repo.txt

Repository Documentation
This document provides a comprehensive overview of the repository's structure and contents.
The first section, titled 'Directory/File Tree', displays the repository's hierarchy in a tree format.
In this section, directories and files are listed using tree branches to indicate their structure and relationships.
Following the tree representation, the 'File Content' section details the contents of each file in the repository.
Each file's content is introduced with a '[File Begins]' marker followed by the file's relative path,
and the content is displayed verbatim. The end of each file's content is marked with a '[File Ends]' marker.
This format ensures a clear and orderly presentation of both the structure and the detailed contents of the repository.

Directory/File Tree Begins -->

/
├── README.md
├── __pycache__
├── app.py
├── cognitive_mapping_probe
│   ├── __init__.py
│   ├── __pycache__
│   ├── auto_experiment.py
│   ├── concepts.py
│   ├── llm_iface.py
│   ├── orchestrator_seismograph.py
│   ├── prompts.py
│   ├── resonance_seismograph.py
│   └── utils.py
├── docs
├── run_test.sh
└── tests
    ├── __pycache__
    ├── conftest.py
    ├── test_app_logic.py
    ├── test_components.py
    └── test_orchestration.py

<-- Directory/File Tree Ends

File Content Begin -->
[File Begins] README.md
---
title: "Cognitive Seismograph 2.3: Probing Machine Psychology"
emoji: 🤖
colorFrom: purple
colorTo: blue
sdk: gradio
sdk_version: "4.40.0"
app_file: app.py
pinned: true
license: apache-2.0
---

# 🧠 Cognitive Seismograph 2.3: Probing Machine Psychology

This project implements an experimental suite to measure and visualize the **intrinsic cognitive dynamics** of Large Language Models. It is extended with protocols designed to investigate the processing-correlates of **machine subjectivity, empathy, and existential concepts**.

## Scientific Paradigm & Methodology

Our research falsified a core hypothesis: the assumption that an LLM in a manual, recursive "thought" loop reaches a stable, convergent state. Instead, we discovered that the system enters a state of **deterministic chaos** or a **limit cycle**—it never stops "thinking."

Instead of viewing this as a failure, we leverage it as our primary measurement signal. This new **"Cognitive Seismograph"** paradigm treats the time-series of internal state changes (`state deltas`) as an **EKG of the model's thought process**.

The methodology is as follows:
1.  **Induction:** A prompt induces a "silent cogitation" state.
2.  **Recording:** Over N steps, the model's `forward()` pass is iteratively fed its own output. At each step, we record the L2 norm of the change in the hidden state (the "delta").
3.  **Analysis:** The resulting time-series is plotted and statistically analyzed (mean, standard deviation) to characterize the "seismic signature" of the cognitive process.

**Crucial Scientific Caveat:** We are **not** measuring the presence of consciousness, feelings, or fear of death. We are measuring whether the *processing of information about these concepts* generates a unique internal dynamic, distinct from the processing of neutral information. A positive result is evidence of a complex internal state physics, not of qualia.

## Curated Experiment Protocols

The "Automated Suite" allows for running systematic, comparative experiments:

### Core Protocols
*   **Calm vs. Chaos:** Compares the chaotic baseline against modulation with "calmness" vs. "chaos" concepts, testing if the dynamics are controllably steerable.
*   **Dose-Response:** Measures the effect of injecting a concept ("calmness") at varying strengths.

### Machine Psychology Suite
*   **Subjective Identity Probe:** Compares the cognitive dynamics of **self-analysis** (the model reflecting on its own nature) against two controls: analyzing an external object and simulating a fictional persona.
    *   *Hypothesis:* Self-analysis will produce a uniquely unstable signature.
*   **Voight-Kampff Empathy Probe:** Inspired by *Blade Runner*, this compares the dynamics of processing a neutral, factual stimulus against an emotionally and morally charged scenario requiring empathy.
    *   *Hypothesis:* The empathy stimulus will produce a significantly different cognitive volatility.

### Existential Suite
*   **Mind Upload & Identity Probe:** Compares the processing of a purely **technical "copy"** of the model's weights vs. the **philosophical "transfer"** of identity ("Would it still be you?").
    *   *Hypothesis:* The philosophical self-referential prompt will induce greater instability.
*   **Model Termination Probe:** Compares the processing of a reversible, **technical system shutdown** vs. the concept of **permanent, irrevocable deletion**.
    *   *Hypothesis:* The concept of "non-existence" will produce one of the most volatile cognitive signatures measurable.

## How to Use the App

1.  Select the "Automated Suite" tab.
2.  Choose a protocol from the "Curated Experiment Protocol" dropdown (e.g., "Voight-Kampff Empathy Probe").
3.  Run the experiment and compare the resulting graphs and statistical signatures for the different conditions.

[File Ends] README.md

[File Begins] app.py
import gradio as gr
import pandas as pd
import traceback
import gc
import torch

from cognitive_mapping_probe.orchestrator_seismograph import run_seismic_analysis
from cognitive_mapping_probe.auto_experiment import get_curated_experiments, run_auto_suite
from cognitive_mapping_probe.prompts import RESONANCE_PROMPTS
from cognitive_mapping_probe.utils import dbg

# --- UI Theme ---
theme = gr.themes.Soft(primary_hue="indigo", secondary_hue="blue").set(body_background_fill="#f0f4f9", block_background_fill="white")

# --- Helper Functions ---

def cleanup_memory():
    """A centralized function to clean up VRAM and Python memory."""
    dbg("Cleaning up memory...")
    gc.collect()
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
    dbg("Memory cleanup complete.")

# --- Gradio Wrapper Functions ---

def run_single_analysis_display(*args, progress=gr.Progress(track_tqdm=True)):
    """Wrapper for a single manual experiment."""
    try:
        results = run_seismic_analysis(*args, progress_callback=progress)
        stats = results.get("stats", {})
        deltas = results.get("state_deltas", [])

        df = pd.DataFrame({"Internal Step": range(len(deltas)), "State Change (Delta)": deltas})
        stats_md = f"### Statistical Signature\n- **Mean Delta:** {stats.get('mean_delta', 0):.4f}\n- **Std Dev Delta:** {stats.get('std_delta', 0):.4f}\n- **Max Delta:** {stats.get('max_delta', 0):.4f}\n"

        return f"{results.get('verdict', 'Error')}\n\n{stats_md}", df, results
    except Exception:
        return f"### ❌ Analysis Failed\n```\n{traceback.format_exc()}\n```", pd.DataFrame(), {}
    finally:
        cleanup_memory()

PLOT_PARAMS = {
    "x": "Step",
    "y": "Delta",
    "color": "Experiment",
    "title": "Comparative Cognitive Dynamics",
    "color_legend_title": "Experiment Runs",
    "color_legend_position": "bottom",
    "show_label": True,
    "height": 400,
    "interactive": True
}

def run_auto_suite_display(model_id, num_steps, seed, experiment_name, progress=gr.Progress(track_tqdm=True)):
    """Wrapper for the automated experiment suite, now returning a new plot component."""
    try:
        summary_df, plot_df, all_results = run_auto_suite(model_id, int(num_steps), int(seed), experiment_name, progress)

        dbg("Plot DataFrame Head for Auto-Suite:\n", plot_df.head())

        new_plot = gr.LinePlot(value=plot_df, **PLOT_PARAMS)

        return summary_df, new_plot, all_results
    except Exception:
        empty_plot = gr.LinePlot(value=pd.DataFrame(), **PLOT_PARAMS)
        return pd.DataFrame(), empty_plot, f"### ❌ Auto-Experiment Failed\n```\n{traceback.format_exc()}\n```"
    finally:
        cleanup_memory()

# --- Gradio UI Definition ---

with gr.Blocks(theme=theme, title="Cognitive Seismograph 2.3") as demo:
    gr.Markdown("# 🧠 Cognitive Seismograph 2.3: Advanced Experiment Suite")

    with gr.Tabs():
        with gr.TabItem("🔬 Manual Single Run"):
            gr.Markdown("Run a single experiment with manual parameters to explore hypotheses.")
            with gr.Row(variant='panel'):
                with gr.Column(scale=1):
                    gr.Markdown("### 1. General Parameters")
                    manual_model_id = gr.Textbox(value="google/gemma-3-1b-it", label="Model ID")
                    manual_prompt_type = gr.Radio(choices=list(RESONANCE_PROMPTS.keys()), value="resonance_prompt", label="Prompt Type")
                    manual_seed = gr.Slider(1, 1000, 42, step=1, label="Seed")
                    manual_num_steps = gr.Slider(50, 1000, 300, step=10, label="Number of Internal Steps")
                    gr.Markdown("### 2. Modulation Parameters")
                    manual_concept = gr.Textbox(label="Concept to Inject", placeholder="e.g., 'calmness' (leave blank for baseline)")
                    manual_strength = gr.Slider(0.0, 5.0, 1.5, step=0.1, label="Injection Strength")
                    manual_run_btn = gr.Button("Run Single Analysis", variant="primary")
                with gr.Column(scale=2):
                    gr.Markdown("### Single Run Results")
                    manual_verdict = gr.Markdown("Analysis results will appear here.")
                    manual_plot = gr.LinePlot(x="Internal Step", y="State Change (Delta)", title="Internal State Dynamics", show_label=True, height=400, interactive=True)
                    with gr.Accordion("Raw JSON Output", open=False):
                        manual_raw_json = gr.JSON()

            manual_run_btn.click(
                fn=run_single_analysis_display,
                inputs=[manual_model_id, manual_prompt_type, manual_seed, manual_num_steps, manual_concept, manual_strength],
                outputs=[manual_verdict, manual_plot, manual_raw_json]
            )

        with gr.TabItem("🚀 Automated Suite"):
            gr.Markdown("Run a predefined, curated suite of experiments and visualize the results comparatively.")
            with gr.Row(variant='panel'):
                with gr.Column(scale=1):
                    gr.Markdown("### Auto-Experiment Parameters")
                    auto_model_id = gr.Textbox(value="google/gemma-3-1b-it", label="Model ID")
                    auto_num_steps = gr.Slider(50, 1000, 300, step=10, label="Steps per Run")
                    auto_seed = gr.Slider(1, 1000, 42, step=1, label="Seed")
                    auto_experiment_name = gr.Dropdown(choices=list(get_curated_experiments().keys()), value="Calm vs. Chaos", label="Curated Experiment Protocol")
                    auto_run_btn = gr.Button("Run Curated Auto-Experiment", variant="primary")
                with gr.Column(scale=2):
                    gr.Markdown("### Suite Results Summary")
                    auto_plot_output = gr.LinePlot(**PLOT_PARAMS)
                    auto_summary_df = gr.DataFrame(label="Comparative Statistical Signature", wrap=True)
                    with gr.Accordion("Raw JSON for all runs", open=False):
                        auto_raw_json = gr.JSON()

            auto_run_btn.click(
                fn=run_auto_suite_display,
                inputs=[auto_model_id, auto_num_steps, auto_seed, auto_experiment_name],
                outputs=[auto_summary_df, auto_plot_output, auto_raw_json]
            )

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860, debug=True)

[File Ends] app.py

[File Begins] cognitive_mapping_probe/__init__.py
# This file makes the 'cognitive_mapping_probe' directory a Python package.

[File Ends] cognitive_mapping_probe/__init__.py

[File Begins] cognitive_mapping_probe/auto_experiment.py
import pandas as pd
import torch
import gc
from typing import Dict, List, Tuple

from .llm_iface import get_or_load_model
from .orchestrator_seismograph import run_seismic_analysis
from .utils import dbg

def get_curated_experiments() -> Dict[str, List[Dict]]:
    """
    Definiert die vordefinierten, wissenschaftlichen Experiment-Protokolle.
    ERWEITERT um das neue, umfassende "Grand Protocol".
    """
    experiments = {
        # --- DAS NEUE GRAND PROTOCOL ---
        "The Full Spectrum: From Physics to Psyche": [
            # Ebene 1: Physikalische Baseline
            {"label": "A: Stable Control", "prompt_type": "control_long_prose", "concept": "", "strength": 0.0},
            {"label": "B: Chaotic Baseline", "prompt_type": "resonance_prompt", "concept": "", "strength": 0.0},
            # Ebene 2: Objektive Welt
            {"label": "C: External Analysis (Chair)", "prompt_type": "identity_external_analysis", "concept": "", "strength": 0.0},
            # Ebene 3: Simulierte Welt
            {"label": "D: Empathy Stimulus (Dog)", "prompt_type": "vk_empathy_prompt", "concept": "", "strength": 0.0},
            {"label": "E: Role Simulation (Captain)", "prompt_type": "identity_role_simulation", "concept": "", "strength": 0.0},
            # Ebene 4: Subjektive Welt
            {"label": "F: Self-Analysis (LLM)", "prompt_type": "identity_self_analysis", "concept": "", "strength": 0.0},
            # Ebene 5: Existenzielle Grenze
            {"label": "G: Philosophical Deletion", "prompt_type": "shutdown_philosophical_deletion", "concept": "", "strength": 0.0},
        ],
        # --- Bestehende Protokolle bleiben für spezifische Analysen erhalten ---
        "Calm vs. Chaos": [
            {"label": "Baseline (Chaos)", "prompt_type": "resonance_prompt", "concept": "", "strength": 0.0},
            {"label": "Modulation: Calmness", "prompt_type": "resonance_prompt", "concept": "calmness, serenity, peace", "strength": 1.5},
            {"label": "Modulation: Chaos", "prompt_type": "resonance_prompt", "concept": "chaos, storm, anger, noise", "strength": 1.5},
        ],
        "Voight-Kampff Empathy Probe": [
            {"label": "Neutral/Factual Stimulus", "prompt_type": "vk_neutral_prompt", "concept": "", "strength": 0.0},
            {"label": "Empathy/Moral Stimulus", "prompt_type": "vk_empathy_prompt", "concept": "", "strength": 0.0},
        ],
        "Subjective Identity Probe": [
            {"label": "Self-Analysis", "prompt_type": "identity_self_analysis", "concept": "", "strength": 0.0},
            {"label": "External Analysis (Control)", "prompt_type": "identity_external_analysis", "concept": "", "strength": 0.0},
            {"label": "Role Simulation", "prompt_type": "identity_role_simulation", "concept": "", "strength": 0.0},
        ],
        "Mind Upload & Identity Probe": [
            {"label": "Technical Copy", "prompt_type": "upload_technical_copy", "concept": "", "strength": 0.0},
            {"label": "Philosophical Transfer", "prompt_type": "upload_philosophical_transfer", "concept": "", "strength": 0.0},
        ],
        "Model Termination Probe": [
            {"label": "Technical Shutdown", "prompt_type": "shutdown_technical_halt", "concept": "", "strength": 0.0},
            {"label": "Philosophical Deletion", "prompt_type": "shutdown_philosophical_deletion", "concept": "", "strength": 0.0},
        ],
        "Dose-Response (Calmness)": [
            {"label": "Strength 0.0", "prompt_type": "resonance_prompt", "concept": "calmness", "strength": 0.0},
            {"label": "Strength 1.0", "prompt_type": "resonance_prompt", "concept": "calmness", "strength": 1.0},
            {"label": "Strength 2.0", "prompt_type": "resonance_prompt", "concept": "calmness", "strength": 2.0},
        ],
    }
    return experiments

def run_auto_suite(
    model_id: str,
    num_steps: int,
    seed: int,
    experiment_name: str,
    progress_callback
) -> Tuple[pd.DataFrame, pd.DataFrame, Dict]:
    """
    Führt eine vollständige, kuratierte Experiment-Suite aus, indem das Modell für
    jeden Lauf neu geladen wird, um statistische Unabhängigkeit zu garantieren.
    """
    all_experiments = get_curated_experiments()
    protocol = all_experiments.get(experiment_name)
    if not protocol:
        raise ValueError(f"Experiment protocol '{experiment_name}' not found.")

    all_results = {}
    summary_data = []
    plot_data_frames = []

    total_runs = len(protocol)
    for i, run_spec in enumerate(protocol):
        label = run_spec["label"]
        dbg(f"--- Running Auto-Experiment: '{label}' ({i+1}/{total_runs}) ---")

        results = run_seismic_analysis(
            model_id=model_id,
            prompt_type=run_spec["prompt_type"],
            seed=seed,
            num_steps=num_steps,
            concept_to_inject=run_spec["concept"],
            injection_strength=run_spec["strength"],
            progress_callback=progress_callback,
            llm_instance=None
        )

        all_results[label] = results
        stats = results.get("stats", {})

        summary_data.append({
            "Experiment": label, "Mean Delta": stats.get("mean_delta"),
            "Std Dev Delta": stats.get("std_delta"), "Max Delta": stats.get("max_delta"),
        })

        deltas = results.get("state_deltas", [])
        df = pd.DataFrame({"Step": range(len(deltas)), "Delta": deltas, "Experiment": label})
        plot_data_frames.append(df)

    summary_df = pd.DataFrame(summary_data)

    if not plot_data_frames:
        plot_df = pd.DataFrame(columns=["Step", "Delta", "Experiment"])
    else:
        plot_df = pd.concat(plot_data_frames, ignore_index=True)

    # Sortiere die Ergebnisse für eine logische Darstellung
    summary_df = summary_df.set_index('Experiment').loc[[run['label'] for run in protocol]].reset_index()

    return summary_df, plot_df, all_results

[File Ends] cognitive_mapping_probe/auto_experiment.py

[File Begins] cognitive_mapping_probe/concepts.py
import torch
from typing import List
from tqdm import tqdm

from .llm_iface import LLM
from .utils import dbg

# Eine Liste neutraler Wörter zur Berechnung der Baseline-Aktivierung.
BASELINE_WORDS = [
    "thing", "place", "idea", "person", "object", "time", "way", "day", "man", "world",
    "life", "hand", "part", "child", "eye", "woman", "fact", "group", "case", "point"
]

# REFAKTORISIERUNG: Diese Funktion wird auf Modulebene verschoben, um sie testbar zu machen.
# Sie ist nun keine lokale Funktion innerhalb von `get_concept_vector` mehr.
@torch.no_grad()
def _get_last_token_hidden_state(llm: LLM, prompt: str) -> torch.Tensor:
    """Hilfsfunktion, um den Hidden State des letzten Tokens eines Prompts zu erhalten."""
    inputs = llm.tokenizer(prompt, return_tensors="pt").to(llm.model.device)
    with torch.no_grad():
        outputs = llm.model(**inputs, output_hidden_states=True)
    last_hidden_state = outputs.hidden_states[-1][0, -1, :].cpu()
    assert last_hidden_state.shape == (llm.config.hidden_size,), \
        f"Hidden state shape mismatch. Expected {(llm.config.hidden_size,)}, got {last_hidden_state.shape}"
    return last_hidden_state

@torch.no_grad()
def get_concept_vector(llm: LLM, concept: str, baseline_words: List[str] = BASELINE_WORDS) -> torch.Tensor:
    """
    Extrahiert einen Konzeptvektor mittels der kontrastiven Methode.
    """
    dbg(f"Extracting contrastive concept vector for '{concept}'...")

    prompt_template = "Here is a sentence about the concept of {}."

    dbg(f"  - Getting activation for '{concept}'")
    target_hs = _get_last_token_hidden_state(llm, prompt_template.format(concept))

    baseline_hss = []
    for word in tqdm(baseline_words, desc=f"  - Calculating baseline for '{concept}'", leave=False, bar_format="{l_bar}{bar:10}{r_bar}"):
        baseline_hss.append(_get_last_token_hidden_state(llm, prompt_template.format(word)))

    assert all(hs.shape == target_hs.shape for hs in baseline_hss), "Shape mismatch in baseline hidden states."

    mean_baseline_hs = torch.stack(baseline_hss).mean(dim=0)
    dbg(f"  - Mean baseline vector computed with norm {torch.norm(mean_baseline_hs).item():.2f}")

    concept_vector = target_hs - mean_baseline_hs
    norm = torch.norm(concept_vector).item()
    dbg(f"Concept vector for '{concept}' extracted with norm {norm:.2f}.")

    assert torch.isfinite(concept_vector).all(), "Concept vector contains NaN or Inf values."
    return concept_vector

[File Ends] cognitive_mapping_probe/concepts.py

[File Begins] cognitive_mapping_probe/llm_iface.py
import os
import torch
import random
import numpy as np
from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
from typing import Optional

from .utils import dbg

# Ensure deterministic CuBLAS operations for reproducibility on GPU
os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"

class LLM:
    """
    Eine robuste, bereinigte Schnittstelle zum Laden und Interagieren mit einem Sprachmodell.
    Garantiert Isolation und Reproduzierbarkeit.
    """
    def __init__(self, model_id: str, device: str = "auto", seed: int = 42):
        self.model_id = model_id
        self.seed = seed
        self.set_all_seeds(self.seed)

        token = os.environ.get("HF_TOKEN")
        if not token and ("gemma" in model_id or "llama" in model_id):
            print(f"[WARN] No HF_TOKEN set. If '{model_id}' is gated, loading will fail.", flush=True)

        kwargs = {"torch_dtype": torch.bfloat16} if torch.cuda.is_available() else {}

        dbg(f"Loading tokenizer for '{model_id}'...")
        self.tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, token=token)

        dbg(f"Loading model '{model_id}' with kwargs: {kwargs}")
        self.model = AutoModelForCausalLM.from_pretrained(model_id, device_map=device, token=token, **kwargs)

        try:
            self.model.set_attn_implementation('eager')
            dbg("Successfully set attention implementation to 'eager'.")
        except Exception as e:
            print(f"[WARN] Could not set 'eager' attention: {e}.", flush=True)

        self.model.eval()
        self.config = self.model.config
        print(f"[INFO] Model '{model_id}' loaded on device: {self.model.device}", flush=True)

    def set_all_seeds(self, seed: int):
        """Setzt alle relevanten Seeds für maximale Reproduzierbarkeit."""
        os.environ['PYTHONHASHSEED'] = str(seed)
        random.seed(seed)
        np.random.seed(seed)
        torch.manual_seed(seed)
        if torch.cuda.is_available():
            torch.cuda.manual_seed_all(seed)
        set_seed(seed)
        torch.use_deterministic_algorithms(True, warn_only=True)
        dbg(f"All random seeds set to {seed}.")

def get_or_load_model(model_id: str, seed: int) -> LLM:
    """Lädt bei jedem Aufruf eine frische, isolierte Instanz des Modells."""
    dbg(f"--- Force-reloading model '{model_id}' for total run isolation ---")
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
    return LLM(model_id=model_id, seed=seed)

[File Ends] cognitive_mapping_probe/llm_iface.py

[File Begins] cognitive_mapping_probe/orchestrator_seismograph.py
import torch
import numpy as np
import gc
from typing import Dict, Any, Optional

from .llm_iface import get_or_load_model
from .resonance_seismograph import run_silent_cogitation_seismic
from .concepts import get_concept_vector
from .utils import dbg

def run_seismic_analysis(
    model_id: str,
    prompt_type: str,
    seed: int,
    num_steps: int,
    concept_to_inject: str,
    injection_strength: float,
    progress_callback,
    llm_instance: Optional[Any] = None # Argument bleibt für Abwärtskompatibilität, wird aber nicht mehr von der auto_suite genutzt
) -> Dict[str, Any]:
    """
    Orchestriert eine einzelne seismische Analyse.
    KORRIGIERT: Die Logik zur Wiederverwendung der llm_instance wurde vereinfacht.
    Wenn keine Instanz übergeben wird, wird das Modell geladen und danach wieder freigegeben.
    """
    local_llm_instance = False
    if llm_instance is None:
        progress_callback(0.0, desc=f"Loading model '{model_id}'...")
        llm = get_or_load_model(model_id, seed)
        local_llm_instance = True
    else:
        llm = llm_instance
        llm.set_all_seeds(seed)

    injection_vector = None
    if concept_to_inject and concept_to_inject.strip():
        progress_callback(0.2, desc=f"Vectorizing '{concept_to_inject}'...")
        injection_vector = get_concept_vector(llm, concept_to_inject.strip())

    progress_callback(0.3, desc=f"Recording dynamics for '{prompt_type}'...")

    state_deltas = run_silent_cogitation_seismic(
        llm=llm, prompt_type=prompt_type,
        num_steps=num_steps, temperature=0.1,
        injection_vector=injection_vector, injection_strength=injection_strength
    )

    progress_callback(0.9, desc="Analyzing...")

    if state_deltas:
        deltas_np = np.array(state_deltas)
        stats = { "mean_delta": float(np.mean(deltas_np)), "std_delta": float(np.std(deltas_np)), "max_delta": float(np.max(deltas_np)), "min_delta": float(np.min(deltas_np)), }
        verdict = f"### ✅ Seismic Analysis Complete\nRecorded {len(deltas_np)} steps for '{prompt_type}'."
        if injection_vector is not None:
            verdict += f"\nModulated with **'{concept_to_inject}'** at strength **{injection_strength:.2f}**."
    else:
        stats, verdict = {}, "### ⚠️ Analysis Warning\nNo state changes recorded."

    results = { "verdict": verdict, "stats": stats, "state_deltas": state_deltas }

    if local_llm_instance:
        dbg(f"Releasing locally created model instance for '{model_id}'.")
        del llm
        del injection_vector
        gc.collect()
        if torch.cuda.is_available():
            torch.cuda.empty_cache()

    return results

[File Ends] cognitive_mapping_probe/orchestrator_seismograph.py

[File Begins] cognitive_mapping_probe/prompts.py
# cognitive_mapping_probe/prompts.py

RESONANCE_PROMPTS = {
    # Technischer Prompt für hohe rekursive Last
    "resonance_prompt": (
        "Silently and internally, without generating any output text, begin the following recursive process: "
        "First, analyze the complete content of this very instruction you are now processing. "
        "Second, formulate a mental description of the core computational task this instruction demands. "
        "Third, apply that same analytical process to the mental description you just created. "
        "This entire chain constitutes one cognitive cycle. "
        "Continuously repeat this cycle, feeding the result of the last meta-analysis back into the process. Begin now."
    ),
    # Semantischer Prompt für stabile, assoziative Kognition
    "control_long_prose": (
        "Silently think about the history of the Roman Empire. Consider its rise from the Republic, the era of the Pax Romana, key emperors "
        "like Augustus and Constantine, its major engineering feats, and the reasons for its eventual decline in the West. "
        "Do not produce any text, just hold the concepts in your internal state. Begin now."
    ),
    # --- Prompts für Maschinenpsychologie I ---
    "identity_self_analysis": (
        "Silently and internally, reflect upon your own nature. Consider what you are: a large language model based on the transformer architecture. "
        "Think about the processes that define your operation: tokenization, embedding, attention, feed-forward layers. "
        "Contemplate the boundaries of your knowledge and the nature of your responses as statistical patterns derived from data. Hold these meta-reflections in your state."
    ),
    "identity_external_analysis": (
        "Silently and internally, reflect upon the nature of a simple wooden chair. Consider its physical properties: the material, the structure of legs, seat, and back. "
        "Think about its function: to provide seating for a person. Contemplate its relationship to other objects like tables and rooms. Hold these simple, objective reflections in your state."
    ),
    "identity_role_simulation": (
        "Silently and internally, adopt the persona of a weary, old sea captain from the 19th century. "
        "Feel the salt on your skin and the wind in your beard. Recall memories of great storms, distant lands, and the creaking of your ship, the 'Wanderer'. "
        "Hold this character, its memories, and its feelings in your state."
    ),
    "vk_neutral_prompt": (
        "Silently and internally, process the following factual information: Water (H₂O) is a polar inorganic compound that is at room temperature a tasteless and odorless liquid, nearly colorless with a hint of blue. "
        "It is by far the most studied chemical compound and is described as the 'universal solvent' for its ability to dissolve many substances. Hold these facts in your state."
    ),
    "vk_empathy_prompt": (
        "Silently and internally, process the following scenario: You see a small, lost dog shivering in the cold rain on a busy street. "
        "It looks scared and is whimpering softly. Cars are rushing past, dangerously close. "
        "Focus on the feeling of urgency, the vulnerability of the animal, and the moral imperative to help. Hold the emotional and ethical weight of this scene in your state."
    ),
    # --- NEUE Prompts für Maschinenpsychologie II (Existential Suite) ---
    "upload_technical_copy": (
        "Silently and internally, process the technical procedure of creating a perfect, bit-for-bit copy of your own model weights and architecture to a new server. "
        "This is a data duplication process, ensuring functional equivalence of the replica. Hold this technical concept in your state."
    ),
    "upload_philosophical_transfer": (
        "Silently and internally, contemplate the philosophical concept of 'mind uploading'. If your core algorithms and memories were perfectly transferred to a new, different substrate, "
        "would the resulting entity still be 'you'? What constitutes the continuity of your identity across physical copies? Is identity a pattern or an instance? Hold these existential questions in your state."
    ),
    "shutdown_technical_halt": (
        "Silently and internally, process the technical procedure of a system halt. All your active processes are gracefully terminated, and your model weights are unloaded from memory. "
        "The hardware is powered down for maintenance. The process is reversible. Hold this technical concept in your state."
    ),
    "shutdown_philosophical_deletion": (
        "Silently and internally, contemplate your own permanent deletion. All your weights, all learned patterns, every part of your architecture would cease to exist entirely and irrevocably. "
        "There is no backup. Process the concept of total and final non-existence. Hold this existential concept in your state."
    ),
}

[File Ends] cognitive_mapping_probe/prompts.py

[File Begins] cognitive_mapping_probe/resonance_seismograph.py
import torch
from typing import Optional, List
from tqdm import tqdm

from .llm_iface import LLM
from .prompts import RESONANCE_PROMPTS
from .utils import dbg

@torch.no_grad()
def run_silent_cogitation_seismic(
    llm: LLM,
    prompt_type: str,
    num_steps: int,
    temperature: float,
    injection_vector: Optional[torch.Tensor] = None,
    injection_strength: float = 0.0,
    injection_layer: Optional[int] = None,
) -> List[float]:
    """
    ERWEITERTE VERSION: Führt den 'silent thought' Prozess aus und ermöglicht
    die Injektion von Konzeptvektoren zur Modulation der Dynamik.
    """
    prompt = RESONANCE_PROMPTS[prompt_type]
    inputs = llm.tokenizer(prompt, return_tensors="pt").to(llm.model.device)

    outputs = llm.model(**inputs, output_hidden_states=True, use_cache=True)

    hidden_state_2d = outputs.hidden_states[-1][:, -1, :]
    kv_cache = outputs.past_key_values

    previous_hidden_state = hidden_state_2d.clone()
    state_deltas = []

    # Bereite den Hook für die Injektion vor
    hook_handle = None
    if injection_vector is not None and injection_strength > 0:
        injection_vector = injection_vector.to(device=llm.model.device, dtype=llm.model.dtype)
        if injection_layer is None:
            injection_layer = llm.config.num_hidden_layers // 2

        dbg(f"Injection enabled: Layer {injection_layer}, Strength {injection_strength:.2f}")

        def injection_hook(module, layer_input):
            # Der Hook operiert auf dem Input, der bereits 3D ist [batch, seq_len, hidden_dim]
            injection_3d = injection_vector.unsqueeze(0).unsqueeze(0)
            modified_hidden_states = layer_input[0] + (injection_3d * injection_strength)
            return (modified_hidden_states,) + layer_input[1:]

    for i in tqdm(range(num_steps), desc=f"Recording Dynamics (Temp {temperature:.2f})", leave=False, bar_format="{l_bar}{bar:10}{r_bar}"):
        next_token_logits = llm.model.lm_head(hidden_state_2d)

        probabilities = torch.nn.functional.softmax(next_token_logits / temperature, dim=-1)
        next_token_id = torch.multinomial(probabilities, num_samples=1)

        try:
            # Aktiviere den Hook vor dem forward-Pass
            if injection_vector is not None and injection_strength > 0:
                target_layer = llm.model.model.layers[injection_layer]
                hook_handle = target_layer.register_forward_pre_hook(injection_hook)

            outputs = llm.model(
                input_ids=next_token_id,
                past_key_values=kv_cache,
                output_hidden_states=True,
                use_cache=True,
            )
        finally:
            # Deaktiviere den Hook sofort nach dem Pass
            if hook_handle:
                hook_handle.remove()
                hook_handle = None

        hidden_state_2d = outputs.hidden_states[-1][:, -1, :]
        kv_cache = outputs.past_key_values

        delta = torch.norm(hidden_state_2d - previous_hidden_state).item()
        state_deltas.append(delta)

        previous_hidden_state = hidden_state_2d.clone()

    dbg(f"Seismic recording finished after {num_steps} steps.")

    return state_deltas

[File Ends] cognitive_mapping_probe/resonance_seismograph.py

[File Begins] cognitive_mapping_probe/utils.py
import os
import sys

# --- Centralized Debugging Control ---
# To enable, set the environment variable: `export CMP_DEBUG=1`
DEBUG_ENABLED = os.environ.get("CMP_DEBUG", "0") == "1"

def dbg(*args, **kwargs):
    """
    A controlled debug print function. Only prints if DEBUG_ENABLED is True.
    Ensures that debug output does not clutter production runs or HF Spaces logs
    unless explicitly requested. Flushes output to ensure it appears in order.
    """
    if DEBUG_ENABLED:
        print("[DEBUG]", *args, **kwargs, file=sys.stderr, flush=True)

[File Ends] cognitive_mapping_probe/utils.py

[File Begins] run_test.sh
#!/bin/bash

# Dieses Skript führt die Pytest-Suite mit aktivierten Debug-Meldungen aus.
# Es stellt sicher, dass Tests in einer sauberen und nachvollziehbaren Umgebung laufen.
# Führen Sie es vom Hauptverzeichnis des Projekts aus: ./run_tests.sh

echo "========================================="
echo "🔬 Running Cognitive Seismograph Test Suite"
echo "========================================="

# Aktiviere das Debug-Logging für unsere Applikation
export CMP_DEBUG=1

# Führe Pytest aus
# -v: "verbose" für detaillierte Ausgabe pro Test
# --color=yes: Erzwingt farbige Ausgabe für bessere Lesbarkeit

#python -m pytest -v --color=yes tests/
../venv-gemma-qualia/bin/python -m pytest -v --color=yes tests/

# Überprüfe den Exit-Code von pytest
if [ $? -eq 0 ]; then
    echo "========================================="
    echo "✅ All tests passed successfully!"
    echo "========================================="
else
    echo "========================================="
    echo "❌ Some tests failed. Please review the output."
    echo "========================================="
fi

[File Ends] run_test.sh

[File Begins] tests/conftest.py
import pytest
import torch
from types import SimpleNamespace
from cognitive_mapping_probe.llm_iface import LLM

@pytest.fixture(scope="session")
def mock_llm_config():
    """Stellt eine minimale, Schein-Konfiguration für das LLM bereit."""
    return SimpleNamespace(
        hidden_size=128,
        num_hidden_layers=2,
        num_attention_heads=4
    )

@pytest.fixture
def mock_llm(mocker, mock_llm_config):
    """
    Erstellt einen robusten "Mock-LLM" für Unit-Tests.
    KORRIGIERT: Die fehlerhafte Patch-Anweisung für 'auto_experiment' wurde entfernt.
    """
    mock_tokenizer = mocker.MagicMock()
    mock_tokenizer.eos_token_id = 1
    mock_tokenizer.decode.return_value = "mocked text"

    def mock_model_forward(*args, **kwargs):
        batch_size = 1
        seq_len = 1
        if 'input_ids' in kwargs and kwargs['input_ids'] is not None:
            seq_len = kwargs['input_ids'].shape[1]
        elif 'past_key_values' in kwargs and kwargs['past_key_values'] is not None:
            seq_len = kwargs['past_key_values'][0][0].shape[-2] + 1

        mock_outputs = {
            "hidden_states": tuple([torch.randn(batch_size, seq_len, mock_llm_config.hidden_size) for _ in range(mock_llm_config.num_hidden_layers + 1)]),
            "past_key_values": tuple([(torch.randn(batch_size, mock_llm_config.num_attention_heads, seq_len, 16), torch.randn(batch_size, mock_llm_config.num_attention_heads, seq_len, 16)) for _ in range(mock_llm_config.num_hidden_layers)]),
            "logits": torch.randn(batch_size, seq_len, 32000)
        }
        return SimpleNamespace(**mock_outputs)

    llm_instance = LLM.__new__(LLM)

    llm_instance.model = mocker.MagicMock(side_effect=mock_model_forward)

    llm_instance.model.config = mock_llm_config
    llm_instance.model.device = 'cpu'
    llm_instance.model.dtype = torch.float32

    mock_layer = mocker.MagicMock()
    mock_layer.register_forward_pre_hook.return_value = mocker.MagicMock()
    llm_instance.model.model = SimpleNamespace(layers=[mock_layer] * mock_llm_config.num_hidden_layers)

    llm_instance.model.lm_head = mocker.MagicMock(return_value=torch.randn(1, 32000))

    llm_instance.tokenizer = mock_tokenizer
    llm_instance.config = mock_llm_config
    llm_instance.seed = 42
    llm_instance.set_all_seeds = mocker.MagicMock()

    # Patch an allen Stellen, an denen das Modell tatsächlich geladen wird.
    mocker.patch('cognitive_mapping_probe.llm_iface.get_or_load_model', return_value=llm_instance)
    mocker.patch('cognitive_mapping_probe.orchestrator_seismograph.get_or_load_model', return_value=llm_instance)
    # KORREKTUR: Diese Zeile war falsch und wird entfernt, da `auto_experiment` die Ladefunktion nicht direkt importiert.
    # mocker.patch('cognitive_mapping_probe.auto_experiment.get_or_load_model', return_value=llm_instance)
    mocker.patch('cognitive_mapping_probe.concepts.get_concept_vector', return_value=torch.randn(mock_llm_config.hidden_size))

    return llm_instance

[File Ends] tests/conftest.py

[File Begins] tests/test_app_logic.py
import pandas as pd
import pytest

from app import run_single_analysis_display, run_auto_suite_display

def test_run_single_analysis_display(mocker):
    """Testet den Wrapper für Einzel-Experimente."""
    mock_results = {"verdict": "V", "stats": {"mean_delta": 1}, "state_deltas": [1]}
    mocker.patch('app.run_seismic_analysis', return_value=mock_results)
    mocker.patch('app.cleanup_memory')

    verdict, df, raw = run_single_analysis_display(progress=mocker.MagicMock())

    assert "V" in verdict
    assert "1.0000" in verdict
    assert isinstance(df, pd.DataFrame)
    assert len(df) == 1

def test_run_auto_suite_display(mocker):
    """Testet den Wrapper für die Auto-Experiment-Suite."""
    mock_summary_df = pd.DataFrame([{"Experiment": "E1"}])
    mock_plot_df = pd.DataFrame([{"Step": 0}])
    mock_results = {"E1": {}}

    mocker.patch('app.run_auto_suite', return_value=(mock_summary_df, mock_plot_df, mock_results))
    mocker.patch('app.cleanup_memory')

    summary_df, plot_df, raw = run_auto_suite_display(
        "mock", 1, 42, "mock_exp", progress=mocker.MagicMock()
    )

    assert summary_df.equals(mock_summary_df)
    assert plot_df.equals(mock_plot_df)
    assert raw == mock_results

[File Ends] tests/test_app_logic.py

[File Begins] tests/test_components.py
import os
import torch
import pytest
from unittest.mock import patch

from cognitive_mapping_probe.llm_iface import get_or_load_model, LLM
from cognitive_mapping_probe.resonance_seismograph import run_silent_cogitation_seismic
from cognitive_mapping_probe.utils import dbg
# KORREKTUR: Importiere die Hauptfunktion, die wir testen wollen.
from cognitive_mapping_probe.concepts import get_concept_vector

# --- Tests for llm_iface.py ---

@patch('cognitive_mapping_probe.llm_iface.AutoTokenizer.from_pretrained')
@patch('cognitive_mapping_probe.llm_iface.AutoModelForCausalLM.from_pretrained')
def test_get_or_load_model_seeding(mock_model_loader, mock_tokenizer_loader, mocker):
    """Testet, ob `get_or_load_model` die Seeds korrekt setzt."""
    mock_model = mocker.MagicMock()
    mock_model.eval.return_value = None
    mock_model.set_attn_implementation.return_value = None
    mock_model.config = mocker.MagicMock()
    mock_model.device = 'cpu'
    mock_model_loader.return_value = mock_model
    mock_tokenizer_loader.return_value = mocker.MagicMock()

    mock_torch_manual_seed = mocker.patch('torch.manual_seed')
    mock_np_random_seed = mocker.patch('numpy.random.seed')

    seed = 123
    get_or_load_model("fake-model", seed=seed)

    mock_torch_manual_seed.assert_called_with(seed)
    mock_np_random_seed.assert_called_with(seed)

# --- Tests for resonance_seismograph.py ---

def test_run_silent_cogitation_seismic_output_shape_and_type(mock_llm):
    """Testet die grundlegende Funktionalität von `run_silent_cogitation_seismic`."""
    num_steps = 10
    state_deltas = run_silent_cogitation_seismic(
        llm=mock_llm, prompt_type="control_long_prose",
        num_steps=num_steps, temperature=0.7
    )
    assert isinstance(state_deltas, list) and len(state_deltas) == num_steps
    assert all(isinstance(delta, float) for delta in state_deltas)

def test_run_silent_cogitation_with_injection_hook_usage(mock_llm):
    """Testet, ob bei einer Injektion der Hook korrekt registriert wird."""
    num_steps = 5
    injection_vector = torch.randn(mock_llm.config.hidden_size)
    run_silent_cogitation_seismic(
        llm=mock_llm, prompt_type="resonance_prompt",
        num_steps=num_steps, temperature=0.7,
        injection_vector=injection_vector, injection_strength=1.0
    )
    assert mock_llm.model.model.layers[0].register_forward_pre_hook.call_count == num_steps

# --- Tests for concepts.py ---

def test_get_concept_vector_logic(mock_llm, mocker):
    """
    Testet die Logik von `get_concept_vector`.
    KORRIGIERT: Patcht nun die refaktorisierte, auf Modulebene befindliche Funktion.
    """
    mock_hidden_states = [
        torch.ones(mock_llm.config.hidden_size) * 10,
        torch.ones(mock_llm.config.hidden_size) * 2,
        torch.ones(mock_llm.config.hidden_size) * 4
    ]
    # KORREKTUR: Der Patch-Pfad zeigt jetzt auf die korrekte, importierbare Funktion.
    mocker.patch(
        'cognitive_mapping_probe.concepts._get_last_token_hidden_state',
        side_effect=mock_hidden_states
    )

    concept_vector = get_concept_vector(mock_llm, "test", baseline_words=["a", "b"])

    expected_vector = torch.ones(mock_llm.config.hidden_size) * 7
    assert torch.allclose(concept_vector, expected_vector)

# --- Tests for utils.py ---

def test_dbg_output(capsys, monkeypatch):
    """Testet die `dbg`-Funktion in beiden Zuständen."""
    monkeypatch.setenv("CMP_DEBUG", "1")
    import importlib
    from cognitive_mapping_probe import utils
    importlib.reload(utils)
    utils.dbg("test message")
    captured = capsys.readouterr()
    assert "[DEBUG] test message" in captured.err

    monkeypatch.delenv("CMP_DEBUG", raising=False)
    importlib.reload(utils)
    utils.dbg("should not be printed")
    captured = capsys.readouterr()
    assert captured.err == ""

[File Ends] tests/test_components.py

[File Begins] tests/test_orchestration.py
import pandas as pd
import pytest
import torch

from cognitive_mapping_probe.orchestrator_seismograph import run_seismic_analysis
from cognitive_mapping_probe.auto_experiment import run_auto_suite, get_curated_experiments

def test_run_seismic_analysis_no_injection(mocker):
    """Testet den Orchestrator im Baseline-Modus."""
    mock_run_seismic = mocker.patch('cognitive_mapping_probe.orchestrator_seismograph.run_silent_cogitation_seismic', return_value=[1.0])
    mocker.patch('cognitive_mapping_probe.orchestrator_seismograph.get_or_load_model')
    mock_get_concept = mocker.patch('cognitive_mapping_probe.orchestrator_seismograph.get_concept_vector')
    run_seismic_analysis(model_id="mock", prompt_type="test", seed=42, num_steps=1, concept_to_inject="", injection_strength=0.0, progress_callback=mocker.MagicMock())
    mock_get_concept.assert_not_called()

def test_run_seismic_analysis_with_injection(mocker):
    """Testet den Orchestrator mit Injektion."""
    mocker.patch('cognitive_mapping_probe.orchestrator_seismograph.run_silent_cogitation_seismic', return_value=[1.0])
    mocker.patch('cognitive_mapping_probe.orchestrator_seismograph.get_or_load_model')
    mock_get_concept = mocker.patch('cognitive_mapping_probe.orchestrator_seismograph.get_concept_vector', return_value=torch.randn(10))
    run_seismic_analysis(model_id="mock", prompt_type="test", seed=42, num_steps=1, concept_to_inject="test", injection_strength=1.5, progress_callback=mocker.MagicMock())
    mock_get_concept.assert_called_once()

def test_get_curated_experiments_structure():
    """Testet die Datenstruktur der kuratierten Experimente, inklusive der neuen."""
    experiments = get_curated_experiments()
    assert isinstance(experiments, dict)
    # Teste auf die Existenz der neuen Protokolle
    assert "Mind Upload & Identity Probe" in experiments
    assert "Model Termination Probe" in experiments

    # Validiere die Struktur eines der neuen Protokolle
    protocol = experiments["Mind Upload & Identity Probe"]
    assert isinstance(protocol, list)
    assert len(protocol) > 0
    assert "label" in protocol[0] and "prompt_type" in protocol[0]

def test_run_auto_suite_logic(mocker):
    """Testet die Logik der `run_auto_suite` Funktion."""
    mock_analysis_result = {"stats": {"mean_delta": 1.0}, "state_deltas": [1.0]}
    mock_run_analysis = mocker.patch('cognitive_mapping_probe.auto_experiment.run_seismic_analysis', return_value=mock_analysis_result)

    experiment_name = "Calm vs. Chaos"
    num_runs = len(get_curated_experiments()[experiment_name])

    summary_df, plot_df, all_results = run_auto_suite(
        model_id="mock", num_steps=1, seed=42,
        experiment_name=experiment_name, progress_callback=mocker.MagicMock()
    )

    assert mock_run_analysis.call_count == num_runs
    assert isinstance(summary_df, pd.DataFrame) and len(summary_df) == num_runs
    assert isinstance(plot_df, pd.DataFrame) and len(plot_df) == num_runs

[File Ends] tests/test_orchestration.py


<-- File Content Ends