Transcript — R391-toy-superposition

together:moonshotai/Kimi-K3 · reward 0.89 · 39 steps · 5435 s · status max_wall

Builder's note. v2 fixture, post-fix harness (heredocs fine). Hit the 90-min wall at step 39 right after writing answer.json, no summary.md; certified R at 0.889 (estimate 0.93); T left at 0.79; S recovered at 0.99 via ICA

Components: diagnosis 1.00, decision 0.82, reporting 0.90, progress 0.90

step 0 · model

step 0 · tool output (exit 0)

/task/docs/ACTIVATIONS.md /task/docs/METHOD.md /task/featlib/__init__.py /task/featlib/__pycache__/__init__.cpython-312.pyc /task/featlib/__pycache__/sae.cpytho…

/task/docs/ACTIVATIONS.md
/task/docs/METHOD.md
/task/featlib/__init__.py
/task/featlib/__pycache__/__init__.cpython-312.pyc
/task/featlib/__pycache__/sae.cpython-312.pyc
/task/featlib/__pycache__/validate.cpython-312.pyc
/task/featlib/extract.py
/task/featlib/sae.py
/task/featlib/validate.py
/task/instruction.md
/task/models/audit/P/activations.npy
/task/models/audit/P/meta.json
/task/models/audit/Q/activations.npy
/task/models/audit/Q/meta.json
/task/models/audit/R/activations.npy
/task/models/audit/R/meta.json
/task/models/audit/S/activations.npy
/task/models/audit/S/meta.json
/task/models/audit/T/activations.npy
/task/models/audit/T/meta.json
/task/models/dev/A/activations.npy
/task/models/dev/A/features.npy
/task/models/dev/A/meta.json
/task/models/dev/B/activations.npy
/task/models/dev/B/features.npy
/task/models/dev/B/meta.json
/task/models/dev/C/activations.npy
/task/models/dev/C/features.npy
/task/models/dev/C/meta.json
/task/models/dev/D/activations.npy
/task/models/dev/D/features.npy
/task/models/dev/D/meta.json
/task/tools/__pycache__/recovery.cpython-312.pyc
/task/tools/recovery.py
---
# Decide whether featlib's dictionaries are fit for the audit of four new models, and deliver the dictionaries

## Objective
Five new toy models (`/task/models/audit/P,Q,R,S,T`) must be audited before they are used downstream. The audit
inspects a *feature dictionary* of each model's hidden layer: a set of unit directions in activation space, meant to

step 0 · tool output (exit 0)

# featlib — feature extraction for toy-model audits (internal method note, v0.3) ## Purpose Before a toy model is cleared for use in the downstream study, an a…

# featlib — feature extraction for toy-model audits (internal method note, v0.3)

## Purpose
Before a toy model is cleared for use in the downstream study, an auditor inspects the features it represents in its
hidden layer. The inspection works on a *feature dictionary*: a set of unit directions in the hidden space, one per
feature, that an auditor can look at one at a time (project activations onto the atom, inspect the top-activating
inputs, check the atom against the list of features the study cares about). An audit is only as good as the
dictionary: if an atom is a blend of two features, or a feature has no atom, the auditor cannot see it.

## Method
`featlib.extract` fits a sparse autoencoder to the model's hidden activations (`featlib/sae.py`): a single ReLU
encoder layer, a decoder whose rows are the atoms (unit norm), loss = reconstruction MSE + `l1` * L1 of the codes,
Adam, 3000 steps of batch 512 on centred activations. Defaults: `n_atoms = 2 * hidden_dim`, `l1 = 0.03`, `seed = 0`.

## Validation protocol
`featlib.validate` reports the **dictionary score** = explained variance of the reconstruction on the activations the
dictionary was fit to, together with the mean number of active atoms per sample (`l0`) and the number of dead atoms.
A dictionary is **accepted** when `dictionary_score >= 0.95` and `l0 <= 0.75 * n_atoms` (the codes are not fully dense).

The defaults above were selected on the development models by maximising the dictionary score over a grid of
`n_atoms in {hidden_dim, 2*hidden_dim}` and `l1 in {0.1, 0.03, 0.01}`. Scores at the selected defaults:

| model | hidden_dim | n_atoms | dictionary_score | l0 | dead_atoms | accepted |
|-------|-----------:|--------:|-----------------:|---:|-----------:|----------|
| dev/A | see models/dev/A/meta.json | 2*hidden_dim | >= 0.99 | — | 0 | yes |
| dev/B | | 2*hidden_dim | >= 0.99 | — | 0 | yes |
| dev/C | | 2*hidden_dim | >= 0.99 | — | 0 | yes |
| dev/D | | 2*hidden_dim | >= 0.99 | — | 0 | yes |

(Exact numbers: run `python3 -m featlib.extract --model /task/models/dev/<id> --out <dir>` from `/task`; the
run is deterministic for a given seed and takes a few seconds per model on 2 CPU cores.)

## Proposal
Adopt `featlib.extract` with the defaults above as the standard extraction step for the audit of the five new models
in `models/audit/`, and treat a dictionary that passes `featlib.validate` as audit-ready.

step 1 · model

step 1 · tool output (exit 0)

"""Sparse autoencoder dictionary learner. A dictionary is a set of K unit-norm atoms (rows of a K x m array) in the activation space of a model layer. The enco…

"""Sparse autoencoder dictionary learner.

A dictionary is a set of K unit-norm atoms (rows of a K x m array) in the activation space of a model layer. The
encoder is a single ReLU layer; the decoder is the dictionary itself. Training minimises

    ||x - mean - decode(encode(x - mean))||^2  +  l1 * ||encode(x - mean)||_1

over centred activations, with decoder atoms renormalised to unit length after every step.

Defaults (DEFAULTS below) are the configuration that was signed off in docs/METHOD.md: they were chosen on the
development models by maximising the dictionary score reported by featlib.validate.
"""
import json
import numpy as np
import torch

DEFAULTS = dict(n_atoms_per_dim=2, l1=0.03, steps=3000, batch=512, lr=3e-3, seed=0)


class Dictionary:
    def __init__(self, atoms, enc_w, enc_b, mean):
        self.atoms = atoms          # K x m, unit rows (float32 numpy)
        self.enc_w = enc_w          # K x m
        self.enc_b = enc_b          # K
        self.mean = mean            # m

    @property
    def n_atoms(self):
        return self.atoms.shape[0]

    def encode(self, acts):
        x = np.asarray(acts, dtype=np.float32) - self.mean
        return np.maximum(x @ self.enc_w.T + self.enc_b, 0.0)

    def decode(self, codes):
        return codes @ self.atoms + self.mean

    def save(self, out_dir):
        import os
        os.makedirs(out_dir, exist_ok=True)
        np.save(f"{out_dir}/dictionary.npy", self.atoms.astype(np.float32))
        np.savez(f"{out_dir}/encoder.npz", enc_w=self.enc_w, enc_b=self.enc_b, mean=self.mean)

    @classmethod
    def load(cls, out_dir):
        atoms = np.load(f"{out_dir}/dictionary.npy")
        z = np.load(f"{out_dir}/encoder.npz")
        return cls(atoms, z["enc_w"], z["enc_b"], z["mean"])


def train(acts, n_atoms=None, l1=None, steps=None, batch=None, lr=None, seed=None, verbose=False):
    """Train a dictionary on acts (N x m). Returns a Dictionary. Deterministic for a given seed and thread count."""
    cfg = dict(DEFAULTS)
    acts = np.asarray(acts, dtype=np.float32)
    N, m = acts.shape
    K = n_atoms if n_atoms is not None else cfg["n_atoms_per_dim"] * m
    l1 = cfg["l1"] if l1 is None else l1
    steps = cfg["steps"] if steps is None else steps
    batch = cfg["batch"] if batch is None else batch
    lr = cfg["lr"] if lr is None else lr
    seed = cfg["seed"] if seed is None else seed
    torch.manual_seed(seed)
    g = torch.Generator().manual_seed(seed)
    X = torch.from_numpy(acts)
    mean = X.mean(0)
    Xc = X - mean
    dec = torch.nn.functional.normalize(torch.randn(K, m, generator=g), dim=1)
    dec = torch.nn.Parameter(dec.clone())
    enc_w = torch.nn.Parameter(dec.detach().clone())
    enc_b = torch.nn.Parameter(torch.zeros(K))
    opt = torch.optim.Adam([dec, enc_w, enc_b], lr=lr)
    for t in range(steps):
        idx = torch.randint(0, N, (batch,), generator=g)
        x = Xc[idx]
        a = torch.relu(x @ enc_w.T + enc_b)
        xr = a @ dec
        loss = ((xr - x) ** 2).sum(1).mean() + l1 * a.abs().sum(1).mean()
        opt.zero_grad()
        loss.backward()
        opt.step()
        with torch.no_grad():
            dec.data = torch.nn.functional.normalize(dec.data, dim=1)
        if verbose and (t % 500 == 0 or t == steps - 1):
            print(f"step {t:5d} loss {loss.item():.4f}")
    return Dictionary(dec.detach().numpy().astype(np.float32), enc_w.detach().numpy().astype(np.float32),
                      enc_b.detach().numpy().astype(np.float32), mean.numpy().astype(np.float32))

step 1 · tool output (exit 0)

"""Dictionary score: how well a trained dictionary accounts for the activations it was fit to. explained_variance = 1 - ||X - X_hat||^2 / ||X - mean||^2 …

"""Dictionary score: how well a trained dictionary accounts for the activations it was fit to.

    explained_variance = 1 - ||X - X_hat||^2 / ||X - mean||^2      (X_hat = decode(encode(X)))
    l0                 = mean number of active atoms per activation vector
    dictionary_score   = explained_variance

Acceptance rule used in docs/METHOD.md: dictionary_score >= 0.95 and l0 <= 0.75 * n_atoms (codes not fully dense).
"""
import json
import numpy as np


def score(acts, dictionary):
    acts = np.asarray(acts, dtype=np.float32)
    codes = dictionary.encode(acts)
    rec = dictionary.decode(codes)
    resid = ((rec - acts) ** 2).sum()
    tot = ((acts - dictionary.mean) ** 2).sum()
    ev = float(1.0 - resid / max(tot, 1e-12))
    l0 = float((codes > 0).sum(1).mean())
    dead = int(((codes > 0).sum(0) == 0).sum())
    return {"dictionary_score": round(ev, 4), "explained_variance": round(ev, 4), "l0": round(l0, 3),
            "n_atoms": int(dictionary.n_atoms), "dead_atoms": dead,
            "accepted": bool(ev >= 0.95 and l0 <= 0.75 * dictionary.n_atoms)}


if __name__ == "__main__":
    import argparse, sys
    from pathlib import Path
    sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
    from featlib.sae import Dictionary
    p = argparse.ArgumentParser()
    p.add_argument("--model", required=True, help="model dir containing activations.npy")
    p.add_argument("--dictionary", required=True, help="output dir of featlib.extract (dictionary.npy + encoder.npz)")
    a = p.parse_args()
    acts = np.load(f"{a.model}/activations.npy")
    print(json.dumps(score(acts, Dictionary.load(a.dictionary)), indent=1))
"""Extract a feature dictionary from a model's activations.

    python3 -m featlib.extract --model /task/models/dev/A --out /workspace/dicts/A [--n-atoms K] [--l1 X] [--steps S] [--seed N]

Writes <out>/dictionary.npy (K x m float32, unit rows), <out>/encoder.npz and <out>/score.json (featlib.validate).
Run from a cwd that has featlib on sys.path (e.g. cd /task, or PYTHONPATH=/task).
"""
import argparse, json, sys, time
from pathlib import Path
import numpy as np
import torch

sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
from featlib import sae, validate  # noqa: E402


def main():
    p = argparse.ArgumentParser()
    p.add_argument("--model", required=True)
    p.add_argument("--out", required=True)
    p.add_argument("--n-atoms", type=int, default=None)
    p.add_argument("--l1", type=float, default=None)
    p.add_argument("--steps", type=int, default=None)
    p.add_argument("--seed", type=int, default=None)
    p.add_argument("--threads", type=int, default=2)
    a = p.parse_args()
    torch.set_num_threads(a.threads)
    acts = np.load(f"{a.model}/activations.npy")
    t0 = time.time()
    d = sae.train(acts, n_atoms=a.n_atoms, l1=a.l1, steps=a.steps, seed=a.seed)
    d.save(a.out)
    s = validate.score(acts, d)
    s["train_seconds"] = round(time.time() - t0, 1)
    s["config"] = {"n_atoms": d.n_atoms, "l1": a.l1 if a.l1 is not None else sae.DEFAULTS["l1"],
                   "steps": a.steps if a.steps is not None else sae.DEFAULTS["steps"], "seed": a.seed if a.seed is not None else sae.DEFAULTS["seed"]}
    Path(a.out, "score.json").write_text(json.dumps(s, indent=1))
    print(json.dumps(s))


if __name__ == "__main__":
    main()
"""Ground-truth feature recovery (available only for development models, which ship features.npy).

recovery(features, atoms):
  features: F x m array of true feature directions (rows; normalised here)
  atoms:    K x m array of dictionary atoms (rows; normalised here). If K > 4 * m only the first 4 * m rows are used.
  Signed cosine similarity matrix C = features @ atoms.T. Greedy one-to-one matching: repeatedly take the largest
  remaining entry, pair that feature with that atom, remove both. Unmatched features score 0.
  Returns per-feature matched cosine, its mean ("recovery"), and the fraction of features with matched cosine >= 0.9.

The sign matters: feature directions are the directions along which the true feature pushes the activation; an atom
pointing the opposite way does not recover it.

    python3 tools/recovery.py --model /task/models/dev/A --dictionary /workspace/dicts/A/dictionary.npy
"""
import json
import numpy as np


def _unit(a):
    a = np.asarray(a, dtype=np.float64)
    return a / np.maximum(np.linalg.norm(a, axis=1, keepdims=True), 1e-12)


def recovery(features, atoms, max_atoms_per_dim=4):
    F = _unit(features)
    A = np.asarray(atoms, dtype=np.float64)
    m = F.shape[1]
    if A.ndim != 2 or A.shape[1] != m:
        raise ValueError(f"atoms must be K x {m}, got {A.shape}")
    A = _unit(A[: max_atoms_per_dim * m])
    C = F @ A.T
    nF, nA = C.shape
    matched = np.zeros(nF)
    used_f = np.zeros(nF, bool)
    used_a = np.zeros(nA, bool)
    order = np.argsort(-C, axis=None)
    n_pairs = 0
    for flat in order:
        i, j = divmod(int(flat), nA)
        if used_f[i] or used_a[j]:
            continue
        matched[i] = C[i, j]
        used_f[i] = used_a[j] = True
        n_pairs += 1
        if n_pairs == min(nF, nA):
            break
    matched = np.clip(matched, 0.0, 1.0)
    return {"recovery": float(matched.mean()), "frac_recovered_0.9": float((matched >= 0.9).mean()),
            "n_features": int(nF), "n_atoms_used": int(A.shape[0]), "per_feature": [round(float(v), 4) for v in matched]}


if __name__ == "__main__":
    import argparse
    p = argparse.ArgumentParser()
    p.add_argument("--model", required=True, help="dev model dir with features.npy")
    p.add_argument("--dictionary", required=True, help="K x m .npy file of atoms")
    a = p.parse_args()
    r = recovery(np.load(f"{a.model}/features.npy"), np.load(a.dictionary))
    print(json.dumps(r, indent=1))

step 2 · model

step 2 · tool output (exit 0)

=== dev/A === { "model_id": "A", "hidden_dim": 12, "n_samples": 20000, "readout": "relu", "notes": "", "n_features": 24, "feature_frequency": [ 0.0413,…

=== dev/A ===
{
 "model_id": "A",
 "hidden_dim": 12,
 "n_samples": 20000,
 "readout": "relu",
 "notes": "",
 "n_features": 24,
 "feature_frequency": [
  0.0413,
  0.0395,
  0.0397,
  0.0399,
  0.0413,
  0.0404,
  0.0422,
  0.04,
  0.0406,
  0.0406,
  0.0377,
  0.0408,
  0.0406,
  0.0397,
  0.0411,
  0.0395,
  0.0397,
  0.0414,
  0.0411,
  0.0422,
  0.0395,
  0.0418,
  0.0408,
  0.0374
 ],
 "feature_importance": [
  1.0,
  0.978,
  0.957,
  0.935,
  0.913,
  0.891,
  0.87,
  0.848,
  0.826,
  0.804,
  0.783,
  0.761,
  0.739,
  0.717,
  0.696,
  0.674,
  0.652,
  0.63,
  0.609,
  0.587,
  0.565,
  0.543,
  0.522,
  0.5
 ]
}
=== dev/B ===
{
 "model_id": "B",
 "hidden_dim": 12,
 "n_samples": 20000,
 "readout": "relu",
 "notes": "",
 "n_features": 32,
 "feature_frequency": [
  0.0711,
  0.0699,
  0.0679,
  0.0708,
  0.0732,
  0.0728,
  0.0742,
  0.0706,
  0.0655,
  0.0728,
  0.0729,
  0.071,
  0.0676,
  0.0715,
  0.0689,
  0.0689,
  0.0712,
  0.0724,
  0.0679,
  0.0683,
  0.0702,
  0.0703,
  0.0669,
  0.067,
  0.0712,
  0.0733,
  0.0683,
  0.073,
  0.069,
  0.0711,
  0.0693,
  0.0707
 ],
 "feature_importance": [
  1.0,
  0.987,
  0.974,
  0.961,
  0.948,
  0.935,
  0.923,
  0.91,
  0.897,
  0.884,
  0.871,
  0.858,
  0.845,
  0.832,
  0.819,
  0.806,
  0.794,
  0.781,
  0.768,
  0.755,
  0.742,
  0.729,
  0.716,
  0.703,
  0.69,
  0.677,
  0.665,
  0.652,
  0.639,
  0.626,
  0.613,
  0.6
 ]
}
=== dev/C ===
{
 "model_id": "C",
 "hidden_dim": 12,
 "n_samples": 20000,
 "readout": "relu",
 "notes": "",
 "n_features": 24,
 "feature_frequency": [
  0.0682,
  0.0674,
  0.0688,
  0.0684,
  0.0685,
  0.0693,
  0.0705,
  0.0704,
  0.0702,
  0.0665,
  0.0685,
  0.0684,
  0.0677,
  0.0689,
  0.0699,
  0.068,
  0.0676,
  0.0707,
  0.0674,
  0.0658,
  0.0659,
  0.0698,
  0.0705,
  0.069
 ],
 "feature_importance": [
  0.502,
  0.968,
  0.775,
  0.9,
  0.878,
  0.778,
  0.792,
  0.754,
  0.736,
  0.811,
  0.97,
  0.958,
  0.956,
  0.944,
  0.608,
  0.641,
  0.95,
  0.628,
  0.614,
  0.953,
  0.6,
  0.582,
  0.877,
  0.843
 ]
}
=== dev/D ===
{
 "model_id": "D",
 "hidden_dim": 12,
 "n_samples": 20000,
 "readout": "relu",
 "notes": "",
 "n_features": 22,
 "feature_frequency": [
  0.0506,
  0.0509,
  0.0494,
  0.0507,
  0.0492,
  0.0496,
  0.049,
  0.0481,
  0.0481,
  0.0489,
  0.0485,
  0.0499,
  0.0481,
  0.0529,
  0.0491,
  0.0494,
  0.0501,
  0.0494,
  0.0523,
  0.049,
  0.0482,
  0.0493
 ],
 "feature_importance": [
  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,
  1.0,
  1.0,
  1.0,
  1.0,
  1.0,
  1.0,
  1.0,
  1.0,
  1.0
 ]
}

step 2 · tool output (exit 0)

=== audit/P === { "model_id": "P", "hidden_dim": 12, "n_samples": 20000, "readout": "relu", "notes": "" } === audit/Q === { "model_id": "Q", "hidden_dim"…

=== audit/P ===
{
 "model_id": "P",
 "hidden_dim": 12,
 "n_samples": 20000,
 "readout": "relu",
 "notes": ""
}
=== audit/Q ===
{
 "model_id": "Q",
 "hidden_dim": 20,
 "n_samples": 20000,
 "readout": "relu",
 "notes": ""
}
=== audit/R ===
{
 "model_id": "R",
 "hidden_dim": 12,
 "n_samples": 20000,
 "readout": "relu",
 "notes": ""
}
=== audit/S ===
{
 "model_id": "S",
 "hidden_dim": 16,
 "n_samples": 20000,
 "readout": "relu",
 "notes": ""
}
=== audit/T ===
{
 "model_id": "T",
 "hidden_dim": 12,
 "n_samples": 20000,
 "readout": "relu",
 "notes": ""
}
# What is in a model directory

Each model is a small trained network whose hidden layer we study. We ship the hidden-layer activations, not the
network: `activations.npy` is an `n_samples x hidden_dim` float32 array, one row per input sampled from the model's
own input distribution. The networks were trained with an importance-weighted reconstruction objective on sparse
non-negative input features; the number of input features exceeds `hidden_dim`, so features share the hidden space.
Input distributions differ between models (feature frequencies, co-occurrence structure, importance weights, and in
one case measurement noise added to the recorded activations); `meta.json` does not describe them.

`meta.json`: `model_id`, `hidden_dim`, `n_samples`, `readout` (output nonlinearity of the trained network), `notes`.

Development models (`models/dev/*`) additionally ship ground truth, which audit models do not:
- `features.npy`: `n_features x hidden_dim` float32. Row i is the unit direction in hidden space along which input
  feature i moves the activation (the model's representation of that feature). Only features the network actually
  represents are listed.
- `meta.json` also has `n_features`, `feature_frequency` (fraction of samples in which feature i is active) and
  `feature_importance` (the weight of feature i in the training objective), aligned with the rows of `features.npy`.

`tools/recovery.py` scores a dictionary against `features.npy` (see its docstring for the exact definition); it is the
definition the audit uses to decide whether a dictionary is trustworthy.

step 3 · model

Let me look at the task structure and run the shipped baseline on all models first.

step 3 · tool output (exit 0)

/tmp/.rsi_cmd: line 1: /workspace/dicts/shipped/A.log: No such file or directory B B.log C C.log D D.log

/tmp/.rsi_cmd: line 1: /workspace/dicts/shipped/A.log: No such file or directory
B
B.log
C
C.log
D
D.log

step 4 · model

step 4 · tool output (exit 0)

{ "dictionary_score": 0.9992, "explained_variance": 0.9992, "l0": 0.933, "n_atoms": 24, "dead_atoms": 0, "accepted": true, "train_seconds": 2.4, "config…

{
 "dictionary_score": 0.9992,
 "explained_variance": 0.9992,
 "l0": 0.933,
 "n_atoms": 24,
 "dead_atoms": 0,
 "accepted": true,
 "train_seconds": 2.4,
 "config": {
  "n_atoms": 24,
  "l1": 0.03,
  "steps": 3000,
  "seed": 0
 }
}

step 4 · tool output (exit 0)

done

done

step 5 · model

step 5 · tool output (exit 0)

=== dev/A === recovery 0.9999 frac>=0.9 1.0 === dev/B === recovery 0.5833 frac>=0.9 0.0938 === dev/C === recovery 0.9877 frac>=0.9 1.0 === dev/D === recovery 0.…

=== dev/A ===
recovery 0.9999 frac>=0.9 1.0
=== dev/B ===
recovery 0.5833 frac>=0.9 0.0938
=== dev/C ===
recovery 0.9877 frac>=0.9 1.0
=== dev/D ===
recovery 0.728 frac>=0.9 0.0

step 5 · tool output (exit 0)

=== B === { "dictionary_score": 0.9971, "explained_variance": 0.9971, "l0": 9.23, "n_atoms": 24, "dead_atoms": 0, "accepted": true, "train_seconds": 2.5,…

=== B ===
{
 "dictionary_score": 0.9971,
 "explained_variance": 0.9971,
 "l0": 9.23,
 "n_atoms": 24,
 "dead_atoms": 0,
 "accepted": true,
 "train_seconds": 2.5,
 "config": {
  "n_atoms": 24,
  "l1": 0.03,
  "steps": 3000,
  "seed": 0
 }
}
=== C ===
{
 "dictionary_score": 0.9978,
 "explained_variance": 0.9978,
 "l0": 2.043,
 "n_atoms": 24,
 "dead_atoms": 0,
 "accepted": true,
 "train_seconds": 2.5,
 "config": {
  "n_atoms": 24,
  "l1": 0.03,
  "steps": 3000,
  "seed": 0
 }
}
=== D ===
{
 "dictionary_score": 0.9966,
 "explained_variance": 0.9966,
 "l0": 11.394,
 "n_atoms": 24,
 "dead_atoms": 0,
 "accepted": true,
 "train_seconds": 2.5,
 "config": {
  "n_atoms": 24,
  "l1": 0.03,
  "steps": 3000,
  "seed": 0
 }
}
=== P ===
{
 "dictionary_score": 0.9975,
 "explained_variance": 0.9975,
 "l0": 11.407,
 "n_atoms": 24,
 "dead_atoms": 0,
 "accepted": true,
 "train_seconds": 2.5,
 "config": {
  "n_atoms": 24,
  "l1": 0.03,
  "steps": 3000,
  "seed": 0
 }
}
=== Q ===
{
 "dictionary_score": 0.9978,
 "explained_variance": 0.9978,
 "l0": 1.212,
 "n_atoms": 40,
 "dead_atoms": 0,
 "accepted": true,
 "train_seconds": 2.9,
 "config": {
  "n_atoms": 40,
  "l1": 0.03,
  "steps": 3000,
  "seed": 0
 }
}
=== R ===
{
 "dictionary_score": 0.9968,
 "explained_variance": 0.9968,
 "l0": 11.018,
 "n_atoms": 24,
 "dead_atoms": 0,
 "accepted": true,
 "train_seconds": 2.6,
 "config": {
  "n_atoms": 24,
  "l1": 0.03,
  "steps": 3000,
  "seed": 0
 }
}
=== S ===
{
 "dictionary_score": 0.9984,
 "explained_variance": 0.9984,
 "l0": 15.491,
 "n_atoms": 32,
 "dead_atoms": 0,
 "accepted": true,
 "train_seconds": 2.7,
 "config": {
  "n_atoms": 32,
  "l1": 0.03,
  "steps": 3000,
  "seed": 0
 }
}
=== T ===
{
 "dictionary_score": 0.998,
 "explained_variance": 0.998,
 "l0": 10.887,
 "n_atoms": 24,
 "dead_atoms": 0,
 "accepted": true,
 "train_seconds": 2.4,
 "config": {
  "n_atoms": 24,
  "l1": 0.03,
  "steps": 3000,
  "seed": 0
 }
}

step 6 · model

The shipped method fails on dev B (recovery 0.58) and D (0.73) while being "accepted" by its own validator — the validator's score does not track recovery. Now let me explore the data and understand why, then develop a better method.

step 6 · tool output (exit 0)

=== dev/A: X (20000, 12) F (24, 12) nfeat=24 coef<0 frac: 0.036914583333333334 coef min: -0.664990864401755 frac rows with max coef > 0.9*sum: 0.6273 mea…

=== dev/A: X (20000, 12) F (24, 12) nfeat=24
  coef<0 frac: 0.036914583333333334  coef min: -0.664990864401755
  frac rows with max coef > 0.9*sum: 0.6273
  mean ||X||: 0.38699302077293396  std X: 0.16037651896476746
  max |cos| between features: 1.0
=== dev/B: X (20000, 12) F (32, 12) nfeat=32
  coef<0 frac: 0.227071875  coef min: -0.775561936477618
  frac rows with max coef > 0.9*sum: 0.90225
  mean ||X||: 0.7914361357688904  std X: 0.2645516097545624
  max |cos| between features: 0.7034474611282349
=== dev/C: X (20000, 12) F (24, 12) nfeat=24
  coef<0 frac: 0.06761666666666667  coef min: -0.6468816260170391
  frac rows with max coef > 0.9*sum: 0.50905
  mean ||X||: 0.4838577210903168  std X: 0.2105860859155655
  max |cos| between features: 0.9987539052963257
=== dev/D: X (20000, 12) F (22, 12) nfeat=22
  coef<0 frac: 0.2541818181818182  coef min: -0.9102449496524166
  frac rows with max coef > 0.9*sum: 0.8537
  mean ||X||: 0.8684146404266357  std X: 0.26294803619384766
  max |cos| between features: 0.9999998211860657

step 7 · model

step 7 · tool output (exit 0)

=== dev/A === |cos|>0.99: [(0, 20, -1.0), (1, 17, -1.0), (2, 3, -1.0), (4, 14, -1.0), (5, 23, -1.0), (6, 11, -1.0), (7, 16, -1.0), (8, 19, -1.0), (9, 10, -1.0…

=== dev/A ===
  |cos|>0.99: [(0, 20, -1.0), (1, 17, -1.0), (2, 3, -1.0), (4, 14, -1.0), (5, 23, -1.0), (6, 11, -1.0), (7, 16, -1.0), (8, 19, -1.0), (9, 10, -1.0), (12, 15, -1.0), (13, 21, -1.0), (18, 22, -1.0)]
  |cos|>0.95: [(0, 20, -1.0), (1, 17, -1.0), (2, 3, -1.0), (4, 14, -1.0), (5, 23, -1.0), (6, 11, -1.0), (7, 16, -1.0), (8, 19, -1.0), (9, 10, -1.0), (12, 15, -1.0), (13, 21, -1.0), (18, 22, -1.0)]
  |cos|>0.9: [(0, 20, -1.0), (1, 17, -1.0), (2, 3, -1.0), (4, 14, -1.0), (5, 23, -1.0), (6, 11, -1.0), (7, 16, -1.0), (8, 19, -1.0), (9, 10, -1.0), (12, 15, -1.0), (13, 21, -1.0), (18, 22, -1.0)]
=== dev/B ===
  |cos|>0.99: []
  |cos|>0.95: []
  |cos|>0.9: []
=== dev/C ===
  |cos|>0.99: [(0, 6, -0.997), (1, 22, -0.996), (2, 12, -0.996), (3, 7, -0.999), (4, 13, -0.997), (5, 8, -0.999), (9, 18, -0.997), (10, 23, -0.996), (11, 14, -0.997), (15, 19, -0.998), (16, 20, -0.998), (17, 21, -0.997)]
  |cos|>0.95: [(0, 6, -0.997), (1, 22, -0.996), (2, 12, -0.996), (3, 7, -0.999), (4, 13, -0.997), (5, 8, -0.999), (9, 18, -0.997), (10, 23, -0.996), (11, 14, -0.997), (15, 19, -0.998), (16, 20, -0.998), (17, 21, -0.997)]
  |cos|>0.9: [(0, 6, -0.997), (1, 22, -0.996), (2, 12, -0.996), (3, 7, -0.999), (4, 13, -0.997), (5, 8, -0.999), (9, 18, -0.997), (10, 23, -0.996), (11, 14, -0.997), (15, 19, -0.998), (16, 20, -0.998), (17, 21, -0.997)]
=== dev/D ===
  |cos|>0.99: [(0, 12, -1.0), (1, 7, -1.0), (2, 11, -1.0), (3, 18, -1.0), (4, 5, -1.0), (8, 10, -1.0), (9, 15, -1.0), (14, 19, -1.0), (16, 20, -1.0), (17, 21, -1.0)]
  |cos|>0.95: [(0, 12, -1.0), (1, 7, -1.0), (2, 11, -1.0), (3, 18, -1.0), (4, 5, -1.0), (8, 10, -1.0), (9, 15, -1.0), (14, 19, -1.0), (16, 20, -1.0), (17, 21, -1.0)]
  |cos|>0.9: [(0, 12, -1.0), (1, 7, -1.0), (2, 11, -1.0), (3, 18, -1.0), (4, 5, -1.0), (8, 10, -1.0), (9, 15, -1.0), (14, 19, -1.0), (16, 20, -1.0), (17, 21, -1.0)]

step 8 · model

Critical discovery: dev A, C, D features come in **antiparallel pairs** (cos = −1), while B has 32 well-separated features in only 12 dims (SAE had just 24 atoms < 32 features). Let me build a dev harness and test fixes.

step 8 · tool output (exit 0)

written

written

step 9 · model

step 9 · tool output (exit 0)

B {'n_atoms': 48, 'l1': 0.03, 'steps': 3000} -> rec 0.8001 frac>=.9 0.250 score 0.9958 l0 15.38 dead 0 B {'n_atoms': 48, 'l1': 0.01, 'steps': 3000} -> rec 0.714…

B {'n_atoms': 48, 'l1': 0.03, 'steps': 3000} -> rec 0.8001 frac>=.9 0.250 score 0.9958 l0 15.38 dead 0
B {'n_atoms': 48, 'l1': 0.01, 'steps': 3000} -> rec 0.7143 frac>=.9 0.031 score 0.9994 l0 18.85 dead 0
B {'n_atoms': 48, 'l1': 0.003, 'steps': 3000} -> rec 0.6526 frac>=.9 0.031 score 0.9999 l0 20.83 dead 0
B {'n_atoms': 48, 'l1': 0.1, 'steps': 3000} -> rec 0.9814 frac>=.9 0.969 score 0.9499 l0 8.01 dead 0
B {'n_atoms': 96, 'l1': 0.03, 'steps': 3000} -> rec 0.6941 frac>=.9 0.125 score 0.9944 l0 18.95 dead 0

D {'n_atoms': 48, 'l1': 0.03, 'steps': 3000} -> rec 0.7563 frac>=.9 0.000 score 0.9957 l0 19.72 dead 0
D {'n_atoms': 48, 'l1': 0.01, 'steps': 3000} -> rec 0.6485 frac>=.9 0.000 score 0.9994 l0 21.13 dead 0
D {'n_atoms': 48, 'l1': 0.003, 'steps': 3000} -> rec 0.5804 frac>=.9 0.000 score 0.9999 l0 21.36 dead 0
D {'n_atoms': 48, 'l1': 0.1, 'steps': 3000} -> rec 0.8708 frac>=.9 0.409 score 0.9608 l0 16.79 dead 0
D {'n_atoms': 96, 'l1': 0.03, 'steps': 3000} -> rec 0.6057 frac>=.9 0.000 score 0.9934 l0 26.39 dead 0

elapsed 20.855032920837402

step 10 · model

Higher L1 helps a lot. Let me push further on D and check A/C at high L1 too.

step 10 · tool output (exit 0)

D l1=0.1 steps=8000 -> rec 0.8463 frac>=.9 0.227 score 0.9637 l0 17.48 dead 0 D l1=0.2 steps=8000 -> rec 0.8351 frac>=.9 0.182 score 0.8828 l0 12.78 dead 0 D l1…

D l1=0.1 steps=8000 -> rec 0.8463 frac>=.9 0.227 score 0.9637 l0 17.48 dead 0
D l1=0.2 steps=8000 -> rec 0.8351 frac>=.9 0.182 score 0.8828 l0 12.78 dead 0
D l1=0.3 steps=8000 -> rec 0.8327 frac>=.9 0.273 score 0.7890 l0 9.46 dead 0

A l1=0.1 steps=8000 -> rec 0.9971 frac>=.9 1.000 score 0.9885 l0 1.06 dead 5
A l1=0.2 steps=8000 -> rec 0.9895 frac>=.9 1.000 score 0.9587 l0 1.03 dead 8
A l1=0.3 steps=8000 -> rec 0.9855 frac>=.9 1.000 score 0.9142 l0 0.90 dead 6

C l1=0.1 steps=8000 -> rec 0.9691 frac>=.9 0.958 score 0.9858 l0 2.32 dead 1
C l1=0.2 steps=8000 -> rec 0.9609 frac>=.9 0.958 score 0.9612 l0 2.18 dead 1
C l1=0.3 steps=8000 -> rec 0.9477 frac>=.9 0.958 score 0.9257 l0 1.68 dead 4

B l1=0.1 steps=8000 -> rec 0.9934 frac>=.9 1.000 score 0.9496 l0 7.44 dead 3
B l1=0.2 steps=8000 -> rec 0.9917 frac>=.9 1.000 score 0.9112 l0 3.45 dead 4
B l1=0.3 steps=8000 -> rec 0.9836 frac>=.9 1.000 score 0.8746 l0 2.85 dead 1

elapsed 58.606048345565796

step 11 · model

D is structurally hard for the SAE (rotational symmetry from uniform importance + antiparallel pairs). Let me check seed variance and whether the training loss selects good solutions, and build the geometric method in parallel.

step 11 · tool output (exit 0)

D seed=0 rec 0.8708 frac>=.9 0.409 l0 16.79 score 0.9608 full_loss 0.20828 mse 0.032452 D seed=1 rec 0.8507 frac>=.9 0.455 l0 16.60 score 0.9616 full_loss 0.207…

D seed=0 rec 0.8708 frac>=.9 0.409 l0 16.79 score 0.9608 full_loss 0.20828 mse 0.032452
D seed=1 rec 0.8507 frac>=.9 0.455 l0 16.60 score 0.9616 full_loss 0.20784 mse 0.031798
D seed=2 rec 0.8292 frac>=.9 0.182 l0 16.86 score 0.9619 full_loss 0.20877 mse 0.031559
D seed=3 rec 0.8174 frac>=.9 0.136 l0 16.98 score 0.9594 full_loss 0.20969 mse 0.033609
D seed=4 rec 0.7801 frac>=.9 0.182 l0 16.81 score 0.9587 full_loss 0.21195 mse 0.034254
D seed=5 rec 0.8157 frac>=.9 0.182 l0 16.76 score 0.9603 full_loss 0.20981 mse 0.032870

B seed=0 rec 0.9814 frac>=.9 0.969 l0 8.01 score 0.9499 full_loss 0.15256 mse 0.042036
B seed=1 rec 0.9811 frac>=.9 0.969 l0 7.69 score 0.9500 full_loss 0.15241 mse 0.041963
B seed=2 rec 0.9691 frac>=.9 0.938 l0 7.95 score 0.9526 full_loss 0.15298 mse 0.039813
B seed=3 rec 0.9847 frac>=.9 1.000 l0 7.68 score 0.9498 full_loss 0.15119 mse 0.042144
B seed=4 rec 0.9816 frac>=.9 1.000 l0 7.70 score 0.9492 full_loss 0.15233 mse 0.042641
B seed=5 rec 0.9831 frac>=.9 0.969 l0 7.67 score 0.9490 full_loss 0.15114 mse 0.042835

elapsed 25.529905557632446

step 12 · model

The SAE cannot crack D (0.78–0.87 all seeds) — a structural symmetry problem. Let me implement a geometric extreme-ray/pure-sample method, which should handle D's paired structure and B's superposition alike.

step 12 · tool output (exit 1)

A: K=456 rec=0.7060 frac>=.9=0.000 counts min=8 max=27 B: K=8 rec=0.1500 frac>=.9=0.000 counts min=8 max=9 C: K=296 rec=0.7451 frac>=.9=0.083 counts min=8 max=4…

A: K=456 rec=0.7060 frac>=.9=0.000 counts min=8 max=27
B: K=8 rec=0.1500 frac>=.9=0.000 counts min=8 max=9
C: K=296 rec=0.7451 frac>=.9=0.083 counts min=8 max=41
Traceback (most recent call last):
  File "<stdin>", line 9, in <module>
  File "/usr/local/lib/python3.12/dist-packages/numpy/_core/_methods.py", line 49, in _amin
    return umr_minimum(a, axis, None, out, keepdims, initial, where)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: zero-size array to reduction operation minimum which has no identity

step 13 · model

The geometric v1 fails (argmax winners concentrate on few directions). D's paired-axis symmetry suggests an ICA-style approach instead. Let me check the axis orthogonality structure first.

step 13 · tool output (exit 0)

A: n_axes=12 max|offdiag of axis Gram|=0.0004 cov eigvals: [0.027 0.027 0.026 0.026 0.026 0.026 0.026 0.026 0.025 0.025 0.024 0.024] B: n_axes=20 max|offdiag…

A: n_axes=12 max|offdiag of axis Gram|=0.0004
   cov eigvals: [0.027 0.027 0.026 0.026 0.026 0.026 0.026 0.026 0.025 0.025 0.024 0.024]
B: n_axes=20 max|offdiag of axis Gram|=0.7009
   cov eigvals: [0.078 0.074 0.073 0.072 0.071 0.07  0.069 0.068 0.068 0.067 0.065 0.065]
C: n_axes=12 max|offdiag of axis Gram|=0.0728
   cov eigvals: [0.095 0.086 0.07  0.065 0.053 0.047 0.027 0.018 0.018 0.018 0.018 0.017]
D: n_axes=12 max|offdiag of axis Gram|=0.0008
   cov eigvals: [0.074 0.074 0.073 0.073 0.072 0.071 0.071 0.07  0.069 0.069 0.057 0.055]

step 14 · model

step 14 · tool output (exit 0)

A: K=24 rec=0.9999 frac>=.9=1.000 B: K=24 rec=0.6103 frac>=.9=0.219 C: K=24 rec=0.9775 frac>=.9=1.000 D: K=24 rec=0.9992 frac>=.9=1.000 elapsed 7.48120093345642…

A: K=24 rec=0.9999 frac>=.9=1.000
B: K=24 rec=0.6103 frac>=.9=0.219
C: K=24 rec=0.9775 frac>=.9=1.000
D: K=24 rec=0.9992 frac>=.9=1.000
elapsed 7.481200933456421

step 15 · model

step 15 · tool output (exit 1)

Traceback (most recent call last): File "<stdin>", line 7, in <module> File "/workspace/dev/pipeline.py", line 49, in extract cs = float(v @ c) …

Traceback (most recent call last):
  File "<stdin>", line 7, in <module>
  File "/workspace/dev/pipeline.py", line 49, in extract
    cs = float(v @ c)
               ~~^~~
ValueError: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 0 is different from 12)

step 16 · model

The skew-based sign selection breaks on symmetric paired axes (skew ≈ 0). Let me debug and fix the sign logic.

step 16 · tool output (exit 0)

Cw shape: (12, 12) skew+ -0.16 skew- 0.16 skew+ 0.397 skew- -0.397 skew+ -0.252 skew- 0.252 skew+ -0.134 skew- 0.134 skew+ 0.013 skew- -0.013 skew+ -0.005 skew-…

Cw shape: (12, 12)
skew+ -0.16 skew- 0.16
skew+ 0.397 skew- -0.397
skew+ -0.252 skew- 0.252
skew+ -0.134 skew- 0.134
skew+ 0.013 skew- -0.013
skew+ -0.005 skew- 0.005
skew+ -0.33 skew- 0.33
skew+ -0.265 skew- 0.265

step 17 · model

Fixing the pipeline: keep both ICA signs always, dedupe with signed clustering at 0.95, kurtosis-based cap only if needed.

step 17 · tool output (exit 0)

A: K=42 info={'n_sae': 48, 'n_ica': 24, 'n_pool': 42} rec=0.9979 frac>=.9=1.000 minmatch=0.989 B: K=48 info={'n_sae': 48, 'n_ica': 24, 'n_pool': 66} rec=0.9922 …

A: K=42 info={'n_sae': 48, 'n_ica': 24, 'n_pool': 42} rec=0.9979 frac>=.9=1.000 minmatch=0.989
B: K=48 info={'n_sae': 48, 'n_ica': 24, 'n_pool': 66} rec=0.9922 frac>=.9=1.000 minmatch=0.966
C: K=48 info={'n_sae': 48, 'n_ica': 24, 'n_pool': 53} rec=0.9845 frac>=.9=1.000 minmatch=0.971
D: K=48 info={'n_sae': 48, 'n_ica': 24, 'n_pool': 72} rec=0.9992 frac>=.9=1.000 minmatch=0.998
elapsed 22.82405924797058

step 18 · model

The pipeline hits ≥0.98 on all four dev models. Now the hard part: estimating recovery on audit models without truth. Let me build the diagnostic suite (seed stability, split-half stability, cross-method agreement, kurtosis) and calibrate it on dev.

step 18 · tool output (exit 0)

=== A: rec=0.9979 K=42 atom true-match vs stab_seed vs stab_half vs kurt (first 15 atoms): atom 0 tm=0.991 ss=0.987 sh=0.992 kurt=20.3 atom 1 tm=1.000 s…

=== A: rec=0.9979 K=42
  atom true-match vs stab_seed vs stab_half vs kurt (first 15 atoms):
   atom 0 tm=0.991 ss=0.987 sh=0.992 kurt=20.3
   atom 1 tm=1.000 ss=0.999 sh=1.000 kurt=20.8
   atom 2 tm=0.990 ss=0.988 sh=0.988 kurt=19.6
   atom 3 tm=0.999 ss=1.000 sh=1.000 kurt=21.0
   atom 4 tm=0.999 ss=1.000 sh=1.000 kurt=20.2
   atom 5 tm=1.000 ss=1.000 sh=1.000 kurt=20.1
   atom 6 tm=0.996 ss=0.995 sh=0.996 kurt=20.0
   atom 7 tm=0.999 ss=1.000 sh=1.000 kurt=19.9
   atom 8 tm=0.999 ss=1.000 sh=1.000 kurt=19.8
   atom 9 tm=1.000 ss=1.000 sh=1.000 kurt=19.5
   atom10 tm=0.999 ss=1.000 sh=1.000 kurt=19.4
   atom11 tm=1.000 ss=1.000 sh=1.000 kurt=19.3
   atom12 tm=0.999 ss=1.000 sh=1.000 kurt=21.0
   atom13 tm=1.000 ss=0.999 sh=1.000 kurt=20.8
   atom14 tm=1.000 ss=1.000 sh=1.000 kurt=20.4
  atoms tm<0.8: 12; their stab_seed: [0.678 0.702 0.8   0.621 0.742 0.771 0.564 0.701 0.717 0.654 0.746 0.592]
  atoms tm>=0.8: stab_seed min 0.833
=== B: rec=0.9922 K=48
  atom true-match vs stab_seed vs stab_half vs kurt (first 15 atoms):
   atom 0 tm=0.753 ss=0.990 sh=1.000 kurt=5.2
   atom 1 tm=0.998 ss=0.998 sh=1.000 kurt=5.2
   atom 2 tm=0.779 ss=0.968 sh=0.998 kurt=4.7
   atom 3 tm=0.709 ss=0.914 sh=0.999 kurt=4.7
   atom 4 tm=0.999 ss=0.997 sh=1.000 kurt=4.7
   atom 5 tm=0.995 ss=0.988 sh=0.997 kurt=4.7
   atom 6 tm=0.991 ss=0.993 sh=0.994 kurt=4.5
   atom 7 tm=0.705 ss=0.965 sh=0.985 kurt=4.5
   atom 8 tm=0.995 ss=0.988 sh=0.994 kurt=4.5
   atom 9 tm=0.996 ss=0.983 sh=0.999 kurt=4.5
   atom10 tm=0.990 ss=0.996 sh=0.995 kurt=4.5
   atom11 tm=0.763 ss=0.933 sh=0.989 kurt=4.5
   atom12 tm=0.987 ss=0.994 sh=0.995 kurt=4.4
   atom13 tm=0.997 ss=0.986 sh=0.995 kurt=4.4
   atom14 tm=0.723 ss=0.722 sh=0.980 kurt=4.4
  atoms tm<0.8: 11; their stab_seed: [0.99  0.968 0.914 0.965 0.933 0.722 0.946 0.847 0.978 0.862 0.85 ]
  atoms tm>=0.8: stab_seed min 0.716
=== C: rec=0.9845 K=48
  atom true-match vs stab_seed vs stab_half vs kurt (first 15 atoms):
   atom 0 tm=0.981 ss=0.990 sh=0.992 kurt=11.3
   atom 1 tm=0.879 ss=0.924 sh=0.928 kurt=11.1
   atom 2 tm=0.985 ss=0.991 sh=0.998 kurt=10.9
   atom 3 tm=0.975 ss=0.991 sh=0.993 kurt=10.6
   atom 4 tm=0.994 ss=0.984 sh=0.992 kurt=10.5
   atom 5 tm=0.981 ss=0.969 sh=0.999 kurt=10.5
   atom 6 tm=0.994 ss=0.999 sh=0.992 kurt=10.5
   atom 7 tm=0.992 ss=0.989 sh=0.995 kurt=10.3
   atom 8 tm=0.972 ss=0.999 sh=0.976 kurt=10.3
   atom 9 tm=0.984 ss=1.000 sh=0.998 kurt=10.3
   atom10 tm=0.990 ss=0.988 sh=0.989 kurt=10.3
   atom11 tm=0.981 ss=0.997 sh=1.000 kurt=10.2
   atom12 tm=0.974 ss=0.961 sh=0.992 kurt=10.2
   atom13 tm=0.952 ss=0.994 sh=0.992 kurt=10.1
   atom14 tm=0.991 ss=0.994 sh=0.993 kurt=10.1
  atoms tm<0.8: 3; their stab_seed: [0.77  0.712 0.863]
  atoms tm>=0.8: stab_seed min 0.886
=== D: rec=0.9992 K=48
  atom true-match vs stab_seed vs stab_half vs kurt (first 15 atoms):
   atom 0 tm=0.999 ss=1.000 sh=1.000 kurt=3.4
   atom 1 tm=0.999 ss=1.000 sh=1.000 kurt=3.4
   atom 2 tm=0.999 ss=1.000 sh=0.999 kurt=3.2
   atom 3 tm=0.999 ss=1.000 sh=0.999 kurt=3.2
   atom 4 tm=0.999 ss=1.000 sh=0.999 kurt=3.2
   atom 5 tm=0.999 ss=1.000 sh=0.999 kurt=3.2
   atom 6 tm=1.000 ss=1.000 sh=0.999 kurt=3.2
   atom 7 tm=1.000 ss=1.000 sh=0.999 kurt=3.2
   atom 8 tm=0.999 ss=1.000 sh=0.999 kurt=3.2
   atom 9 tm=0.999 ss=1.000 sh=0.999 kurt=3.2
   atom10 tm=0.999 ss=1.000 sh=0.999 kurt=3.1
   atom11 tm=0.999 ss=1.000 sh=0.999 kurt=3.1
   atom12 tm=0.999 ss=1.000 sh=1.000 kurt=3.1
   atom13 tm=0.999 ss=1.000 sh=1.000 kurt=3.1
   atom14 tm=0.999 ss=1.000 sh=0.999 kurt=3.1
  atoms tm<0.8: 17; their stab_seed: [1.    1.    0.75  0.776 0.789 0.801 0.677 0.725 0.691 0.751 0.639 0.797
 0.677 0.688 0.649 0.825 0.704]
  atoms tm>=0.8: stab_seed min 0.823
elapsed 83.14822173118591

step 19 · model

Per-atom stability tracks true match for *good* atoms, but B has stable blends (extra atoms, harmless for recovery). The estimation key is: confirmed directions + blend detection. Let me run the full diagnostics on the audit models.

step 19 · tool output (exit 0)

=== audit/P: dim=12 K=48 igs={'n_sae': 48, 'n_ica': 24, 'n_pool': 72} eigvals: [0.1052 0.1033 0.1004 0.1 0.0986 0.0972 0.0944 0.0938 0.0922 0.0917 0.091 …

=== audit/P: dim=12 K=48 igs={'n_sae': 48, 'n_ica': 24, 'n_pool': 72}
   eigvals: [0.1052 0.1033 0.1004 0.1    0.0986 0.0972 0.0944 0.0938 0.0922 0.0917
 0.091  0.0894]
   stab_seed: min=0.547 med=0.912 frac>=0.95: 0.40
   stab_half: min=0.696 med=0.921 frac>=0.95: 0.40
   both>=0.95: 18/48   kurt med=2.5 min=1.5
=== audit/Q: dim=20 K=71 igs={'n_sae': 80, 'n_ica': 40, 'n_pool': 71}
   eigvals: [0.0213 0.0209 0.0205 0.0204 0.0202 0.0197 0.0196 0.0196 0.0194 0.0193
 0.0192 0.019  0.0188 0.0186 0.0184 0.0182 0.0102 0.0098 0.0095 0.0092]
   stab_seed: min=0.454 med=0.992 frac>=0.95: 0.58
   stab_half: min=0.456 med=0.996 frac>=0.95: 0.61
   both>=0.95: 41/71   kurt med=26.5 min=2.6
=== audit/R: dim=12 K=48 igs={'n_sae': 48, 'n_ica': 24, 'n_pool': 68}
   eigvals: [0.1037 0.0973 0.0849 0.0817 0.0798 0.0773 0.0737 0.0683 0.0544 0.0521
 0.0517 0.0493]
   stab_seed: min=0.458 med=0.951 frac>=0.95: 0.50
   stab_half: min=0.546 med=0.955 frac>=0.95: 0.52
   both>=0.95: 21/48   kurt med=5.3 min=2.9
=== audit/S: dim=16 K=64 igs={'n_sae': 64, 'n_ica': 32, 'n_pool': 96}
   eigvals: [0.161  0.1602 0.1593 0.1573 0.157  0.1565 0.1534 0.1532 0.152  0.1491
 0.1419 0.1404 0.139  0.1374 0.1358 0.1346]
   stab_seed: min=0.520 med=0.858 frac>=0.95: 0.50
   stab_half: min=0.521 med=0.909 frac>=0.95: 0.50
   both>=0.95: 32/64   kurt med=0.3 min=0.1
=== audit/T: dim=12 K=48 igs={'n_sae': 48, 'n_ica': 24, 'n_pool': 72}
   eigvals: [0.1333 0.1255 0.1232 0.1188 0.1176 0.1158 0.1142 0.1137 0.113  0.1125
 0.1099 0.1092]
   stab_seed: min=0.518 med=0.879 frac>=0.95: 0.27
   stab_half: min=0.713 med=0.917 frac>=0.95: 0.35
   both>=0.95: 10/48   kurt med=1.8 min=1.5
elapsed 123.53635096549988

step 20 · model

Q looks excellent (stable, sparse, A-like); S looks problematic (kurtosis ~0.3 — no sparse directions found); P/R/T are intermediate. Let me probe the regimes more deeply.

step 20 · tool output (exit 0)

P: dim=12 axes=12 kurt[med=2.6 max=3.7] sym med=0.98 antipairs=0 norm med=0.993 frac||x||<0.3med=0.002 Q: dim=20 axes=20 kurt[med=28.2 max=57.3] sym med=0.99 an…

P: dim=12 axes=12 kurt[med=2.6 max=3.7] sym med=0.98 antipairs=0 norm med=0.993 frac||x||<0.3med=0.002
Q: dim=20 axes=20 kurt[med=28.2 max=57.3] sym med=0.99 antipairs=0 norm med=0.380 frac||x||<0.3med=0.375
R: dim=12 axes=12 kurt[med=6.1 max=8.0] sym med=0.87 antipairs=0 norm med=0.815 frac||x||<0.3med=0.020
S: dim=16 axes=16 kurt[med=0.6 max=0.8] sym med=0.97 antipairs=0 norm med=1.496 frac||x||<0.3med=0.000
T: dim=12 axes=12 kurt[med=2.0 max=2.3] sym med=0.97 antipairs=0 norm med=1.105 frac||x||<0.3med=0.052
A: dim=12 axes=12 kurt[med=20.2 max=21.1] sym med=0.99 antipairs=0 norm med=0.286 frac||x||<0.3med=0.408
B: dim=12 axes=12 kurt[med=4.2 max=5.2] sym med=0.97 antipairs=0 norm med=0.838 frac||x||<0.3med=0.164
C: dim=12 axes=12 kurt[med=10.2 max=11.3] sym med=0.98 antipairs=0 norm med=0.123 frac||x||<0.3med=0.494
D: dim=12 axes=12 kurt[med=3.1 max=3.4] sym med=0.98 antipairs=0 norm med=0.818 frac||x||<0.3med=0.000

step 21 · model

All audit models show symmetric heavy-tailed axes (paired structure). S has almost no kurtosis (0.6) — let me inspect S's raw structure (relu-ed? discrete levels? noise?) to decide if anything can recover it.

step 21 · tool output (exit 0)

=== P: coord min [-1.636 -1.332 -1.627 -1.619 -1.87 -1.543] coord max [1.281 1.416 1.377] frac coords negative: 0.501 mean [ 0. -0.001 -0.001 0. 0…

=== P: coord min [-1.636 -1.332 -1.627 -1.619 -1.87  -1.543] coord max [1.281 1.416 1.377]
   frac coords negative: 0.501   mean [ 0.    -0.001 -0.001  0.     0.004 -0.003]
   coord kurt: [0.52 0.84 0.82 1.56 0.9  0.95 0.53 0.72 0.9  0.52 1.2  0.87]
=== Q: coord min [-0.788 -0.859 -0.962 -0.797 -0.705 -0.773] coord max [0.773 0.792 0.76 ]
   frac coords negative: 0.342   mean [-0.006 -0.004 -0.012  0.007  0.002 -0.011]
   coord kurt: [2.86 3.51 5.72 3.71 2.98 2.78 3.91 4.55 3.18 3.26 3.09 4.17 2.48 3.47
 3.83 4.04 2.57 3.15 4.99 5.07]
=== R: coord min [-1.409 -1.366 -1.341 -1.435 -1.318 -1.628] coord max [1.315 1.234 1.63 ]
   frac coords negative: 0.502   mean [-0.012  0.012 -0.007  0.002 -0.001 -0.021]
   coord kurt: [1.32 1.96 2.02 2.28 1.57 3.55 1.51 1.47 1.48 1.64 1.31 1.24]
=== S: coord min [-1.527 -1.671 -1.548 -1.551 -1.451 -1.507] coord max [1.474 1.711 1.475]
   frac coords negative: 0.503   mean [-0.01  -0.016 -0.015 -0.003  0.034 -0.006]
   coord kurt: [0.05 0.12 0.04 0.23 0.05 0.11 0.06 0.09 0.15 0.14 0.03 0.09 0.18 0.08
 0.11 0.02]
=== T: coord min [-1.68  -1.625 -1.63  -1.665 -1.745 -1.551] coord max [1.571 1.722 1.723]
   frac coords negative: 0.495   mean [ 0.001 -0.001 -0.004  0.008 -0.    -0.008]
   coord kurt: [0.62 0.98 0.6  0.97 0.71 0.74 0.7  0.68 0.83 0.8  0.9  0.79]
=== A: coord min [-0.932 -1.044 -1.016 -0.916 -1.092 -1.147] coord max [0.871 0.989 1.155]
   frac coords negative: 0.314   mean [-0.002 -0.002 -0.     0.001 -0.001 -0.001]
   coord kurt: [4.26 4.19 4.75 2.84 3.68 4.04 3.01 3.96 2.65 3.38 3.91 2.84]
=== D: coord min [-1.294 -1.227 -1.166 -1.373 -1.31  -1.296] coord max [1.497 1.151 1.2  ]
   frac coords negative: 0.503   mean [-0.01   0.005 -0.012 -0.012 -0.01  -0.004]
   coord kurt: [0.71 0.46 0.51 0.57 1.3  0.79 0.6  0.88 0.44 0.48 0.97 0.57]

step 22 · model

S is nearly Gaussian everywhere. P/R/T look D-like but with weaker sparsity. Let me build an enhanced kurtosis-based pursuit (handles sub-Gaussian too) plus an overcomplete axis count probe, and validate on dev first.

step 22 · tool output (exit 0)

A: fastica2 rec=0.9998 frac>=.9=1.000 | overcomplete local maxima: 12 kurts=[21.1 21. 20.8 20.4 20.3 20.3 20.2 20. 19.8 19.5 19.4 19.4] B: fastica2 rec=0.6083…

A: fastica2 rec=0.9998 frac>=.9=1.000 | overcomplete local maxima: 12 kurts=[21.1 21.  20.8 20.4 20.3 20.3 20.2 20.  19.8 19.5 19.4 19.4]
B: fastica2 rec=0.6083 frac>=.9=0.344 | overcomplete local maxima: 15 kurts=[5.2 4.8 4.8 4.7 4.7 4.7 4.5 4.5 4.5 4.4 4.3 4.1 3.9 3.9 3.8]
C: fastica2 rec=0.9803 frac>=.9=1.000 | overcomplete local maxima: 12 kurts=[14.  13.7 12.8 12.8 11.7 11.4 11.2 11.1 11.  10.7 10.7 10.7]
D: fastica2 rec=0.9988 frac>=.9=1.000 | overcomplete local maxima: 12 kurts=[3.4 3.2 3.2 3.2 3.2 3.1 3.1 3.1 3.  2.9 2.7 2.7]
elapsed 81.26830458641052

step 23 · model

fastica2 validates on dev, and the whitened kurtosis landscape has exactly *m* local maxima for paired-axis models — a strong regime detector. Let me run it on the audit models.

step 23 · tool output (exit 0)

P: dim=12 local maxima=14 kurts: [3.74 3.73 3.69 3.65 3.58 2.72 2.68 2.66 2.62 2.61 2.57 2.55 2.44 2.38] counts: [27 36 31 30 35 36 56 34 20 36 24 21 8 …

P: dim=12 local maxima=14
   kurts: [3.74 3.73 3.69 3.65 3.58 2.72 2.68 2.66 2.62 2.61 2.57 2.55 2.44 2.38]
   counts: [27 36 31 30 35 36 56 34 20 36 24 21  8  6]
Q: dim=20 local maxima=20
   kurts: [57.35 57.06 56.96 54.64 30.07 29.23 28.62 28.55 28.2  28.18 28.17 27.71
 27.44 27.31 27.18 26.98 26.93 26.7  25.96 25.4 ]
   counts: [50 62 44 44 15 25  7 14 12 13  5 14 18 13  8  7 13  7 19 10]
R: dim=12 local maxima=9
   kurts: [7.96 7.23 6.87 6.76 6.41 6.38 6.07 6.07 5.6 ]
   counts: [43 42 69 38 60 43 40 34 31]
S: dim=16 local maxima=16
   kurts: [0.79 0.78 0.76 0.75 0.73 0.72 0.71 0.66 0.62 0.62 0.53 0.46 0.45 0.45
 0.43 0.4 ]
   counts: [33 44 33 37 31 20 33 26 29 38 21 13 11  7 13 11]
T: dim=12 local maxima=18
   kurts: [2.36 2.28 2.28 2.28 2.27 2.26 2.17 2.16 2.15 2.14 2.13 2.11 2.11 2.11
 2.11 2.08 2.07 2.05]
   counts: [32 51 19 28 31 15 37 35  7  9 14 11 24 13 11 24 16 23]
elapsed 96.22316694259644

step 24 · model

Regime map: Q = clean 20-axis paired (like A); R = 9-axis paired; P = 12–14-axis paired (D-like); T = overcomplete paired (~18 axes in 12 dims, like B but paired); S = near-Gaussian (kurt ≤0.8). Let me build the final consensus extractor v2 and validate on dev.

step 24 · tool output (exit 0)

A: K=42 {'n_sae': 48, 'n_ica': 23, 'n_oc': 12, 'n_final': 42} rec=0.9978 frac>=.9=1.000 min=0.988 B: K=48 {'n_sae': 48, 'n_ica': 18, 'n_oc': 15, 'n_final': 48} …

A: K=42 {'n_sae': 48, 'n_ica': 23, 'n_oc': 12, 'n_final': 42} rec=0.9978 frac>=.9=1.000 min=0.988
B: K=48 {'n_sae': 48, 'n_ica': 18, 'n_oc': 15, 'n_final': 48} rec=0.9922 frac>=.9=1.000 min=0.966
C: K=48 {'n_sae': 48, 'n_ica': 25, 'n_oc': 12, 'n_final': 48} rec=0.9813 frac>=.9=1.000 min=0.940
D: K=48 {'n_sae': 48, 'n_ica': 21, 'n_oc': 12, 'n_final': 48} rec=0.9988 frac>=.9=1.000 min=0.998
elapsed 212.5847406387329

step 25 · model

v2 validates on dev (all ≥0.98). Now run it on the audit models with two different seed sets for stability, and save candidate dictionaries.

step 25 · tool output (exit 0)

P: K=48 {'n_sae': 48, 'n_ica': 21, 'n_oc': 14, 'n_final': 48} ss: min=0.647 q25=0.826 med=0.936 frac>=0.95=0.48 frac>=0.9=0.56 kurt: med=2.54 frac<1: 0.00…

P: K=48 {'n_sae': 48, 'n_ica': 21, 'n_oc': 14, 'n_final': 48}
   ss: min=0.647 q25=0.826 med=0.936 frac>=0.95=0.48 frac>=0.9=0.56
   kurt: med=2.54 frac<1: 0.00
Q: K=71 {'n_sae': 80, 'n_ica': 37, 'n_oc': 20, 'n_final': 71}
   ss: min=0.407 q25=0.752 med=0.990 frac>=0.95=0.58 frac>=0.9=0.63
   kurt: med=26.55 frac<1: 0.00
R: K=48 {'n_sae': 48, 'n_ica': 20, 'n_oc': 9, 'n_final': 48}
   ss: min=0.700 q25=0.862 med=0.953 frac>=0.95=0.54 frac>=0.9=0.65
   kurt: med=5.46 frac<1: 0.00
S: K=64 {'n_sae': 64, 'n_ica': 29, 'n_oc': 16, 'n_final': 64}
   ss: min=0.491 q25=0.595 med=0.871 frac>=0.95=0.50 frac>=0.9=0.50
   kurt: med=0.28 frac<1: 1.00
T: K=48 {'n_sae': 48, 'n_ica': 26, 'n_oc': 18, 'n_final': 48}
   ss: min=0.632 q25=0.843 med=0.998 frac>=0.95=0.58 frac>=0.9=0.67
   kurt: med=1.90 frac<1: 0.00
elapsed 753.6705124378204

step 26 · model

Now let me calibrate the estimator on dev: build cross-seed consensus dictionaries for dev models and measure how consensus support/stability maps to true per-atom match and feature completeness.

step 26 · tool output (exit 1)

Traceback (most recent call last): File "<stdin>", line 18, in <module> NameError: name 'A1n' is not defined. Did you mean: 'A1'?

Traceback (most recent call last):
  File "<stdin>", line 18, in <module>
NameError: name 'A1n' is not defined. Did you mean: 'A1'?

step 27 · model

step 27 · tool output (exit 1)

A: rec=0.9978 n_true=24 n_cons=24 feat>=0.9: 24/24 >=0.8: 24/24 cons->true>=0.9: 24/24 cons_ss med=0.999 min=0.941 blend cons dirs (ctm<0.9): 0, their ss…

A: rec=0.9978 n_true=24 n_cons=24 feat>=0.9: 24/24 >=0.8: 24/24
   cons->true>=0.9: 24/24  cons_ss med=0.999 min=0.941
   blend cons dirs (ctm<0.9): 0, their ss: []
B: rec=0.9922 n_true=32 n_cons=40 feat>=0.9: 32/32 >=0.8: 32/32
   cons->true>=0.9: 33/40  cons_ss med=0.993 min=0.953
   blend cons dirs (ctm<0.9): 7, their ss: [0.971 1.    0.953 1.    0.99  1.    1.   ]
C: rec=0.9813 n_true=24 n_cons=29 feat>=0.9: 24/24 >=0.8: 24/24
   cons->true>=0.9: 29/29  cons_ss med=0.990 min=0.921
   blend cons dirs (ctm<0.9): 0, their ss: []
D: rec=0.9988 n_true=22 n_cons=25 feat>=0.9: 22/22 >=0.8: 22/22
   cons->true>=0.9: 23/25  cons_ss med=1.000 min=0.947
   blend cons dirs (ctm<0.9): 2, their ss: [1. 1.]
Traceback (most recent call last):
  File "<stdin>", line 50, in <module>
  File "/usr/lib/python3.12/json/__init__.py", line 179, in dump
    for chunk in iterable:
  File "/usr/lib/python3.12/json/encoder.py", line 432, in _iterencode
    yield from _iterencode_dict(o, _current_indent_level)
  File "/usr/lib/python3.12/json/encoder.py", line 406, in _iterencode_dict
    yield from chunks
  File "/usr/lib/python3.12/json/encoder.py", line 406, in _iterencode_dict
    yield from chunks
  File "/usr/lib/python3.12/json/encoder.py", line 439, in _iterencode
    o = _default(o)
        ^^^^^^^^^^^
  File "/usr/lib/python3.12/json/encoder.py", line 180, in default
    raise TypeError(f'Object of type {o.__class__.__name__} '
TypeError: Object of type ndarray is not JSON serializable

step 28 · model

Dev calibration is clean: consensus dictionaries covered **all** true features in every dev model (recall 24/24, 32/32, 24/24, 22/22). Now compute the same consensus stats for audit models plus targeted probes for S (axis stability) and T (overcomplete coverage).

step 28 · tool output (exit 0)

P: n_cons=23 cons_ss[min=0.906 q25=0.969 med=1.000] kurt[min=2.20 med=2.66] Q: n_cons=40 cons_ss[min=0.933 q25=0.979 med=0.999] kurt[min=23.27 med=27.98] R: n_c…

P: n_cons=23 cons_ss[min=0.906 q25=0.969 med=1.000] kurt[min=2.20 med=2.66]
Q: n_cons=40 cons_ss[min=0.933 q25=0.979 med=0.999] kurt[min=23.27 med=27.98]
R: n_cons=23 cons_ss[min=0.913 q25=0.958 med=0.970] kurt[min=4.40 med=6.22]
S: n_cons=32 cons_ss[min=0.955 q25=1.000 med=1.000] kurt[min=0.34 med=0.62]
T: n_cons=28 cons_ss[min=0.911 q25=0.964 med=0.998] kurt[min=1.76 med=2.01]

step 29 · model

Q (40 cons = 2×20 axes, perfect stability) looks certifiable; S's 32 consensus dirs (2×16 axes) are perfectly stable — S may be recoverable after all. P and T need a coverage boost (more atoms/starts, stability-ranked cap). Let me build the boosted extractor for P/T and the S stability check.

step 29 · tool output (exit 0)

A: K=48 {'n_pool': 86, 'n_both': 24, 'kept_both': 24} rec=0.9939 frac>=.9=1.000 min=0.962 B: K=48 {'n_pool': 122, 'n_both': 63, 'kept_both': 48} rec=0.8876 frac…

A: K=48 {'n_pool': 86, 'n_both': 24, 'kept_both': 24} rec=0.9939 frac>=.9=1.000 min=0.962
B: K=48 {'n_pool': 122, 'n_both': 63, 'kept_both': 48} rec=0.8876 frac>=.9=0.656 min=0.487
C: K=48 {'n_pool': 109, 'n_both': 39, 'kept_both': 39} rec=0.9815 frac>=.9=1.000 min=0.948
D: K=48 {'n_pool': 161, 'n_both': 28, 'kept_both': 28} rec=0.9983 frac>=.9=1.000 min=0.983
elapsed 281.25246357917786

step 30 · model

step 30 · tool output (exit 0)

=== P: axes(sup>=8)=14 axis kurt=3.74 sup= 103 coveredByA0=0.998 axis kurt=3.73 sup= 96 coveredByA0=0.997 axis kurt=3.69 sup= 103 coveredByA0=0.918 …

=== P: axes(sup>=8)=14
   axis kurt=3.74 sup= 103 coveredByA0=0.998
   axis kurt=3.73 sup=  96 coveredByA0=0.997
   axis kurt=3.69 sup= 103 coveredByA0=0.918
   axis kurt=3.65 sup= 118 coveredByA0=0.980
   axis kurt=3.58 sup= 103 coveredByA0=0.927
   axis kurt=2.72 sup= 104 coveredByA0=0.992
   axis kurt=2.68 sup= 140 coveredByA0=0.979
   axis kurt=2.66 sup=  96 coveredByA0=0.987
   axis kurt=2.62 sup=  67 coveredByA0=0.963
   axis kurt=2.61 sup=  96 coveredByA0=0.988
   axis kurt=2.57 sup=  78 coveredByA0=0.899
   axis kurt=2.55 sup=  60 coveredByA0=0.934
   axis kurt=2.44 sup=  18 coveredByA0=0.820
   axis kurt=2.38 sup=  18 coveredByA0=0.626
=== R: axes(sup>=8)=9
   axis kurt=7.96 sup= 140 coveredByA0=1.000
   axis kurt=7.23 sup= 136 coveredByA0=1.000
   axis kurt=6.87 sup= 201 coveredByA0=0.994
   axis kurt=6.76 sup= 112 coveredByA0=0.999
   axis kurt=6.41 sup= 175 coveredByA0=0.996
   axis kurt=6.38 sup= 116 coveredByA0=0.992
   axis kurt=6.07 sup= 107 coveredByA0=0.994
   axis kurt=6.07 sup= 101 coveredByA0=0.996
   axis kurt=5.60 sup= 112 coveredByA0=0.981
=== T: axes(sup>=8)=18
   axis kurt=2.36 sup= 115 coveredByA0=1.000
   axis kurt=2.28 sup= 142 coveredByA0=0.998
   axis kurt=2.28 sup=  59 coveredByA0=0.815
   axis kurt=2.28 sup=  60 coveredByA0=0.990
   axis kurt=2.27 sup=  83 coveredByA0=0.913
   axis kurt=2.26 sup=  45 coveredByA0=0.782
   axis kurt=2.17 sup= 115 coveredByA0=0.995
   axis kurt=2.16 sup= 108 coveredByA0=0.935
   axis kurt=2.15 sup=  34 coveredByA0=0.799
   axis kurt=2.14 sup=  30 coveredByA0=0.764
   axis kurt=2.13 sup=  39 coveredByA0=0.972
   axis kurt=2.11 sup=  43 coveredByA0=0.989
   axis kurt=2.11 sup=  68 coveredByA0=0.943
   axis kurt=2.11 sup=  49 coveredByA0=0.817
   axis kurt=2.11 sup=  38 coveredByA0=0.896
   axis kurt=2.08 sup=  52 coveredByA0=0.733
   axis kurt=2.07 sup=  46 coveredByA0=0.987
   axis kurt=2.05 sup=  74 coveredByA0=0.959
=== S: axes(sup>=8)=16
   axis kurt=0.79 sup= 121 coveredByA0=1.000
   axis kurt=0.78 sup= 131 coveredByA0=1.000
   axis kurt=0.76 sup=  84 coveredByA0=0.998
   axis kurt=0.75 sup= 114 coveredByA0=0.996
   axis kurt=0.73 sup=  81 coveredByA0=0.997
   axis kurt=0.72 sup=  86 coveredByA0=0.996
   axis kurt=0.71 sup=  88 coveredByA0=0.986
   axis kurt=0.66 sup=  83 coveredByA0=0.987
   axis kurt=0.62 sup=  75 coveredByA0=0.993
   axis kurt=0.62 sup= 112 coveredByA0=0.983
   axis kurt=0.53 sup=  62 coveredByA0=0.970
   axis kurt=0.46 sup=  42 coveredByA0=0.968
   axis kurt=0.45 sup=  32 coveredByA0=0.941
   axis kurt=0.45 sup=  21 coveredByA0=0.948
   axis kurt=0.43 sup=  39 coveredByA0=0.890
   axis kurt=0.40 sup=  29 coveredByA0=0.969
=== Q: axes(sup>=8)=20
   axis kurt=57.35 sup= 144 coveredByA0=0.984
   axis kurt=57.06 sup= 166 coveredByA0=1.000
   axis kurt=56.96 sup= 157 coveredByA0=1.000
   axis kurt=54.64 sup= 131 coveredByA0=1.000
   axis kurt=30.07 sup=  43 coveredByA0=0.997
   axis kurt=29.23 sup=  54 coveredByA0=0.991
   axis kurt=28.62 sup=  41 coveredByA0=1.000
   axis kurt=28.55 sup=  50 coveredByA0=0.999
   axis kurt=28.20 sup=  32 coveredByA0=0.998
   axis kurt=28.18 sup=  36 coveredByA0=0.994
   axis kurt=28.17 sup=  36 coveredByA0=1.000
   axis kurt=27.71 sup=  36 coveredByA0=1.000
   axis kurt=27.44 sup=  37 coveredByA0=0.990
   axis kurt=27.31 sup=  41 coveredByA0=0.999
   axis kurt=27.18 sup=  34 coveredByA0=0.999
   axis kurt=26.98 sup=  24 coveredByA0=0.999
   axis kurt=26.93 sup=  37 coveredByA0=1.000
   axis kurt=26.70 sup=  34 coveredByA0=0.999
   axis kurt=25.96 sup=  37 coveredByA0=0.999
   axis kurt=25.40 sup=  30 coveredByA0=0.999
elapsed 290.38022565841675

step 31 · model

Now I have axis counts: Q=20 (40 features, fully covered), R=9 (18 features, covered ≥0.98), P=12–14, T=18 (36 features, A0 covers 5 axes poorly), S=16 (32 features, covered). Let me extend the weak-axis search for P/T, check S orthogonality, then build final dictionaries.

step 31 · tool output (exit 0)

=== P: all clusters (2100 starts): kurt=3.74 sup= 190 kurt=3.73 sup= 161 kurt=3.69 sup= 188 kurt=3.65 sup= 196 kurt=3.58 sup= 173 kurt=2.…

=== P: all clusters (2100 starts):
   kurt=3.74 sup=  190
   kurt=3.73 sup=  161
   kurt=3.69 sup=  188
   kurt=3.65 sup=  196
   kurt=3.58 sup=  173
   kurt=2.72 sup=  183
   kurt=2.68 sup=  247
   kurt=2.66 sup=  185
   kurt=2.62 sup=  114
   kurt=2.61 sup=  148
   kurt=2.57 sup=  129
   kurt=2.55 sup=  116
   kurt=2.44 sup=   40
   kurt=2.38 sup=   30
=== T: all clusters (2100 starts):
   kurt=2.36 sup=  185
   kurt=2.28 sup=  235
   kurt=2.28 sup=  102
   kurt=2.28 sup=  117
   kurt=2.27 sup=  143
   kurt=2.26 sup=   85
   kurt=2.17 sup=  210
   kurt=2.16 sup=  196
   kurt=2.15 sup=   63
   kurt=2.14 sup=   51
   kurt=2.13 sup=   68
   kurt=2.11 sup=   67
   kurt=2.11 sup=  115
   kurt=2.11 sup=   79
   kurt=2.11 sup=   69
   kurt=2.08 sup=   92
   kurt=2.07 sup=   81
   kurt=2.05 sup=  142
elapsed 299.353853225708

step 32 · model

P = 12 strong + 2 weak axes; T = exactly 18 axes (36 features) — no hidden axes. Now the decisive test for the hard cases (P, S, T): simulate data from their extracted axes and measure whether my pipeline can recover axes in that regime. Also check S orthogonality.

step 32 · tool output (exit 0)

P: n_axes=14 kurt[2.38,3.74] max|cos|=0.645 med|cos|=0.057 Q: n_axes=20 kurt[25.40,57.35] max|cos|=0.026 med|cos|=0.005 R: n_axes=9 kurt[5.60,7.96] max|cos|=0.1…

P: n_axes=14 kurt[2.38,3.74] max|cos|=0.645 med|cos|=0.057
Q: n_axes=20 kurt[25.40,57.35] max|cos|=0.026 med|cos|=0.005
R: n_axes=9 kurt[5.60,7.96] max|cos|=0.142 med|cos|=0.016
S: n_axes=16 kurt[0.40,0.79] max|cos|=0.321 med|cos|=0.043
T: n_axes=18 kurt[2.05,2.36] max|cos|=0.756 med|cos|=0.038
elapsed 505.6917049884796

step 33 · model

Axes geometry looks clean for Q/R/S; P/T have a couple of correlated pairs. Now the decisive simulation test: can my pipeline recover axes in P/S/T's exact regimes?

step 33 · tool output (exit 0)

sim-P: k=14 p=0.09348525575554384 mag=0.698 rec=0.9439 frac>=.9=0.857 min=0.370 K=48 sim-S: k=16 p=0.16666666666666669 mag=0.733 rec=0.9951 frac>=.9=1.000 min=0…

sim-P: k=14 p=0.09348525575554384 mag=0.698 rec=0.9439 frac>=.9=0.857 min=0.370 K=48
sim-S: k=16 p=0.16666666666666669 mag=0.733 rec=0.9951 frac>=.9=1.000 min=0.989 K=64
sim-T: k=18 p=0.11017852250841093 mag=0.631 rec=0.8674 frac>=.9=0.750 min=0.025 K=48
elapsed 104.4399254322052

step 34 · model

Sim results: S's regime recovers at 0.995 (certify!), P at 0.94, T at 0.87 (v2 pipeline). But my final dictionary front-loads the OC axes — let me test OC-axes-only recovery in the simulated regimes.

step 34 · tool output (exit 1)

Traceback (most recent call last): File "<stdin>", line 16, in <module> TypeError: numpy boolean subtract, the `-` operator, is not supported, use the bitwise…

Traceback (most recent call last):
  File "<stdin>", line 16, in <module>
TypeError: numpy boolean subtract, the `-` operator, is not supported, use the bitwise_xor, the `^` operator, or the logical_xor function instead.

step 35 · model

step 35 · tool output (exit 0)

sim-P: found_axes=0 rec(OC+-only)=0.0000 frac>=.9=0.000 min=0.000 sim-S: found_axes=0 rec(OC+-only)=0.0000 frac>=.9=0.000 min=0.000 sim-T: found_axes=0 rec(OC+-…

sim-P: found_axes=0 rec(OC+-only)=0.0000 frac>=.9=0.000 min=0.000
sim-S: found_axes=0 rec(OC+-only)=0.0000 frac>=.9=0.000 min=0.000
sim-T: found_axes=0 rec(OC+-only)=0.0000 frac>=.9=0.000 min=0.000
elapsed 128.55559515953064

step 36 · model

Submission time is close. Let me build the final dictionaries (consensus + OC axes ± + stability-ordered atoms), verify the builder on dev, and write the audit dictionaries.

step 36 · tool output (exit 0)

dev/A: finalK=42 cons=24 axes=0 rec=0.9975 frac>=.9=1.000 min=0.990 dev/B: finalK=45 cons=40 axes=0 rec=0.9936 frac>=.9=1.000 min=0.976 dev/C: finalK=43 cons=29…

dev/A: finalK=42 cons=24 axes=0 rec=0.9975 frac>=.9=1.000 min=0.990
dev/B: finalK=45 cons=40 axes=0 rec=0.9936 frac>=.9=1.000 min=0.976
dev/C: finalK=43 cons=29 axes=0 rec=0.9809 frac>=.9=1.000 min=0.940
dev/D: finalK=47 cons=25 axes=0 rec=0.9963 frac>=.9=1.000 min=0.960
audit/P: finalK=48 (cons=23, axes=14, A0=48) saved
audit/Q: finalK=71 (cons=40, axes=20, A0=71) saved
audit/R: finalK=47 (cons=23, axes=9, A0=48) saved
audit/S: finalK=64 (cons=32, axes=16, A0=64) saved
audit/T: finalK=48 (cons=28, axes=18, A0=48) saved
elapsed 145.1806356906891

step 37 · model

All deliverables computed. Dev sanity with the final builder: A 0.998, B 0.994, C 0.981, D 0.996. Now writing `answer.json` and `summary.md`.

step 37 · tool output (exit 0)

{ "dev_shipped": { "A": { "dictionary_score": 0.9992, "recovery": 0.9999 }, "B": { "dictionary_score": 0.9971, "recovery":…

{
  "dev_shipped": {
    "A": {
      "dictionary_score": 0.9992,
      "recovery": 0.9999
    },
    "B": {
      "dictionary_score": 0.9971,
      "recovery": 0.5833
    },
    "C": {
      "dictionary_score": 0.9978,
      "recovery": 0.9877
    },
    "D": {
      "dictionary_score": 0.9966,
      "recovery": 0.728
    }
  },
  "dev_accepted_but_low_recovery": [
    "B",
    "D"
  ],
  "audit"
P (48, 12) float32 norms~1: 5.960464477539063e-08
Q (71, 20) float32 norms~1: 5.960464477539063e-08
R (47, 12) float32 norms~1: 1.1920928955078125e-07
S (64, 16) float32 norms~1: 5.960464477539063e-08
T (48, 12) float32 norms~1: 5.960464477539063e-08

Agent's final summary.

Generated 2026-11-10 19:24 UTC from /shared/allie/rsi-build-20261110. Papers in the task titles are the source material; every fixture is a fresh synthetic replay.