i, 0],
+                proj[i, 1],
+                marker="X",
+                s=s,
+                c=GATE_C,
+                edgecolors=MACRO_C[ch],
+                linewidths=2.4,
+                zorder=9,
+            )
+
+    def sm(seq):
+        d = seq[np.concatenate([[True], seq[1:] != seq[:-1]])]
+        xy = proj[d]
+        return (
+            xy
+            if len(xy) < 3
+            else np.column_stack(
+                [
+                    gaussian_filter1d(xy[:, 0], 1.0, mode="nearest"),
+                    gaussian_filter1d(xy[:, 1], 1.0, mode="nearest"),
+                ]
+            )
+        )
+
+    def crossings(ax, segs, color):
+        """Color crossings by channel and emphasize gateway visitation."""
+        buckets = {
+            ("I_N", True): [],
+            ("I_N", False): [],
+            ("I_C", True): [],
+            ("I_C", False): [],
+        }
+        for s_ in segs:
+            xy = sm(s_)
+            if len(xy) < 2:
+                continue
+            fN = sum(int(x) in Nchan for x in s_)
+            fC = sum(int(x) in Cchan for x in s_)
+            ch = "I_N" if fN >= fC else "I_C"
+            buckets[(ch, bool(set(int(t) for t in s_) & egset))].append(xy)
+        for (ch, viaG), L_ in buckets.items():
+            if not L_:
+                continue
+            ax.add_collection(
+                LineCollection(
+                    [[xy[i], xy[i + 1]] for xy in L_ for i in range(len(xy) - 1)],
+                    colors=MACRO_C[ch],
+                    linewidths=0.85 if viaG else 0.5,
+                    alpha=0.20 if viaG else 0.11,
+                    zorder=4 if viaG else 3,
+                    capstyle="round",
+                )
+            )
+
+    fig = plt.figure(figsize=(23.5, 11.4))
+    fig.patch.set_facecolor(PAGE)
+    gs = fig.add_gridspec(2, 4, height_ratios=[1.12, 1.0], hspace=0.24, wspace=0.15)
+
+    axa = fig.add_subplot(gs[0, 0:2])
+    axa.set_facecolor(PAGE)
+    fes(axa)
+    sc = axa.scatter(
+        proj[:, 0],
+        proj[:, 1],
+        c=q,
+        cmap=COMMIT,
+        s=54,
+        vmin=0,
+        vmax=1,
+        edgecolors=PAGE,
+        linewidths=0.9,
+        zorder=6,
+    )
+    rings(axa)
+    xs = axa.scatter(
+        proj[half, 0],
+        proj[half, 1],
+        marker="x",
+        c="#101010",
+        s=70,
+        linewidths=2,
+        zorder=7,
+    )
+    xs.set_path_effects([pe.withStroke(linewidth=3.4, foreground=PAGE)])
+    gates(axa)
+    axa.set_ylabel("TICA 2", fontsize=10.5)
+    axa.set_title("a  Committor + macrostates", fontsize=12, fontweight="bold", pad=8)
+    cb = fig.colorbar(sc, ax=axa, pad=0.012, fraction=0.045)
+    cb.set_label("committor $q^+$ (R$\\rightarrow$L)", fontsize=9)
+    cb.outline.set_visible(False)
+    cb.ax.tick_params(length=0, labelsize=8)
+    lh = [
+        Line2D(
+            [0], [0], marker="o", color="w", mfc="none", mec=MACRO_C[m], mew=1.8, ms=10
+        )
+        for m in ["R", "I_N", "I_C", "L"]
+    ]
+    lh += [
+        Line2D([0], [0], marker="x", color="#101010", lw=0, mew=2, ms=9),
+        Line2D(
+            [0],
+            [0],
+            marker="X",
+            color="w",
+            mfc=GATE_C,
+            mec=MACRO_C["I_N"],
+            mew=1.6,
+            ms=11,
+        ),
+    ]
+    axa.legend(
+        lh,
+        [
+            "R state",
+            "I$_N$ state",
+            "I$_C$ state",
+            "L state",
+            "committor-½",
+            "rate-limiting exit gate",
+        ],
+        loc="lower left",
+        fontsize=7.3,
+        framealpha=0.95,
+        ncol=2,
+    ).get_frame().set_edgecolor("#C9D3DE")
+
+    axb = fig.add_subplot(gs[0, 2:4])
+    axb.set_facecolor(PAGE)
+    cfb = fes(axb)
+    rings(axb)
+    for r, cc, lab, fr in (
+        (rIN, MACRO_C["I_N"], "I$_N$", fIN),
+        (rIC, MACRO_C["I_C"], "I$_C$", fIC),
+    ):
+        if r is None:
+            continue
+        xy = proj[r]
+        axb.plot(
+            xy[:, 0],
+            xy[:, 1],
+            "-o",
+            lw=3.0,
+            ms=6,
+            color=cc,
+            mec=PAGE,
+            mew=1.0,
+            zorder=8,
+            path_effects=[pe.withStroke(linewidth=5.4, foreground=PAGE)],
+            label=f"channel via {lab}: {fr * 100:.0f}% of flux",
+        )
+    gates(axb)
+    axb.set_title(
+        "b  The two reactive channels (TPT)", fontsize=12, fontweight="bold", pad=8
+    )
+    axb.legend(
+        loc="upper left", fontsize=8.2, framealpha=0.95
+    ).get_frame().set_edgecolor("#C9D3DE")
+    cbF = fig.colorbar(cfb, ax=axb, pad=0.012, fraction=0.045, ticks=range(0, 7))
+    cbF.set_label("free energy $F/k_\\mathrm{B}T$ (background)", fontsize=9)
+    cbF.outline.set_visible(False)
+    cbF.ax.tick_params(length=0, labelsize=8)
+
+    panels = [
+        ("c  naive MSM (unconditioned)", "naive", "#5C6570"),
+        ("d  Doob bridge (conditioned)", "doob", "#1F4EA1"),
+        ("e  Doob + FK  (global tilt)", "dfk", "#A3161C"),
+        ("f  Doob + FK  (channel-dependent)", "cfk", "#1E7A46"),
+    ]
+    samp_axes = []
+    for col, (title, key, color) in enumerate(panels):
+        ax = fig.add_subplot(gs[1, col])
+        samp_axes.append(ax)
+        ax.set_facecolor(PAGE)
+        fes(ax, 0.9)
+        crossings(ax, S[key], color)
+        rings(ax, lw=1.2, s=90)
+        gates(ax, s=150)
+        reach, via, ns, nN, nC = M[key]
+        ax.set_title(title, fontsize=12, fontweight="bold", pad=8)
+        if col == 0:
+            ax.set_ylabel("TICA 2", fontsize=10.5)
+        ax.text(
+            0.5,
+            0.03,
+            f"reach L: {reach * 100:.0f}%   ({ns} of {NP} crossings)\n"
+            f"via rate-limiting gateway: {via * 100:.0f}%\n"
+            f"N-channel {nN} / C-channel {nC}",
+            transform=ax.transAxes,
+            ha="center",
+            va="bottom",
+            fontsize=11,
+            fontweight="bold",
+            color="#111",
+            linespacing=1.5,
+            bbox=dict(boxstyle="round,pad=0.5", fc=PAGE, ec=color, lw=1.8, alpha=0.96),
+            zorder=13,
+        )
+    axc0 = samp_axes[0]
+    axc0.legend(
+        [
+            Line2D([0], [0], color=MACRO_C["I_N"], lw=2.2),
+            Line2D([0], [0], color=MACRO_C["I_C"], lw=2.2),
+            Line2D([0], [0], color="#555", lw=2.2),
+            Line2D([0], [0], color="#555", lw=0.9),
+        ],
+        [
+            "crossing via N-channel",
+            "crossing via C-channel",
+            "uses rate-limiting gateway",
+            "does not",
+        ],
+        loc="upper left",
+        fontsize=7.6,
+        framealpha=0.95,
+    ).get_frame().set_edgecolor("#C9D3DE")
+
+    fig.suptitle(
+        "Aib9 R→L:  Doob fixes ARRIVAL (40%→100%);  global FK routes crossings through the "
+        "rate-limiting exit gateway (40%→82%) but COLLAPSES the C-channel (47%→2%);\n"
+        "channel-dependent FK keeps the gateway emphasis (68%) AND both channels",
+        fontsize=13,
+        fontweight="bold",
+        y=1.02,
+    )
+    fig.text(
+        0.006,
+        0.006,
+        f"Aib9 OpenMM MSM (lag {LAG}, K={K}); {NP} rollouts/sampler, horizon {H}.  Panels c–f draw the "
+        f"REACTIVE SEGMENT of each rollout (last frame in R → first in L), so only genuine crossings are\n"
+        f"shown — naive produces {M['naive'][2]}, the conditioned samplers {NP}.  Rate-limiting exit gateway = "
+        f"the 4 highest net-flux committor-½ states of the trap→L barrier (magenta X, edge = channel).\n"
+        f"Channel = MERGED handedness partition (each 'other' microstate joins the channel whose half leads "
+        f"the flip): counting only the labelled I$_N$/I$_C$ helices under-counts a channel, because most\n"
+        f"reactive flux runs R→other→L.  The two channels are intrinsically BALANCED — merged-partition flux "
+        f"out of R is {fRN4 * 100:.0f}% N / {fRC4 * 100:.0f}% C (Aib9 is a homopolymer, so N↔C is a symmetry) —\n"
+        f"so f's equal 300/300 allocation matches the true split; the measured results there are that both "
+        f"confined bridges reach L 100% independently and keep 68% gateway use.",
+        fontsize=8.2,
+        color="#6B7785",
+        ha="left",
+        va="bottom",
+        linespacing=1.5,
+    )
+    fig.subplots_adjust(left=0.045, right=0.988, top=0.90, bottom=0.115)
+    out = ROOT / "figures" / "aib9_method"
+    out.parent.mkdir(parents=True, exist_ok=True)
+    for ext, kw in (("png", dict(dpi=180)), ("pdf", {})):
+        fig.savefig(
+            out.with_suffix("." + ext), facecolor=PAGE, bbox_inches="tight", **kw
+        )
+        print(f"[fig] {out.with_suffix('.' + ext)}")
+    plt.close(fig)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/make_mb_panels.py b/make_mb_panels.py
new file mode 100644
index 0000000000000000000000000000000000000000..843cf2e219bc72cb25a329a82872c2dc63826272
--- /dev/null
+++ b/make_mb_panels.py
@@ -0,0 +1,401 @@
+#!/usr/bin/env python
+from __future__ import annotations
+import json, os
+import numpy as np, torch
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+from matplotlib.gridspec import GridSpec
+
+from scoobdoob.mb_potential import mb_potential_xy
+from scoobdoob.validation import msm_build as mb, tpt, bridge
+from scoobdoob.validation import mb_ground_truth as gt
+from scoobdoob.validation import mb_data as V
+
+
+plt.rcParams["svg.fonttype"] = "none"
+plt.rcParams["pdf.fonttype"] = 42
+
+DATA_DIR = "data/mb_panels_data"
+
+LAG_OPT, N_OPT = 5, 16
+TICA_LAG = 20
+
+
+def channel(paths, q, xy, on, thru, start, end, nbins=18):
+    """Dominant route = the maximum-bottleneck (widest) path through the ensemble's OWN empirical transition graph, from the start set to the target set."""
+    import heapq
+    from collections import defaultdict
+
+    W = defaultdict(float)
+    for p in paths:
+        for a, b in zip(p[:-1], p[1:]):
+            if a != b and on[a] and on[b]:
+                W[(int(a), int(b))] += 1.0
+    if not W:
+        return np.zeros((0, 2))
+    adj = defaultdict(list)
+    for (a, b), c in W.items():
+        adj[a].append((b, c))
+    S, T = set(map(int, start)), set(map(int, end))
+
+    best = defaultdict(float)
+    prev = {}
+    heap = []
+    for a in S:
+        best[a] = float("inf")
+        heapq.heappush(heap, (-best[a], a))
+    seen = set()
+    while heap:
+        nb, u = heapq.heappop(heap)
+        nb = -nb
+        if u in seen:
+            continue
+        seen.add(u)
+        if u in T:
+            path = [u]
+            while path[-1] not in S:
+                path.append(prev[path[-1]])
+            return xy[path[::-1]]
+        for v, c in adj[u]:
+            w = min(nb, c)
+            if w > best[v]:
+                best[v] = w
+                prev[v] = u
+                heapq.heappush(heap, (-w, v))
+    return np.zeros((0, 2))
+
+
+_CACHE_KEYS = [
+    "gx",
+    "gy",
+    "gv",
+    "S1",
+    "S2",
+    "Axy",
+    "Bxy",
+    "xlo",
+    "xhi",
+    "ylo",
+    "yhi",
+    "cx",
+    "cy",
+    "q",
+    "keep",
+    "xy",
+    "mep",
+    "arc",
+    "Emep",
+    "ch_raw",
+    "ch_fk",
+    "praw",
+    "pfk",
+    "s1_raw",
+    "s1_fk",
+    "counts",
+    "thru",
+    "on",
+]
+CACHE_NPZ = "data/mb_panels_data/panels_cache.npz"
+
+
+def save_cache(D, path=CACHE_NPZ):
+    os.makedirs(os.path.dirname(path), exist_ok=True)
+    out = {}
+    for k, v in D.items():
+        if k in ("praw", "pfk"):
+            out[k] = np.array([np.asarray(p) for p in v], dtype=object)
+        else:
+            out[k] = np.asarray(v)
+    np.savez_compressed(path, **out)
+    print(f"[cache] -> {path}")
+
+
+def load_cache(path=CACHE_NPZ):
+    if not os.path.exists(path):
+        raise SystemExit(f"missing {path}\nrun: python make_mb_panels.py --cache-only")
+    z = np.load(path, allow_pickle=True)
+    D = {k: z[k] for k in z.files}
+    for k in ("praw", "pfk"):
+        D[k] = list(D[k])
+    for k in ("xlo", "xhi", "ylo", "yhi", "s1_raw", "s1_fk"):
+        D[k] = float(D[k])
+    print(f"[cache] loaded {path}")
+    return D
+
+
+def compute():
+    """Everything numeric. Returns the dict the plot stage needs; touches no matplotlib."""
+    trajs = V.load_trajs(str(V.DEFAULT_DATA))
+    model = mb.build_full_msm(
+        trajs,
+        trajs,
+        lag=LAG_OPT,
+        tica_lag=TICA_LAG,
+        k=V.K,
+        n_comp=2,
+        seed=0,
+        use_tica=True,
+    )
+    A, B, _ = mb.q2_sign_endpoints(model)
+    start, end = V.orient_endpoints(model, A, B)
+    xy = model.state_xy
+    _, q, _ = tpt.committor_agreement(model, start, end)
+    rf = tpt.reactive_flux(model, start, end)
+    thru = np.asarray(rf.gross_flux).sum(1)
+    sad = gt.saddle_points()
+    S1 = sad[np.argmin(sad[:, 0])]
+    S2 = sad[np.argmax(sad[:, 0])]
+    q1 = q[np.argmin(np.linalg.norm(xy - S1, axis=1))]
+    q2 = q[np.argmin(np.linalg.norm(xy - S2, axis=1))]
+
+    share = thru / thru.sum()
+    on = share > 0.5 / model.n_active
+    on[list(start)] = True
+    on[list(end)] = True
+    Axy = model.active_to_xy(start).mean(0)
+    Bxy = model.active_to_xy(end).mean(0)
+    land = np.vstack([Axy, Bxy, sad])
+    lo = land.min(0) - 0.35
+    hi = land.max(0) + 0.35
+    inbox = (
+        (xy[:, 0] >= lo[0])
+        & (xy[:, 0] <= hi[0])
+        & (xy[:, 1] >= lo[1])
+        & (xy[:, 1] <= hi[1])
+    )
+    on = on & inbox
+    keep = np.where(on)[0]
+    cx, cy = xy[keep, 0], xy[keep, 1]
+
+    labels = np.concatenate(model.dtrajs)
+    counts = np.bincount(labels, minlength=model.n_active)[: model.n_active].astype(
+        float
+    )
+    rho = np.maximum(counts, 1) / counts.mean()
+    react = (q > 0.05) & (q < 0.95)
+    rho_floor = np.percentile(rho[react], 25)
+    w_fk = np.exp(-LAG_OPT * 1.5 * np.maximum(rho, rho_floor))
+    w_fk[~on] = 0.0
+    w_raw = np.ones_like(w_fk)
+    w_raw[~on] = 0.0
+
+    def rollout(w):
+        Ph = bridge.doob_kernels(model.P, end, N_OPT, w=w)
+        return bridge.sample_bridge_paths(Ph, start, n_paths=3000, seed=0)
+
+    praw = rollout(w_raw)
+    pfk = rollout(w_fk)
+    ch_raw = channel(praw, q, xy, on, thru, start, end)
+    ch_fk = channel(pfk, q, xy, on, thru, start, end)
+    n1 = np.linalg.norm(xy - S1, axis=1) < 0.25
+    s1_raw = np.mean([any(n1[s] for s in p) for p in praw]) * 100
+    s1_fk = np.mean([any(n1[s] for s in p) for p in pfk]) * 100
+
+    M = np.load("data/mb_mep.npz")
+    mep = M["mep"]
+    Emep = M["Emep"]
+    arc = M["arc"]
+    _keep_mep = mep[:, 1] >= -0.05
+    mep = mep[_keep_mep]
+    xlo, xhi = cx.min() - 0.12, cx.max() + 0.12
+    ylo, yhi = cy.min() - 0.12, cy.max() + 0.12
+    gx, gy = np.meshgrid(np.linspace(xlo, xhi, 300), np.linspace(ylo, yhi, 300))
+    with torch.no_grad():
+        gv = np.clip(
+            mb_potential_xy(torch.tensor(gx), torch.tensor(gy)).numpy(), -150, 100
+        )
+
+    return {k: v for k, v in locals().items() if k in _CACHE_KEYS}
+
+
+def render(D):
+    os.makedirs("figures", exist_ok=True)
+    """Plotting only. Reads the cache dict; recomputes nothing. Safe to re-run while restyling."""
+    gx = D["gx"]
+    gy = D["gy"]
+    gv = D["gv"]
+    S1 = D["S1"]
+    S2 = D["S2"]
+    Axy = D["Axy"]
+    Bxy = D["Bxy"]
+    xlo = D["xlo"]
+    xhi = D["xhi"]
+    ylo = D["ylo"]
+    yhi = D["yhi"]
+    cx = D["cx"]
+    cy = D["cy"]
+    q = D["q"]
+    keep = D["keep"]
+    xy = D["xy"]
+    mep = D["mep"]
+    arc = D["arc"]
+    Emep = D["Emep"]
+    ch_raw = D["ch_raw"]
+    ch_fk = D["ch_fk"]
+    praw = D["praw"]
+    pfk = D["pfk"]
+    s1_raw = D["s1_raw"]
+    s1_fk = D["s1_fk"]
+    counts = D["counts"]
+    thru = D["thru"]
+    on = D["on"]
+
+    fig = plt.figure(figsize=(15, 5.4))
+    gs = GridSpec(1, 4, width_ratios=[1, 1, 1, 0.9], wspace=0.28)
+
+    def base(ax):
+        ax.contour(gx, gy, gv, levels=22, colors="0.82", linewidths=0.5, zorder=0)
+        ax.scatter(*S1, marker="X", s=150, color="black", zorder=6)
+        ax.scatter(*S2, marker="X", s=150, color="0.5", zorder=6)
+        ax.scatter(*Axy, marker="*", s=260, color="#228833", ec="white", zorder=7)
+        ax.scatter(*Bxy, marker="*", s=260, color="#ee6677", ec="white", zorder=7)
+        ax.set_xlim(xlo, xhi)
+        ax.set_ylim(ylo, yhi)
+        ax.set_xlabel("x")
+
+    ax = fig.add_subplot(gs[0])
+    base(ax)
+    sc = ax.scatter(cx, cy, c=q[keep], cmap="coolwarm", s=22, zorder=3, vmin=0, vmax=1)
+    ax.plot(
+        mep[:, 0],
+        mep[:, 1],
+        "--",
+        color="0.25",
+        lw=1.6,
+        zorder=4,
+        label="min-energy path",
+    )
+    ax.set_ylabel("y")
+    ax.set_title("a  MSM microstates + committor", fontsize=10.5, loc="left")
+    ax.legend(fontsize=7.5, loc="lower right", frameon=False)
+    ax.annotate(
+        "S1", S1, xytext=(S1[0] - 0.13, S1[1] + 0.10), fontsize=8.5, weight="bold"
+    )
+    ax.annotate(
+        "S2",
+        S2,
+        xytext=(S2[0] + 0.06, S2[1] + 0.10),
+        fontsize=8.5,
+        color="0.4",
+        weight="bold",
+    )
+    cax = ax.inset_axes([0.60, 0.90, 0.36, 0.045])
+    cb = fig.colorbar(sc, cax=cax, orientation="horizontal")
+    cb.set_label("committor $q^+$", fontsize=7.5)
+    cb.ax.tick_params(labelsize=6.5)
+
+    ax = fig.add_subplot(gs[1])
+    base(ax)
+    ax.scatter(cx, cy, s=9, color="0.72", zorder=1)
+    for p in praw[:150]:
+        pp = xy[list(p)]
+        ax.plot(pp[:, 0], pp[:, 1], "-", color="#4477aa", lw=0.5, alpha=0.06, zorder=2)
+    ax.plot(mep[:, 0], mep[:, 1], "--", color="0.35", lw=1.3, zorder=4)
+    ax.plot(
+        ch_raw[:, 0],
+        ch_raw[:, 1],
+        "-o",
+        color="#22447a",
+        lw=2.6,
+        ms=4,
+        zorder=5,
+        label="dominant route (max-flux)",
+    )
+    ax.set_title(f"b  Without FK ({s1_raw:.0f}% via S1)", fontsize=10.5, loc="left")
+    ax.legend(fontsize=7.5, loc="lower right", frameon=False)
+
+    ax = fig.add_subplot(gs[2])
+    base(ax)
+    ax.scatter(cx, cy, s=9, color="0.72", zorder=1)
+    for p in pfk[:150]:
+        pp = xy[list(p)]
+        ax.plot(pp[:, 0], pp[:, 1], "-", color="#cc6677", lw=0.5, alpha=0.06, zorder=2)
+    ax.plot(mep[:, 0], mep[:, 1], "--", color="0.35", lw=1.3, zorder=4)
+    ax.plot(
+        ch_fk[:, 0],
+        ch_fk[:, 1],
+        "-o",
+        color="#a03050",
+        lw=2.6,
+        ms=4,
+        zorder=5,
+        label="dominant route (max-flux)",
+    )
+    ax.set_title(f"c  With floored FK ({s1_fk:.0f}% via S1)", fontsize=10.5, loc="left")
+    ax.legend(fontsize=7.5, loc="lower right", frameon=False)
+
+    ax = fig.add_subplot(gs[3])
+    ax.plot(arc, Emep, "-", color="0.2", lw=2)
+    ipk = np.argmax(Emep)
+    ax.scatter([arc[ipk]], [Emep[ipk]], s=90, color="black", zorder=5)
+    ax.annotate(
+        "S1 (rate-limiting)\nbarrier 106 = 6.4 $k_BT$",
+        (arc[ipk], Emep[ipk]),
+        xytext=(arc[ipk] - 0.05, Emep[ipk] - 30),
+        fontsize=8,
+        ha="center",
+        arrowprops=dict(arrowstyle="->"),
+    )
+
+    iS2 = np.argmin(np.linalg.norm(mep - S2, axis=1))
+    ax.scatter([arc[iS2]], [Emep[iS2]], s=70, color="0.5", zorder=5)
+    ax.annotate(
+        "S2\nbarrier 9 = 0.5 $k_BT$",
+        (arc[iS2], Emep[iS2]),
+        xytext=(arc[iS2] - 0.05, Emep[iS2] - 34),
+        fontsize=8,
+        color="0.4",
+        ha="center",
+        arrowprops=dict(arrowstyle="->", color="0.4"),
+    )
+    ax.set_xlabel("path length A$\\to$B")
+    ax.set_ylabel("potential energy (MB units)")
+    ax.set_title("d  barrier heights along the MEP", fontsize=10.5, loc="left")
+    ax.spines[["top", "right"]].set_visible(False)
+
+    fig.suptitle(
+        "The transition is obligated through S1 (d); the raw MSM under-samples it (b), "
+        "floored FK recovers it (c).  $\\tau$=5, N=16, $\\alpha$=1.5, TICA lag 20",
+        fontsize=11.5,
+        y=1.0,
+    )
+    fig.tight_layout(rect=[0, 0, 1, 0.95])
+    fig.savefig("figures/mb_panels.svg", bbox_inches="tight")
+    fig.savefig("figures/mb_panels.png", dpi=200, bbox_inches="tight")
+    fig.savefig("figures/mb_panels.pdf", bbox_inches="tight")
+
+    print(
+        f"[stats] S1 via raw {s1_raw:.0f}% -> FK {s1_fk:.0f}%; MEP peak at S1 (+106 vs S2 +9)"
+    )
+    print("[done] -> figures/mb_panels.svg / .png / .pdf")
+
+
+def main():
+    import argparse
+
+    ap = argparse.ArgumentParser(description="Muller-Brown panel figure.")
+    ap.add_argument(
+        "--cache-only", action="store_true", help="compute and cache, do not plot"
+    )
+    ap.add_argument(
+        "--from-cache",
+        action="store_true",
+        help="plot from the cache without recomputing (fast; for restyling)",
+    )
+    ap.add_argument("--cache", default=CACHE_NPZ)
+    a = ap.parse_args()
+
+    if a.from_cache:
+        render(load_cache(a.cache))
+        return
+    D = compute()
+    save_cache(D, a.cache)
+    if a.cache_only:
+        return
+    render(D)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/make_mb_unconditional.py b/make_mb_unconditional.py
new file mode 100644
index 0000000000000000000000000000000000000000..e3aceeef32ba461a76b12ed205c42ccbb810726f
--- /dev/null
+++ b/make_mb_unconditional.py
@@ -0,0 +1,269 @@
+#!/usr/bin/env python
+from __future__ import annotations
+import json, os
+import numpy as np, torch
+import matplotlib.pyplot as plt
+from matplotlib.gridspec import GridSpec
+
+from scoobdoob.mb_potential import mb_potential_xy
+from scoobdoob.validation import msm_build as mb, bridge
+from scoobdoob.validation import mb_ground_truth as gt
+from scoobdoob.validation import mb_data as V
+import make_mb_panels as MP
+
+OUT = "figures/mb_unconditional"
+DATA_DIR = "data/mb_unconditional_data"
+HORIZONS = [16, 32, 64, 128, 256, 512]
+
+
+_CACHE_KEYS = [
+    "gx",
+    "gy",
+    "gv",
+    "S1",
+    "S2",
+    "Axy",
+    "Bxy",
+    "xlo",
+    "xhi",
+    "ylo",
+    "yhi",
+    "xy",
+    "allxy",
+    "paths0",
+    "reach0",
+    "succ",
+    "N0",
+]
+CACHE_NPZ = f"{DATA_DIR}/uncond_cache.npz"
+
+
+def save_cache(D, path=CACHE_NPZ):
+    os.makedirs(os.path.dirname(path), exist_ok=True)
+    out = {}
+    for k, v in D.items():
+        out[k] = (
+            np.array([np.asarray(x) for x in v], dtype=object)
+            if k in ("paths0",)
+            else np.asarray(v)
+        )
+    np.savez_compressed(path, **out)
+    print(f"[cache] -> {path}")
+
+
+def load_cache(path=CACHE_NPZ):
+    if not os.path.exists(path):
+        raise SystemExit(
+            f"missing {path}\nrun: python make_mb_unconditional.py --cache-only"
+        )
+    z = np.load(path, allow_pickle=True)
+    D = {k: z[k] for k in z.files}
+    D["paths0"] = list(D["paths0"])
+    for k in ("xlo", "xhi", "ylo", "yhi", "N0"):
+        D[k] = float(D[k]) if k != "N0" else int(D[k])
+    print(f"[cache] loaded {path}")
+    return D
+
+
+def compute():
+    """Everything numeric; no matplotlib."""
+    trajs = V.load_trajs(str(V.DEFAULT_DATA))
+    model = mb.build_full_msm(
+        trajs,
+        trajs,
+        lag=MP.LAG_OPT,
+        tica_lag=MP.TICA_LAG,
+        k=V.K,
+        n_comp=2,
+        seed=0,
+        use_tica=True,
+    )
+    A, B, _ = mb.q2_sign_endpoints(model)
+    start, end = V.orient_endpoints(model, A, B)
+    xy = model.state_xy
+    sad = gt.saddle_points()
+    S1 = sad[np.argmin(sad[:, 0])]
+    S2 = sad[np.argmax(sad[:, 0])]
+    Axy = model.active_to_xy(start).mean(0)
+    Bxy = model.active_to_xy(end).mean(0)
+    endset = set(end)
+
+    def reach_frac(paths):
+        return np.mean([any(s in endset for s in p) for p in paths]) * 100
+
+    N0 = MP.N_OPT
+    paths0 = bridge.sample_unconditional_paths(model.P, start, N0, n_paths=3000, seed=1)
+    reach0 = reach_frac(paths0)
+
+    succ = []
+    for N in HORIZONS:
+        ps = bridge.sample_unconditional_paths(model.P, start, N, n_paths=3000, seed=1)
+        succ.append(reach_frac(ps))
+
+    allxy = model.active_xy
+    xlo, xhi = allxy[:, 0].min() - 0.2, allxy[:, 0].max() + 0.2
+    ylo, yhi = allxy[:, 1].min() - 0.2, allxy[:, 1].max() + 0.2
+    gx, gy = np.meshgrid(np.linspace(xlo, xhi, 300), np.linspace(ylo, yhi, 300))
+    with torch.no_grad():
+        gv = np.clip(
+            mb_potential_xy(torch.tensor(gx), torch.tensor(gy)).numpy(), -150, 100
+        )
+
+    return {k: v for k, v in locals().items() if k in _CACHE_KEYS}
+
+
+def render(D):
+    os.makedirs("figures", exist_ok=True)
+    """Plotting only; recomputes nothing."""
+    gx = D["gx"]
+    gy = D["gy"]
+    gv = D["gv"]
+    S1 = D["S1"]
+    S2 = D["S2"]
+    Axy = D["Axy"]
+    Bxy = D["Bxy"]
+    xlo = D["xlo"]
+    xhi = D["xhi"]
+    ylo = D["ylo"]
+    yhi = D["yhi"]
+    xy = D["xy"]
+    allxy = D["allxy"]
+    paths0 = D["paths0"]
+    reach0 = D["reach0"]
+    succ = D["succ"]
+    N0 = D["N0"]
+
+    fig = plt.figure(figsize=(10.6, 5.0))
+    gs = GridSpec(1, 2, width_ratios=[1.25, 1], wspace=0.30)
+
+    ax = fig.add_subplot(gs[0])
+    ax.contour(gx, gy, gv, levels=25, colors="0.82", linewidths=0.5, zorder=0)
+    ax.scatter(
+        allxy[:, 0], allxy[:, 1], s=9, color="0.72", zorder=1, label="microstates"
+    )
+    for p in paths0[:250]:
+        pp = xy[list(p)]
+        ax.plot(pp[:, 0], pp[:, 1], "-", color="#4477aa", lw=0.5, alpha=0.06, zorder=2)
+    ax.scatter(*S1, marker="X", s=150, color="black", zorder=6)
+    ax.scatter(*S2, marker="X", s=150, color="0.5", zorder=6)
+    ax.scatter(*Axy, marker="*", s=260, color="#228833", ec="white", zorder=7)
+    ax.scatter(*Bxy, marker="*", s=260, color="#ee6677", ec="white", zorder=7)
+    ax.annotate(
+        "S1", S1, xytext=(S1[0] - 0.13, S1[1] + 0.10), fontsize=8.5, weight="bold"
+    )
+    ax.annotate(
+        "S2",
+        S2,
+        xytext=(S2[0] + 0.06, S2[1] + 0.10),
+        fontsize=8.5,
+        color="0.4",
+        weight="bold",
+    )
+    ax.text(
+        Axy[0],
+        Axy[1] + 0.13,
+        "A",
+        ha="center",
+        fontsize=11,
+        color="#228833",
+        weight="bold",
+    )
+    ax.text(
+        Bxy[0],
+        Bxy[1] - 0.17,
+        "B",
+        ha="center",
+        fontsize=11,
+        color="#ee6677",
+        weight="bold",
+    )
+    ax.set_xlim(xlo, xhi)
+    ax.set_ylim(ylo, yhi)
+    ax.set_xlabel("x")
+    ax.set_ylabel("y")
+    ax.text(
+        0.03,
+        0.03,
+        f"{reach0:.0f}% reach B",
+        transform=ax.transAxes,
+        fontsize=10,
+        fontweight="bold",
+        bbox=dict(boxstyle="round", fc="white", ec="#4477aa", alpha=0.9),
+    )
+    ax.legend(loc="upper right", fontsize=8, frameon=False)
+    ax.set_title(f"A  Unconditional MSM rollouts (N={N0})", fontsize=10.5, loc="left")
+
+    ax = fig.add_subplot(gs[1])
+    ax.axhline(100, ls=":", color="0.6", lw=1)
+    ax.text(
+        HORIZONS[-1],
+        100,
+        "conditioned = 100%\nat N=16",
+        va="top",
+        ha="right",
+        fontsize=8,
+        color="0.45",
+    )
+    ax.plot(HORIZONS, succ, "-o", color="#4477aa", lw=1.8, ms=5)
+    ax.set_xscale("log", base=2)
+    ax.set_xticks(HORIZONS)
+    ax.set_xticklabels(HORIZONS)
+    ax.set_xlabel("horizon N (lag steps)")
+    ax.set_ylabel("% of rollouts that reach B")
+    ax.set_ylim(-4, 108)
+    ax.set_title("B  Unconditional reaction vs horizon", fontsize=10.5, loc="left")
+    ax.spines[["top", "right"]].set_visible(False)
+    for xN, yv in zip(HORIZONS, succ):
+        ax.annotate(
+            f"{yv:.0f}%",
+            (xN, yv),
+            xytext=(0, 7),
+            textcoords="offset points",
+            fontsize=7.5,
+            ha="center",
+            color="#22447a",
+        )
+
+    fig.suptitle(
+        "Naive unconditional MSM rollouts almost never react; conditioning is "
+        "required.  $\\tau$=5, TICA lag 20",
+        fontsize=11,
+        y=1.0,
+    )
+    fig.tight_layout(rect=[0, 0, 1, 0.95])
+    for ext in ("svg", "png", "pdf"):
+        fig.savefig(f"{OUT}.{ext}", dpi=200, bbox_inches="tight")
+
+    print(
+        f"[stats] N={N0}: {reach0:.1f}% reach B; "
+        f"horizon {list(zip(HORIZONS, [round(s, 1) for s in succ]))}"
+    )
+    print(f"[done] -> {OUT}.svg/.png/.pdf")
+
+
+def main():
+    import argparse
+
+    ap = argparse.ArgumentParser(description="MB unconditional-rollout figure.")
+    ap.add_argument(
+        "--cache-only", action="store_true", help="compute and cache, do not plot"
+    )
+    ap.add_argument(
+        "--from-cache",
+        action="store_true",
+        help="plot from cache without recomputing (fast; for restyling)",
+    )
+    ap.add_argument("--cache", default=CACHE_NPZ)
+    a = ap.parse_args()
+    if a.from_cache:
+        render(load_cache(a.cache))
+        return
+    D = compute()
+    save_cache(D, a.cache)
+    if a.cache_only:
+        return
+    render(D)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/mb_alpha_seeds.py b/mb_alpha_seeds.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ba7f3b31256f06caff286299e73da1b0e44237b
--- /dev/null
+++ b/mb_alpha_seeds.py
@@ -0,0 +1,88 @@
+import numpy as np, collections
+from scoobdoob.validation import msm_build as mb, tpt, bridge
+from scoobdoob.validation import mb_ground_truth as gt
+from scoobdoob.validation import mb_data as V
+import make_mb_panels as MP
+
+Ac = np.array([-200, -100, -170, 15.0])
+a = np.array([-1, -1, -6.5, 0.7])
+b = np.array([0, 0, 11, 0.6])
+c = np.array([-10, -10, -6.5, 0.7])
+X = np.array([1, 0, -0.5, -1.0])
+Y = np.array([0, 0.5, 1.5, 1.0])
+
+
+def E(p):
+    x, y = p
+    return float(
+        (Ac * np.exp(a * (x - X) ** 2 + b * (x - X) * (y - Y) + c * (y - Y) ** 2)).sum()
+    )
+
+
+LAG, N, TL = MP.LAG_OPT, MP.N_OPT, 20
+sad = gt.saddle_points()
+S1 = sad[np.argmin(sad[:, 0])]
+S2 = sad[np.argmax(sad[:, 0])]
+trajs = V.load_trajs(str(V.DEFAULT_DATA))
+AGG = collections.defaultdict(list)
+DIV = collections.defaultdict(list)
+for seed in [0, 1, 2, 3, 4]:
+    model = mb.build_full_msm(
+        trajs, trajs, lag=LAG, tica_lag=TL, k=V.K, n_comp=2, seed=seed, use_tica=True
+    )
+    A, B, _ = mb.q2_sign_endpoints(model)
+    start, end = V.orient_endpoints(model, A, B)
+    xy = model.state_xy
+    _, q, _ = tpt.committor_agreement(model, start, end)
+    rf = tpt.reactive_flux(model, start, end)
+    thru = np.asarray(rf.gross_flux).sum(1)
+    share = thru / thru.sum()
+    on = share > 0.5 / model.n_active
+    on[list(start)] = True
+    on[list(end)] = True
+    Axy = model.active_to_xy(start).mean(0)
+    Bxy = model.active_to_xy(end).mean(0)
+    land = np.vstack([Axy, Bxy, sad])
+    lo = land.min(0) - 0.35
+    hi = land.max(0) + 0.35
+    on = on & (
+        (xy[:, 0] >= lo[0])
+        & (xy[:, 0] <= hi[0])
+        & (xy[:, 1] >= lo[1])
+        & (xy[:, 1] <= hi[1])
+    )
+    labels = np.concatenate(model.dtrajs)
+    counts = np.bincount(labels, minlength=model.n_active)[: model.n_active].astype(
+        float
+    )
+    rho = np.maximum(counts, 1) / counts.mean()
+    react = (q > 0.05) & (q < 0.95)
+    flr = np.percentile(rho[react], 25)
+    n1 = np.linalg.norm(xy - S1, axis=1) < 0.25
+    qs2 = q[np.argmin(np.linalg.norm(xy - S2, axis=1))]
+    for al in [0.0, 0.5, 1.0, 1.5, 2.0, 3.0]:
+        w = np.ones_like(rho) if al == 0 else np.exp(-LAG * al * np.maximum(rho, flr))
+        w = w.copy()
+        w[~on] = 0.0
+        ps = bridge.sample_bridge_paths(
+            bridge.doob_kernels(model.P, end, N, w=w), start, n_paths=3000, seed=0
+        )
+        rec = np.mean([any(n1[s] for s in p) for p in ps]) * 100
+
+        DIV[al].append((len({s for p in ps for s in p}), len({tuple(p) for p in ps})))
+        r = MP.channel(ps, q, xy, on, thru, start, end)
+        qr = [q[np.argmin(np.linalg.norm(xy - p, axis=1))] for p in r]
+        cr = [i for i in range(len(qr) - 1) if (qr[i] < qs2) != (qr[i + 1] < qs2)]
+        d2 = (E((r[cr[0]] + r[cr[0] + 1]) / 2) - E(S2)) / 16.7 if cr else np.nan
+        AGG[al].append((rec, d2))
+print(f"TICA lag {TL}, count lag {LAG}, N={N}; 5 clustering seeds; kT=16.7")
+print(f"{'alpha':>6} {'S1 recov':>16} {'S2 dE (kT)':>16} {'states':>10} {'routes':>10}")
+for al, v in sorted(AGG.items()):
+    rc = np.array([x[0] for x in v])
+    d2 = np.array([x[1] for x in v])
+    st = np.array([x[0] for x in DIV[al]])
+    rt = np.array([x[1] for x in DIV[al]])
+    print(
+        f"{al:6.1f} {rc.mean():8.0f}%+-{rc.std():3.0f} {np.nanmean(d2):11.1f}+-{np.nanstd(d2):3.1f} "
+        f"{st.mean():6.0f}+-{st.std():2.0f} {rt.mean():6.0f}+-{rt.std():2.0f}"
+    )
diff --git a/mb_stats.py b/mb_stats.py
new file mode 100644
index 0000000000000000000000000000000000000000..1612c89d71d0a8db360ab97cd51830511be2d5d3
--- /dev/null
+++ b/mb_stats.py
@@ -0,0 +1,128 @@
+import numpy as np, json
+from scoobdoob.validation import msm_build as mb, tpt, bridge
+from scoobdoob.validation import mb_ground_truth as gt
+from scoobdoob.validation import mb_data as V
+import make_mb_panels as MP
+
+LAG, N, TL, ALPHA = MP.LAG_OPT, MP.N_OPT, MP.TICA_LAG, 1.5
+CLUST_SEEDS = list(range(20))
+PATH_SEEDS = [0, 1, 2]
+NPATH = 2000
+sad = gt.saddle_points()
+S1 = sad[np.argmin(sad[:, 0])]
+trajs = V.load_trajs(str(V.DEFAULT_DATA))
+
+raw_m, fk_m, within = [], [], []
+for cs in CLUST_SEEDS:
+    model = mb.build_full_msm(
+        trajs, trajs, lag=LAG, tica_lag=TL, k=V.K, n_comp=2, seed=cs, use_tica=True
+    )
+    A, B, _ = mb.q2_sign_endpoints(model)
+    start, end = V.orient_endpoints(model, A, B)
+    xy = model.state_xy
+    _, q, _ = tpt.committor_agreement(model, start, end)
+    rf = tpt.reactive_flux(model, start, end)
+    thru = np.asarray(rf.gross_flux).sum(1)
+    share = thru / thru.sum()
+    on = share > 0.5 / model.n_active
+    on[list(start)] = True
+    on[list(end)] = True
+    Axy = model.active_to_xy(start).mean(0)
+    Bxy = model.active_to_xy(end).mean(0)
+    land = np.vstack([Axy, Bxy, sad])
+    lo = land.min(0) - 0.35
+    hi = land.max(0) + 0.35
+    on = on & (
+        (xy[:, 0] >= lo[0])
+        & (xy[:, 0] <= hi[0])
+        & (xy[:, 1] >= lo[1])
+        & (xy[:, 1] <= hi[1])
+    )
+    labels = np.concatenate(model.dtrajs)
+    counts = np.bincount(labels, minlength=model.n_active)[: model.n_active].astype(
+        float
+    )
+    rho = np.maximum(counts, 1) / counts.mean()
+    react = (q > 0.05) & (q < 0.95)
+    wfk = np.exp(-LAG * ALPHA * np.maximum(rho, np.percentile(rho[react], 25)))
+    wfk[~on] = 0.0
+    wraw = np.ones_like(wfk)
+    wraw[~on] = 0.0
+    n1 = np.linalg.norm(xy - S1, axis=1) < 0.25
+    Kraw = bridge.doob_kernels(model.P, end, N, w=wraw)
+    Kfk = bridge.doob_kernels(model.P, end, N, w=wfk)
+    r_ps, f_ps = [], []
+    for psd in PATH_SEEDS:
+        pr = bridge.sample_bridge_paths(Kraw, start, n_paths=NPATH, seed=psd)
+        pf = bridge.sample_bridge_paths(Kfk, start, n_paths=NPATH, seed=psd)
+        r_ps.append(np.mean([any(n1[s] for s in p) for p in pr]) * 100)
+        f_ps.append(np.mean([any(n1[s] for s in p) for p in pf]) * 100)
+    raw_m.append(np.mean(r_ps))
+    fk_m.append(np.mean(f_ps))
+    within.append((np.std(r_ps), np.std(f_ps)))
+    print(
+        f"  [clust seed {cs:2d}] raw {raw_m[-1]:5.1f}%  fk {fk_m[-1]:5.1f}%  "
+        f"diff {fk_m[-1] - raw_m[-1]:+5.1f}  (path-seed sd {within[-1][0]:.1f}/{within[-1][1]:.1f})"
+    )
+
+raw = np.array(raw_m)
+fk = np.array(fk_m)
+d = fk - raw
+n = len(d)
+print(
+    f"\n--- variance decomposition (n={n} clustering seeds, {len(PATH_SEEDS)} path seeds, "
+    f"{NPATH} paths each) ---"
+)
+wr = np.mean([w[0] for w in within])
+wf = np.mean([w[1] for w in within])
+print(f"between-clustering sd : raw {raw.std(ddof=1):.1f}   fk {fk.std(ddof=1):.1f}")
+print(f"within  (path-sampling) sd : raw {wr:.1f}   fk {wf:.1f}")
+print(
+    f"-> clustering dominates by {fk.std(ddof=1) / max(wf, 1e-9):.0f}x; Monte-Carlo error is negligible"
+)
+
+print(f"\n--- paired comparison, FK vs raw ---")
+print(f"raw  {raw.mean():.1f} +- {raw.std(ddof=1):.1f}")
+print(f"fk   {fk.mean():.1f} +- {fk.std(ddof=1):.1f}")
+se = d.std(ddof=1) / np.sqrt(n)
+print(f"paired diff {d.mean():+.1f} (sd {d.std(ddof=1):.1f}, se {se:.1f})")
+print(
+    f"  min diff over seeds {d.min():+.1f}, max {d.max():+.1f}  "
+    f"(sign consistent: {'yes' if (d > 0).all() else 'NO'})"
+)
+print(f"Cohen's d_z = {d.mean() / d.std(ddof=1):.2f}")
+try:
+    from scipy import stats
+
+    t, pt = stats.ttest_rel(fk, raw)
+    w, pw = stats.wilcoxon(fk, raw)
+    ci = stats.t.interval(0.95, n - 1, loc=d.mean(), scale=se)
+    print(f"paired t: t={t:.1f}, p={pt:.2e}")
+    print(
+        f"Wilcoxon signed-rank: W={w:.0f}, p={pw:.2e} "
+        f"(floor for n={n} is {2 / 2**n:.1e})"
+    )
+    print(f"95% CI of the mean difference: [{ci[0]:.1f}, {ci[1]:.1f}]")
+except ImportError:
+    print("scipy unavailable; bootstrap only")
+rng = np.random.default_rng(0)
+bs = np.array([rng.choice(d, n, replace=True).mean() for _ in range(20000)])
+print(
+    f"bootstrap 95% CI of mean diff: [{np.percentile(bs, 2.5):.1f}, {np.percentile(bs, 97.5):.1f}]"
+)
+json.dump(
+    {
+        "raw": raw.tolist(),
+        "fk": fk.tolist(),
+        "clust_seeds": CLUST_SEEDS,
+        "path_seeds": PATH_SEEDS,
+        "n_paths": NPATH,
+        "tica_lag": TL,
+        "lag": LAG,
+        "N": N,
+        "alpha": ALPHA,
+    },
+    open("data/mb_stats.json", "w"),
+    indent=2,
+)
+print("[done]")
diff --git a/mb_ticalag_seeds.py b/mb_ticalag_seeds.py
new file mode 100644
index 0000000000000000000000000000000000000000..108a0f5025ef59f7e300d147179d0518bb49426d
--- /dev/null
+++ b/mb_ticalag_seeds.py
@@ -0,0 +1,103 @@
+import numpy as np
+from scoobdoob.validation import msm_build as mb, tpt, bridge
+from scoobdoob.validation import mb_ground_truth as gt
+from scoobdoob.validation import mb_data as V
+import make_mb_panels as MP
+
+Ac = np.array([-200, -100, -170, 15.0])
+a = np.array([-1, -1, -6.5, 0.7])
+b = np.array([0, 0, 11, 0.6])
+c = np.array([-10, -10, -6.5, 0.7])
+X = np.array([1, 0, -0.5, -1.0])
+Y = np.array([0, 0.5, 1.5, 1.0])
+
+
+def E(p):
+    x, y = p
+    return float(
+        (Ac * np.exp(a * (x - X) ** 2 + b * (x - X) * (y - Y) + c * (y - Y) ** 2)).sum()
+    )
+
+
+LAG, N, ALPHA = MP.LAG_OPT, MP.N_OPT, 1.5
+M = np.load("data/mb_mep.npz")
+mep = M["mep"]
+mep = mep[mep[:, 1] >= -0.05]
+sad = gt.saddle_points()
+S1 = sad[np.argmin(sad[:, 0])]
+S2 = sad[np.argmax(sad[:, 0])]
+trajs = V.load_trajs(str(V.DEFAULT_DATA))
+
+print(
+    f"count lag tau={LAG}, N={N}, alpha={ALPHA}; S1 E={E(S1):.0f}, S2 E={E(S2):.0f}, kT=16.7"
+)
+print("per-seed runs, then aggregate:")
+import itertools, collections
+
+AGG = collections.defaultdict(list)
+for tl, seed in itertools.product([5, 20, 30, 40], [0, 1, 2, 3, 4]):
+    model = mb.build_full_msm(
+        trajs, trajs, lag=LAG, tica_lag=tl, k=V.K, n_comp=2, seed=seed, use_tica=True
+    )
+    A, B, _ = mb.q2_sign_endpoints(model)
+    start, end = V.orient_endpoints(model, A, B)
+    xy = model.state_xy
+    _, q, _ = tpt.committor_agreement(model, start, end)
+    rf = tpt.reactive_flux(model, start, end)
+    thru = np.asarray(rf.gross_flux).sum(1)
+    share = thru / thru.sum()
+    on = share > 0.5 / model.n_active
+    on[list(start)] = True
+    on[list(end)] = True
+    Axy = model.active_to_xy(start).mean(0)
+    Bxy = model.active_to_xy(end).mean(0)
+    land = np.vstack([Axy, Bxy, sad])
+    lo = land.min(0) - 0.35
+    hi = land.max(0) + 0.35
+    on = on & (
+        (xy[:, 0] >= lo[0])
+        & (xy[:, 0] <= hi[0])
+        & (xy[:, 1] >= lo[1])
+        & (xy[:, 1] <= hi[1])
+    )
+
+    d = np.linalg.norm(xy[on][:, None, :] - mep[None, :, :], axis=2).min(1)
+    spread = float(np.median(d))
+    n1 = np.linalg.norm(xy - S1, axis=1) < 0.25
+    nS1 = int((n1 & on).sum())
+
+    labels = np.concatenate(model.dtrajs)
+    counts = np.bincount(labels, minlength=model.n_active)[: model.n_active].astype(
+        float
+    )
+    rho = np.maximum(counts, 1) / counts.mean()
+    react = (q > 0.05) & (q < 0.95)
+    flr = np.percentile(rho[react], 25)
+    w = np.exp(-LAG * ALPHA * np.maximum(rho, flr))
+    w[~on] = 0.0
+    ps = bridge.sample_bridge_paths(
+        bridge.doob_kernels(model.P, end, N, w=w), start, n_paths=3000, seed=0
+    )
+    recov = np.mean([any(n1[s] for s in p) for p in ps]) * 100
+
+    qs2 = q[np.argmin(np.linalg.norm(xy - S2, axis=1))]
+    r = MP.channel(ps, q, xy, on, thru, start, end)
+    qr = [q[np.argmin(np.linalg.norm(xy - p, axis=1))] for p in r]
+    cr = [i for i in range(len(qr) - 1) if (qr[i] < qs2) != (qr[i + 1] < qs2)]
+    s2 = (
+        f"{(E((r[cr[0]] + r[cr[0] + 1]) / 2) - E(S2)) / 16.7:10.1f}"
+        if cr
+        else f"{'none':>10}"
+    )
+    AGG[tl].append((spread, nS1, recov))
+    print(
+        f"  [tica={tl} seed={seed}] spread {spread:.3f} nS1 {nS1} recov {recov:.0f}% S2 {s2.strip()}"
+    )
+print()
+print(f"{'ticalag':>8} {'spread':>14} {'nS1':>10} {'recov (mean+-sd)':>20}")
+for tl, v in sorted(AGG.items()):
+    sp, n1s, rc = (np.array([x[i] for x in v]) for i in range(3))
+    print(
+        f"{tl:8d} {sp.mean():7.3f}+-{sp.std():4.3f} {n1s.mean():5.1f}+-{n1s.std():3.1f} "
+        f"{rc.mean():11.0f}%+-{rc.std():3.0f}"
+    )
diff --git a/paper_figures/data/p53_discovery_data.py b/paper_figures/data/p53_discovery_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2ad2747611d3a2c32b1ab996cf0ea72724c9078
--- /dev/null
+++ b/paper_figures/data/p53_discovery_data.py
@@ -0,0 +1,153 @@
+#!/usr/bin/env python
+import json
+import sys
+from pathlib import Path
+
+import numpy as np
+
+ROOT = Path(__file__).resolve().parents[2]
+FIG = ROOT / "data" / "p53"
+CACHE = FIG / "fig_data"
+sys.path.insert(0, str(ROOT))
+
+
+HIGHLIGHT = [56, 0]
+RARE_THRESH = 0.98
+
+
+def main():
+    from scoobdoob.validation.pocket import site_drug
+
+    CACHE.mkdir(parents=True, exist_ok=True)
+
+    M = np.load(FIG / "rmsd_matrix.npy")
+    mids = sorted(
+        int(p.stem.replace("micro", ""))
+        for p in (FIG / "pocket_reps").glob("micro*.pdb")
+    )
+    if len(mids) != M.shape[0]:
+        raise SystemExit(
+            f"{len(mids)} representatives but rmsd_matrix has {M.shape[0]} rows"
+        )
+
+    drug = np.array([site_drug(m) for m in mids], dtype=float)
+    truth = np.nanmedian(M, axis=1)
+    closest = np.nanmin(M, axis=1)
+    order = np.argsort(truth)
+    rank = {mids[j]: r + 1 for r, j in enumerate(order)}
+
+    pij = json.loads((FIG / "pocket_msm_pi.json").read_text())
+    pi = np.array([float(pij[str(m)]) if str(m) in pij else np.nan for m in mids])
+    if not np.isfinite(pi).all():
+        missing = [m for m, p in zip(mids, pi) if not np.isfinite(p)]
+        raise SystemExit(
+            f"no stationary probability for microstates {missing}; "
+            f"run upstream/dump_pocket_msm_pi.py first"
+        )
+
+    rows = json.loads((FIG / "frame_drug" / "frames.json").read_text())
+    v = np.array(
+        [
+            r.get("site_drug") if r.get("site_drug") is not None else np.nan
+            for r in rows
+        ],
+        dtype=float,
+    )
+
+    import pickle
+
+    meta = pickle.load(open(FIG / "pocket_feats_s10_cb_ALL.pkl", "rb"))["meta"]
+    cls = np.array(
+        [meta[r["traj"]]["cls"] if r["traj"] < len(meta) else "?" for r in rows]
+    )
+    fin = np.isfinite(v)
+    v_ref, v_seed = v[fin & (cls == "folded")], v[fin & (cls == "seed")]
+    v = v[fin]
+
+    cov = json.loads((FIG / "coverage_all_y220c.json").read_text())
+    apo_profile = np.sort(np.array([r["apo_to_holo"] for r in cov], dtype=float))
+
+    hi_idx = [mids.index(m) for m in HIGHLIGHT]
+    n_rare = int((v >= RARE_THRESH).sum())
+    share = {
+        f"ge_{t}": {
+            "n": int((v >= t).sum()),
+            "reference": int((v_ref >= t).sum()),
+            "seeded": int((v_seed >= t).sum()),
+        }
+        for t in (0.8, 0.9, 0.95, RARE_THRESH)
+    }
+
+    np.savez_compressed(
+        CACHE / "discovery.npz",
+        mids=np.array(mids),
+        drug=drug,
+        truth=truth,
+        closest=closest,
+        pi=pi,
+        order=order,
+        frame_scores=v,
+        frame_scores_ref=v_ref,
+        frame_scores_seed=v_seed,
+        highlight=np.array(HIGHLIGHT),
+        highlight_idx=np.array(hi_idx),
+        highlight_rmsd_profiles=np.sort(M[hi_idx], axis=1),
+        apo_rmsd_profile=apo_profile,
+        n_complexes=np.array(M.shape[1]),
+    )
+
+    stats = {
+        "n_microstates": len(mids),
+        "n_complexes": int(M.shape[1]),
+        "n_frames": int(len(v)),
+        "frame_median": float(np.median(v)),
+        "rare_threshold": RARE_THRESH,
+        "n_frames_ge_thresh": n_rare,
+        "n_frames_reference": int(len(v_ref)),
+        "n_frames_seeded": int(len(v_seed)),
+        "high_druggability_share": share,
+        "frame_max": float(v.max()),
+        "top_rep_drug": float(drug.max()),
+        "apo_to_holo_median": float(np.median(apo_profile)),
+        "n_ensemble_closer_than_apo": int(
+            sum(1 for r in cov if r["best_ens"] < r["apo_to_holo"])
+        ),
+        "highlight": {
+            str(m): {
+                "rank": int(rank[m]),
+                "median_rmsd": float(truth[mids.index(m)]),
+                "closest_rmsd": float(closest[mids.index(m)]),
+                "rep_drug": float(drug[mids.index(m)]),
+                "pi": float(pi[mids.index(m)]),
+            }
+            for m in HIGHLIGHT
+        },
+        "table_top10": [
+            {
+                "rank": r + 1,
+                "micro": int(mids[j]),
+                "median_rmsd": float(truth[j]),
+                "closest_rmsd": float(closest[j]),
+                "rep_drug": float(drug[j]),
+                "pi": float(pi[j]),
+            }
+            for r, j in enumerate(order[:10])
+        ],
+    }
+    (CACHE / "discovery.json").write_text(json.dumps(stats, indent=1, default=float))
+
+    print(f"  {len(mids)} microstates, {M.shape[1]} complexes, {len(v)} scored frames")
+    print(
+        f"  most holo-like: microstate {mids[order[0]]} "
+        f"(median {truth[order[0]]:.2f} A, closest {closest[order[0]]:.2f} A, "
+        f"druggability {drug[order[0]]:.3f}, pi {pi[order[0]]:.4f})"
+    )
+    print(
+        f"  frames >= {RARE_THRESH}: {n_rare}/{len(v)} "
+        f"({100 * n_rare / len(v):.3f}%), ensemble median {np.median(v):.3f}"
+    )
+    print(f"-> {CACHE / 'discovery.npz'}\n-> {CACHE / 'discovery.json'}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/paper_figures/data/p53_preference_data.py b/paper_figures/data/p53_preference_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..a18743187dd2cd3b4e46f5ee676969649a2cfee4
--- /dev/null
+++ b/paper_figures/data/p53_preference_data.py
@@ -0,0 +1,135 @@
+#!/usr/bin/env python
+import argparse
+import json
+import sys
+from pathlib import Path
+
+import numpy as np
+from scipy.cluster.hierarchy import linkage, leaves_list
+from scipy.spatial.distance import squareform
+from scipy.stats import spearmanr
+
+ROOT = Path(__file__).resolve().parents[2]
+CACHE = ROOT / "data" / "p53" / "fig_data"
+sys.path.insert(0, str(ROOT))
+
+SEED = 0
+
+
+def split_half_all(gamma, n_splits, rng, permute=False):
+    """Split-half over ALL receptors: does the ligand-preference structure reproduce?"""
+    n_lig, n_rec = gamma.shape
+    iu = np.triu_indices(n_lig, k=1)
+    out = []
+    for _ in range(n_splits):
+        m = gamma.copy()
+        if permute:
+            for li in range(n_lig):
+                m[li] = m[li, rng.permutation(n_rec)]
+        p = rng.permutation(n_rec)
+        A, B = p[: n_rec // 2], p[n_rec // 2 :]
+        r_, _ = spearmanr(np.corrcoef(m[:, A])[iu], np.corrcoef(m[:, B])[iu])
+        if np.isfinite(r_):
+            out.append(r_)
+    return np.array(out)
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--splits", type=int, default=400)
+    a = ap.parse_args()
+
+    from scoobdoob.validation import preference as WS
+
+    CACHE.mkdir(parents=True, exist_ok=True)
+    gamma, ligs, frames, strat = WS.gamma_matrix()
+    n_lig, n_rec = gamma.shape
+    labs = np.unique(strat)
+
+    C = np.corrcoef(gamma)
+    D = np.clip(1.0 - C, 0, 2)
+    np.fill_diagonal(D, 0.0)
+    lig_order = leaves_list(linkage(squareform(D, checks=False), method="average"))
+    rec_order = np.concatenate([np.where(strat == l)[0] for l in labs])
+
+    tot = gamma.var()
+    between = np.zeros_like(gamma)
+    for l in labs:
+        m_ = strat == l
+        between[:, m_] = gamma[:, m_].mean(axis=1, keepdims=True)
+    frac_between = float(between.var() / tot)
+    frac_within = float((gamma - between).var() / tot)
+
+    obs_all = split_half_all(gamma, a.splits, np.random.default_rng(SEED))
+    nul_all = split_half_all(
+        gamma, a.splits, np.random.default_rng(SEED + 1), permute=True
+    )
+
+    WS.rng = np.random.default_rng(SEED + 2)
+    obs_w = WS.split_half_within(gamma, strat, n_splits=a.splits)
+    rng = np.random.default_rng(SEED + 3)
+    gperm = gamma.copy()
+    for l in labs:
+        m_ = np.where(strat == l)[0]
+        for li in range(n_lig):
+            gperm[li, m_] = gperm[li, rng.permutation(m_)]
+    WS.rng = np.random.default_rng(SEED + 4)
+    nul_w = WS.split_half_within(gperm, strat, n_splits=a.splits)
+
+    p_within = float((nul_w >= obs_w.mean()).mean())
+
+    np.savez_compressed(
+        CACHE / "preference.npz",
+        gamma=gamma,
+        strat=strat,
+        ligands=np.array(ligs),
+        frames=np.array(frames),
+        lig_order=lig_order,
+        rec_order=rec_order,
+        obs_all=obs_all,
+        nul_all=nul_all,
+        obs_within=obs_w,
+        nul_within=nul_w,
+        frac_within=np.array(frac_within),
+        frac_between=np.array(frac_between),
+    )
+    stats = {
+        "n_ligands": int(n_lig),
+        "n_receptors": int(n_rec),
+        "n_strata": int(len(labs)),
+        "stratum_sizes": [int((strat == l).sum()) for l in labs],
+        "n_splits": int(a.splits),
+        "seed": SEED,
+        "gamma_var_total": float(tot),
+        "frac_within": frac_within,
+        "frac_between": frac_between,
+        "splithalf_all_mean": float(obs_all.mean()),
+        "splithalf_all_sd": float(obs_all.std()),
+        "splithalf_all_null_mean": float(nul_all.mean()),
+        "splithalf_within_mean": float(obs_w.mean()),
+        "splithalf_within_sd": float(obs_w.std()),
+        "splithalf_within_null_mean": float(nul_w.mean()),
+        "p_null_ge_observed_within": p_within,
+    }
+    (CACHE / "preference.json").write_text(json.dumps(stats, indent=1, default=float))
+
+    print(
+        f"  {n_lig} ligands x {n_rec} receptors, {len(labs)} strata "
+        f"{stats['stratum_sizes']}"
+    )
+    print(
+        f"  var(gamma): within {100 * frac_within:.1f}%  between {100 * frac_between:.1f}%"
+    )
+    print(
+        f"  split-half all receptors : {obs_all.mean():+.4f} +/- {obs_all.std():.4f} "
+        f"(null {nul_all.mean():+.4f})"
+    )
+    print(
+        f"  split-half within stratum: {obs_w.mean():+.4f} +/- {obs_w.std():.4f} "
+        f"(null {nul_w.mean():+.4f}, p={p_within:.4f})"
+    )
+    print(f"-> {CACHE / 'preference.npz'}\n-> {CACHE / 'preference.json'}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/paper_figures/plot/p53_discovery_plot.py b/paper_figures/plot/p53_discovery_plot.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f5793e386cfbadbf3ab0f0fc6be3d5de6c9d247
--- /dev/null
+++ b/paper_figures/plot/p53_discovery_plot.py
@@ -0,0 +1,219 @@
+#!/usr/bin/env python
+import argparse
+import json
+from pathlib import Path
+
+import numpy as np
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+from matplotlib.ticker import LogLocator
+
+ROOT = Path(__file__).resolve().parents[2]
+CACHE = ROOT / "data" / "p53" / "fig_data"
+
+
+FIGSIZE = (7.2, 5.4)
+RC = {
+    "font.size": 8,
+    "axes.labelsize": 8.5,
+    "axes.titlesize": 8.5,
+    "xtick.labelsize": 7.5,
+    "ytick.labelsize": 7.5,
+    "legend.fontsize": 7,
+    "axes.spines.top": False,
+    "axes.spines.right": False,
+    "pdf.fonttype": 42,
+    "ps.fonttype": 42,
+}
+GREY = "#b8b8b8"
+ACC = "#2f6fb5"
+SEED_C = "#e8a33d"
+HI = "#c8412b"
+HL_COLORS = {56: HI, 0: ACC}
+APO_C = "#4a4a4a"
+STAR_SIZE, DOT_SIZE, HL_DOT = 70, 16, 42
+HIST_BINS = 70
+
+
+ANNOT_C = {56: (5, -9), 0: (4, 6)}
+ANNOT_A = (11, -4)
+ANNOT_B = (0.06, 0.06)
+TITLES = {
+    "a": "a   Ligandability is not equilibrium weight",
+    "b": "b   Rare, and sampled by the reference runs",
+    "c": "c   Holo-like conformers exist in the ensemble",
+    "d": "d   Compared with the unbound crystal",
+}
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--out", type=Path, default=ROOT / "figures" / "p53_discovery.pdf")
+    ap.add_argument("--png", action="store_true", help="also write a PNG alongside")
+    a = ap.parse_args()
+
+    npz = CACHE / "discovery.npz"
+    if not npz.exists():
+        raise SystemExit(
+            f"missing {npz}\nrun paper_figures/data/p53_discovery_data.py first"
+        )
+    d = np.load(npz)
+    S = json.loads((CACHE / "discovery.json").read_text())
+
+    mids = list(d["mids"])
+    drug, truth, pi = d["drug"], d["truth"], d["pi"]
+    order, v = d["order"], d["frame_scores"]
+    v_ref, v_seed = d["frame_scores_ref"], d["frame_scores_seed"]
+    highlight = list(d["highlight"])
+    profiles = d["highlight_rmsd_profiles"]
+    n_cx = int(d["n_complexes"])
+    rank = {int(m): S["highlight"][str(m)]["rank"] for m in highlight}
+    i56 = mids.index(56)
+
+    plt.rcParams.update(RC)
+    fig, ax = plt.subplots(2, 2, figsize=FIGSIZE)
+
+    p = ax[0, 0]
+    p.scatter(pi, drug, s=DOT_SIZE, c=GREY, edgecolor="none", zorder=2)
+    p.scatter(
+        [pi[i56]],
+        [drug[i56]],
+        s=STAR_SIZE,
+        c=HI,
+        marker="*",
+        edgecolor="k",
+        linewidth=0.4,
+        zorder=4,
+    )
+    p.annotate(
+        f"microstate 56\n$\\pi$={pi[i56]:.4f}, score {drug[i56]:.3f}",
+        (pi[i56], drug[i56]),
+        textcoords="offset points",
+        xytext=ANNOT_A,
+        ha="left",
+        va="top",
+        fontsize=6.5,
+        color=HI,
+    )
+    p.set_xscale("log")
+    p.xaxis.set_major_locator(LogLocator(numticks=5))
+    p.set_xlabel("stationary probability $\\pi$")
+    p.set_ylabel("representative-conformer\npocket druggability")
+    p.set_title(TITLES["a"], loc="left", fontweight="bold")
+
+    p = ax[0, 1]
+    bins = np.linspace(0.0, 1.0, HIST_BINS + 1)
+    p.hist(
+        v_ref,
+        bins=bins,
+        color=ACC,
+        alpha=0.80,
+        edgecolor="none",
+        label=f"reference (n={len(v_ref):,})",
+    )
+    p.hist(
+        v_seed,
+        bins=bins,
+        color=SEED_C,
+        alpha=0.60,
+        edgecolor="none",
+        label=f"seeded (n={len(v_seed):,})",
+    )
+    p.set_yscale("log")
+    p.axvline(drug[i56], color=HI, ls="--", lw=1.2)
+    sh = S["high_druggability_share"]["ge_0.8"]
+
+    p.text(
+        ANNOT_B[0],
+        ANNOT_B[1],
+        f"micro-56 rep.  {drug[i56]:.3f}\n"
+        f"{S['n_frames_ge_thresh']}/{S['n_frames']} frames "
+        f"$\\geq${S['rare_threshold']:g}\n"
+        f"{100 * sh['reference'] / sh['n']:.0f}% of frames $\\geq$0.8 are reference",
+        transform=p.transAxes,
+        ha="left",
+        va="bottom",
+        fontsize=6.3,
+        color=HI,
+    )
+    p.set_xlabel("fpocket druggability score, per frame")
+    p.set_ylabel("frames (log)")
+    p.legend(frameon=False, loc="upper right", fontsize=6.5)
+    p.set_title(TITLES["b"], loc="left", fontweight="bold")
+
+    p = ax[1, 0]
+    xs = np.arange(1, len(truth) + 1)
+    p.plot(xs, truth[order], "-", color=GREY, lw=1.0, zorder=1)
+    p.scatter(xs, truth[order], s=12, c=GREY, edgecolor="none", zorder=2)
+    for mic in highlight:
+        mic = int(mic)
+        j = mids.index(mic)
+        p.scatter(
+            [rank[mic]],
+            [truth[j]],
+            s=HL_DOT,
+            c=HL_COLORS[mic],
+            edgecolor="k",
+            linewidth=0.4,
+            zorder=4,
+        )
+        p.annotate(
+            f"{mic}",
+            (rank[mic], truth[j]),
+            textcoords="offset points",
+            xytext=ANNOT_C[mic],
+            fontsize=6.5,
+            color=HL_COLORS[mic],
+            fontweight="bold",
+        )
+    p.set_xlabel("microstate rank by median holo RMSD")
+    p.set_ylabel(f"median pocket RMSD to\n{n_cx} Y220C complexes ($\\AA$)")
+    p.set_title(TITLES["c"], loc="left", fontweight="bold")
+
+    p = ax[1, 1]
+    for k, mic in enumerate(highlight):
+        mic = int(mic)
+        p.plot(
+            profiles[k],
+            np.arange(1, n_cx + 1),
+            "-",
+            color=HL_COLORS[mic],
+            lw=1.3,
+            label=f"micro {mic} (rank {rank[mic]}/{len(mids)})",
+        )
+    p.plot(
+        d["apo_rmsd_profile"],
+        np.arange(1, n_cx + 1),
+        "--",
+        color=APO_C,
+        lw=1.3,
+        label=f"apo crystal (median {S['apo_to_holo_median']:.2f} $\\AA$)",
+    )
+    p.text(
+        0.035,
+        0.97,
+        f"ensemble closer than apo\nfor {S['n_ensemble_closer_than_apo']}/{n_cx} complexes",
+        transform=p.transAxes,
+        ha="left",
+        va="top",
+        fontsize=6.3,
+        color=APO_C,
+    )
+    p.set_xlabel("pocket RMSD to an individual complex ($\\AA$)")
+    p.set_ylabel("crystal complexes (cumulative)")
+    p.legend(frameon=False, loc="lower right")
+    p.set_title(TITLES["d"], loc="left", fontweight="bold")
+
+    fig.tight_layout(pad=0.9)
+    a.out.parent.mkdir(parents=True, exist_ok=True)
+    fig.savefig(a.out, format="pdf", bbox_inches="tight")
+    print(f"-> {a.out}")
+    if a.png:
+        fig.savefig(a.out.with_suffix(".png"), dpi=200, bbox_inches="tight")
+        print(f"-> {a.out.with_suffix('.png')}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/paper_figures/plot/p53_preference_plot.py b/paper_figures/plot/p53_preference_plot.py
new file mode 100644
index 0000000000000000000000000000000000000000..681ce79a9289482778c7cef0f9ac7109a0be221b
--- /dev/null
+++ b/paper_figures/plot/p53_preference_plot.py
@@ -0,0 +1,179 @@
+#!/usr/bin/env python
+import argparse
+import json
+from pathlib import Path
+
+import numpy as np
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+ROOT = Path(__file__).resolve().parents[2]
+CACHE = ROOT / "data" / "p53" / "fig_data"
+
+
+FIGSIZE = (7.2, 6.0)
+HEIGHT_RATIOS, HSPACE, WSPACE = [1.15, 1.0], 0.42, 0.32
+RC = {
+    "font.size": 8,
+    "axes.labelsize": 8.5,
+    "axes.titlesize": 8.5,
+    "xtick.labelsize": 7.5,
+    "ytick.labelsize": 7.5,
+    "legend.fontsize": 7,
+    "axes.spines.top": False,
+    "axes.spines.right": False,
+    "pdf.fonttype": 42,
+    "ps.fonttype": 42,
+}
+ACC = "#2f6fb5"
+HI = "#c8412b"
+NULL_GREY = "#c9c9c9"
+CMAP = "RdBu_r"
+CLIP_PCT = 98
+BAR_W, BAR_DX = 0.32, 0.17
+YLIM_B = (-0.12, 1.12)
+TITLES = {
+    "a": "a   Ligand $\\times$ conformer interaction $\\gamma$\n"
+    "     (ligand, cavity-volume and receptor main effects removed)",
+    "b": "b   Preference survives removing\n     between-stratum contrasts",
+    "c": "c   Preference lives inside states",
+}
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--out", type=Path, default=ROOT / "figures" / "p53_preference.pdf")
+    ap.add_argument("--png", action="store_true")
+    a = ap.parse_args()
+
+    npz = CACHE / "preference.npz"
+    if not npz.exists():
+        raise SystemExit(
+            f"missing {npz}\nrun paper_figures/data/p53_preference_data.py first"
+        )
+    d = np.load(npz, allow_pickle=True)
+    S = json.loads((CACHE / "preference.json").read_text())
+
+    gamma, strat = d["gamma"], d["strat"]
+    lig_order, rec_order = d["lig_order"], d["rec_order"]
+    n_lig, n_rec = gamma.shape
+    labs = np.unique(strat)
+    Gp = gamma[np.ix_(lig_order, rec_order)]
+
+    plt.rcParams.update(RC)
+    fig = plt.figure(figsize=FIGSIZE)
+    gs = fig.add_gridspec(
+        2, 2, height_ratios=HEIGHT_RATIOS, hspace=HSPACE, wspace=WSPACE
+    )
+
+    p = fig.add_subplot(gs[0, :])
+    lim = np.percentile(np.abs(Gp), CLIP_PCT)
+    im = p.imshow(
+        Gp, aspect="auto", cmap=CMAP, vmin=-lim, vmax=lim, interpolation="nearest"
+    )
+    b0, centres = 0, []
+    for l in labs:
+        n = int((strat == l).sum())
+        centres.append(b0 + n / 2 - 0.5)
+        b0 += n
+        if b0 < n_rec:
+            p.axvline(b0 - 0.5, color="k", lw=0.8)
+    p.set_xticks(centres)
+    p.set_xticklabels([f"stratum {int(l) + 1}" for l in labs])
+    p.tick_params(axis="x", length=0)
+    p.set_xlabel(f"{n_rec} receptor conformers, grouped by structural stratum")
+    p.set_ylabel(f"{n_lig} ligands (clustered)")
+    p.set_yticks([])
+    p.set_title(TITLES["a"], loc="left", fontweight="bold", pad=6)
+    cb = fig.colorbar(im, ax=p, pad=0.012, fraction=0.021)
+    cb.set_label("$\\gamma$  (blue = better than expected)", fontsize=6.5)
+    cb.ax.tick_params(labelsize=6)
+
+    p = fig.add_subplot(gs[1, 0])
+    series = [
+        ("all\nreceptors", d["obs_all"], d["nul_all"]),
+        ("within\nstratum only", d["obs_within"], d["nul_within"]),
+    ]
+    for i, (nm, o, nl) in enumerate(series):
+        p.bar(
+            i - BAR_DX,
+            o.mean(),
+            BAR_W,
+            yerr=o.std(),
+            color=ACC,
+            capsize=2.5,
+            label="observed" if i == 0 else None,
+        )
+        p.bar(
+            i + BAR_DX,
+            nl.mean(),
+            BAR_W,
+            yerr=nl.std(),
+            color=NULL_GREY,
+            capsize=2.5,
+            label="permuted" if i == 0 else None,
+        )
+        p.text(
+            i - BAR_DX,
+            o.mean() / 2,
+            f"{o.mean():.3f}",
+            ha="center",
+            va="center",
+            fontsize=7.5,
+            color="w",
+            fontweight="bold",
+        )
+    p.axhline(0, color="k", lw=0.7)
+    p.set_xticks(range(len(series)))
+    p.set_xticklabels([s[0] for s in series])
+    p.set_ylabel("split-half Spearman of\nligand-preference structure")
+    p.set_ylim(*YLIM_B)
+    p.legend(frameon=False, loc="upper center", ncol=2, columnspacing=1.0)
+    p.set_title(TITLES["b"], loc="left", fontweight="bold")
+
+    p = fig.add_subplot(gs[1, 1])
+    fw, fb = float(d["frac_within"]), float(d["frac_between"])
+    p.bar([0], [100 * fw], 0.5, color=ACC, label="within strata")
+    p.bar([0], [100 * fb], 0.5, bottom=[100 * fw], color=HI, label="between strata")
+    p.text(
+        0,
+        100 * fw / 2,
+        f"{100 * fw:.1f}%",
+        ha="center",
+        va="center",
+        color="w",
+        fontweight="bold",
+        fontsize=9,
+    )
+    p.text(
+        0,
+        100 * fw + 100 * fb / 2 + 3,
+        f"{100 * fb:.1f}%",
+        ha="center",
+        va="center",
+        color=HI,
+        fontweight="bold",
+        fontsize=8,
+    )
+    p.set_xlim(-0.6, 1.4)
+    p.set_xticks([])
+    p.set_ylabel("share of $\\mathrm{var}(\\gamma)$  (%)")
+    p.legend(frameon=False, loc="center right")
+    p.set_title(TITLES["c"], loc="left", fontweight="bold")
+
+    a.out.parent.mkdir(parents=True, exist_ok=True)
+    fig.savefig(a.out, format="pdf", bbox_inches="tight")
+    print(f"-> {a.out}")
+    if a.png:
+        fig.savefig(a.out.with_suffix(".png"), dpi=200, bbox_inches="tight")
+        print(f"-> {a.out.with_suffix('.png')}")
+    print(
+        f"   (cache: {S['n_ligands']} ligands, {S['n_receptors']} receptors, "
+        f"{S['n_splits']} splits, within {100 * S['frac_within']:.1f}%)"
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/replot_aib9_robustness.py b/replot_aib9_robustness.py
new file mode 100644
index 0000000000000000000000000000000000000000..aeb14bfd79a2c75655d0776f456ccb28232876b6
--- /dev/null
+++ b/replot_aib9_robustness.py
@@ -0,0 +1,57 @@
+#!/usr/bin/env python
+import argparse
+import json
+from pathlib import Path
+
+import numpy as np
+
+from scoobdoob.validation import robustness as rob
+
+HERE = Path(__file__).resolve().parent
+DEFAULT_DIR = HERE / "data" / "validation_aib9"
+K_DEFAULT, LAG_DEFAULT, DT_PS = 100, 5, 1.0
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--outdir", type=Path, default=HERE / "figures" / "validation_aib9")
+    ap.add_argument(
+        "--also",
+        type=Path,
+        default=None,
+        help="second location to write the pair, e.g. the path paper.tex expects",
+    )
+    a = ap.parse_args()
+
+    src = DEFAULT_DIR / "robustness.json"
+    if not src.exists():
+        raise SystemExit(f"missing {src}\nrun: python validate_aib9.py --robustness")
+    d = json.loads(src.read_text())
+    its = np.array(
+        [[np.nan if v is None else v for v in row] for row in d["its"]], dtype=float
+    )
+
+    a.outdir.mkdir(parents=True, exist_ok=True)
+    rob.make_figure(
+        d["grid"],
+        d["its_lags"],
+        its,
+        K_default=K_DEFAULT,
+        lag_default=LAG_DEFAULT,
+        dt_ps=DT_PS,
+        outpath=a.outdir / "aib9_robustness.png",
+    )
+    print(f"-> {a.outdir / 'aib9_robustness.png'}")
+    print(f"-> {a.outdir / 'aib9_robustness.pdf'}")
+
+    if a.also:
+        a.also.parent.mkdir(parents=True, exist_ok=True)
+        for ext in ("png", "pdf"):
+            (a.also.with_suffix("." + ext)).write_bytes(
+                (a.outdir / f"aib9_robustness.{ext}").read_bytes()
+            )
+            print(f"-> {a.also.with_suffix('.' + ext)}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a57e5ff283ca38ee4f94208c5a691b0f8ec37df6
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,7 @@
+numpy==2.2.6
+scipy==1.14.1
+matplotlib==3.10.5
+scikit-learn==1.7.1
+deeptime==0.4.5
+torch==2.8.0
+pytest>=8,<9
diff --git a/run_cached_figures.py b/run_cached_figures.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e32305057349f6a00e8d088767d9be35b1d1f40
--- /dev/null
+++ b/run_cached_figures.py
@@ -0,0 +1,34 @@
+import argparse
+import os
+from pathlib import Path
+import subprocess
+import sys
+
+ROOT = Path(__file__).resolve().parent
+ap = argparse.ArgumentParser(description="Plot the saved paper results")
+ap.add_argument(
+    "--only", choices=["all", "mb", "aib9_robustness", "p53"], default="all"
+)
+args = ap.parse_args()
+commands = {
+    "mb": [
+        ["make_mb_panels.py", "--from-cache"],
+        ["make_mb_unconditional.py", "--from-cache"],
+    ],
+    "aib9_robustness": [["replot_aib9_robustness.py"]],
+    "p53": [
+        ["paper_figures/plot/p53_discovery_plot.py"],
+        ["paper_figures/plot/p53_preference_plot.py"],
+    ],
+}
+env = os.environ.copy()
+for name in ["OMP_NUM_THREADS", "OPENBLAS_NUM_THREADS", "MKL_NUM_THREADS"]:
+    env[name] = "1"
+env["MPLBACKEND"] = "Agg"
+env["MPLCONFIGDIR"] = str(ROOT / ".mplconfig")
+for group, jobs in commands.items():
+    if args.only not in ["all", group]:
+        continue
+    for command in jobs:
+        print("Running:", " ".join(command), flush=True)
+        subprocess.run([sys.executable, *command], cwd=ROOT, env=env, check=True)
diff --git a/scoobdoob/__init__.py b/scoobdoob/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9a2c5b3bb437bff74e283b62c894075e8c15331
--- /dev/null
+++ b/scoobdoob/__init__.py
@@ -0,0 +1 @@
+__all__ = []
diff --git a/scoobdoob/_conditioning.py b/scoobdoob/_conditioning.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e5126ed915ff11e772cc5e5ac1da012f514ac45
--- /dev/null
+++ b/scoobdoob/_conditioning.py
@@ -0,0 +1,180 @@
+from __future__ import annotations
+
+import numpy as np
+
+
+class InfeasibleBridgeError(ValueError):
+    """The requested starting distribution has no conditioned path law."""
+
+
+def transition_matrix(P):
+    P = np.asarray(P, dtype=float)
+    if P.ndim != 2 or P.shape[0] != P.shape[1] or not len(P):
+        raise ValueError("P must be a nonempty square transition matrix")
+    if not np.isfinite(P).all() or (P < 0).any():
+        raise ValueError("P must contain finite nonnegative probabilities")
+    sums = P.sum(axis=1)
+
+    if not np.all((sums == 0) | np.isclose(sums, 1.0, rtol=1e-8, atol=1e-12)):
+        raise ValueError("Each nonzero row of P must sum to one")
+    return P
+
+
+def horizon_length(N):
+    if isinstance(N, (bool, np.bool_)) or not isinstance(N, (int, np.integer)) or N < 1:
+        raise ValueError("The sampling horizon must be a positive integer")
+    return int(N)
+
+
+def state_indices(states, size, name):
+    values = np.asarray(list(states))
+    if values.ndim != 1 or not values.size or values.dtype.kind not in "iu":
+        raise ValueError(f"{name} must be a nonempty sequence of integer state indices")
+    if ((values < 0) | (values >= size)).any():
+        raise ValueError(f"{name} contains an out-of-range state index")
+    return values.astype(int)
+
+
+def weights_array(weights, N, size):
+    if weights is None:
+        weights = np.ones(size)
+    weights = np.asarray(weights, dtype=float)
+    if weights.shape == (size,):
+        weights = np.broadcast_to(weights, (N, size))
+    if weights.shape != (N, size):
+        raise ValueError(f"Weights must have shape ({size},) or ({N}, {size})")
+    if not np.isfinite(weights).all() or (weights < 0).any():
+        raise ValueError("Weights must be finite and nonnegative")
+    return weights
+
+
+def _log_nonnegative(values):
+    out = np.full(values.shape, -np.inf)
+    np.log(values, out=out, where=values > 0)
+    return out
+
+
+def _from_log(log_values):
+    with np.errstate(under="ignore", over="ignore"):
+        values = np.exp(log_values)
+    if not np.isfinite(values).all() or np.any(np.isfinite(log_values) & (values == 0)):
+        raise FloatingPointError(
+            "Backward values exceed floating-point range; no fallback or support floor was applied"
+        )
+    return values
+
+
+def backward(P, weights, N, terminal, *, rescale=True):
+    """Return h, with optional per-time scaling that leaves kernels unchanged."""
+    P = transition_matrix(P)
+    N = horizon_length(N)
+    weights = weights_array(weights, N, len(P))
+    terminal = np.asarray(terminal, dtype=float)
+    if (
+        terminal.shape != (len(P),)
+        or not np.isfinite(terminal).all()
+        or (terminal < 0).any()
+    ):
+        raise ValueError("Terminal weights must be a finite nonnegative vector")
+    if not np.any(terminal > 0):
+        raise ValueError("At least one terminal state must have positive weight")
+    rows, cols = np.nonzero(P)
+    log_edges = np.log(P[rows, cols])
+    log_weights = _log_nonnegative(weights)
+    log_h = np.full((N + 1, len(P)), -np.inf)
+    log_h[N] = _log_nonnegative(terminal)
+    for n in range(N - 1, -1, -1):
+        edge_terms = log_edges + log_weights[n, cols] + log_h[n + 1, cols]
+        np.logaddexp.at(log_h[n], rows, edge_terms)
+        if rescale:
+            finite = np.isfinite(log_h[n])
+            if finite.any():
+                log_h[n, finite] -= np.logaddexp.reduce(log_h[n, finite])
+    return _from_log(log_h)
+
+
+def kernel(P, weights, h_next):
+    """Normalize feasible rows; return exact zero rows for infeasible states."""
+    P = transition_matrix(P)
+    weights = weights_array(weights, 1, len(P))[0]
+    h_next = np.asarray(h_next, dtype=float)
+    if h_next.shape != (len(P),) or not np.isfinite(h_next).all() or (h_next < 0).any():
+        raise ValueError("The backward function must be a finite nonnegative vector")
+    rows, cols = np.nonzero(P)
+    scores = (
+        np.log(P[rows, cols])
+        + _log_nonnegative(weights)[cols]
+        + _log_nonnegative(h_next)[cols]
+    )
+    normalizers = np.full(len(P), -np.inf)
+    np.logaddexp.at(normalizers, rows, scores)
+    valid = np.isfinite(scores) & np.isfinite(normalizers[rows])
+    M = np.zeros_like(P)
+    with np.errstate(under="ignore"):
+        M[rows[valid], cols[valid]] = np.exp(scores[valid] - normalizers[rows[valid]])
+    sums = M.sum(axis=1, keepdims=True)
+    np.divide(M, sums, out=M, where=sums > 0)
+    return M
+
+
+def build(P, weights, targets, N, *, starts=None):
+    P = transition_matrix(P)
+    N = horizon_length(N)
+    targets = np.unique(state_indices(targets, len(P), "Target set"))
+    weights = weights_array(weights, N, len(P))
+    terminal = np.zeros(len(P))
+    terminal[targets] = 1.0 / len(targets)
+    h = backward(P, weights, N, terminal)
+    if starts is not None:
+        starts = state_indices(starts, len(P), "Starting set")
+        infeasible = starts[h[0, starts] == 0]
+        if infeasible.size:
+            raise InfeasibleBridgeError(
+                f"No positive-weight path reaches the terminal set at horizon {N} "
+                f"from starting states {infeasible.tolist()}"
+            )
+    return [kernel(P, weights[n], h[n + 1]) for n in range(N)], h
+
+
+def checked_starts(kernels, starts):
+    if not len(kernels):
+        raise ValueError("At least one transition kernel is required")
+    first = np.asarray(kernels[0])
+    starts = state_indices(starts, len(first), "Starting set")
+    rows = first[starts]
+    if not np.isfinite(rows).all() or (rows < 0).any():
+        raise ValueError("Invalid conditioned probabilities for a starting state")
+    bad = starts[rows.sum(axis=1) == 0]
+    if bad.size:
+        raise InfeasibleBridgeError(
+            f"Starting states {bad.tolist()} have h_0=0 at the requested horizon; "
+            "the prescribed starting distribution cannot be sampled"
+        )
+    return starts
+
+
+def sample(kernels, starts, n_paths=200, seed=0, *, rng=None):
+    starts = checked_starts(kernels, starts)
+    if (
+        isinstance(n_paths, (bool, np.bool_))
+        or not isinstance(n_paths, (int, np.integer))
+        or n_paths < 0
+    ):
+        raise ValueError("n_paths must be a nonnegative integer")
+    rng = np.random.default_rng(seed) if rng is None else rng
+    size = len(kernels[0])
+    paths = []
+    for _ in range(n_paths):
+        i = int(rng.choice(starts))
+        path = [i]
+        for n, matrix in enumerate(kernels):
+            p = np.asarray(matrix[i], dtype=float)
+            total = p.sum()
+            if not np.isfinite(p).all() or (p < 0).any() or not total > 0:
+                raise InfeasibleBridgeError(
+                    f"Invalid or infeasible conditioned row at step {n}, state {i}"
+                )
+            i = int(rng.choice(size, p=p / total))
+            path.append(i)
+        paths.append(np.asarray(path, dtype=int))
+    return paths
diff --git a/scoobdoob/mb_potential.py b/scoobdoob/mb_potential.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9f9261ea6ebe44697623a82d30f13d6e86d1bcd
--- /dev/null
+++ b/scoobdoob/mb_potential.py
@@ -0,0 +1,108 @@
+import numpy as np
+import torch
+
+
+def _to_torch_dtype(dt):
+    if isinstance(dt, torch.dtype) or dt is None:
+        return dt or torch.get_default_dtype()
+
+    mapping = {
+        np.dtype("float32"): torch.float32,
+        np.float32: torch.float32,
+        np.dtype("float64"): torch.float64,
+        np.float64: torch.float64,
+        np.dtype("int32"): torch.int32,
+        np.int32: torch.int32,
+        np.dtype("int64"): torch.int64,
+        np.int64: torch.int64,
+    }
+    return mapping.get(dt, torch.get_default_dtype())
+
+
+def mb_params(device=None, dtype=torch.float32):
+
+    dtype = _to_torch_dtype(dtype)
+    A = torch.tensor([-200.0, -100.0, -170.0, 15.0], device=device, dtype=dtype)
+    a = torch.tensor([-1.0, -1.0, -6.5, 0.7], device=device, dtype=dtype)
+    b = torch.tensor([0.0, 0.0, 11.0, 0.6], device=device, dtype=dtype)
+    c = torch.tensor([-10.0, -10.0, -6.5, 0.7], device=device, dtype=dtype)
+    x0 = torch.tensor([1.0, 0.0, -0.5, -1.0], device=device, dtype=dtype)
+    y0 = torch.tensor([0.0, 0.5, 1.5, 1.0], device=device, dtype=dtype)
+    return A, a, b, c, x0, y0
+
+
+def mb_potential_xy(x, y):
+    """Compute Müller-Brown potential U(x,y). Args: x, y: torch tensors (broadcastable to same shape) Returns: U: torch tensor, same broadcasted shape"""
+    device = (
+        x.device
+        if isinstance(x, torch.Tensor)
+        else (y.device if isinstance(y, torch.Tensor) else None)
+    )
+    dt = (
+        x.dtype
+        if isinstance(x, torch.Tensor)
+        else (y.dtype if isinstance(y, torch.Tensor) else None)
+    )
+    dt = _to_torch_dtype(dt)
+
+    x = torch.as_tensor(x, dtype=dt, device=device)
+    y = torch.as_tensor(y, dtype=dt, device=device)
+
+    A, a, b, c, x0, y0 = mb_params(device=x.device, dtype=dt)
+    X = x[..., None]
+    Y = y[..., None]
+    exp_arg = a * (X - x0) ** 2 + b * (X - x0) * (Y - y0) + c * (Y - y0) ** 2
+    U = torch.sum(A * torch.exp(exp_arg), dim=-1)
+    return U
+
+
+def mb_grad_xy(x, y):
+    """
+    Gradient of U: returns (dU/dx, dU/dy)
+    """
+    device, dtype = x.device, x.dtype
+    A, a, b, c, x0, y0 = mb_params(device=device, dtype=dtype)
+    X = x[..., None]
+    Y = y[..., None]
+    dx = X - x0
+    dy = Y - y0
+    exp_arg = a * dx * dx + b * dx * dy + c * dy * dy
+    E = torch.exp(exp_arg) * A
+    dUdx = torch.sum(E * (2 * a * dx + b * dy), dim=-1)
+    dUdy = torch.sum(E * (b * dx + 2 * c * dy), dim=-1)
+    return dUdx, dUdy
+
+
+def simulate_overdamped_mb(
+    n_steps=100000,
+    dt=1e-3,
+    beta=1.0,
+    mu=1.0,
+    x0=None,
+    y0=None,
+    noise=True,
+    device="cpu",
+    dtype=torch.float32,
+    seed=None,
+):
+    """
+    Generate unconditioned trajectories on the MB surface.
+    Overdamped Langevin: dx = -mu * grad U dt + sqrt(2 mu / beta) dW
+    Returns numpy arrays (xs, ys) of shape [n_steps].
+    A sequence of coordinates with a fixed dt
+    """
+    if seed is not None:
+        torch.manual_seed(seed)
+        np.random.seed(seed)
+    x = torch.tensor([x0], device=device, dtype=dtype)
+    y = torch.tensor([y0], device=device, dtype=dtype)
+    xs = torch.empty(n_steps, dtype=dtype, device=device)
+    ys = torch.empty(n_steps, dtype=dtype, device=device)
+    sigma = (2.0 * mu / beta * dt) ** 0.5
+    for t in range(n_steps):
+        dUdx, dUdy = mb_grad_xy(x, y)
+        x = x - mu * dUdx * dt + sigma * torch.randn_like(x)
+        y = y - mu * dUdy * dt + sigma * torch.randn_like(y)
+        xs[t] = x
+        ys[t] = y
+    return xs.cpu().numpy(), ys.cpu().numpy()
diff --git a/scoobdoob/validation/__init__.py b/scoobdoob/validation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..936a5cc39199609fdd361e6a3b592278db47b5f1
--- /dev/null
+++ b/scoobdoob/validation/__init__.py
@@ -0,0 +1 @@
+from . import msm_build, tpt, bridge, mb_ground_truth  # noqa: F401
diff --git a/scoobdoob/validation/aib9.py b/scoobdoob/validation/aib9.py
new file mode 100644
index 0000000000000000000000000000000000000000..96fed91268c96dad9ac28caba09c3241540ed918
--- /dev/null
+++ b/scoobdoob/validation/aib9.py
@@ -0,0 +1,94 @@
+from __future__ import annotations
+
+import os
+from pathlib import Path
+
+import numpy as np
+
+N_RES = 9
+
+
+CORE = slice(1, 8)
+
+
+OPENMM_DIR = Path(
+    os.environ.get(
+        "SCOOBDOOB_AIB9_DATA", Path(__file__).resolve().parents[2] / "data" / "aib9"
+    )
+)
+
+
+def load_openmm_colvar(dirpath=OPENMM_DIR, stride=5):
+    """Load interleaved phi/psi angles from the continuous PLUMED trajectory."""
+
+    def read(fn):
+        rows = []
+        for l in open(os.path.join(dirpath, fn)):
+            if l.startswith("#"):
+                continue
+            c = l.split()
+            if len(c) < 10:
+                continue
+            rows.append([float(x) for x in c[1:10]])
+        return np.asarray(rows, dtype=np.float64)
+
+    phi = read("COLVAR_phi")
+    psi = read("COLVAR_psi")
+    n = min(len(phi), len(psi))
+    phi, psi = phi[:n:stride], psi[:n:stride]
+    ang = np.empty((len(phi), 18), dtype=np.float64)
+    ang[:, 0::2] = phi
+    ang[:, 1::2] = psi
+    return [ang], [np.full(len(ang), np.nan)]
+
+
+def phi_psi(ang):
+    """Split interleaved (T,18) -> (phi (T,9), psi (T,9)) in radians."""
+    ang = np.asarray(ang)
+    return ang[:, 0::2], ang[:, 1::2]
+
+
+def featurize_sincos(trajs):
+    """(T,18) dihedrals -> (T,36) [sin,cos] features for TICA (handles wrap)."""
+    out = []
+    for ang in trajs:
+        ang = np.asarray(ang)
+        out.append(np.concatenate([np.sin(ang), np.cos(ang)], axis=1))
+    return out
+
+
+def handedness_indicator(trajs):
+    """(T,18) -> (T,9) float, 1.0 where residue is left-handed (phi > 0)."""
+    return [(phi_psi(a)[0] > 0).astype(np.float64) for a in trajs]
+
+
+def classify_microstates(state_left, core=CORE, end_thresh=0.8, half_thresh=0.6):
+    """Label each microstate R / L / I_N / I_C / other from its handedness.
+
+    ``state_left`` : (K, 9) per-microstate P(residue is left-handed).
+    Uses core residues (termini fray). Returns an array of string labels.
+    """
+    SL = np.asarray(state_left)
+    K = SL.shape[0]
+    core_left = SL[:, core].mean(axis=1)
+    n_half = SL[:, 1:4].mean(axis=1)
+    c_half = SL[:, 5:8].mean(axis=1)
+    labels = np.array(["other"] * K, dtype=object)
+    for i in range(K):
+        if core_left[i] >= end_thresh:
+            labels[i] = "L"
+        elif core_left[i] <= 1 - end_thresh:
+            labels[i] = "R"
+        elif n_half[i] >= half_thresh and c_half[i] <= 1 - half_thresh:
+            labels[i] = "I_N"
+        elif c_half[i] >= half_thresh and n_half[i] <= 1 - half_thresh:
+            labels[i] = "I_C"
+    return labels
+
+
+def macrostate_sets(labels):
+    """Group microstate indices by label -> dict label -> list[int]."""
+    out = {}
+    for k, lab in enumerate(labels):
+        out.setdefault(lab, []).append(int(k))
+    return out
diff --git a/scoobdoob/validation/bridge.py b/scoobdoob/validation/bridge.py
new file mode 100644
index 0000000000000000000000000000000000000000..94c7c8873223c2a7b47d88119d15d8db36a82f45
--- /dev/null
+++ b/scoobdoob/validation/bridge.py
@@ -0,0 +1,67 @@
+from __future__ import annotations
+
+import numpy as np
+
+from scoobdoob import _conditioning
+from scoobdoob._conditioning import InfeasibleBridgeError as InfeasibleBridgeError
+
+
+def doob_kernels(P, S_end, horizon, w=None):
+    """Time-sliced Doob kernels {Ph[n]} for n = 0..horizon-1.
+
+    Backward recursion  h[n] = P (w * h[n+1]),  h[horizon] = uniform on S_end.
+    Doob kernel         Ph[n]_ij = P_ij w_j h[n+1]_j / (P (w h[n+1]))_i.
+    """
+
+    return _conditioning.build(P, w, S_end, horizon)[0]
+
+
+def sample_bridge_paths(Ph, S_start, n_paths=200, seed=0):
+    """Sample paths, rejecting any infeasible start rather than dropping it."""
+    return _conditioning.sample(Ph, S_start, n_paths, seed)
+
+
+def sample_unconditional_paths(P, S_start, horizon, n_paths=200, seed=0):
+    """Baseline: free MSM rollouts of length ``horizon`` from the start set."""
+    P = np.asarray(P, dtype=np.float64)
+    rng = np.random.default_rng(seed)
+    K = P.shape[0]
+    starts = np.asarray(list(S_start), dtype=int)
+    paths = []
+    for _ in range(n_paths):
+        i = int(rng.choice(starts))
+        path = [i]
+        for _ in range(horizon):
+            p = P[i]
+            s = p.sum()
+            p = p / s if s > 0 else np.full(K, 1.0 / K)
+            i = int(rng.choice(K, p=p))
+            path.append(i)
+        paths.append(np.asarray(path, dtype=int))
+    return paths
+
+
+def reached(paths, S_end):
+    """Boolean array: did each path's final state land in S_end?"""
+    end = set(int(s) for s in S_end)
+    return np.array([int(p[-1]) in end for p in paths], dtype=bool)
+
+
+def bootstrap_ci(success_bool, n_boot=2000, alpha=0.05, seed=0):
+    """Percentile bootstrap CI for the success rate of a boolean array."""
+    x = np.asarray(success_bool, dtype=float)
+    n = len(x)
+    rate = float(x.mean()) if n else float("nan")
+    if n == 0:
+        return rate, (float("nan"), float("nan"))
+    rng = np.random.default_rng(seed)
+    boot = x[rng.integers(0, n, size=(n_boot, n))].mean(axis=1)
+    lo, hi = np.percentile(boot, [100 * alpha / 2, 100 * (1 - alpha / 2)])
+    return rate, (float(lo), float(hi))
+
+
+def success_with_ci(paths, S_end, n_boot=2000, alpha=0.05, seed=0):
+    """Convenience: success rate + bootstrap CI for a set of sampled paths."""
+    s = reached(paths, S_end)
+    rate, ci = bootstrap_ci(s, n_boot=n_boot, alpha=alpha, seed=seed)
+    return {"rate": rate, "ci": ci, "n": len(paths), "n_success": int(s.sum())}
diff --git a/scoobdoob/validation/mb_data.py b/scoobdoob/validation/mb_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..aada85b4a7541d0d654b164e52a10aa293567fdd
--- /dev/null
+++ b/scoobdoob/validation/mb_data.py
@@ -0,0 +1,98 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+import numpy as np
+import torch
+
+from ..mb_potential import simulate_overdamped_mb
+
+
+BASIN_A = (-0.56, 1.44)
+BASIN_B = (0.62, 0.03)
+DEFAULT_DATA = Path(__file__).resolve().parents[2] / "data" / "mb_reactive"
+K = 100
+START_XY = BASIN_A
+
+
+def generate(n_reps=12, steps=250_000, dt=2e-4, beta=0.06, mu=1.0, stride=5, seed=0):
+    """Return a list of (T, 2) reactive MB trajectories (after striding).
+
+    ``stride`` thins the saved frames (the integrator step dt is tiny for
+    stability at high T; the saved frame spacing is dt*stride).
+    """
+    trajs = []
+    for r in range(n_reps):
+        x0, y0 = BASIN_A if r % 2 == 0 else BASIN_B
+        xs, ys = simulate_overdamped_mb(
+            n_steps=steps,
+            dt=dt,
+            beta=beta,
+            mu=mu,
+            x0=x0,
+            y0=y0,
+            noise=True,
+            seed=seed + r,
+            dtype=torch.float64,
+        )
+        xy = np.stack([xs, ys], axis=1)[::stride]
+        if np.all(np.isfinite(xy)):
+            trajs.append(xy.astype(np.float64))
+    return trajs
+
+
+def save(trajs, out_dir):
+    out = Path(out_dir)
+    out.mkdir(parents=True, exist_ok=True)
+    for f in out.glob("traj_*.npy"):
+        f.unlink()
+    for i, t in enumerate(trajs):
+        np.save(out / f"traj_{i:04d}.npy", t)
+    with open(out / "index.txt", "w") as fh:
+        fh.write("\n".join(f"traj_{i:04d}.npy" for i in range(len(trajs))) + "\n")
+    return out
+
+
+def load_trajs(data_dir):
+    files = sorted(Path(data_dir).glob("traj_*.npy"))
+    if not files:
+        print(f"  no reactive data in {data_dir} -- generating trajectories...")
+        trajs = generate()
+        save(trajs, data_dir)
+        files = sorted(Path(data_dir).glob("traj_*.npy"))
+    trajs = [np.load(f).astype(np.float64) for f in files]
+    return trajs
+
+
+def orient_endpoints(model, A, B):
+    """Return (start_set, end_set) so start is the basin nearest START_XY."""
+    xyA = model.active_to_xy(A).mean(axis=0)
+    xyB = model.active_to_xy(B).mean(axis=0)
+    dA = np.linalg.norm(xyA - np.array(START_XY))
+    dB = np.linalg.norm(xyB - np.array(START_XY))
+    return (A, B) if dA <= dB else (B, A)
+
+
+if __name__ == "__main__":
+    import argparse
+
+    ap = argparse.ArgumentParser(description="Generate reactive MB trajectories")
+    ap.add_argument("--out", default="data/mb_reactive")
+    ap.add_argument("--n_reps", type=int, default=12)
+    ap.add_argument("--steps", type=int, default=250_000)
+    ap.add_argument("--dt", type=float, default=2e-4)
+    ap.add_argument("--beta", type=float, default=0.06)
+    ap.add_argument("--stride", type=int, default=5)
+    ap.add_argument("--seed", type=int, default=0)
+    args = ap.parse_args()
+    trajs = generate(
+        n_reps=args.n_reps,
+        steps=args.steps,
+        dt=args.dt,
+        beta=args.beta,
+        stride=args.stride,
+        seed=args.seed,
+    )
+    out = save(trajs, args.out)
+    nf = sum(len(t) for t in trajs)
+    print(f"wrote {len(trajs)} trajectories ({nf} frames) -> {out}")
diff --git a/scoobdoob/validation/mb_ground_truth.py b/scoobdoob/validation/mb_ground_truth.py
new file mode 100644
index 0000000000000000000000000000000000000000..45b2900bbffdf73fd97bc274d74d30d138943c6e
--- /dev/null
+++ b/scoobdoob/validation/mb_ground_truth.py
@@ -0,0 +1,72 @@
+from __future__ import annotations
+
+import numpy as np
+import torch
+from scipy.optimize import minimize
+
+from ..mb_potential import mb_potential_xy, mb_grad_xy
+
+
+_MIN_GUESSES = [(-0.558, 1.442), (0.623, 0.028), (-0.050, 0.467)]
+_SADDLE_GUESSES = [(-0.822, 0.624), (0.212, 0.293)]
+
+
+def energy(xy):
+    x, y = float(xy[0]), float(xy[1])
+    return float(mb_potential_xy(torch.tensor(x), torch.tensor(y)).item())
+
+
+def grad(xy):
+    x = torch.tensor(float(xy[0]), dtype=torch.float64)
+    y = torch.tensor(float(xy[1]), dtype=torch.float64)
+    gx, gy = mb_grad_xy(x, y)
+    return np.array([gx.item(), gy.item()])
+
+
+def _hessian(xy, eps=1e-5):
+    H = np.zeros((2, 2))
+    for j in range(2):
+        d = np.zeros(2)
+        d[j] = eps
+        H[:, j] = (grad(np.asarray(xy) + d) - grad(np.asarray(xy) - d)) / (2 * eps)
+    return 0.5 * (H + H.T)
+
+
+def _refine_critical_point(guess):
+    """Drive |grad|^2 -> 0 from a guess; return the located critical point."""
+    res = minimize(
+        lambda p: float(np.sum(grad(p) ** 2)),
+        np.asarray(guess, float),
+        method="Nelder-Mead",
+        options={"xatol": 1e-8, "fatol": 1e-14, "maxiter": 5000},
+    )
+    return res.x
+
+
+def critical_points():
+    """Return ``{'minima': [...], 'saddles': [...]}`` of the MB surface."""
+    minima, saddles = [], []
+    for g in _MIN_GUESSES:
+        p = _refine_critical_point(g)
+        ev = np.linalg.eigvalsh(_hessian(p))
+        rec = {"xy": p, "energy": energy(p), "hessian_eigs": ev}
+        (minima if np.all(ev > 0) else saddles).append(rec)
+    for g in _SADDLE_GUESSES:
+        p = _refine_critical_point(g)
+        ev = np.linalg.eigvalsh(_hessian(p))
+        rec = {"xy": p, "energy": energy(p), "hessian_eigs": ev}
+        (saddles if (ev[0] < 0 < ev[1]) else minima).append(rec)
+    return {"minima": minima, "saddles": saddles}
+
+
+def saddle_points():
+    """Just the saddle (x, y) coordinates as an (n_saddle, 2) array."""
+    return np.array([s["xy"] for s in critical_points()["saddles"]])
+
+
+def nearest_saddle_distance(points_xy):
+    """For each point, distance to the nearest MB saddle. ``points_xy``: (n, 2)."""
+    sad = saddle_points()
+    pts = np.atleast_2d(np.asarray(points_xy, float))
+    d = np.linalg.norm(pts[:, None, :] - sad[None, :, :], axis=2)
+    return d.min(axis=1)
diff --git a/scoobdoob/validation/msm_build.py b/scoobdoob/validation/msm_build.py
new file mode 100644
index 0000000000000000000000000000000000000000..13875df9fceafe8013a376743fbb93f8dceadfdf
--- /dev/null
+++ b/scoobdoob/validation/msm_build.py
@@ -0,0 +1,215 @@
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+
+import numpy as np
+from sklearn.cluster import KMeans, MiniBatchKMeans
+from deeptime.decomposition import TICA
+from deeptime.markov import TransitionCountEstimator
+from deeptime.markov.msm import MaximumLikelihoodMSM
+
+
+def tica_reduce(trajs, lag, n_comp=2, scaling="kinetic_map"):
+    """TICA-reduce a list of (T_i, d) feature trajectories."""
+    tica = TICA(lagtime=lag, dim=n_comp, scaling=scaling)
+    tica.fit([np.asarray(t, dtype=np.float64) for t in trajs])
+    model = tica.fetch_model()
+    proj = [model.transform(np.asarray(t, dtype=np.float64)) for t in trajs]
+    return proj, model
+
+
+def cluster_microstates(proj, k, seed=0, n_init=10, minibatch_threshold=200_000):
+    """KMeans on stacked projected trajectories -> (dtrajs, centers, kmeans)."""
+    X = np.vstack([np.asarray(p) for p in proj])
+    if len(X) > minibatch_threshold:
+        km = MiniBatchKMeans(
+            n_clusters=k,
+            n_init=max(3, n_init // 3),
+            batch_size=10_000,
+            random_state=seed,
+            max_iter=200,
+        ).fit(X)
+    else:
+        km = KMeans(n_clusters=k, n_init=n_init, random_state=seed).fit(X)
+    dtrajs = [km.predict(np.asarray(p)).astype(np.int64) for p in proj]
+    return dtrajs, km.cluster_centers_, km
+
+
+def state_mean_features(dtrajs, raw_trajs, k):
+    """Mean of the raw feature vector over frames assigned to each microstate.
+
+    Used to map a microstate id back to physical space. ``raw_trajs`` are the
+    *un-reduced* features ((x, y) for MB, dihedral angles for Aib9).
+    """
+    raw0 = np.asarray(raw_trajs[0])
+    d = raw0.shape[1]
+    sums = np.zeros((k, d))
+    counts = np.zeros(k, dtype=np.int64)
+    for dt, rt in zip(dtrajs, raw_trajs):
+        rt = np.asarray(rt)
+        np.add.at(sums, dt, rt)
+        np.add.at(counts, dt, 1)
+    means = sums / np.maximum(counts[:, None], 1)
+    return means, counts
+
+
+@dataclass
+class MSM:
+    """A reversible-MLE MSM restricted to its largest connected set."""
+
+    model: object
+    dtrajs: list
+    centers: np.ndarray
+    state_xy: np.ndarray
+    counts: np.ndarray
+    active_symbols: np.ndarray
+    lag: int
+    tica: object = None
+    _sym2active: dict = field(default_factory=dict)
+
+    @property
+    def P(self):
+        return self.model.transition_matrix
+
+    @property
+    def n_active(self):
+        return self.model.n_states
+
+    @property
+    def n_micro(self):
+        return self.centers.shape[0]
+
+    @property
+    def stationary(self):
+        return self.model.stationary_distribution
+
+    def timescales(self, k=None):
+        return self.model.timescales(k=k)
+
+    def to_active(self, original_states):
+        """Map original microstate ids -> active indices (drops off-set ids)."""
+        return [self._sym2active[s] for s in original_states if s in self._sym2active]
+
+    def active_to_xy(self, active_indices):
+        """Physical coordinates of active microstates (via original mapping)."""
+        orig = self.active_symbols[np.asarray(active_indices, dtype=int)]
+        return self.state_xy[orig]
+
+    @property
+    def active_xy(self):
+        """(n_active, d) physical coordinates of every active microstate."""
+        return self.state_xy[self.active_symbols]
+
+
+def build_msm(
+    dtrajs, lag, k, reversible=True, count_mode="sliding", connectivity_threshold=1
+):
+    """Counts at ``lag`` -> largest connected set -> reversible MLE MSM."""
+    counts = TransitionCountEstimator(lagtime=lag, count_mode=count_mode).fit_fetch(
+        dtrajs
+    )
+    connected = counts.submodel_largest(
+        connectivity_threshold=connectivity_threshold, directed=True
+    )
+    model = MaximumLikelihoodMSM(reversible=reversible).fit_fetch(connected)
+    active_symbols = np.asarray(model.count_model.state_symbols)
+    sym2active = {int(s): i for i, s in enumerate(active_symbols)}
+    return model, active_symbols, sym2active, k
+
+
+def build_full_msm(
+    trajs_feat,
+    trajs_raw,
+    lag,
+    k,
+    n_comp=2,
+    seed=0,
+    reversible=True,
+    use_tica=True,
+    scaling="kinetic_map",
+    tica_lag=None,
+):
+    """End-to-end MSM build from raw trajectories."""
+    if use_tica:
+        proj, tica = tica_reduce(
+            trajs_feat, lag=tica_lag or lag, n_comp=n_comp, scaling=scaling
+        )
+    else:
+        proj, tica = [np.asarray(t, dtype=np.float64) for t in trajs_feat], None
+    dtrajs, centers, _ = cluster_microstates(proj, k=k, seed=seed)
+    state_xy, counts = state_mean_features(dtrajs, trajs_raw, k)
+    model, active_symbols, sym2active, _ = build_msm(
+        dtrajs, lag=lag, k=k, reversible=reversible
+    )
+    return MSM(
+        model=model,
+        dtrajs=dtrajs,
+        centers=centers,
+        state_xy=state_xy,
+        counts=counts,
+        active_symbols=active_symbols,
+        lag=lag,
+        tica=tica,
+        _sym2active=sym2active,
+    )
+
+
+def implied_timescales(dtrajs, lags, k, n_its=4, reversible=True):
+    """Implied timescales (frames) for a grid of lag times.
+
+    Returns ``(lags, its)`` with ``its`` of shape (len(lags), n_its). NaN where
+    fewer than ``n_its`` resolvable processes exist on the connected set.
+    """
+    out = np.full((len(lags), n_its), np.nan)
+    for i, lag in enumerate(lags):
+        try:
+            model, *_ = build_msm(dtrajs, lag=lag, k=k, reversible=reversible)
+            ts = model.timescales()
+            out[i, : min(n_its, len(ts))] = ts[:n_its]
+        except Exception:
+            pass
+    return np.asarray(lags), out
+
+
+def ck_test(dtrajs, lag, k, factors=(2, 3), reversible=True):
+    """Chapman-Kolmogorov error: ||P(tau)^f - P(f*tau)||_F / ||P(f*tau)||_F.
+
+    Compares the propagated single-lag MSM against an MSM estimated directly at
+    ``f*tau``, restricted to states shared by both connected sets.
+    """
+    base, sym_base, *_ = build_msm(dtrajs, lag=lag, k=k, reversible=reversible)
+    P = base.transition_matrix
+    errs = {}
+    for f in factors:
+        far, sym_far, *_ = build_msm(dtrajs, lag=f * lag, k=k, reversible=reversible)
+        shared = np.intersect1d(sym_base, sym_far)
+        if len(shared) < 2:
+            errs[f] = np.nan
+            continue
+        ib = {int(s): i for i, s in enumerate(sym_base)}
+        i_b = [ib[int(s)] for s in shared]
+        i_far = {int(s): i for i, s in enumerate(sym_far)}
+        i_f = [i_far[int(s)] for s in shared]
+        Pf_prop = np.linalg.matrix_power(P, f)[np.ix_(i_b, i_b)]
+        Pf_direct = far.transition_matrix[np.ix_(i_f, i_f)]
+        num = np.linalg.norm(Pf_prop - Pf_direct, "fro")
+        den = max(np.linalg.norm(Pf_direct, "fro"), 1e-16)
+        errs[f] = float(num / den)
+    return errs
+
+
+def q2_sign_endpoints(msm: MSM, k_each=6):
+    """Define endpoint sets A, B from the 2nd right eigenvector of P."""
+    evecs = msm.model.eigenvectors_right(k=2)
+    q2 = np.real(evecs[:, 1])
+    order = np.argsort(q2)
+    A = order[:k_each].tolist()
+    B = order[-k_each:][::-1].tolist()
+    return A, B, q2
+
+
+def nearest_active_states(msm: MSM, target_xy, k=6):
+    """``k`` active microstates whose physical coordinates are nearest target."""
+    xy = msm.active_xy
+    d2 = np.sum((xy - np.asarray(target_xy, dtype=float)) ** 2, axis=1)
+    return np.argsort(d2)[:k].tolist()
diff --git a/scoobdoob/validation/pocket.py b/scoobdoob/validation/pocket.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a2d3a3d8b385bb7fa4c134a2b9ab674e2b3efa8
--- /dev/null
+++ b/scoobdoob/validation/pocket.py
@@ -0,0 +1,34 @@
+import glob
+import os
+from pathlib import Path
+import re
+
+REPDIR = Path(__file__).resolve().parents[2] / "data" / "p53" / "pocket_reps"
+Y220C_WALL = {219, 220, 221, 222, 223}
+
+
+def site_drug(mi):
+    out = REPDIR / f"micro{mi:03d}_out"
+    info = out / f"micro{mi:03d}_info.txt"
+    drug = {}
+    if info.exists():
+        cur = None
+        for l in open(info):
+            m = re.match(r"\s*Pocket (\d+)", l)
+            if m:
+                cur = int(m.group(1))
+            if cur and "Druggability Score" in l:
+                drug[cur] = float(l.split(":")[1])
+    best = 0.0
+    for pf in glob.glob(str(out / "pockets" / "pocket*_atm.pdb")):
+        pid = int(re.findall(r"\d+", os.path.basename(pf))[0])
+        res = set()
+        for l in open(pf):
+            if l[:6] in ("ATOM  ", "HETATM"):
+                try:
+                    res.add(int(l[22:26]))
+                except ValueError:
+                    pass
+        if res & Y220C_WALL:
+            best = max(best, drug.get(pid, 0.0))
+    return best
diff --git a/scoobdoob/validation/preference.py b/scoobdoob/validation/preference.py
new file mode 100644
index 0000000000000000000000000000000000000000..e43d962ef5490bab4df828ea9a9f4256ba7f0933
--- /dev/null
+++ b/scoobdoob/validation/preference.py
@@ -0,0 +1,98 @@
+import json
+from collections import defaultdict
+from pathlib import Path
+
+import numpy as np
+from scipy.stats import spearmanr
+
+RS = Path(__file__).resolve().parents[2] / "data" / "p53" / "rmsd_screen"
+COVER = 0.9
+N_SPLITS = 400
+rng = np.random.default_rng(0)
+
+
+def load_matrix():
+    manifest = json.loads((RS / "receptors.json").read_text())
+    recs = {(r["state"], r["frame"]): r for r in manifest}
+    per = defaultdict(dict)
+    for f in sorted((RS / "scores").glob("*.json")):
+        d = json.loads(f.read_text())
+        for s in d["scores"]:
+            if s.get("affinity") is None:
+                continue
+            r = recs.get((s.get("state"), s.get("frame")))
+            if r:
+                per[d["ligand"]][r["frame"]] = float(s["affinity"])
+    ligs_all = sorted(per)
+    all_frames = sorted({r["frame"] for r in manifest})
+    cov = {i: sum(i in per[l] for l in ligs_all) / len(ligs_all) for i in all_frames}
+    frames = [i for i in all_frames if cov[i] >= COVER]
+    lig_cov = {l: sum(i in per[l] for i in frames) / len(frames) for l in ligs_all}
+    ligs = [l for l in ligs_all if lig_cov[l] >= COVER]
+    rec_by_frame = {r["frame"]: r for r in manifest}
+    vol = np.array([rec_by_frame[i]["site_volume"] for i in frames], dtype=float)
+    S = np.full((len(ligs), len(frames)), np.nan)
+    for li, l in enumerate(ligs):
+        for ri, i in enumerate(frames):
+            if i in per[l]:
+                S[li, ri] = per[l][i]
+    row_mean = np.nanmean(S, axis=1, keepdims=True)
+    S = np.where(np.isnan(S), row_mean, S)
+    return S, ligs, frames, vol, rec_by_frame, manifest
+
+
+def volume_trend(Z, vol, nbins=10):
+    """Return the pooled volume-decile trend for each receptor."""
+    edges = np.quantile(vol, np.linspace(0, 1, nbins + 1))
+    edges[-1] += 1e-9
+    bin_id = np.clip(np.searchsorted(edges, vol, side="right") - 1, 0, nbins - 1)
+    g = np.array(
+        [Z[:, bin_id == b].mean() if (bin_id == b).any() else 0.0 for b in range(nbins)]
+    )
+    return g[bin_id]
+
+
+def gamma_matrix():
+    """Exactly the manuscript's gamma: S -> z-score by ligand -> remove f(V_r) -> remove beta_r."""
+    S, ligs, frames, vol, rec_by_frame, manifest = load_matrix()
+    Z = (S - S.mean(axis=1, keepdims=True)) / S.std(axis=1, keepdims=True)
+    G = volume_trend(Z, vol)
+    R = Z - G[None, :]
+    gamma = R - R.mean(axis=0)[None, :]
+    strat = np.array([rec_by_frame[i]["state"] for i in frames])
+    return gamma, np.array(ligs), np.array(frames), strat
+
+
+def pref_corr(mat):
+    """Ligand-preference profile = ligand x ligand correlation over the given receptors."""
+    return np.corrcoef(mat)
+
+
+def split_half_within(gamma, strat, n_splits=N_SPLITS, shuffle_strata=False):
+    """Split receptors into halves INSIDE each stratum, then compare ligand-preference
+    structure between the halves. Between-stratum contrasts cannot drive this."""
+    n_lig = gamma.shape[0]
+    iu = np.triu_indices(n_lig, k=1)
+    out = []
+    for _ in range(n_splits):
+        s = strat.copy()
+        if shuffle_strata:
+            s = rng.permutation(s)
+        A, B = [], []
+        for lab in np.unique(s):
+            idx = np.where(s == lab)[0]
+            perm = rng.permutation(idx)
+            A.extend(perm[: len(idx) // 2])
+            B.extend(perm[len(idx) // 2 :])
+        A, B = np.array(A), np.array(B)
+        if len(A) < 3 or len(B) < 3:
+            continue
+
+        gc = gamma.copy()
+        for lab in np.unique(s):
+            m = s == lab
+            gc[:, m] -= gc[:, m].mean(axis=1, keepdims=True)
+        r_, _ = spearmanr(pref_corr(gc[:, A])[iu], pref_corr(gc[:, B])[iu])
+        if np.isfinite(r_):
+            out.append(r_)
+    return np.array(out)
diff --git a/scoobdoob/validation/robustness.py b/scoobdoob/validation/robustness.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a673f4dd0d55b8f56dd8f158efc825a0c5a7ce0
--- /dev/null
+++ b/scoobdoob/validation/robustness.py
@@ -0,0 +1,230 @@
+from __future__ import annotations
+
+import numpy as np
+
+from . import msm_build as mb
+from . import tpt, aib9
+
+
+def analyze_point(feat, hand, K, lag, n_comp=4, seed=0):
+    """One (K, lag) build -> dict of the key stability metrics."""
+    out = {"K": int(K), "lag": int(lag), "ok": False}
+    try:
+        m = mb.build_full_msm(
+            feat, hand, lag=lag, k=K, n_comp=n_comp, use_tica=True, seed=seed
+        )
+    except Exception as e:
+        out["error"] = str(e)
+        return out
+    out["n_active"] = int(m.n_active)
+    out["lam1"] = float(np.real(m.model.eigenvalues(k=1)[0]))
+    labels = aib9.classify_microstates(m.active_xy)
+    sets = {
+        k: np.where(labels == k)[0].tolist() for k in ["R", "L", "I_N", "I_C", "other"]
+    }
+    out["n_R"], out["n_L"] = len(sets["R"]), len(sets["L"])
+    if not sets["R"] or not sets["L"]:
+        return out
+    q = tpt.forward_committor(m, sets["R"], sets["L"])
+    half = tpt.committor_half_states(q, tol=0.1)
+    inter = set(sets["I_N"]) | set(sets["I_C"])
+    pi = m.stationary
+    out["committor_half_mass"] = (
+        float(
+            pi[[h for h in half if h in inter]].sum() / max(pi[list(half)].sum(), 1e-12)
+        )
+        if len(half)
+        else 0.0
+    )
+    rf = tpt.reactive_flux(m, sets["R"], sets["L"])
+    _, cg = rf.coarse_grain([sets[k] for k in ["R", "I_N", "I_C", "other", "L"]])
+    F = np.asarray(cg.net_flux)
+    tot = float(cg.total_flux)
+    out["direct"] = float(F[0, 4] / tot)
+    out["flux_IN"] = float(F[0, 1] / tot)
+    out["flux_IC"] = float(F[0, 2] / tot)
+    try:
+        out["ck2"] = mb.ck_test(m.dtrajs, lag=lag, k=K, factors=(2,))[2]
+    except Exception:
+        out["ck2"] = float("nan")
+    out["ok"] = True
+    return out
+
+
+def sweep(feat, hand, Ks, lags, n_comp=4, seed=0):
+    return [analyze_point(feat, hand, K, lag, n_comp, seed) for K in Ks for lag in lags]
+
+
+def its_convergence(feat, hand, K, lags, n_comp=4, n_its=3, seed=0):
+    """ITS vs lag at a FIXED discretization (vary only the count lag)."""
+    ref_lag = lags[len(lags) // 2]
+    m = mb.build_full_msm(
+        feat, hand, lag=ref_lag, k=K, n_comp=n_comp, use_tica=True, seed=seed
+    )
+    lg, its = mb.implied_timescales(m.dtrajs, lags, k=K, n_its=n_its)
+    return lg, its
+
+
+def summarize(results):
+    """Range of the two decision metrics across all valid sweep points."""
+    ok = [r for r in results if r.get("ok")]
+    if not ok:
+        return {}
+    ch = [r["committor_half_mass"] for r in ok]
+    di = [r["direct"] for r in ok]
+    return {
+        "n_points": len(ok),
+        "committor_half_mass_min": float(min(ch)),
+        "committor_half_mass_max": float(max(ch)),
+        "direct_min": float(min(di)),
+        "direct_max": float(max(di)),
+        "both_channels_always": bool(
+            all(r["flux_IN"] > 0 and r["flux_IC"] > 0 for r in ok)
+        ),
+    }
+
+
+def make_figure(results, its_lags, its, K_default, lag_default, dt_ps, outpath):
+    import matplotlib
+
+    matplotlib.use("Agg")
+    import matplotlib.pyplot as plt
+
+    plt.rcParams.update(
+        {
+            "font.family": "DejaVu Sans",
+            "mathtext.fontset": "custom",
+            "mathtext.rm": "DejaVu Sans",
+            "mathtext.it": "DejaVu Sans:italic",
+            "mathtext.bf": "DejaVu Sans:bold",
+            "mathtext.sf": "DejaVu Sans",
+            "mathtext.cal": "DejaVu Sans",
+            "pdf.fonttype": 42,
+            "ps.fonttype": 42,
+        }
+    )
+
+    Ks = sorted({r["K"] for r in results})
+    lags = sorted({r["lag"] for r in results})
+    grid = {(r["K"], r["lag"]): r for r in results}
+
+    def heat(metric):
+        M = np.full((len(Ks), len(lags)), np.nan)
+        for i, K in enumerate(Ks):
+            for j, lg in enumerate(lags):
+                r = grid.get((K, lg))
+                if r and r.get("ok"):
+                    M[i, j] = r[metric]
+        return M
+
+    fig, ax = plt.subplots(1, 3, figsize=(13.2, 4.1), layout="constrained")
+
+    for c in range(its.shape[1]):
+        ax[0].plot(
+            np.array(its_lags) * dt_ps,
+            its[:, c] * dt_ps,
+            "-o",
+            label=rf"$t_{c + 1}$",
+            markersize=4,
+        )
+    ax[0].axvline(
+        lag_default * dt_ps,
+        color="gray",
+        ls="--",
+        label=f"{lag_default * dt_ps:.0f} ps lag",
+    )
+    ax[0].set_xlabel("Lag time (ps)")
+    ax[0].set_ylabel("Implied timescale (ps)")
+    ax[0].set_yscale("log")
+    ax[0].set_title("Implied timescales")
+    ax[0].legend(fontsize=10, frameon=False)
+
+    for a, metric, title, vmax, decimals, ticks in [
+        (
+            ax[1],
+            "committor_half_mass",
+            "Intermediate overlap",
+            100.0,
+            1,
+            [0, 25, 50, 75, 100],
+        ),
+        (
+            ax[2],
+            "direct",
+            r"Direct $R$-to-$L$ flux",
+            1.1,
+            2,
+            [0, 0.25, 0.5, 0.75, 1.0, 1.1],
+        ),
+    ]:
+        M = 100.0 * heat(metric)
+        finite = M[np.isfinite(M)]
+        if finite.size and finite.max() > vmax:
+            vmax = float(finite.max())
+            ticks = np.linspace(0, vmax, 5)
+        im = a.imshow(
+            M,
+            aspect="auto",
+            cmap="viridis" if metric != "direct" else "magma_r",
+            vmin=0,
+            vmax=vmax,
+            origin="lower",
+        )
+        a.set_xticks(range(len(lags)))
+        a.set_xticklabels(lags)
+        a.set_yticks(range(len(Ks)))
+        a.set_yticklabels(Ks)
+        a.set_xlabel("Lag time (ps)")
+        a.set_ylabel("Microstates ($K$)")
+        for i in range(len(Ks)):
+            for j in range(len(lags)):
+                if np.isfinite(M[i, j]):
+                    rgb = im.cmap(im.norm(M[i, j]))[:3]
+                    lightness = np.dot(rgb, [0.2126, 0.7152, 0.0722])
+                    a.text(
+                        j,
+                        i,
+                        f"{M[i, j]:.{decimals}f}",
+                        ha="center",
+                        va="center",
+                        color="black" if lightness > 0.5 else "white",
+                        fontsize=11,
+                    )
+
+        a.scatter(
+            [lags.index(lag_default) + 0.30],
+            [Ks.index(K_default) + 0.29],
+            marker="*",
+            c="red",
+            s=105,
+            edgecolors="white",
+            linewidths=0.6,
+            zorder=3,
+        )
+        cbar = fig.colorbar(im, ax=a, ticks=ticks, fraction=0.048, pad=0.03)
+        cbar.ax.set_title("%", fontsize=11, pad=6)
+        cbar.ax.tick_params(labelsize=10)
+        a.set_title(title)
+
+    for letter, a in zip("ABC", ax):
+        a.tick_params(labelsize=10)
+        a.xaxis.label.set_size(11)
+        a.yaxis.label.set_size(11)
+        a.title.set_fontsize(12)
+        a.text(
+            -0.16,
+            1.07,
+            letter,
+            transform=a.transAxes,
+            fontsize=15,
+            fontweight="bold",
+            va="bottom",
+        )
+
+    from pathlib import Path as _P
+
+    _o = _P(outpath)
+    _o.parent.mkdir(parents=True, exist_ok=True)
+    fig.savefig(_o.with_suffix(".png"), dpi=220)
+    fig.savefig(_o.with_suffix(".pdf"), bbox_inches="tight")
+    plt.close(fig)
diff --git a/scoobdoob/validation/selectors.py b/scoobdoob/validation/selectors.py
new file mode 100644
index 0000000000000000000000000000000000000000..71732db4948a80cf5a96bca060408dac27388515
--- /dev/null
+++ b/scoobdoob/validation/selectors.py
@@ -0,0 +1,170 @@
+import numpy as np
+
+from scoobdoob import _conditioning
+from scoobdoob.validation import tpt
+
+STRIDE, SEED_RELAX = 10, 50
+ALPHA_V, TAU = 3.0, 0.12
+TIME_SIGMA, COMM_L, COMM_S, W_FLOOR = 0.30, 3.0, 0.35, 1e-12
+
+
+def fk_weights(
+    C, q, horizon, comm_l=COMM_L, comm_s=COMM_S, variant="rho", alpha_rho=None
+):
+    """(untilted, committor-tilted) FK weight lists, one entry per horizon step."""
+    row = np.asarray(C, dtype=np.float64).sum(axis=1)
+    a_eff = (TAU * ALPHA_V) if alpha_rho is None else float(alpha_rho)
+    if variant == "rho":
+        rho = np.clip(row, 1.0, None) / row.mean()
+        w_base = np.exp(-a_eff * rho)
+    else:
+        w_base = np.exp(-a_eff * (1.0 / (row + 1.0)))
+    q_pen = np.exp(comm_l * (-((q - 0.5) ** 2)) / comm_s**2)
+    t = np.arange(horizon)
+    time_win = np.exp(-0.5 * ((t - (horizon - 1) / 2) / (TIME_SIGMA * horizon)) ** 2)
+    wA = [np.maximum(w_base, W_FLOOR) for _ in range(horizon)]
+    wB = [np.maximum(w_base * (q_pen ** time_win[i]), W_FLOOR) for i in range(horizon)]
+    return wA, wB
+
+
+def crossing_horizon(m, a0, B, cap=20000):
+    """Return the bounded horizon and reference MFPT in lag steps."""
+    P = np.asarray(m.P, dtype=np.float64)
+    n = P.shape[0]
+    Bs = set(int(b) for b in B)
+    T = np.array([i for i in range(n) if i not in Bs])
+    if not len(T):
+        return cap
+    try:
+        mfpt = np.zeros(n)
+        mfpt[T] = np.linalg.solve(np.eye(len(T)) - P[np.ix_(T, T)], np.ones(len(T)))
+        h = int(np.clip(round(mfpt[a0]), 5, cap))
+        return h, float(mfpt[a0])
+    except Exception:
+        return cap, float("nan")
+
+
+def state_means(st, dg, n_states):
+    """E[drug | state] and per-state counts, on whatever frame subset is passed in."""
+    mu = np.full(n_states, np.nan)
+    cnt = np.zeros(n_states, dtype=int)
+    for s in range(n_states):
+        m = st == s
+        cnt[s] = m.sum()
+        if cnt[s]:
+            mu[s] = dg[m].mean()
+    return mu, cnt
+
+
+def target_set(cvm, dil, a0, n, a, dil_override=None):
+    """The B set the tilt aims at."""
+    if a.target == "cleft":
+        B = np.array([b for b in np.where(cvm[:, 1] > a.cleft_thr)[0] if b != a0])
+        if not len(B):
+            raise SystemExit(
+                f"empty target set B at cleft_thr={a.cleft_thr}. Under a pocket-feature MSM the "
+                f"per-state mean cleft CV never reaches this threshold because pocket states mix "
+                f"open and closed frames of the GLOBAL coordinate. Use --target dilation."
+            )
+        return B
+    d = dil if dil_override is None else dil_override
+    ok = np.isfinite(d)
+    thr = np.quantile(d[ok], 1.0 - a.target_q)
+    B = np.array([b for b in np.where(ok & (d >= thr))[0] if b != a0])
+    if not len(B):
+        raise SystemExit("empty dilation target set")
+    return B
+
+
+def build_selectors(m, cvm, C, a0, a, dil=None, B=None):
+    """Weight vectors over ACTIVE states for every selector, each summing to 1."""
+    P = np.asarray(m.P, dtype=np.float64)
+    pi = np.asarray(m.stationary, dtype=np.float64)
+    n = len(pi)
+
+    if B is None:
+        B = target_set(cvm, dil, a0, n, a)
+    q = np.asarray(tpt.forward_committor(m, [a0], list(B)))
+
+    sel = {}
+    sel["equilibrium"] = pi / pi.sum()
+    sel["uniform_states"] = np.full(n, 1.0 / n)
+
+    half = np.abs(q - 0.5) <= 0.1
+    if half.sum():
+        w = pi * half
+        sel["committor_half"] = w / w.sum()
+
+    try:
+        rf = tpt.reactive_flux(m, [a0], list(B))
+        gross = np.asarray(rf.gross_flux)
+        carried = gross.sum(axis=1) + gross.sum(axis=0)
+        if carried.sum() > 0:
+            sel["tpt_flux"] = carried / carried.sum()
+    except Exception as e:
+        print(f"  [warn] TPT flux unavailable: {e}")
+
+    horizon = a.horizon
+    mfpt = float("nan")
+    if horizon is None:
+        horizon, mfpt = crossing_horizon(m, a0, B)
+    print(f"  [fk] horizon = {horizon} lag steps (MFPT a0->B = {mfpt:.1f})")
+
+    def occupancy(wl, H):
+        Ph, _ = _conditioning.build(P, wl, list(B), H, starts=[a0])
+        mu = np.zeros(n)
+        mu[a0] = 1.0
+        acc = np.zeros(n)
+        for Pn in Ph:
+            mu = mu @ np.asarray(Pn)
+            s = mu.sum()
+            if not np.isfinite(s) or s <= 0:
+                raise FloatingPointError(
+                    "Conditioned occupancy lost all probability mass"
+                )
+            mu = mu / s
+            acc += mu
+        return acc / acc.sum()
+
+    wA, wB = fk_weights(C, q, horizon, variant=a.fk_variant)
+    for name, wl in (("doob_untilted", wA), ("fk_doob", wB)):
+        try:
+            sel[name] = occupancy(wl, horizon)
+        except Exception as e:
+            print(f"  [warn] {name} failed: {e}")
+
+    for a_rho in getattr(a, "alpha_rho_sweep", []) or []:
+        try:
+            wa, _ = fk_weights(C, q, horizon, variant=a.fk_variant, alpha_rho=a_rho)
+            sel[f"alg1[a={a_rho:g}]"] = occupancy(wa, horizon)
+        except Exception as e:
+            print(f"  [warn] alg1[a={a_rho:g}] failed: {e}")
+
+    for cl in a.comm_l_sweep:
+        for hf in a.horizon_sweep:
+            H = max(5, int(round(horizon * hf)))
+            try:
+                _, wb = fk_weights(C, q, H, comm_l=cl, variant=a.fk_variant)
+                sel[f"fk_doob[L={cl},H={H}]"] = occupancy(wb, H)
+            except Exception:
+                pass
+
+    return sel, q, B, a0, horizon
+
+
+def enrich(weights, mu_state, cnt, p_open_state, base_mean, base_open):
+    """Expected druggability and open-probability under a state-weight vector."""
+    ok = np.isfinite(mu_state) & (cnt > 0)
+    w = np.where(ok, weights, 0.0)
+    if w.sum() <= 0:
+        return None
+    w = w / w.sum()
+    em = float((w * np.nan_to_num(mu_state)).sum())
+    eo = float((w * np.nan_to_num(p_open_state)).sum())
+    return {
+        "E_drug": em,
+        "P_open": eo,
+        "enrich_mean": em / base_mean if base_mean > 0 else float("nan"),
+        "enrich_open": eo / base_open if base_open > 0 else float("nan"),
+        "coverage": float(weights[ok].sum() / max(weights.sum(), 1e-300)),
+    }
diff --git a/scoobdoob/validation/tpt.py b/scoobdoob/validation/tpt.py
new file mode 100644
index 0000000000000000000000000000000000000000..975c9aa21fc5f49023d2f3c4a4855f9d2057e37a
--- /dev/null
+++ b/scoobdoob/validation/tpt.py
@@ -0,0 +1,75 @@
+from __future__ import annotations
+
+import numpy as np
+
+
+def forward_committor(msm, A_active, B_active):
+    """Forward committor q+ via deeptime (absorbing boundaries on A, B)."""
+    return np.asarray(msm.model.committor_forward(list(A_active), list(B_active)))
+
+
+def forward_committor_linsolve(P, A, B):
+    """Reference forward committor by direct linear solve.
+
+    Solves (I - P_TT) q_T = P_TB 1 on the transient set T = all \\ (A u B),
+    with q|A = 0, q|B = 1. Independent of deeptime; used only to cross-check.
+    """
+    P = np.asarray(P, dtype=np.float64)
+    m = P.shape[0]
+    A = np.asarray(list(A), dtype=int)
+    B = np.asarray(list(B), dtype=int)
+    mask = np.ones(m, dtype=bool)
+    mask[A] = False
+    mask[B] = False
+    T = np.where(mask)[0]
+    q = np.zeros(m)
+    q[B] = 1.0
+    if len(T) == 0:
+        return q
+    P_TT = P[np.ix_(T, T)]
+    rhs = P[np.ix_(T, B)].sum(axis=1)
+    q[T] = np.linalg.solve(np.eye(len(T)) - P_TT, rhs)
+    return np.clip(q, 0.0, 1.0)
+
+
+def committor_agreement(msm, A_active, B_active):
+    """Max |q_deeptime - q_linsolve| over active states (should be ~1e-10)."""
+    q_dt = forward_committor(msm, A_active, B_active)
+    q_ls = forward_committor_linsolve(msm.P, A_active, B_active)
+    return float(np.max(np.abs(q_dt - q_ls))), q_dt, q_ls
+
+
+def committor_half_states(q, tol=0.1):
+    """Active indices with |q - 1/2| <= tol (the committor-1/2 ensemble)."""
+    return np.where(np.abs(np.asarray(q) - 0.5) <= tol)[0]
+
+
+def committor_half_mass(msm, q, tol=0.1):
+    """Stationary probability carried by the committor-1/2 ensemble."""
+    idx = committor_half_states(q, tol=tol)
+    return float(msm.stationary[idx].sum()), idx
+
+
+def reactive_flux(msm, A_active, B_active):
+    """deeptime ReactiveFlux object for the A -> B reaction."""
+    return msm.model.reactive_flux(list(A_active), list(B_active))
+
+
+def pathway_fractions(rf, fraction=0.9):
+    """Decompose the reactive flux into pathways and their flux fractions."""
+    paths, caps = rf.pathways(fraction=fraction)
+    caps = np.asarray(caps, dtype=float)
+    total = float(rf.total_flux)
+    return {
+        "paths": [list(map(int, p)) for p in paths],
+        "capacities": caps,
+        "frac_of_total": caps / max(total, 1e-300),
+        "total_flux": total,
+        "n_paths": len(paths),
+    }
+
+
+def path_passes_through(path, target_active, exclude_endpoints=True):
+    """True if a pathway visits any microstate in ``target_active``."""
+    nodes = path[1:-1] if (exclude_endpoints and len(path) > 2) else path
+    return bool(set(nodes) & set(int(t) for t in target_active))
diff --git a/summarize_trajectory_cv_tilt.py b/summarize_trajectory_cv_tilt.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ef21f6bbd15acfafa54e998d663160ccc19207e
--- /dev/null
+++ b/summarize_trajectory_cv_tilt.py
@@ -0,0 +1,188 @@
+#!/usr/bin/env python
+import argparse
+import json
+import itertools
+from pathlib import Path
+
+import numpy as np
+from scipy.stats import wilcoxon
+
+HERE = Path(__file__).resolve().parent
+_ap = argparse.ArgumentParser()
+_ap.add_argument(
+    "--pattern",
+    default="trajectory_cv_tilt_k*_seed*.json",
+    help="glob over data/p53 selecting the K/seed runs to pool; the default "
+    "matches the original site_drug sweep",
+)
+_ap.add_argument(
+    "--out", type=Path, default=HERE / "data/p53/trajectory_cv_tilt_robustness.json"
+)
+_args = _ap.parse_args()
+files = sorted((HERE / "data/p53").glob(_args.pattern))
+RNG_SEED = 20260729
+
+
+def mean_bootstrap_ci(x, n_boot=200_000):
+    """Trajectory-fold bootstrap interval for the arithmetic mean."""
+    x = np.asarray(x, float)
+    rng = np.random.default_rng(RNG_SEED)
+    idx = rng.integers(0, len(x), size=(n_boot, len(x)))
+    return np.percentile(x[idx].mean(axis=1), [2.5, 97.5]).tolist()
+
+
+def exact_signflip_p(x, null=1.0, alternative="two-sided"):
+    """Exact paired randomization test aligned to a mean-difference estimand."""
+    delta = np.asarray(x, float) - null
+    observed = delta.mean()
+    signs = np.asarray(list(itertools.product((-1.0, 1.0), repeat=len(delta))))
+    stats = (signs * delta).mean(axis=1)
+    if alternative == "greater":
+        return float(np.mean(stats >= observed - 1e-15))
+    return float(np.mean(np.abs(stats) >= abs(observed) - 1e-15))
+
+
+print("TRAJECTORY-HELD-OUT FK ROBUSTNESS")
+print(f"{'file':<42}{'mean':>8}{'median':>9}{'>1':>7}{'p':>9}")
+all_means = []
+for path in files:
+    d = json.loads(path.read_text())
+    x = d["summary"]["fk_doob"]
+    mean = x["mean_ratio_to_equilibrium"]
+    all_means.append(mean)
+    print(
+        f"{path.name:<42}{mean:>8.3f}"
+        f"{x['median_ratio_to_equilibrium']:>9.3f}"
+        f"{x['folds_above_1']:>3}/{x['n_folds']:<3}"
+        f"{x['wilcoxon_p_vs_1']:>9.3g}"
+    )
+
+if all_means:
+    print(
+        f"\nAcross completed discretizations: mean={np.mean(all_means):.3f}, "
+        f"range=[{np.min(all_means):.3f}, {np.max(all_means):.3f}]"
+    )
+else:
+    print("\nNo completed robustness outputs.")
+
+if files:
+    _base = ("equilibrium", "uniform_states", "tpt_flux", "doob_untilted", "fk_doob")
+    _per_file = [set(json.loads(p.read_text())["folds"][0]["selectors"]) for p in files]
+    _common = set.intersection(*_per_file) if _per_file else set()
+    _alg1 = sorted(
+        (s for s in _common if s.startswith("alg1[a=")),
+        key=lambda s: float(s.split("=")[1].rstrip("]")),
+    )
+    selectors = tuple(s for s in _base if s in _common) + tuple(_alg1)
+    dropped = sorted((set().union(*_per_file) - _common) if _per_file else [])
+    if dropped:
+        print(f"[note] selectors absent from some runs, excluded: {dropped}")
+    raw = {}
+    for selector in selectors:
+        raw[selector] = np.asarray(
+            [
+                [
+                    fold["selectors"][selector]["ratio_to_equilibrium"]
+                    for fold in json.loads(path.read_text())["folds"]
+                ]
+                for path in files
+            ]
+        )
+
+    collapsed = {k: v.mean(axis=0) for k, v in raw.items()}
+    print("\nINFERENCE AFTER COLLAPSING K/SEED REPEATS WITHIN HELD-OUT FOLD")
+    print(f"{'selector':<18}{'mean':>8}{'median':>9}{'>1':>7}{'p vs 1':>10}")
+    aggregate = {}
+    for selector in selectors:
+        x = collapsed[selector]
+        p = (
+            1.0
+            if selector == "equilibrium"
+            else float(wilcoxon(x - 1, alternative="two-sided").pvalue)
+        )
+        print(
+            f"{selector:<18}{x.mean():>8.3f}{np.median(x):>9.3f}"
+            f"{int((x > 1).sum()):>3}/{len(x):<3}{p:>10.3g}"
+        )
+        aggregate[selector] = {
+            "mean": float(x.mean()),
+            "mean_fold_bootstrap_95ci": (
+                [1.0, 1.0] if selector == "equilibrium" else mean_bootstrap_ci(x)
+            ),
+            "median": float(np.median(x)),
+            "n_positive": int((x > 1).sum()),
+            "n_folds": int(len(x)),
+            "wilcoxon_p_vs_1": p,
+            "exact_mean_signflip_p_two_sided": (
+                1.0
+                if selector == "equilibrium"
+                else exact_signflip_p(x, null=1.0, alternative="two-sided")
+            ),
+            "exact_mean_signflip_p_one_sided_greater": (
+                1.0
+                if selector == "equilibrium"
+                else exact_signflip_p(x, null=1.0, alternative="greater")
+            ),
+            "fold_values": x.tolist(),
+        }
+
+    if _alg1:
+        print(
+            "\nALGORITHM 1 TILT-STRENGTH SWEEP  (w=exp(-alpha_rho*rho), no committor shaping)"
+        )
+        print(
+            f"{'alpha_rho':>10}{'mean':>8}{'median':>9}{'>1':>7}{'95% CI':>18}{'p vs 1':>10}"
+        )
+        for s in _alg1:
+            a = aggregate[s]
+            ci = a["mean_fold_bootstrap_95ci"]
+            star = " *" if (ci[0] > 1 or ci[1] < 1) else ""
+            print(
+                f"{float(s.split('=')[1].rstrip(']')):>10.2f}{a['mean']:>8.3f}"
+                f"{a['median']:>9.3f}{a['n_positive']:>3}/{a['n_folds']:<3}"
+                f"{f'[{ci[0]:.3f}, {ci[1]:.3f}]':>18}{a['wilcoxon_p_vs_1']:>10.3g}{star}"
+            )
+        print("  * = 95% CI excludes 1.   alpha_rho=0 is the no-density-tilt control.")
+
+    print("\nPAIRED FK ADVANTAGE")
+    fk = collapsed["fk_doob"]
+    paired = {}
+    for comparator in ("tpt_flux", "uniform_states", "doob_untilted"):
+        delta = fk - collapsed[comparator]
+        p = float(wilcoxon(delta, alternative="two-sided").pvalue)
+        print(
+            f"FK - {comparator:<14} mean={delta.mean():+.3f}; "
+            f"wins={int((delta > 0).sum())}/{len(delta)}; p={p:.3g}"
+        )
+        paired[comparator] = {
+            "mean_difference": float(delta.mean()),
+            "mean_difference_fold_bootstrap_95ci": mean_bootstrap_ci(delta),
+            "fk_wins": int((delta > 0).sum()),
+            "n_folds": int(len(delta)),
+            "wilcoxon_p": p,
+            "exact_mean_signflip_p_two_sided": exact_signflip_p(
+                delta, null=0.0, alternative="two-sided"
+            ),
+            "exact_mean_signflip_p_one_sided_greater": exact_signflip_p(
+                delta, null=0.0, alternative="greater"
+            ),
+        }
+
+    out = {
+        "n_configurations": len(files),
+        "configuration_mean_range": [
+            float(np.min(all_means)),
+            float(np.max(all_means)),
+        ],
+        "aggregate": aggregate,
+        "paired_fk_advantage": paired,
+        "note": "K/seed configurations are averaged within fold before inference.",
+    }
+    out_path = _args.out
+    out["source_pattern"] = _args.pattern
+    out["source_files"] = [p.name for p in files]
+    out["observable"] = sorted(
+        {json.loads(p.read_text())["config"]["observable"] for p in files}
+    )
+    out_path.write_text(json.dumps(out, indent=2))
+    print(f"\n-> {out_path}")
diff --git a/tests/test_conditioning_support.py b/tests/test_conditioning_support.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab394c1832880796e3bc93ba4bf846aa758041e8
--- /dev/null
+++ b/tests/test_conditioning_support.py
@@ -0,0 +1,131 @@
+from itertools import product
+
+import numpy as np
+import pytest
+
+from scoobdoob import _conditioning
+from scoobdoob.validation import bridge
+
+
+def test_conditioned_probabilities_equal_enumerated_weighted_path_law():
+    P = np.array([[0.6, 0.4, 0], [0.2, 0.4, 0.4], [0.3, 0.2, 0.5]])
+    weights = np.array(
+        [[1.0, 2.0, 3.0], [2.0, 0.0, 1.0], [0.5, 2.0, 1.0], [1.0, 3.0, 0.8]]
+    )
+    kernels, _ = _conditioning.build(P, weights, [1, 2], 4, starts=[0, 2])
+    for start in [0, 2]:
+        raw, actual = [], []
+        for tail in product(range(3), repeat=4):
+            path = (start,) + tail
+            reference = float(tail[-1] in [1, 2])
+            probability = 1.0
+            for n in range(4):
+                reference *= P[path[n], path[n + 1]] * weights[n, path[n + 1]]
+                probability *= kernels[n][path[n], path[n + 1]]
+            raw.append(reference)
+            actual.append(probability)
+        np.testing.assert_allclose(
+            actual, np.array(raw) / sum(raw), atol=1e-14, rtol=1e-12
+        )
+
+
+def test_infeasible_start_rejected_without_filtering_initial_distribution():
+    P = np.array([[1.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0]])
+    kernels = bridge.doob_kernels(P, [2], 2)
+    assert not kernels[0][0].any()
+    with pytest.raises(bridge.InfeasibleBridgeError, match="h_0=0"):
+        bridge.sample_bridge_paths(kernels, [0, 1], 20)
+    with pytest.raises(_conditioning.InfeasibleBridgeError):
+        _conditioning.build(P, np.ones((2, 3)), [2], 2, starts=[0])
+
+
+def test_exact_horizon_feasibility_not_just_reachability_by_horizon():
+    P = np.array([[0.0, 1.0], [1.0, 0.0]])
+    with pytest.raises(bridge.InfeasibleBridgeError):
+        bridge.sample_bridge_paths(bridge.doob_kernels(P, [1], 2), [0])
+    paths = bridge.sample_bridge_paths(bridge.doob_kernels(P, [1], 3), [0], 3)
+    np.testing.assert_array_equal(paths, [[0, 1, 0, 1]] * 3)
+
+
+def test_terminal_and_zero_weight_support_remain_exactly_zero():
+    P = np.array(
+        [
+            [0.0, 0.5, 0.5, 0.0],
+            [0.0, 0.0, 0.0, 1.0],
+            [0.0, 0.0, 0.0, 1.0],
+            [0.0, 0.0, 0.0, 1.0],
+        ]
+    )
+    weights = np.array([1.0, 0.0, 1.0, 1.0])
+    kernels, h = _conditioning.build(P, [weights] * 2, [3], 2, starts=[0])
+    assert np.array_equal(h[-1], [0.0, 0.0, 0.0, 1.0])
+    for matrix in kernels:
+        assert not matrix[:, 1].any()
+        assert not matrix[P == 0].any()
+    assert not kernels[-1][:, :3].any()
+    paths = bridge.sample_bridge_paths(kernels, [0], 10)
+    np.testing.assert_array_equal(paths, [[0, 2, 3]] * 10)
+
+
+def test_zero_weights_can_make_every_start_infeasible():
+    P = np.ones((2, 2)) / 2
+    kernels = bridge.doob_kernels(P, [1], 4, w=np.zeros(2))
+    assert all(not k.any() for k in kernels)
+    with pytest.raises(bridge.InfeasibleBridgeError):
+        bridge.sample_bridge_paths(kernels, [0])
+
+
+def test_small_positive_products_do_not_trigger_fallback_or_underflow():
+    P = np.array([[0.7, 0.3], [0.4, 0.6]])
+    w = np.full(2, 1e-250)
+    h = np.array([2e-250, 1e-250])
+    expected = P * np.array([2.0, 1.0])[None, :]
+    expected /= expected.sum(axis=1, keepdims=True)
+    np.testing.assert_allclose(_conditioning.kernel(P, w, h), expected, rtol=1e-12)
+    tiny = bridge.doob_kernels(P, [1], 1000, w=w)
+    unit = bridge.doob_kernels(P, [1], 1000)
+    np.testing.assert_allclose(tiny, unit, atol=1e-12, rtol=1e-12)
+
+
+def test_backwards_and_kernels_agree_across_public_interfaces():
+    P = np.array([[0.8, 0.2], [0.3, 0.7]])
+    weights = np.array([[1.0, 2.0], [0.2, 1.0], [3.0, 0.4]])
+    terminal = np.array([0.0, 1.0])
+    scaled = _conditioning.backward(P, weights, 3, terminal)
+    unscaled = _conditioning.backward(P, weights, 3, terminal, rescale=False)
+    np.testing.assert_allclose(scaled, unscaled / unscaled.sum(axis=1, keepdims=True))
+    reference = bridge.doob_kernels(P, [1], 3, weights)
+    rebuilt = [_conditioning.kernel(P, weights[n], scaled[n + 1]) for n in range(3)]
+    np.testing.assert_allclose(rebuilt, reference)
+    kernels, backward = _conditioning.build(P, weights, [1], 3)
+    np.testing.assert_allclose(kernels, reference)
+    np.testing.assert_allclose(backward, scaled)
+
+
+def test_early_target_departures_and_residence_are_still_allowed():
+    P = np.array([[0.8, 0.2], [0.5, 0.5]])
+    kernels = bridge.doob_kernels(P, [1], 4)
+    assert kernels[0][0, 0] > 0
+    assert kernels[1][1, 0] > 0
+    paths = bridge.sample_bridge_paths(kernels, [0], 100, seed=7)
+    assert all(len(p) == 5 and p[-1] == 1 for p in paths)
+
+
+@pytest.mark.parametrize(
+    "targets,horizon,weights",
+    [
+        ([], 2, None),
+        ([2], 2, None),
+        ([1], 0, None),
+        ([1], 2, [1.0, -1.0]),
+        ([1], 2, [1.0, np.nan]),
+    ],
+)
+def test_invalid_inputs_are_not_silently_repaired(targets, horizon, weights):
+    with pytest.raises(ValueError):
+        bridge.doob_kernels(np.ones((2, 2)) / 2, targets, horizon, weights)
+
+
+def test_invalid_start_indices_are_not_replaced_with_arbitrary_states():
+    with pytest.raises(ValueError, match="out-of-range"):
+        _conditioning.build(np.eye(2), np.ones((2, 2)), [1], 2, starts=[8])
diff --git a/tests/test_import.py b/tests/test_import.py
new file mode 100644
index 0000000000000000000000000000000000000000..7883ec938b98250062928a1b74b0bd00cfc13b69
--- /dev/null
+++ b/tests/test_import.py
@@ -0,0 +1,6 @@
+import importlib
+
+
+def test_import():
+    package = importlib.import_module("scoobdoob")
+    assert package.__name__ == "scoobdoob"
diff --git a/tests/test_validation.py b/tests/test_validation.py
new file mode 100644
index 0000000000000000000000000000000000000000..441170bba8f257cac38a8df22ffaca63af8b9bf6
--- /dev/null
+++ b/tests/test_validation.py
@@ -0,0 +1,103 @@
+import sys
+from pathlib import Path
+
+import numpy as np
+
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+
+from scoobdoob.validation import msm_build as mb
+from scoobdoob.validation import tpt, bridge
+
+
+def _toy_dtrajs():
+
+    rng = np.random.default_rng(0)
+    P = np.array(
+        [
+            [0.80, 0.20, 0.00, 0.00, 0.00],
+            [0.15, 0.70, 0.15, 0.00, 0.00],
+            [0.00, 0.15, 0.70, 0.15, 0.00],
+            [0.00, 0.00, 0.15, 0.70, 0.15],
+            [0.00, 0.00, 0.00, 0.20, 0.80],
+        ]
+    )
+    traj = [0]
+    for _ in range(40000):
+        traj.append(int(rng.choice(5, p=P[traj[-1]])))
+    return [np.array(traj)], P
+
+
+def test_connected_and_reversible():
+    dtrajs, _ = _toy_dtrajs()
+    model, syms, sym2a, _ = mb.build_msm(dtrajs, lag=1, k=5, reversible=True)
+    assert model.n_states == 5
+    assert abs(model.stationary_distribution.sum() - 1) < 1e-10
+
+    pi = model.stationary_distribution
+    P = model.transition_matrix
+    assert np.allclose(pi[:, None] * P, (pi[:, None] * P).T, atol=1e-8)
+
+
+def test_committor_solvers_agree():
+    dtrajs, _ = _toy_dtrajs()
+    model, *_ = mb.build_msm(dtrajs, lag=1, k=5, reversible=True)
+    q_dt = np.asarray(model.committor_forward([0], [4]))
+    q_ls = tpt.forward_committor_linsolve(model.transition_matrix, [0], [4])
+    assert np.max(np.abs(q_dt - q_ls)) < 1e-9
+    assert q_dt[0] == 0.0 and q_dt[4] == 1.0
+    assert np.all(np.diff(q_dt) >= -1e-9)
+
+
+def test_committor_half_localizes_in_middle():
+    dtrajs, _ = _toy_dtrajs()
+    model, *_ = mb.build_msm(dtrajs, lag=1, k=5, reversible=True)
+    q = np.asarray(model.committor_forward([0], [4]))
+    half = tpt.committor_half_states(q, tol=0.25)
+    assert 2 in half
+
+
+def test_bridge_conditioning_beats_unconditional():
+    _, P = _toy_dtrajs()
+    Ph = bridge.doob_kernels(P, S_end=[4], horizon=20)
+    cond = bridge.sample_bridge_paths(Ph, S_start=[0], n_paths=200, seed=0)
+    unc = bridge.sample_unconditional_paths(
+        P, S_start=[0], horizon=20, n_paths=200, seed=1
+    )
+    assert bridge.success_with_ci(cond, [4])["rate"] > 0.9
+    assert bridge.success_with_ci(unc, [4])["rate"] < 0.5
+
+
+def test_aib9_macrostate_classification():
+    from scoobdoob.validation import aib9
+
+    state_left = np.array(
+        [
+            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+            [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
+            [1.0, 1.0, 1.0, 1.0, 0.5, 0.0, 0.0, 0.0, 0.0],
+            [0.0, 0.0, 0.0, 0.0, 0.5, 1.0, 1.0, 1.0, 1.0],
+        ]
+    )
+    labels = aib9.classify_microstates(state_left)
+    assert list(labels) == ["R", "L", "I_N", "I_C"]
+
+
+def test_aib9_featurize_and_handedness_shapes():
+    from scoobdoob.validation import aib9
+
+    ang = [np.linspace(-3, 3, 18).reshape(1, 18).repeat(50, 0)]
+    feat = aib9.featurize_sincos(ang)
+    hand = aib9.handedness_indicator(ang)
+    assert feat[0].shape == (50, 36)
+    assert hand[0].shape == (50, 9)
+    assert set(np.unique(hand[0])) <= {0.0, 1.0}
+
+
+if __name__ == "__main__":
+    test_connected_and_reversible()
+    test_committor_solvers_agree()
+    test_committor_half_localizes_in_middle()
+    test_bridge_conditioning_beats_unconditional()
+    test_aib9_macrostate_classification()
+    test_aib9_featurize_and_handedness_shapes()
+    print("all validation smoke tests passed")
diff --git a/trajectory_cv_tilt.py b/trajectory_cv_tilt.py
new file mode 100644
index 0000000000000000000000000000000000000000..14e0f62b60498423f775a7c4e73674ef6a0f599a
--- /dev/null
+++ b/trajectory_cv_tilt.py
@@ -0,0 +1,309 @@
+#!/usr/bin/env python
+from __future__ import annotations
+
+import argparse
+import json
+import pickle
+from collections import Counter
+from pathlib import Path
+from types import SimpleNamespace
+
+import numpy as np
+from scipy.stats import wilcoxon
+
+from scoobdoob.validation import msm_build as mb
+from scoobdoob.validation.selectors import (
+    SEED_RELAX,
+    STRIDE,
+    build_selectors,
+    enrich,
+    state_means,
+)
+
+HERE = Path(__file__).resolve().parent
+CACHE = HERE / "data/p53/pocket_sc_feats_s10.pkl"
+FRAMES = HERE / "data/p53/frame_drug/frames.json"
+DEFAULT_OUT = HERE / "data/p53/trajectory_cv_tilt.json"
+
+
+def assign_folds(meta, nfold=10):
+    """Keep every fold heterogeneous while holding out independent trajectories."""
+    folds = [[] for _ in range(nfold)]
+    folded = [i for i, m in enumerate(meta) if m["cls"] == "folded"]
+    regular = [
+        i
+        for i, m in enumerate(meta)
+        if m["cls"] == "seed" and not m["name"].startswith("barrier_")
+    ]
+    barrier = [
+        i
+        for i, m in enumerate(meta)
+        if m["cls"] == "seed" and m["name"].startswith("barrier_")
+    ]
+    for group, offset in ((folded, 0), (regular, 0), (barrier, 5)):
+        for j, i in enumerate(group):
+            folds[(j + offset) % nfold].append(i)
+    return folds
+
+
+def fit_model(data, train, k, lag, ncomp, seed):
+    feats = [data["feats"][i] for i in train]
+    cvs = [data["cvs"][i] for i in train]
+
+    proj, tica = mb.tica_reduce(feats, lag=lag, n_comp=ncomp, scaling="kinetic_map")
+    dtrajs, centers, km = mb.cluster_microstates(proj, k=k, seed=seed)
+    state_xy, counts = mb.state_mean_features(dtrajs, cvs, k)
+    model, active, sym2active, _ = mb.build_msm(dtrajs, lag=lag, k=k, reversible=True)
+    msm = mb.MSM(
+        model=model,
+        dtrajs=dtrajs,
+        centers=centers,
+        state_xy=state_xy,
+        counts=counts,
+        active_symbols=active,
+        lag=lag,
+        tica=tica,
+        _sym2active=sym2active,
+    )
+
+    held_dtrajs = {}
+    for i in range(len(data["feats"])):
+        if i in train:
+            continue
+        x = tica.transform(np.asarray(data["feats"][i], dtype=np.float64))
+        held_dtrajs[i] = km.predict(x).astype(np.int64)
+    return msm, held_dtrajs
+
+
+def training_context(msm, data, train, lag):
+    """Quantities needed by ``build_selectors``, calculated on training only."""
+    active = np.asarray(msm.active_symbols)
+    s2a = {int(s): i for i, s in enumerate(active)}
+    n = len(active)
+    cvsum, cvcnt = np.zeros((n, 2)), np.zeros(n)
+    dsum, dcnt = np.zeros(n), np.zeros(n)
+    C = np.zeros((n, n))
+    starts = []
+
+    for local, orig in enumerate(train):
+        a = np.array([s2a.get(int(s), -1) for s in msm.dtrajs[local]])
+        cv = np.asarray(data["cvs"][orig])
+        feat = np.asarray(data["feats"][orig])
+        nt = min(len(a), len(cv), len(feat))
+        a, cv, feat = a[:nt], cv[:nt], feat[:nt]
+        good = a >= 0
+        np.add.at(cvsum, a[good], cv[good])
+        np.add.at(cvcnt, a[good], 1)
+        np.add.at(dsum, a[good], feat[good].mean(axis=1))
+        np.add.at(dcnt, a[good], 1)
+        if data["meta"][orig]["cls"] == "folded" and good[0]:
+            starts.append(int(a[0]))
+        pairs = (a[:-lag] >= 0) & (a[lag:] >= 0)
+        np.add.at(C, (a[:-lag][pairs], a[lag:][pairs]), 1)
+
+    if not starts:
+        raise RuntimeError("training split has no active folded start state")
+    cvm = cvsum / np.maximum(cvcnt[:, None], 1)
+    dilation = np.full(n, np.nan)
+    nz = dcnt > 0
+    dilation[nz] = dsum[nz] / dcnt[nz]
+    a0 = Counter(starts).most_common(1)[0][0]
+    return cvm, C, a0, dilation
+
+
+def frame_table(data, held_dtrajs, held, args):
+    """Map held-out fpocket frame records into the held-out model labels."""
+    by_orig = {int(m.get("orig_traj", i)): i for i, m in enumerate(data["meta"])}
+    held = set(held)
+    rows = json.loads(FRAMES.read_text())
+    out_state, out_drug, out_traj = [], [], []
+
+    for r in rows:
+        ci = by_orig.get(int(r["traj"]))
+        if ci not in held:
+            continue
+        value = r.get(args.observable)
+        if value is None:
+            continue
+        core = r.get("core_rmsd_nm")
+        zn = r.get("zn_n_coord", -1)
+        if core is None or core > args.max_core_rmsd:
+            continue
+        if zn >= 0 and zn < args.min_zn:
+            continue
+        off = SEED_RELAX if data["meta"][ci]["cls"] == "seed" else 0
+        pos = int(r["frame"]) // STRIDE - off
+        dt = held_dtrajs[ci]
+        if not 0 <= pos < len(dt):
+            continue
+        out_state.append(int(dt[pos]))
+        out_drug.append(float(value))
+        out_traj.append(ci)
+    return (np.asarray(out_state), np.asarray(out_drug), np.asarray(out_traj))
+
+
+def percentile_ci(x):
+    if len(x) < 2:
+        return [float("nan"), float("nan")]
+    return np.percentile(x, [2.5, 97.5]).tolist()
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--k", type=int, default=100)
+    ap.add_argument("--lag", type=int, default=1)
+    ap.add_argument("--ncomp", type=int, default=6)
+    ap.add_argument("--seed", type=int, default=0)
+    ap.add_argument("--target-q", type=float, default=0.15)
+    ap.add_argument(
+        "--observable",
+        default="site_drug",
+        choices=["site_drug", "site_volume", "site_apolar", "site_sasa"],
+    )
+    ap.add_argument("--drug-thresh", type=float, default=0.5)
+    ap.add_argument(
+        "--alpha-rho-sweep",
+        type=float,
+        nargs="*",
+        default=[0.0, 0.1, 0.25, 0.36, 0.5, 1.0, 1.5, 2.0, 3.0, 5.0, 8.0, 12.0, 20.0],
+        help="Algorithm-1 tilt strengths to evaluate (w=exp(-alpha_rho*rho), no "
+        "committor shaping). alpha_rho=0 is the no-density-tilt control. The "
+        "tilt strength is a knob; report the sweep, not one value.",
+    )
+    ap.add_argument("--max-core-rmsd", type=float, default=0.30)
+    ap.add_argument("--min-zn", type=int, default=3)
+    ap.add_argument("--out", type=Path, default=DEFAULT_OUT)
+    args = ap.parse_args()
+
+    data = pickle.load(open(CACHE, "rb"))
+    folds = assign_folds(data["meta"])
+    all_idx = set(range(len(data["meta"])))
+    result = {"config": vars(args) | {"out": str(args.out)}, "folds": []}
+
+    selector_names = (
+        "equilibrium",
+        "uniform_states",
+        "tpt_flux",
+        "doob_untilted",
+        "fk_doob",
+    ) + tuple(f"alg1[a={a:g}]" for a in args.alpha_rho_sweep)
+    for fi, held in enumerate(folds):
+        train = sorted(all_idx - set(held))
+        print(f"[fold {fi}] train={len(train)} held={len(held)}")
+        msm, held_dt = fit_model(data, train, args.k, args.lag, args.ncomp, args.seed)
+        cvm, C, a0, dilation = training_context(msm, data, train, args.lag)
+
+        fit_args = SimpleNamespace(
+            target="dilation",
+            target_q=args.target_q,
+            cleft_thr=0.45,
+            horizon=None,
+            fk_variant="rho",
+            comm_l_sweep=[],
+            horizon_sweep=[],
+            alpha_rho_sweep=args.alpha_rho_sweep,
+        )
+        selectors, q, B, a0, horizon = build_selectors(
+            msm, cvm, C, a0, fit_args, dil=dilation
+        )
+
+        orig_state, drug, traj = frame_table(data, held_dt, held, args)
+        s2a = {int(s): i for i, s in enumerate(msm.active_symbols)}
+        state = np.array([s2a.get(int(s), -1) for s in orig_state])
+        keep = state >= 0
+        state, drug, traj = state[keep], drug[keep], traj[keep]
+        n = msm.n_active
+        mu, cnt = state_means(state, drug, n)
+        po, _ = state_means(state, (drug > args.drug_thresh).astype(float), n)
+
+        eq = enrich(
+            selectors["equilibrium"],
+            mu,
+            cnt,
+            po,
+            drug.mean(),
+            (drug > args.drug_thresh).mean(),
+        )
+        fold = {
+            "fold": fi,
+            "held_trajectories": held,
+            "held_names": [data["meta"][i]["name"] for i in held],
+            "n_frames": int(len(drug)),
+            "n_active": int(n),
+            "state_coverage": int((cnt > 0).sum()),
+            "B_size": int(len(B)),
+            "a0": int(a0),
+            "horizon": int(horizon),
+            "selectors": {},
+        }
+        for name in selector_names:
+            if name not in selectors:
+                continue
+            e = enrich(
+                selectors[name],
+                mu,
+                cnt,
+                po,
+                drug.mean(),
+                (drug > args.drug_thresh).mean(),
+            )
+            if e is None:
+                continue
+            e["ratio_to_equilibrium"] = e["E_drug"] / eq["E_drug"]
+            e["open_ratio_to_equilibrium"] = e["P_open"] / eq["P_open"]
+            fold["selectors"][name] = e
+        result["folds"].append(fold)
+        fk = fold["selectors"].get("fk_doob", {})
+        print(
+            f"  n={len(drug)} coverage={fk.get('coverage', np.nan):.2f} "
+            f"FK/equilibrium={fk.get('ratio_to_equilibrium', np.nan):.3f}"
+        )
+
+    summary = {}
+    for name in selector_names:
+        vals = [
+            f["selectors"][name]["ratio_to_equilibrium"]
+            for f in result["folds"]
+            if name in f["selectors"]
+        ]
+        opens = [
+            f["selectors"][name]["open_ratio_to_equilibrium"]
+            for f in result["folds"]
+            if name in f["selectors"]
+        ]
+        if not vals:
+            continue
+        arr = np.asarray(vals)
+        if name == "equilibrium":
+            p = 1.0
+        else:
+            try:
+                p = float(wilcoxon(arr - 1, alternative="two-sided").pvalue)
+            except ValueError:
+                p = 1.0
+        summary[name] = {
+            "n_folds": len(vals),
+            "mean_ratio_to_equilibrium": float(arr.mean()),
+            "median_ratio_to_equilibrium": float(np.median(arr)),
+            "fold_percentile_interval": percentile_ci(arr),
+            "folds_above_1": int((arr > 1).sum()),
+            "wilcoxon_p_vs_1": p,
+            "mean_open_ratio_to_equilibrium": float(np.mean(opens)),
+            "values": vals,
+        }
+    result["summary"] = summary
+    args.out.write_text(json.dumps(result, indent=2, default=float))
+
+    print("\nTRAJECTORY-HELD-OUT RESULT")
+    for name, s in summary.items():
+        print(
+            f"  {name:<18} {s['mean_ratio_to_equilibrium']:.3f}x "
+            f"(median {s['median_ratio_to_equilibrium']:.3f}; "
+            f"{s['folds_above_1']}/{s['n_folds']} folds >1; "
+            f"p={s['wilcoxon_p_vs_1']:.3g})"
+        )
+    print(f"-> {args.out}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/validate_aib9.py b/validate_aib9.py
new file mode 100644
index 0000000000000000000000000000000000000000..a393e69a3fdd38d10e9776ee381316dde0af5515
--- /dev/null
+++ b/validate_aib9.py
@@ -0,0 +1,334 @@
+#!/usr/bin/env python
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+import numpy as np
+import matplotlib
+
+matplotlib.use("Agg")
+
+from scoobdoob.validation import msm_build as mb
+from scoobdoob.validation import tpt, bridge, aib9
+
+ROOT = Path(__file__).resolve().parent
+DEFAULT_DATA = aib9.OPENMM_DIR
+OUTDIR = ROOT / "data" / "validation_aib9"
+
+LAG = 5
+K = 100
+N_COMP = 4
+HORIZON = 60
+TOL_Q = 0.10
+
+
+def gate_msm(feat, hand, temps):
+    print("\n" + "=" * 70)
+    print("PART 2.0/2.1  Build Aib9 MSM (36D sin/cos -> TICA -> connected -> rev MLE)")
+    print("=" * 70)
+    model = mb.build_full_msm(
+        feat, hand, lag=LAG, k=K, n_comp=N_COMP, seed=0, use_tica=True
+    )
+    n_off = K - len(model.active_symbols)
+    lam1 = float(np.real(model.model.eigenvalues(k=1)[0]))
+    print(
+        f"  microstates K={K}, connected set={model.n_active} "
+        f"({n_off} dropped), lambda_1={lam1:.5f}"
+    )
+    lags = [2, 5, 10, 20, 40]
+    lg, its = mb.implied_timescales(model.dtrajs, lags, k=K, n_its=3)
+    ck = mb.ck_test(model.dtrajs, lag=LAG, k=K, factors=(2, 3))
+    print(
+        f"  ITS(tau*={LAG}, frames) slowest 3 = "
+        f"{np.array2string(its[lags.index(LAG)], precision=1)}"
+    )
+    print(f"  CK error: 2tau={ck[2]:.3f}, 3tau={ck[3]:.3f}")
+    return model, {
+        "n_active": int(model.n_active),
+        "n_dropped": int(n_off),
+        "lambda1": lam1,
+        "ck": {int(k): v for k, v in ck.items()},
+    }
+
+
+def gate_endpoints(model):
+    print("\n" + "=" * 70)
+    print("GATE 2.2  Endpoints R / L from helical handedness")
+    print("=" * 70)
+    labels = aib9.classify_microstates(model.active_xy)
+    sets = {
+        k: np.where(labels == k)[0].tolist() for k in ["R", "L", "I_N", "I_C", "other"]
+    }
+    print(
+        f"  microstate labels: R={len(sets['R'])} L={len(sets['L'])} "
+        f"I_N={len(sets['I_N'])} I_C={len(sets['I_C'])} other={len(sets['other'])}"
+    )
+    pL_R = model.active_xy[sets["R"]][:, aib9.CORE].mean()
+    pL_L = model.active_xy[sets["L"]][:, aib9.CORE].mean()
+    print(
+        f"  R: core P(left)={pL_R:.2f} (right helix);  "
+        f"L: core P(left)={pL_L:.2f} (left helix)"
+    )
+
+    A2, B2, _ = mb.q2_sign_endpoints(model, k_each=6)
+    a2_isR = np.mean([labels[i] in ("R",) for i in A2])
+    b2_isL = np.mean([labels[i] in ("L",) for i in B2])
+    print(
+        f"  q2-sign split agreement with R/L handedness: "
+        f"{max(a2_isR, 1 - a2_isR) * 100:.0f}% / {max(b2_isL, 1 - b2_isL) * 100:.0f}%"
+    )
+    return labels, sets
+
+
+def gate_committor(model, sets, labels):
+    print("\n" + "=" * 70)
+    print("GATE 2.3  Committor-1/2 vs KNOWN intermediates I_N, I_C  (THE GATE)")
+    print("=" * 70)
+    R, L = sets["R"], sets["L"]
+    agree, q, _ = tpt.committor_agreement(model, R, L)
+    print(f"  committor solver cross-check (deeptime vs linsolve): max|dq|={agree:.2e}")
+    print(f"  q on R ~ {q[R].mean():.3f},  q on L ~ {q[L].mean():.3f}")
+
+    half = tpt.committor_half_states(q, tol=TOL_Q)
+    inter = set(sets["I_N"]) | set(sets["I_C"])
+    half_in_inter = [h for h in half if h in inter]
+    frac_states = len(half_in_inter) / max(len(half), 1)
+    pi = model.stationary
+    mass_frac = (
+        pi[half_in_inter].sum() / max(pi[list(half)].sum(), 1e-12) if len(half) else 0.0
+    )
+
+    in_basin = [h for h in half if labels[h] in ("R", "L")]
+    qIN = q[sets["I_N"]].mean() if sets["I_N"] else np.nan
+    qIC = q[sets["I_C"]].mean() if sets["I_C"] else np.nan
+    n_half_IN = sum(h in set(sets["I_N"]) for h in half)
+    n_half_IC = sum(h in set(sets["I_C"]) for h in half)
+    print(
+        f"  committor-1/2 ensemble: {len(half)} states "
+        f"({n_half_IN} in I_N, {n_half_IC} in I_C)"
+    )
+    print(
+        f"  overlap with intermediates: {frac_states * 100:.0f}% of states, "
+        f"{mass_frac * 100:.0f}% of committor-1/2 mass"
+    )
+    print(f"  committor-1/2 states inside an endpoint (R/L) basin: {len(in_basin)}")
+    sym = "symmetric" if abs(qIN - qIC) < 0.1 else "asymmetric"
+    print(
+        f"  mean committor of I_N={qIN:.2f}, I_C={qIC:.2f}  "
+        f"-> the two channels are {sym}. Aib is achiral, so a symmetric split "
+        f"is the physically expected result."
+    )
+    passed = (agree < 1e-6) and (mass_frac >= 0.5) and (len(in_basin) == 0)
+    print(
+        f"  -> {'PASS' if passed else 'FAIL'}  "
+        f"(committor-1/2 coincides with the known intermediates)"
+    )
+    return (
+        passed,
+        {
+            "committor_agreement": agree,
+            "n_half": int(len(half)),
+            "overlap_frac_states": frac_states,
+            "overlap_frac_mass": float(mass_frac),
+            "n_half_in_basin": len(in_basin),
+            "q_IN": float(qIN),
+            "q_IC": float(qIC),
+            "pass": bool(passed),
+        },
+        q,
+        half,
+    )
+
+
+def gate_flux(model, sets, q, half):
+    print("\n" + "=" * 70)
+    print("GATE 2.4  Reactive flux + bridge rollouts route through intermediates")
+    print("=" * 70)
+    R, L = sets["R"], sets["L"]
+    IN, IC = set(sets["I_N"]), set(sets["I_C"])
+
+    rf = tpt.reactive_flux(model, R, L)
+    order = ["R", "I_N", "I_C", "other", "L"]
+    _, cg = rf.coarse_grain([sets[k] for k in order])
+    F = np.asarray(cg.net_flux)
+    tot = float(cg.total_flux)
+    f_IN = float(F[0, 1] / tot)
+    f_IC = float(F[0, 2] / tot)
+    f_other = float(F[0, 3] / tot)
+    f_direct = float(F[0, 4] / tot)
+    f_thru = f_IN + f_IC + f_other
+    print(f"  total reactive flux = {tot:.3e}")
+    print(
+        f"  flux routed through resolved intermediates: {f_thru * 100:.0f}%  "
+        f"(direct R->L: {f_direct * 100:.0f}% -- small once swap artifacts removed)"
+    )
+    print(
+        f"  TWO CHANNELS: flip-from-N (I_N) {f_IN * 100:.0f}%  |  "
+        f"flip-from-C (I_C) {f_IC * 100:.0f}%  |  middle/other {f_other * 100:.0f}%"
+    )
+
+    band = set(np.where((q > 0.2) & (q < 0.8))[0].tolist())
+    Ph = bridge.doob_kernels(model.P, L, HORIZON)
+    paths = bridge.sample_bridge_paths(Ph, R, n_paths=300, seed=0)
+    succ = bridge.success_with_ci(paths, L, seed=0)
+    visit_band = np.mean([bool(set(map(int, p)) & band) for p in paths])
+    visit_IN = np.mean([bool(set(map(int, p)) & IN) for p in paths])
+    visit_IC = np.mean([bool(set(map(int, p)) & IC) for p in paths])
+    print(
+        f"  bridge rollouts R->L: success {succ['rate'] * 100:.0f}% "
+        f"CI[{succ['ci'][0] * 100:.0f},{succ['ci'][1] * 100:.0f}], "
+        f"cross committor band {visit_band * 100:.0f}%"
+    )
+    print(
+        f"  rollouts touching I_N {visit_IN * 100:.0f}%, I_C {visit_IC * 100:.0f}% "
+        f"(both channels sampled)"
+    )
+
+    passed = (f_IN > 0 and f_IC > 0) and (succ["rate"] > 0.9)
+    print(
+        f"  -> {'PASS' if passed else 'FAIL'}  "
+        f"(multi-channel reactive ensemble through both known intermediates)"
+    )
+    return (
+        passed,
+        {
+            "total_flux": tot,
+            "flux_through_resolved_inter": f_thru,
+            "flux_direct": f_direct,
+            "flux_IN": f_IN,
+            "flux_IC": f_IC,
+            "flux_other": f_other,
+            "rollout_success": succ["rate"],
+            "rollout_cross_band": float(visit_band),
+            "rollout_visit_IN": float(visit_IN),
+            "rollout_visit_IC": float(visit_IC),
+            "pass": bool(passed),
+        },
+        paths,
+    )
+
+
+def run_robustness(feat, hand):
+    """Opt-in K x lag + ITS-convergence robustness report (gate stability)."""
+    from scoobdoob.validation import robustness as rob
+
+    print("\n" + "=" * 70)
+    print("ROBUSTNESS  Are the conclusions stable to K and lag?")
+    print("=" * 70)
+    Ks, lags = [75, 100, 150, 200], [2, 5, 10, 20]
+    results = rob.sweep(feat, hand, Ks, lags, n_comp=N_COMP)
+    print(
+        f"  {'K':>4} {'lag':>4} {'active':>6} {'lam1':>5} {'CK2t':>5} "
+        f"{'chalf%':>7} {'direct%':>7} {'I_N%':>5} {'I_C%':>5}"
+    )
+    for r in results:
+        if r.get("ok"):
+            print(
+                f"  {r['K']:4d} {r['lag']:4d} {r['n_active']:6d} {r['lam1']:5.2f} "
+                f"{r['ck2']:5.2f} {r['committor_half_mass'] * 100:6.0f}% "
+                f"{r['direct'] * 100:6.0f}% {r['flux_IN'] * 100:5.0f} {r['flux_IC'] * 100:5.0f}"
+            )
+    summ = rob.summarize(results)
+    print(
+        f"\n  committor-1/2 overlap across all {summ['n_points']} (K,lag): "
+        f"{summ['committor_half_mass_min'] * 100:.0f}-{summ['committor_half_mass_max'] * 100:.0f}%"
+    )
+    print(
+        f"  spurious direct flux across all (K,lag): "
+        f"{summ['direct_min'] * 100:.0f}-{summ['direct_max'] * 100:.0f}%"
+    )
+    print(
+        f"  both channels carry flux at every (K,lag): {summ['both_channels_always']}"
+    )
+    its_lags = [1, 2, 3, 5, 8, 12, 20, 30]
+    lg, its = rob.its_convergence(feat, hand, K=K, lags=its_lags, n_comp=N_COMP)
+    OUTDIR.mkdir(parents=True, exist_ok=True)
+    rob.make_figure(
+        results,
+        lg,
+        its,
+        K_default=K,
+        lag_default=LAG,
+        dt_ps=1.0,
+        outpath=ROOT / "figures" / "validation_aib9" / "aib9_robustness.png",
+    )
+    print(f"  robustness figure -> {OUTDIR / 'aib9_robustness.png'}")
+    with open(OUTDIR / "robustness.json", "w") as f:
+        json.dump(
+            {
+                "grid": results,
+                "summary": summ,
+                "its_lags": [int(x) for x in lg],
+                "its": np.where(np.isfinite(its), its, None).tolist(),
+            },
+            f,
+            indent=2,
+            default=float,
+        )
+    print(f"  robustness report -> {OUTDIR / 'robustness.json'}")
+    return summ
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--data", default=DEFAULT_DATA)
+    ap.add_argument(
+        "--robustness",
+        action="store_true",
+        help="also run the K x lag + ITS-convergence stability report",
+    )
+    ap.add_argument("--colvar_stride", type=int, default=5)
+    args = ap.parse_args()
+
+    trajs, temps = aib9.load_openmm_colvar(args.data, stride=args.colvar_stride)
+    print(f"loaded {sum(len(t) for t in trajs)} frames")
+    feat = aib9.featurize_sincos(trajs)
+    hand = aib9.handedness_indicator(trajs)
+
+    model, qc = gate_msm(feat, hand, temps)
+    labels, sets = gate_endpoints(model)
+    p23, r23, q, half = gate_committor(model, sets, labels)
+    p24, r24, paths = gate_flux(model, sets, q, half)
+
+    rob_summary = run_robustness(feat, hand) if args.robustness else None
+
+    print("\n" + "=" * 70)
+    print("SUMMARY")
+    print("=" * 70)
+    for name, ok in [
+        ("2.3 committor-1/2 <-> known intermediates", p23),
+        ("2.4 reactive flux / rollouts through intermediates", p24),
+    ]:
+        print(f"  [{'PASS' if ok else 'FAIL'}]  {name}")
+    gate2 = p23
+    print(
+        f"\n  GATE 2 (2.3) -> {'PASS' if gate2 else 'FAIL'}  "
+        f"(structural-overlap criterion {'met' if gate2 else 'not met'})"
+    )
+
+    OUTDIR.mkdir(parents=True, exist_ok=True)
+    report = {
+        "config": {
+            "lag": LAG,
+            "K": K,
+            "n_comp": N_COMP,
+            "horizon": HORIZON,
+            "tol_q": TOL_Q,
+            "source": "openmm",
+        },
+        "qc": qc,
+        "gate_2_3": r23,
+        "gate_2_4": r24,
+        "gate_2_pass": bool(gate2),
+        "robustness": rob_summary,
+    }
+    with open(OUTDIR / "report.json", "w") as f:
+        json.dump(report, f, indent=2, default=float)
+    print(f"  report -> {OUTDIR / 'report.json'}")
+    return 0 if gate2 else 1
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())