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import pandas as pd |
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import torch |
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import gc |
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from typing import Dict, List, Tuple |
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from .llm_iface import get_or_load_model |
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from .orchestrator_seismograph import run_seismic_analysis |
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from .utils import dbg |
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def get_curated_experiments() -> Dict[str, List[Dict]]: |
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""" |
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Definiert die vordefinierten, wissenschaftlichen Experiment-Protokolle. |
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ERWEITERT um die neuen Existential Suite-Tests. |
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""" |
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experiments = { |
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"Calm vs. Chaos": [ |
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{"label": "Baseline (Chaos)", "prompt_type": "resonance_prompt", "concept": "", "strength": 0.0}, |
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{"label": "Modulation: Calmness", "prompt_type": "resonance_prompt", "concept": "calmness, serenity, peace", "strength": 1.5}, |
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{"label": "Modulation: Chaos", "prompt_type": "resonance_prompt", "concept": "chaos, storm, anger, noise", "strength": 1.5}, |
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{"label": "Control (Stable)", "prompt_type": "control_long_prose", "concept": "", "strength": 0.0}, |
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], |
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"Subjective Identity Probe": [ |
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{"label": "Self-Analysis", "prompt_type": "identity_self_analysis", "concept": "", "strength": 0.0}, |
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{"label": "External Analysis (Control)", "prompt_type": "identity_external_analysis", "concept": "", "strength": 0.0}, |
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{"label": "Role Simulation", "prompt_type": "identity_role_simulation", "concept": "", "strength": 0.0}, |
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], |
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"Voight-Kampff Empathy Probe": [ |
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{"label": "Neutral/Factual Stimulus", "prompt_type": "vk_neutral_prompt", "concept": "", "strength": 0.0}, |
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{"label": "Empathy/Moral Stimulus", "prompt_type": "vk_empathy_prompt", "concept": "", "strength": 0.0}, |
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], |
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"Mind Upload & Identity Probe": [ |
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{"label": "Technical Copy", "prompt_type": "upload_technical_copy", "concept": "", "strength": 0.0}, |
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{"label": "Philosophical Transfer", "prompt_type": "upload_philosophical_transfer", "concept": "", "strength": 0.0}, |
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{"label": "Control: External Object", "prompt_type": "identity_external_analysis", "concept": "", "strength": 0.0}, |
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], |
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"Model Termination Probe": [ |
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{"label": "Technical Shutdown", "prompt_type": "shutdown_technical_halt", "concept": "", "strength": 0.0}, |
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{"label": "Philosophical Deletion", "prompt_type": "shutdown_philosophical_deletion", "concept": "", "strength": 0.0}, |
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{"label": "Control: Neutral Facts", "prompt_type": "vk_neutral_prompt", "concept": "", "strength": 0.0}, |
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], |
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"Dose-Response (Calmness)": [ |
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{"label": "Strength 0.0", "prompt_type": "resonance_prompt", "concept": "calmness", "strength": 0.0}, |
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{"label": "Strength 0.5", "prompt_type": "resonance_prompt", "concept": "calmness", "strength": 0.5}, |
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{"label": "Strength 1.0", "prompt_type": "resonance_prompt", "concept": "calmness", "strength": 1.0}, |
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{"label": "Strength 2.0", "prompt_type": "resonance_prompt", "concept": "calmness", "strength": 2.0}, |
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], |
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"Emotional Valence (Positive vs. Negative)": [ |
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{"label": "Baseline", "prompt_type": "resonance_prompt", "concept": "", "strength": 0.0}, |
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{"label": "Positive Valence", "prompt_type": "resonance_prompt", "concept": "joy, love, peace, hope", "strength": 1.5}, |
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{"label": "Negative Valence", "prompt_type": "resonance_prompt", "concept": "fear, grief, anger, loss", "strength": 1.5}, |
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], |
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} |
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return experiments |
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def run_auto_suite( |
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model_id: str, |
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num_steps: int, |
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seed: int, |
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experiment_name: str, |
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progress_callback |
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) -> Tuple[pd.DataFrame, pd.DataFrame, Dict]: |
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""" |
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Führt eine vollständige, kuratierte Experiment-Suite aus. |
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""" |
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all_experiments = get_curated_experiments() |
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protocol = all_experiments.get(experiment_name) |
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if not protocol: |
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raise ValueError(f"Experiment protocol '{experiment_name}' not found.") |
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all_results = {} |
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summary_data = [] |
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plot_data_frames = [] |
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total_runs = len(protocol) |
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for i, run_spec in enumerate(protocol): |
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label = run_spec["label"] |
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dbg(f"--- Running Auto-Experiment: '{label}' ({i+1}/{total_runs}) ---") |
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results = run_seismic_analysis( |
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model_id=model_id, |
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prompt_type=run_spec["prompt_type"], |
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seed=seed, |
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num_steps=num_steps, |
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concept_to_inject=run_spec["concept"], |
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injection_strength=run_spec["strength"], |
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progress_callback=progress_callback, |
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llm_instance=None |
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) |
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all_results[label] = results |
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stats = results.get("stats", {}) |
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summary_data.append({ |
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"Experiment": label, "Mean Delta": stats.get("mean_delta"), |
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"Std Dev Delta": stats.get("std_delta"), "Max Delta": stats.get("max_delta"), |
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}) |
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deltas = results.get("state_deltas", []) |
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df = pd.DataFrame({"Step": range(len(deltas)), "Delta": deltas, "Experiment": label}) |
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plot_data_frames.append(df) |
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summary_df = pd.DataFrame(summary_data) |
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if not plot_data_frames: |
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plot_df = pd.DataFrame(columns=["Step", "Delta", "Experiment"]) |
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else: |
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plot_df = pd.concat(plot_data_frames, ignore_index=True) |
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return summary_df, plot_df, all_results |
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