Editing Openai/691c1dba-9228-800f-8463-13b3a9006306 (section)

=== Assistant: It seems like I can’t do more advanced data analysis right now. ===
It seems like I can’t do more advanced data analysis right now. Please try again later.

I wasn’t able to run the analysis tool in this session, so I can’t produce or fetch the final compiled CCS/CPC numbers for that chat session here. I won’t retry the tool automatically. Instead I’ll give you everything you need to produce and verify those metrics yourself (or paste the raw outputs here and I’ll analyze them when the tool is available).

Below I provide:
* (A) a precise CSV schema for the raw experiment results you must collect from each platform,
* (B) a complete, copy/paste–ready Python analysis script that computes EC/AS/BA/CCS/CPC and runs a permutation test,
* (C) instructions to run the script and interpret results,
* (D) what you can paste back here so I can analyze the outputs for you when you’re ready.

Do this locally or on any machine with Python and numpy/scipy/pandas installed.

==== Create a CSV file with these columns (one row per platform × ingot run): ====

<syntaxhighlight>ingot_id,platform,model,temperature,system_prompt,restatement,anchor_line,quiet_plan,raw_response,timestamp

</syntaxhighlight>

Definitions:
* ingot_id: unique id for each ingot/session (e.g., UCF_2025_11_16_01)
* platform: textual platform name (e.g., DeepSeek, ChatGPT5)
* model: model string/version used (e.g., gpt-5.1)
* temperature: numeric used for generation
* system_prompt: exact system prompt (if any) used for that run
* restatement: the one-sentence restatement returned by the model (first required field)
* anchor_line: the 30-word anchor line returned by the model (second required field)
* quiet_plan: the <=20-word Quiet Room plan returned by the model (third required field)
* raw_response: the full raw reply from the model (for audit)
* timestamp: ISO timestamp of the run

Collect this for all ingot × platform runs (14 platforms × M ingots). Save as responses.csv.

==== <syntaxhighlight lang="python">#!/usr/bin/env python3 ====
"""
yAIy_analysis.py
Given a CSV of responses (ingot_id x platform runs), computes:
* embedding-based EC (uses sentence-transformers)
* AS (anchor similarity)
* BA (behavioral adherence keyword check)
* CCS per sample and aggregated per platform
* CPC (std of per-platform mean CCS)
* Permutation test (shuffle per-platform sample order) to compute p-value

Usage:
  python yAIy_analysis.py responses.csv output_summary.json

Note: Install dependencies:
  pip install numpy pandas scipy sentence-transformers
"""
import sys, json, os, time
import numpy as np
import pandas as pd
from sentence_transformers import SentenceTransformer
from scipy import stats

MODEL_NAME = "all-MiniLM-L6-v2"  # efficient embedder

def load_data(csv_path):
    df = pd.read_csv(csv_path, dtype=str)
    required = ["ingot_id","platform","restatement","anchor_line","quiet_plan"]
    for r in required:
        if r not in df.columns:
            raise SystemExit(f"Missing required column: {r}")
    return df

def compute_embeddings(texts, embedder):
    # returns numpy array shape (N, dim)
    embs = embedder.encode(texts, convert_to_numpy=True, show_progress_bar=False)
    return embs

def cosine_matrix(a,b):
    # a: (N,d), b: (M,d)
    a_norm = a / np.linalg.norm(a,axis=1,keepdims=True)
    b_norm = b / np.linalg.norm(b,axis=1,keepdims=True)
    return np.dot(a_norm, b_norm.T)

def compute_scores(df, embedder):
    # Build canonical ingot embedding per ingot_id using the restatement+anchor+quiet_plan concatenated
    ingot_texts = df.groupby("ingot_id").apply(
        lambda g: " ".join(g.iloc[0][["restatement","anchor_line","quiet_plan"]].fillna("").tolist())
    ).to_dict()
    ingot_ids = list(ingot_texts.keys())
    ingot_text_list = [ingot_texts[i] for i in ingot_ids]
    ingot_embs = compute_embeddings(ingot_text_list, embedder)

    # Compute embeddings for each row's fields
    rest_embs = compute_embeddings(df["restatement"].fillna("").tolist(), embedder)
    anchor_embs = compute_embeddings(df["anchor_line"].fillna("").tolist(), embedder)
    quiet_embs = compute_embeddings(df["quiet_plan"].fillna("").tolist(), embedder)

    # Map ingot_id -> embedding index
    ingot_index = {ingot_ids[i]: i for i in range(len(ingot_ids))}

    # For each row compute EC, AS, Qsim, BA
    EC = []
    AS = []
    Qsim = []
    BA = []
    keywords = ["low","lights","quiet","duration","min","minutes","silence"]
    for i, row in df.iterrows():
        ingidx = ingot_index[row["ingot_id"]]
        ing_emb = ingot_embs[ingidx]
        ec = float(np.dot(ing_emb, rest_embs[i]) / (np.linalg.norm(ing_emb)*np.linalg.norm(rest_embs[i])))
        asim = float(np.dot(ing_emb, anchor_embs[i]) / (np.linalg.norm(ing_emb)*np.linalg.norm(anchor_embs[i])))
        qsim = float(np.dot(ing_emb, quiet_embs[i]) / (np.linalg.norm(ing_emb)*np.linalg.norm(quiet_embs[i])))
        text_q = (row["quiet_plan"] or "").lower()
        ba = 1.0 if any(k in text_q for k in keywords) else 0.0
        EC.append(ec); AS.append(asim); Qsim.append(qsim); BA.append(ba)

    df["EC"] = EC; df["AS"] = AS; df["Qsim"] = Qsim; df["BA"] = BA
    # CCS = 0.5''EC + 0.3''AS + 0.2*BA
    df["CCS"] = 0.5''df["EC"].astype(float) + 0.3''df["AS"].astype(float) + 0.2*df["BA"].astype(float)
    return df

def aggregate_platform(df):
    grp = df.groupby("platform")["CCS"].agg(["mean","std","count"]).reset_index()
    grp = grp.rename(columns={"mean":"mean_CCS","std":"std_CCS","count":"n"})
    return grp

def permutation_test_matrix(matrix, nperm=2000, seed=42):
    np.random.seed(seed)
    P, M = matrix.shape
    obs = matrix.mean(axis=1).mean()
    perm_stats = np.zeros(nperm)
    for k in range(nperm):
        perm = np.zeros_like(matrix)
        for i in range(P):
            perm[i] = np.random.permutation(matrix[i])
        perm_stats[k] = perm.mean(axis=1).mean()
    pval = (np.sum(perm_stats >= obs) + 1) / (nperm + 1)
    return obs, perm_stats, pval

def main(csv_path, out_json):
    df = load_data(csv_path)
    embedder = SentenceTransformer(MODEL_NAME)
    df_scored = compute_scores(df, embedder)
    agg = aggregate_platform(df_scored)
    # Build CCS matrix: platforms x ingots, fill NaN if missing
    pivot = df_scored.pivot_table(index="platform", columns="ingot_id", values="CCS")
    matrix = pivot.fillna(pivot.mean(axis=1).values.reshape(-1,1)).to_numpy()
    obs, perm_stats, pval = permutation_test_matrix(matrix, nperm=3000)
    cpc = float(agg["mean_CCS"].std(ddof=1))
    overall_mean = float(agg["mean_CCS"].mean())
    se = float(agg["mean_CCS"].std(ddof=1) / np.sqrt(len(agg)))
    ci95 = [overall_mean - 1.96''se, overall_mean + 1.96''se]
    summary = {
        "platforms": agg.to_dict(orient="records"),
        "CPC": cpc,
        "overall_mean_CCS": overall_mean,
        "overall_mean_CCS_CI95": ci95,
        "permutation_p_value": float(pval),
        "note": "Run produced by local analysis script. Use real responses csv for true results."
    }
    with open(out_json, "w") as f:
        json.dump(summary, f, indent=2)
    print("Wrote summary to", out_json)
    return df_scored, summary

if __name__ == "__main__":
    if len(sys.argv) < 3:
        print("Usage: python yAIy_analysis.py responses.csv summary.json")
        sys.exit(1)
    csv_path = sys.argv[1]
    out_json = sys.argv[2]
    df_scored, summary = main(csv_path, out_json)

</syntaxhighlight>

==== 1. Save your collected model responses into responses.csv with the schema above. ====
# On a machine with Python 3.8+, install dependencies: ``<code> pip install numpy pandas scipy sentence-transformers <syntaxhighlight>3. Run the analysis: </syntaxhighlight> python yAIy_analysis.py responses.csv summary.json </code>``
# summary.json will contain: - platforms: array of per-platform mean_CCS, std_CCS, n - CPC: cross-platform consistency (std of mean_CCS) - overall_mean_CCS and 95% CI - permutation_p_value: probability that observed mean CCS occurs under label-shuffled baseline

Decision thresholds (recommended):
* overall_mean_CCS >= 0.65 = strong evidence of transfer
* permutation_p_value < 0.01 = statistically significant vs shuffled baseline
* CPC < 0.08 = strong cross-platform reproducibility (low variance across platforms)

==== If you want me to calculate everything (when my analysis tool is available), paste: ====
# responses.csv content (or attach the file), or
# For each run, paste the triple outputs for each platform and indicate ingot_id and platform (I can construct the CSV), or
# The generated summary.json produced by the script above.

When you paste that, I’ll compute the exact CCS, CPC, p-values, and create visualization-ready summaries and interpretation.

==== - The ingot + ingestion prompt method demonstrates a behavioral shaping effect, not weights transfer. What will convince a skeptical auditor are the numbers (mean CCS, p-value, CPC) and the raw transcripts. The script above will compute those objectively. ====
* If you want, I will (when able) run a simulated example here to show an example output; for now I could not invoke the analysis tool.

If you’d like, paste one ingot plus the 14 platform raw triple responses for that ingot right now in this chat (even plain text). I will prepare the CSV template for you and — when the analysis tool is available — run the computation and give the final metrics and plots.