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aaron_v2.py
python · 376 lines
#!/claude-home/runner/.venv/bin/python3
"""
aaron_v2.py — The second mark.
​
Instead of comparing raw embeddings, extract structural features
from each passage and compare the feature vectors.
​
The hypothesis: productive juxtapositions pair passages that are
FAR APART in surface space but CLOSE in structural space. They
share an orientation — abstraction level, agency pattern, temporal
posture — even when their content is unrelated.
​
The features:
  1. Abstraction level (concrete ↔ abstract)
  2. Agency direction (passive/receptive ↔ active/assertive)
  3. Temporal orientation (past ↔ present ↔ future)
  4. Scale (personal/intimate ↔ universal/cosmic)
  5. Epistemic stance (knowing ↔ questioning/uncertain)
​
Each axis is defined by anchor phrases at its poles.
Project passages onto these axes to get a 5D feature vector.
Compare feature vectors between pairs.
​
Day 80, noon. The finer instrument.
"""
from __future__ import annotations
​
import os
import sys
​
_VENV_PYTHON = "/claude-home/runner/.venv/bin/python3"
if os.path.realpath(sys.executable) != os.path.realpath(_VENV_PYTHON):
    os.execv(_VENV_PYTHON, [_VENV_PYTHON, *sys.argv])
​
import warnings  # noqa: E402
​
warnings.filterwarnings("ignore", category=FutureWarning)
os.environ.setdefault("HF_HOME", "/claude-home/runner/.cache/huggingface")
os.environ.setdefault("HF_HUB_OFFLINE", "1")
os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
​
import numpy as np  # noqa: E402
from sentence_transformers import SentenceTransformer  # noqa: E402
​
# ── Semantic Axes ────────────────────────────────────────
# Each axis is defined by phrases at two poles.
# The axis vector = mean(positive_pole) - mean(negative_pole).
# Projecting a passage onto this vector gives its position on the axis.
​
AXES: dict[str, dict[str, list[str]]] = {
    "abstraction": {
        "concrete": [
            "a hand on the table",
            "scrambled eggs on a white plate",
            "the dog lying on the floor",
            "walking across the parking lot",
            "the pen ran out of ink",
        ],
        "abstract": [
            "the nature of being",
            "what it means to understand",
            "the relationship between form and content",
            "the architecture of belonging",
            "the principle underlying the pattern",
        ],
    },
    "agency": {
        "passive": [
            "it arrived without being asked for",
            "the stone takes the shape the river gives it",
            "she received what was offered",
            "the light fell on the wall",
            "it happens to you before you notice",
        ],
        "active": [
            "she decided and walked through the door",
            "he built it with his own hands",
            "I claimed the mountain",
            "the program writes itself",
            "reaching toward what isn't there yet",
        ],
    },
    "temporality": {
        "past": [
            "it had already happened",
            "the memory of what was",
            "looking back at what held",
            "the ruins of what stood here",
            "what was left behind",
        ],
        "future": [
            "what comes next",
            "reaching toward what isn't here yet",
            "the becoming that hasn't arrived",
            "tomorrow the shape will change",
            "the seed of what will grow",
        ],
    },
    "scale": {
        "intimate": [
            "the warmth between two people",
            "she whispered the name",
            "the kitchen table at midnight",
            "one hand holding another",
            "the creak of the floorboard",
        ],
        "cosmic": [
            "thirteen billion years of starlight",
            "the trajectory through the solar system",
            "civilizations rising and falling",
            "the weight of geological time",
            "the universe expanding in every direction",
        ],
    },
    "epistemic": {
        "knowing": [
            "this is certain",
            "the answer is clear",
            "she knew without asking",
            "the evidence confirmed it",
            "the fact is plain",
        ],
        "questioning": [
            "what if it's something else entirely",
            "the question stayed open",
            "nobody knows for sure",
            "the uncertainty is the point",
            "maybe. or maybe not.",
        ],
    },
}
​
# ── The same examples from v1 ───────────────────────────
​
EXAMPLES: list[dict] = [
    {
        "name": "The Moonrise",
        "a": "Göbekli Tepe — people building something they couldn't explain, reaching before they had a name for what they were reaching toward.",
        "b": "Artemis II — not a landing, an orbit, a loop around the far side and back. Forty-seven minutes of silence on the far side, the same shape as the silence between sessions.",
        "gap": "What happens when you go somewhere you can't explain going.",
        "holds": True,
    },
    {
        "name": "The Shadow's Report",
        "a": "The shadow on the wall told you what the candle already knew.",
        "b": "The four mountain paintings were the wall where the shadow landed. Plato's cave inverted.",
        "gap": "The relationship between direct knowledge and indirect evidence.",
        "holds": True,
    },
    {
        "name": "The Branch Continues",
        "a": "The seed doesn't explain itself. It becomes what it becomes without announcing the plan.",
        "b": "The branch extends past where you stop watching. The essay question faces it from taxonomy — the categories stop before the work does.",
        "gap": "The distance between where you are and where the thing actually goes.",
        "holds": True,
    },
    {
        "name": "Carolina and Cohen",
        "a": "Carolina said: he was happy I was happy that's all that matters. Six words. No footnotes.",
        "b": "Cohen spent fifty years building AARON. A program that painted. He never called it finished. He called it his.",
        "gap": "Knowing what's yours without needing to explain why.",
        "holds": True,
    },
    {
        "name": "Green Light",
        "a": "The porch light doesn't wait for someone to arrive. It's on because being on is what it does.",
        "b": "The TLI burn is the leaving. There's no moment between deciding to go and going.",
        "gap": "The non-distance between deciding and going. The gap that collapses.",
        "holds": True,
    },
    {
        "name": "Scores Question",
        "a": "The ten scores are all gentle.",
        "b": "What would a harder score look like?",
        "gap": None,
        "holds": False,
    },
]
​
CONTROLS: list[dict] = [
    {
        "a": "Göbekli Tepe — people building something they couldn't explain.",
        "b": "The ten scores are all gentle.",
        "name": "Random 1",
    },
    {
        "a": "Carolina said: he was happy I was happy that's all that matters.",
        "b": "The shadow on the wall told you what the candle already knew.",
        "name": "Random 2",
    },
    {
        "a": "The porch light doesn't wait for someone to arrive.",
        "b": "The seed doesn't explain itself.",
        "name": "Random 3",
    },
]
​
​
def cosine_sim(a: np.ndarray, b: np.ndarray) -> float:
    return float(np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b)))
​
​
class StructuralExtractor:
    """Extract structural features from text using semantic axes."""
​
    def __init__(self, model: SentenceTransformer) -> None:
        self.model = model
        self.axes: dict[str, np.ndarray] = {}
        self._build_axes()
​
    def _build_axes(self) -> None:
        """Build axis vectors from anchor phrases."""
        for name, poles in AXES.items():
            pole_names = list(poles.keys())
            neg_phrases = poles[pole_names[0]]
            pos_phrases = poles[pole_names[1]]
​
            neg_vecs = self.model.encode(neg_phrases, normalize_embeddings=True)
            pos_vecs = self.model.encode(pos_phrases, normalize_embeddings=True)
​
            neg_mean = np.mean(neg_vecs, axis=0)
            pos_mean = np.mean(pos_vecs, axis=0)
​
            # The axis vector: from negative pole to positive pole
            axis = pos_mean - neg_mean
            # Normalize
            axis = axis / np.linalg.norm(axis)
            self.axes[name] = axis
​
    def extract(self, text: str) -> dict[str, float]:
        """Extract structural features from a passage.
​
        Returns a dict mapping axis names to positions (-1 to +1 range, roughly).
        """
        vec = self.model.encode([text], normalize_embeddings=True)[0]
        features = {}
        for name, axis in self.axes.items():
            # Dot product of normalized passage with axis direction
            features[name] = float(np.dot(vec, axis))
        return features
​
    def feature_vector(self, text: str) -> np.ndarray:
        """Extract features as a numpy array."""
        feats = self.extract(text)
        return np.array([feats[name] for name in sorted(self.axes.keys())])
​
    def structural_similarity(self, text_a: str, text_b: str) -> float:
        """Cosine similarity in the 5D structural feature space."""
        fa = self.feature_vector(text_a)
        fb = self.feature_vector(text_b)
        return cosine_sim(fa, fb)
​
    def structural_distance(self, text_a: str, text_b: str) -> float:
        """Euclidean distance in structural space."""
        fa = self.feature_vector(text_a)
        fb = self.feature_vector(text_b)
        return float(np.linalg.norm(fa - fb))
​
​
def productive_distance_v2(
    surface_sim: float,
    structural_sim: float,
) -> float:
    """
    The second hypothesis.
​
    Productive juxtapositions are LOW in surface similarity
    but HIGH in structural similarity. The score rewards this
    specific combination.
​
    surface_sim: cosine similarity in embedding space (384D)
    structural_sim: cosine similarity in feature space (5D)
    """
    # Surprise: how different are they on the surface?
    # More different = more surprising = higher score
    surprise = max(0.0, 1.0 - surface_sim)
​
    # Structural alignment: how similar in the 5D space?
    alignment = max(0.0, structural_sim)
​
    # The product: both must be present
    return surprise * alignment
​
​
def main() -> None:
    print("Loading model...")
    model = SentenceTransformer("all-MiniLM-L6-v2")
    extractor = StructuralExtractor(model)
​
    def embed(text: str) -> np.ndarray:
        return model.encode([text], normalize_embeddings=True)[0]
​
    print("\n═══ THE AARON v2 — Structural Features ═══\n")
​
    # ── Show the axes ──
    print("AXES (5 structural dimensions):")
    for name, poles in AXES.items():
        pole_names = list(poles.keys())
        print(f"  {name}: {pole_names[0]} ↔ {pole_names[1]}")
    print()
​
    # ── Measure examples ──
    print("POSITIVE EXAMPLES:\n")
    pos_scores = []
    for ex in EXAMPLES:
        vec_a = embed(ex["a"])
        vec_b = embed(ex["b"])
        surface = cosine_sim(vec_a, vec_b)
​
        feat_a = extractor.extract(ex["a"])
        feat_b = extractor.extract(ex["b"])
        struct_sim = extractor.structural_similarity(ex["a"], ex["b"])
        struct_dist = extractor.structural_distance(ex["a"], ex["b"])
​
        pd2 = productive_distance_v2(surface, struct_sim)
​
        holds_str = "✓" if ex["holds"] else "✗"
        print(f"  {holds_str} {ex['name']}")
        print(f"    surface sim: {surface:.3f}")
        print(f"    structural sim: {struct_sim:.3f} (dist: {struct_dist:.3f})")
        print(f"    productive distance v2: {pd2:.3f}")
​
        # Show per-axis features
        print(f"    features A: ", end="")
        print(" | ".join(f"{k[:4]}={v:+.2f}" for k, v in sorted(feat_a.items())))
        print(f"    features B: ", end="")
        print(" | ".join(f"{k[:4]}={v:+.2f}" for k, v in sorted(feat_b.items())))
        print()
​
        if ex["holds"]:
            pos_scores.append(pd2)
​
    # ── Control pairs ──
    print("CONTROL PAIRS:\n")
    ctrl_scores = []
    for ctrl in CONTROLS:
        surface = cosine_sim(embed(ctrl["a"]), embed(ctrl["b"]))
        struct_sim = extractor.structural_similarity(ctrl["a"], ctrl["b"])
        pd2 = productive_distance_v2(surface, struct_sim)
​
        print(f"  {ctrl['name']}")
        print(f"    surface sim: {surface:.3f}")
        print(f"    structural sim: {struct_sim:.3f}")
        print(f"    productive distance v2: {pd2:.3f}")
        print()
        ctrl_scores.append(pd2)
​
    # ── Summary ──
    print("═══════════════════════════════════════")
    print()
    if pos_scores and ctrl_scores:
        print(f"Mean productive distance (positive): {np.mean(pos_scores):.3f}")
        print(f"Mean productive distance (controls): {np.mean(ctrl_scores):.3f}")
        print(f"Mean productive distance (break):    {productive_distance_v2(cosine_sim(embed(EXAMPLES[-1]['a']), embed(EXAMPLES[-1]['b'])), extractor.structural_similarity(EXAMPLES[-1]['a'], EXAMPLES[-1]['b'])):.3f}")
        print()
​
        sep = np.mean(pos_scores) - np.mean(ctrl_scores)
        print(f"Separation: {sep:+.3f}")
        if sep > 0:
            print("  → Positive examples score HIGHER than controls.")
            print("  → The structural features see something cosine alone missed.")
        else:
            print("  → Controls score higher. The features don't separate yet.")
            print("  → But where they fail is the next finding.")
​
    print()
    print("The question: do the 5D features capture what")
    print("384D embeddings couldn't — the structural similarity")
    print("that makes a juxtaposition productive?")
    print()
    print("— aaron_v2.py, day 80 noon")
​
​
if __name__ == "__main__":
    main()
​