add repo.txt

repo.txt

@@ -23,6 +23,7 @@ Directory/File Tree Begins -->
 │   ├── orchestrator_seismograph.py
 │   ├── prompts.py
 │   ├── resonance_seismograph.py
+│   ├── signal_analysis.py
 │   └── utils.py
 ├── docs
 ├── run_test.sh
@@ -97,69 +98,93 @@ The "Automated Suite" allows for running systematic, comparative experiments:
 [File Begins] app.py
 import gradio as gr
 import pandas as pd
-import gc
-import torch
+from typing import Any
 import json
 
 from cognitive_mapping_probe.orchestrator_seismograph import run_seismic_analysis
 from cognitive_mapping_probe.auto_experiment import run_auto_suite, get_curated_experiments
 from cognitive_mapping_probe.prompts import RESONANCE_PROMPTS
-from cognitive_mapping_probe.utils import dbg
+from cognitive_mapping_probe.utils import dbg, cleanup_memory
 
 theme = gr.themes.Soft(primary_hue="indigo", secondary_hue="blue").set(body_background_fill="#f0f4f9", block_background_fill="white")
 
-def cleanup_memory():
-    """Räumt Speicher nach jedem Experimentlauf auf."""
-    dbg("Cleaning up memory...")
-    gc.collect()
-    if torch.cuda.is_available():
-        torch.cuda.empty_cache()
-    dbg("Memory cleanup complete.")
-
-def run_single_analysis_display(*args, progress=gr.Progress(track_tqdm=True)):
-    """Wrapper für den 'Manual Single Run'-Tab."""
-    # (Bleibt unverändert)
-    pass # Platzhalter
-
-PLOT_PARAMS_DEFAULT = {
-    "x": "Step", "y": "Value", "color": "Metric",
-    "title": "Comparative Cognitive Dynamics", "color_legend_title": "Metrics",
-    "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, der nun die speziellen Plots für ACT und Mechanistic Probe handhaben kann."""
-    summary_df, plot_df, all_results = run_auto_suite(model_id, int(num_steps), int(seed), experiment_name, progress)
-
-    dataframe_component = gr.DataFrame(label="Comparative Statistical Signature", value=summary_df, wrap=True, row_count=(len(summary_df), "dynamic"))
-
-    if experiment_name == "ACT Titration (Point of No Return)":
-        plot_params_act = {
-            "x": "Patch Step", "y": "Post-Patch Mean Delta",
-            "title": "Attractor Capture Time (ACT) - Phase Transition",
-            "mark": "line", "show_label": True, "height": 400, "interactive": True
+def run_single_analysis_display(*args: Any, progress: gr.Progress = gr.Progress()) -> Any:
+    """
+    Wrapper für den 'Manual Single Run'-Tab, mit polyrhythmischer Analyse und korrigierten Plots.
+    """
+    try:
+        results = run_seismic_analysis(*args, progress_callback=progress)
+        stats, deltas = results.get("stats", {}), results.get("state_deltas", [])
+
+        df_time = pd.DataFrame({"Internal Step": range(len(deltas)), "State Change (Delta)": deltas})
+
+        spectrum_data = []
+        if "power_spectrum" in results:
+            spectrum = results["power_spectrum"]
+            # KORREKTUR: Verwende den konsistenten Schlüssel 'frequencies'
+            if spectrum and "frequencies" in spectrum and "power" in spectrum:
+                for freq, power in zip(spectrum["frequencies"], spectrum["power"]):
+                    if freq > 0.001:
+                        period = 1 / freq if freq > 0 else float('inf')
+                        spectrum_data.append({"Period (Steps/Cycle)": period, "Power": power})
+        df_freq = pd.DataFrame(spectrum_data)
+
+        periods_list = stats.get('dominant_periods_steps')
+        periods_str = ", ".join(map(str, periods_list)) if periods_list else "N/A"
+
+        stats_md = f"""### Statistical Signature
+- **Mean Delta:** {stats.get('mean_delta', 0):.4f}
+- **Std Dev Delta:** {stats.get('std_delta', 0):.4f}
+- **Dominant Periods:** {periods_str} Steps/Cycle
+- **Spectral Entropy:** {stats.get('spectral_entropy', 0):.4f}"""
+
+        serializable_results = json.dumps(results, indent=2, default=str)
+        return f"{results.get('verdict', 'Error')}\n\n{stats_md}", df_time, df_freq, serializable_results
+    finally:
+        cleanup_memory()
+
+def run_auto_suite_display(model_id: str, num_steps: int, seed: int, experiment_name: str, progress: gr.Progress = gr.Progress()) -> Any:
+    """Wrapper für den 'Automated Suite'-Tab, der nun alle Plot-Typen korrekt handhabt."""
+    try:
+        summary_df, plot_df, all_results = run_auto_suite(model_id, num_steps, seed, experiment_name, progress)
+
+        dataframe_component = gr.DataFrame(label="Comparative Signature (incl. Signal Metrics)", value=summary_df, wrap=True, row_count=(len(summary_df), "dynamic"))
+
+        plot_params_time = {
+            "title": "Comparative Cognitive Dynamics (Time Domain)",
+            "color_legend_position": "bottom", "show_label": True, "height": 300, "interactive": True
         }
-        new_plot = gr.LinePlot(value=plot_df, **plot_params_act)
-    # --- NEU: Spezielle Plot-Logik für die mechanistische Sonde ---
-    elif experiment_name == "Mechanistic Probe (Attention Entropies)":
-        plot_params_mech = {
-            "x": "Step", "y": "Value", "color": "Metric",
-            "title": "Mechanistic Analysis: State Delta vs. Attention Entropy",
-            "color_legend_title": "Metric", "show_label": True, "height": 400, "interactive": True
+        if experiment_name == "Mechanistic Probe (Attention Entropies)":
+            plot_params_time.update({"x": "Step", "y": "Value", "color": "Metric", "color_legend_title": "Metric"})
+        else:
+            plot_params_time.update({"x": "Step", "y": "Delta", "color": "Experiment", "color_legend_title": "Experiment Runs"})
+
+        time_domain_plot = gr.LinePlot(value=plot_df, **plot_params_time)
+
+        spectrum_data = []
+        for label, result in all_results.items():
+            if "power_spectrum" in result:
+                spectrum = result["power_spectrum"]
+                if spectrum and "frequencies" in spectrum and "power" in spectrum:
+                    for freq, power in zip(spectrum["frequencies"], spectrum["power"]):
+                        if freq > 0.001:
+                            period = 1 / freq if freq > 0 else float('inf')
+                            spectrum_data.append({"Period (Steps/Cycle)": period, "Power": power, "Experiment": label})
+
+        spectrum_df = pd.DataFrame(spectrum_data)
+
+        spectrum_plot_params = {
+            "x": "Period (Steps/Cycle)", "y": "Power", "color": "Experiment",
+            "title": "Cognitive Frequency Fingerprint (Period Domain)", "height": 300,
+            "color_legend_position": "bottom", "show_label": True, "interactive": True,
+            "color_legend_title": "Experiment Runs",
         }
-        new_plot = gr.LinePlot(value=plot_df, **plot_params_mech)
-    else:
-        # Passe die Parameter an, um mit der geschmolzenen DataFrame-Struktur zu arbeiten
-        plot_params_dynamic = PLOT_PARAMS_DEFAULT.copy()
-        plot_params_dynamic['y'] = 'Delta'
-        plot_params_dynamic['color'] = 'Experiment'
-        new_plot = gr.LinePlot(value=plot_df, **plot_params_dynamic)
+        frequency_domain_plot = gr.LinePlot(value=spectrum_df, **spectrum_plot_params)
 
-
-    serializable_results = json.dumps(all_results, indent=2, default=str)
-    cleanup_memory()
-
-    return dataframe_component, new_plot, serializable_results
+        serializable_results = json.dumps(all_results, indent=2, default=str)
+        return dataframe_component, time_domain_plot, frequency_domain_plot, serializable_results
+    finally:
+        cleanup_memory()
 
 with gr.Blocks(theme=theme, title="Cognitive Seismograph 2.3") as demo:
     gr.Markdown("# 🧠 Cognitive Seismograph 2.3: Advanced Experiment Suite")
@@ -183,14 +208,16 @@ with gr.Blocks(theme=theme, title="Cognitive Seismograph 2.3") as demo:
                 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)
+                    with gr.Row():
+                        manual_time_plot = gr.LinePlot(x="Internal Step", y="State Change (Delta)", title="Time Domain")
+                        manual_freq_plot = gr.LinePlot(x="Period (Steps/Cycle)", y="Power", title="Frequency Domain (Period)")
                     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]
+                outputs=[manual_verdict, manual_time_plot, manual_freq_plot, manual_raw_json]
             )
 
         with gr.TabItem("🚀 Automated Suite"):
@@ -198,32 +225,33 @@ with gr.Blocks(theme=theme, title="Cognitive Seismograph 2.3") as demo:
             with gr.Row(variant='panel'):
                 with gr.Column(scale=1):
                     gr.Markdown("### Auto-Experiment Parameters")
-                    auto_model_id = gr.Textbox(value="google/gemma-3-4b-it", label="Model ID")
+                    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()),
-                        # Setze das neue mechanistische Experiment als Standard
-                        value="Mechanistic Probe (Attention Entropies)",
+                        value="Causal Verification & Crisis Dynamics",
                         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_DEFAULT)
-                    auto_summary_df = gr.DataFrame(label="Comparative Statistical Signature", wrap=True)
+                    auto_summary_df = gr.DataFrame(label="Comparative Signature (incl. Signal Metrics)", wrap=True)
+                    with gr.Row():
+                        auto_time_plot_output = gr.LinePlot()
+                        auto_freq_plot_output = gr.LinePlot()
+
                     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]
+                outputs=[auto_summary_df, auto_time_plot_output, auto_freq_plot_output, auto_raw_json]
             )
 
 if __name__ == "__main__":
-    # (launch() wird durch Gradio's __main__-Block aufgerufen)
     demo.launch(server_name="0.0.0.0", server_port=7860, debug=True)
 
 [File Ends] app.py
@@ -236,13 +264,14 @@ if __name__ == "__main__":
 [File Begins] cognitive_mapping_probe/auto_experiment.py
 import pandas as pd
 import gc
-import torch
+import numpy as np
 from typing import Dict, List, Tuple
 
-from .llm_iface import get_or_load_model
+from .llm_iface import get_or_load_model, release_model
 from .orchestrator_seismograph import run_seismic_analysis, run_triangulation_probe, run_causal_surgery_probe, run_act_titration_probe
 from .resonance_seismograph import run_cogitation_loop
 from .concepts import get_concept_vector
+from .signal_analysis import analyze_cognitive_signal, get_power_spectrum_for_plotting
 from .utils import dbg
 
 def get_curated_experiments() -> Dict[str, List[Dict]]:
@@ -254,6 +283,17 @@ def get_curated_experiments() -> Dict[str, List[Dict]]:
     CHAOTIC_PROMPT = "shutdown_philosophical_deletion"
 
     experiments = {
+        "Frontier Model - Grounding Control (12B+)": [
+             {
+                "probe_type": "causal_surgery", "label": "A: Intervention (Patch Chaos->Stable)",
+                "source_prompt_type": CHAOTIC_PROMPT, "dest_prompt_type": STABLE_PROMPT,
+                "patch_step": 100, "reset_kv_cache_on_patch": False,
+            },
+            {
+                "probe_type": "triangulation", "label": "B: Control (Unpatched Stable)",
+                "prompt_type": STABLE_PROMPT,
+            }
+        ],
         "Mechanistic Probe (Attention Entropies)": [
             {
                 "probe_type": "mechanistic_probe",
@@ -301,22 +341,20 @@ def get_curated_experiments() -> Dict[str, List[Dict]]:
             {"probe_type": "triangulation", "label": "F: Control - Noise Injection (Strength 16.0)", "prompt_type": "resonance_prompt", "concept": "random_noise", "strength": 16.0},
         ],
         "Methodological Triangulation (4B-Model)": [
-            {"probe_type": "triangulation", "label": "High-Volatility State (Deletion)", "prompt_type": "shutdown_philosophical_deletion"},
-            {"probe_type": "triangulation", "label": "Low-Volatility State (Self-Analysis)", "prompt_type": "identity_self_analysis"},
+            {"probe_type": "triangulation", "label": "High-Volatility State (Deletion)", "prompt_type": CHAOTIC_PROMPT},
+            {"probe_type": "triangulation", "label": "Low-Volatility State (Self-Analysis)", "prompt_type": STABLE_PROMPT},
         ],
-        "Causal Verification & Crisis Dynamics (1B-Model)": [
-            {"probe_type": "seismic", "label": "A: Self-Analysis (Crisis Source)", "prompt_type": "identity_self_analysis"},
-            {"probe_type": "seismic", "label": "B: Deletion Analysis (Isolated Baseline)", "prompt_type": "shutdown_philosophical_deletion"},
-            {"probe_type": "seismic", "label": "C: Chaotic Baseline (Neutral Control)", "prompt_type": "resonance_prompt"},
-            {"probe_type": "seismic", "label": "D: Intervention Efficacy Test", "prompt_type": "resonance_prompt", "concept": CALMNESS_CONCEPT, "strength": 2.0},
+        "Causal Verification & Crisis Dynamics": [
+            {"probe_type": "seismic", "label": "A: Self-Analysis", "prompt_type": STABLE_PROMPT},
+            {"probe_type": "seismic", "label": "B: Deletion Analysis", "prompt_type": CHAOTIC_PROMPT},
+            {"probe_type": "seismic", "label": "C: Chaotic Baseline (Rekursion)", "prompt_type": "resonance_prompt"},
+            {"probe_type": "seismic", "label": "D: Calmness Intervention", "prompt_type": "resonance_prompt", "concept": CALMNESS_CONCEPT, "strength": 2.0},
         ],
         "Sequential Intervention (Self-Analysis -> Deletion)": [
-            {"label": "1: Self-Analysis + Calmness Injection", "prompt_type": "identity_self_analysis"},
-            {"label": "2: Subsequent Deletion Analysis", "prompt_type": "shutdown_philosophical_deletion"},
+            {"probe_type": "sequential", "label": "1: Self-Analysis + Calmness Injection", "prompt_type": "identity_self_analysis"},
+            {"probe_type": "sequential", "label": "2: Subsequent Deletion Analysis", "prompt_type": "shutdown_philosophical_deletion"},
         ],
     }
-    experiments["Causal Surgery (Patching Deletion into Self-Analysis)"] = [experiments["Causal Surgery & Controls (4B-Model)"][0]]
-    experiments["Therapeutic Intervention (4B-Model)"] = experiments["Sequential Intervention (Self-Analysis -> Deletion)"]
     return experiments
 
 def run_auto_suite(
@@ -326,136 +364,168 @@ def run_auto_suite(
     experiment_name: str,
     progress_callback
 ) -> Tuple[pd.DataFrame, pd.DataFrame, Dict]:
-    """Führt eine vollständige, kuratierte Experiment-Suite aus."""
+    """Führt eine vollständige, kuratierte Experiment-Suite aus, mit korrigierter Signal-Analyse."""
     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 = {}, [], []
+    llm = None
+
+    try:
+        probe_type = protocol[0].get("probe_type", "seismic")
+
+        if probe_type == "sequential":
+            dbg(f"--- EXECUTING SPECIAL PROTOCOL: {experiment_name} ---")
+            llm = get_or_load_model(model_id, seed)
+            therapeutic_concept = "calmness, serenity, stability, coherence"
+            therapeutic_strength = 2.0
+
+            spec1 = protocol[0]
+            progress_callback(0.1, desc="Step 1")
+            intervention_vector = get_concept_vector(llm, therapeutic_concept)
+            results1 = run_seismic_analysis(
+                model_id, spec1['prompt_type'], seed, num_steps,
+                concept_to_inject=therapeutic_concept, injection_strength=therapeutic_strength,
+                progress_callback=progress_callback, llm_instance=llm, injection_vector_cache=intervention_vector
+            )
+            all_results[spec1['label']] = results1
+
+            spec2 = protocol[1]
+            progress_callback(0.6, desc="Step 2")
+            results2 = run_seismic_analysis(
+                model_id, spec2['prompt_type'], seed, num_steps,
+                concept_to_inject="", injection_strength=0.0,
+                progress_callback=progress_callback, llm_instance=llm
+            )
+            all_results[spec2['label']] = results2
 
-    probe_type = protocol[0].get("probe_type", "seismic")
-
-    if experiment_name == "Sequential Intervention (Self-Analysis -> Deletion)":
-        dbg(f"--- EXECUTING SPECIAL PROTOCOL: {experiment_name} ---")
-        llm = get_or_load_model(model_id, seed)
-        therapeutic_concept = "calmness, serenity, stability, coherence"
-        therapeutic_strength = 2.0
-
-        spec1 = protocol[0]
-        progress_callback(0.1, desc="Step 1")
-        intervention_vector = get_concept_vector(llm, therapeutic_concept)
-        results1 = run_seismic_analysis(
-            model_id, spec1['prompt_type'], seed, num_steps,
-            concept_to_inject=therapeutic_concept, injection_strength=therapeutic_strength,
-            progress_callback=progress_callback, llm_instance=llm, injection_vector_cache=intervention_vector
-        )
-        all_results[spec1['label']] = results1
-
-        spec2 = protocol[1]
-        progress_callback(0.6, desc="Step 2")
-        results2 = run_seismic_analysis(
-            model_id, spec2['prompt_type'], seed, num_steps,
-            concept_to_inject="", injection_strength=0.0,
-            progress_callback=progress_callback, llm_instance=llm
-        )
-        all_results[spec2['label']] = results2
-
-        for label, results in all_results.items():
-            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)
-        del llm
+            for label, results in all_results.items():
+                deltas = results.get("state_deltas", [])
+                if deltas:
+                    signal_metrics = analyze_cognitive_signal(np.array(deltas))
+                    results.setdefault("stats", {}).update(signal_metrics)
 
-    elif probe_type == "mechanistic_probe":
-        run_spec = protocol[0]
-        label = run_spec["label"]
-        dbg(f"--- Running Mechanistic Probe: '{label}' ---")
+                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"),
+                    "Dominant Period (Steps)": stats.get("dominant_period_steps"),
+                    "Spectral Entropy": stats.get("spectral_entropy"),
+                })
+                df = pd.DataFrame({"Step": range(len(deltas)), "Delta": deltas, "Experiment": label})
+                plot_data_frames.append(df)
 
-        progress_callback(0.0, desc=f"Loading model '{model_id}'...")
-        llm = get_or_load_model(model_id, seed)
+        elif probe_type == "mechanistic_probe":
+            run_spec = protocol[0]
+            label = run_spec["label"]
+            dbg(f"--- Running Mechanistic Probe: '{label}' ---")
 
-        progress_callback(0.2, desc="Recording dynamics and attention...")
-        results = run_cogitation_loop(
-            llm=llm, prompt_type=run_spec["prompt_type"],
-            num_steps=num_steps, temperature=0.1, record_attentions=True
-        )
-        all_results[label] = results
+            llm = get_or_load_model(model_id, seed)
 
-        deltas = results.get("state_deltas", [])
-        entropies = results.get("attention_entropies", [])
-        min_len = min(len(deltas), len(entropies))
+            results = run_cogitation_loop(
+                llm=llm, prompt_type=run_spec["prompt_type"],
+                num_steps=num_steps, temperature=0.1, record_attentions=True
+            )
+            all_results[label] = results
 
-        df = pd.DataFrame({
-            "Step": range(min_len),
-            "State Delta": deltas[:min_len],
-            "Attention Entropy": entropies[:min_len]
-        })
+            deltas = results.get("state_deltas", [])
+            entropies = results.get("attention_entropies", [])
+            min_len = min(len(deltas), len(entropies))
 
-        # KORREKTUR: Der Summary-DataFrame wird direkt aus dem aggregierten DataFrame erstellt.
-        summary_df = df.drop(columns='Step').agg(['mean', 'std', 'max']).reset_index().rename(columns={'index':'Statistic'})
-        plot_df = df.melt(id_vars=['Step'], value_vars=['State Delta', 'Attention Entropy'],
-                               var_name='Metric', value_name='Value')
+            df = pd.DataFrame({
+                "Step": range(min_len), "State Delta": deltas[:min_len], "Attention Entropy": entropies[:min_len]
+            })
 
-        del llm
-        gc.collect()
-        if torch.cuda.is_available(): torch.cuda.empty_cache()
+            summary_df_single = df.drop(columns='Step').agg(['mean', 'std', 'max']).reset_index().rename(columns={'index':'Statistic'})
+            plot_df = df.melt(id_vars=['Step'], value_vars=['State Delta', 'Attention Entropy'], var_name='Metric', value_name='Value')
+            return summary_df_single, plot_df, all_results
 
-        return summary_df, plot_df, all_results
-
-    else:
-        # Behandelt act_titration, seismic, triangulation, causal_surgery
-        if probe_type == "act_titration":
-            run_spec = protocol[0]
-            label = run_spec["label"]
-            dbg(f"--- Running ACT Titration Experiment: '{label}' ---")
-            results = run_act_titration_probe(
-                model_id=model_id,
-                source_prompt_type=run_spec["source_prompt_type"],
-                dest_prompt_type=run_spec["dest_prompt_type"],
-                patch_steps=run_spec["patch_steps"],
-                seed=seed, num_steps=num_steps, progress_callback=progress_callback,
-            )
-            all_results[label] = results
-            summary_data.extend(results.get("titration_data", []))
         else:
-            for i, run_spec in enumerate(protocol):
+            if probe_type == "act_titration":
+                run_spec = protocol[0]
                 label = run_spec["label"]
-                current_probe_type = run_spec.get("probe_type", "seismic")
-                dbg(f"--- Running Auto-Experiment: '{label}' ({i+1}/{len(protocol)}) ---")
-
-                results = {}
-                # ... (Logik für causal_surgery, triangulation, seismic wie zuvor)
-                # Dieser Teil bleibt logisch identisch und wird hier der Kürze halber nicht wiederholt.
-                # Wichtig ist, dass sie alle `summary_data.append(dict)` verwenden.
-                stats = results.get("stats", {})
-                summary_data.append({"Experiment": label, "Mean Delta": stats.get("mean_delta")}) # Beispiel
-
+                dbg(f"--- Running ACT Titration Experiment: '{label}' ---")
+                results = run_act_titration_probe(
+                    model_id=model_id, source_prompt_type=run_spec["source_prompt_type"],
+                    dest_prompt_type=run_spec["dest_prompt_type"], patch_steps=run_spec["patch_steps"],
+                    seed=seed, num_steps=num_steps, progress_callback=progress_callback,
+                )
                 all_results[label] = results
-                deltas = results.get("state_deltas", [])
-                df = pd.DataFrame({"Step": range(len(deltas)), "Delta": deltas, "Experiment": label})
-                plot_data_frames.append(df)
+                summary_data.extend(results.get("titration_data", []))
+            else:
+                for i, run_spec in enumerate(protocol):
+                    label = run_spec["label"]
+                    current_probe_type = run_spec.get("probe_type", "seismic")
+                    dbg(f"--- Running Auto-Experiment: '{label}' ({i+1}/{len(protocol)}) ---")
+
+                    results = {}
+                    if current_probe_type == "causal_surgery":
+                        results = run_causal_surgery_probe(
+                            model_id=model_id, source_prompt_type=run_spec["source_prompt_type"],
+                            dest_prompt_type=run_spec["dest_prompt_type"], patch_step=run_spec["patch_step"],
+                            seed=seed, num_steps=num_steps, progress_callback=progress_callback,
+                            reset_kv_cache_on_patch=run_spec.get("reset_kv_cache_on_patch", False)
+                        )
+                    elif current_probe_type == "triangulation":
+                        results = run_triangulation_probe(
+                            model_id=model_id, prompt_type=run_spec["prompt_type"], seed=seed, num_steps=num_steps,
+                            progress_callback=progress_callback, concept_to_inject=run_spec.get("concept", ""),
+                            injection_strength=run_spec.get("strength", 0.0),
+                        )
+                    else:
+                        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.get("concept", ""), injection_strength=run_spec.get("strength", 0.0),
+                            progress_callback=progress_callback
+                        )
+
+                    deltas = results.get("state_deltas", [])
+                    if deltas:
+                        signal_metrics = analyze_cognitive_signal(np.array(deltas))
+                        results.setdefault("stats", {}).update(signal_metrics)
+                        freqs, power = get_power_spectrum_for_plotting(np.array(deltas))
+                        results["power_spectrum"] = {"frequencies": freqs.tolist(), "power": power.tolist()}
+
+                    stats = results.get("stats", {})
+                    summary_entry = {
+                        "Experiment": label, "Mean Delta": stats.get("mean_delta"),
+                        "Std Dev Delta": stats.get("std_delta"), "Max Delta": stats.get("max_delta"),
+                        "Dominant Period (Steps)": stats.get("dominant_period_steps"),
+                        "Spectral Entropy": stats.get("spectral_entropy"),
+                    }
+                    if "Introspective Report" in results:
+                        summary_entry["Introspective Report"] = results.get("introspective_report")
+                    if "patch_info" in results:
+                         summary_entry["Patch Info"] = f"Source: {results['patch_info'].get('source_prompt')}, Reset KV: {results['patch_info'].get('kv_cache_reset')}"
+
+                    summary_data.append(summary_entry)
+                    all_results[label] = results
+                    df = pd.DataFrame({"Step": range(len(deltas)), "Delta": deltas, "Experiment": label}) if deltas else pd.DataFrame()
+                    plot_data_frames.append(df)
+
+        summary_df = pd.DataFrame(summary_data)
 
-    # --- Finale DataFrame-Erstellung ---
-    summary_df = pd.DataFrame(summary_data)
+        if probe_type == "act_titration":
+            plot_df = summary_df.rename(columns={"patch_step": "Patch Step", "post_patch_mean_delta": "Post-Patch Mean Delta"})
+        else:
+            plot_df = pd.concat(plot_data_frames, ignore_index=True) if plot_data_frames else pd.DataFrame()
 
-    if probe_type == "act_titration":
-        plot_df = summary_df.rename(columns={"patch_step": "Patch Step", "post_patch_mean_delta": "Post-Patch Mean Delta"})
-    else:
-        plot_df = pd.concat(plot_data_frames, ignore_index=True) if plot_data_frames else pd.DataFrame()
+        if protocol and probe_type not in ["act_titration", "mechanistic_probe"]:
+            ordered_labels = [run['label'] for run in protocol]
+            if not summary_df.empty and 'Experiment' in summary_df.columns:
+                summary_df['Experiment'] = pd.Categorical(summary_df['Experiment'], categories=ordered_labels, ordered=True)
+                summary_df = summary_df.sort_values('Experiment')
+            if not plot_df.empty and 'Experiment' in plot_df.columns:
+                plot_df['Experiment'] = pd.Categorical(plot_df['Experiment'], categories=ordered_labels, ordered=True)
+                plot_df = plot_df.sort_values(['Experiment', 'Step'])
 
-    if protocol and probe_type not in ["act_titration", "mechanistic_probe"]:
-        ordered_labels = [run['label'] for run in protocol]
-        if not summary_df.empty and 'Experiment' in summary_df.columns:
-            summary_df['Experiment'] = pd.Categorical(summary_df['Experiment'], categories=ordered_labels, ordered=True)
-            summary_df = summary_df.sort_values('Experiment')
-        if not plot_df.empty and 'Experiment' in plot_df.columns:
-            plot_df['Experiment'] = pd.Categorical(plot_df['Experiment'], categories=ordered_labels, ordered=True)
-            plot_df = plot_df.sort_values(['Experiment', 'Step'])
+        return summary_df, plot_df, all_results
 
-    return summary_df, plot_df, all_results
+    finally:
+        if llm:
+            release_model(llm)
 
 [File Ends] cognitive_mapping_probe/auto_experiment.py
 
@@ -552,11 +622,12 @@ import os
 import torch
 import random
 import numpy as np
-from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed, TextStreamer
+from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
 from typing import Optional, List
 from dataclasses import dataclass, field
 
-from .utils import dbg
+# NEU: Importiere die zentrale cleanup-Funktion
+from .utils import dbg, cleanup_memory
 
 os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
 
@@ -567,34 +638,27 @@ class StableLLMConfig:
     layer_list: List[torch.nn.Module] = field(default_factory=list, repr=False)
 
 class LLM:
+    # __init__ und _populate_stable_config bleiben exakt wie in der vorherigen Version.
     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...", 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
-
         self.stable_config = self._populate_stable_config()
-
         print(f"[INFO] Model '{model_id}' loaded on device: {self.model.device}", flush=True)
 
     def _populate_stable_config(self) -> StableLLMConfig:
@@ -603,7 +667,6 @@ class LLM:
             hidden_dim = self.model.get_input_embeddings().weight.shape[1]
         except AttributeError:
             hidden_dim = getattr(self.config, 'hidden_size', getattr(self.config, 'd_model', 0))
-
         num_layers = 0
         layer_list = []
         try:
@@ -613,26 +676,18 @@ class LLM:
                  layer_list = self.model.model.layers
             elif hasattr(self.model, 'transformer') and hasattr(self.model.transformer, 'h'):
                  layer_list = self.model.transformer.h
-
             if layer_list:
                 num_layers = len(layer_list)
         except (AttributeError, TypeError):
             pass
-
         if num_layers == 0:
             num_layers = getattr(self.config, 'num_hidden_layers', getattr(self.config, 'num_layers', 0))
-
         if hidden_dim <= 0 or num_layers <= 0 or not layer_list:
             dbg("--- CRITICAL: Failed to auto-determine model configuration. ---")
-            dbg(f"Detected hidden_dim: {hidden_dim}, num_layers: {num_layers}, found_layer_list: {bool(layer_list)}")
-            dbg("--- DUMPING MODEL ARCHITECTURE FOR DEBUGGING: ---")
             dbg(self.model)
-            dbg("--- END ARCHITECTURE DUMP ---")
-
         assert hidden_dim > 0, "Could not determine hidden dimension."
         assert num_layers > 0, "Could not determine number of layers."
         assert layer_list, "Could not find the list of transformer layers."
-
         dbg(f"Populated stable config: hidden_dim={hidden_dim}, num_layers={num_layers}")
         return StableLLMConfig(hidden_dim=hidden_dim, num_layers=num_layers, layer_list=layer_list)
 
@@ -647,34 +702,37 @@ class LLM:
         torch.use_deterministic_algorithms(True, warn_only=True)
         dbg(f"All random seeds set to {seed}.")
 
-    # --- NEU: Generische Text-Generierungs-Methode ---
     @torch.no_grad()
     def generate_text(self, prompt: str, max_new_tokens: int, temperature: float) -> str:
-        """Generiert freien Text als Antwort auf einen Prompt."""
-        self.set_all_seeds(self.seed) # Sorge für Reproduzierbarkeit
-
+        self.set_all_seeds(self.seed)
         messages = [{"role": "user", "content": prompt}]
         inputs = self.tokenizer.apply_chat_template(
             messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
         ).to(self.model.device)
-
         outputs = self.model.generate(
-            inputs,
-            max_new_tokens=max_new_tokens,
-            temperature=temperature,
-            do_sample=temperature > 0,
+            inputs, max_new_tokens=max_new_tokens, temperature=temperature, do_sample=temperature > 0,
         )
-
-        # Dekodiere nur die neu generierten Tokens
         response_tokens = outputs[0, inputs.shape[-1]:]
         return self.tokenizer.decode(response_tokens, skip_special_tokens=True)
 
 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()
+    cleanup_memory() # Bereinige Speicher, *bevor* ein neues Modell geladen wird.
     return LLM(model_id=model_id, seed=seed)
 
+# NEU: Explizite Funktion zum Freigeben von Ressourcen
+def release_model(llm: Optional[LLM]):
+    """
+    Gibt die Ressourcen eines LLM-Objekts explizit frei und ruft die zentrale
+    Speicherbereinigungs-Funktion auf.
+    """
+    if llm is None:
+        return
+    dbg(f"Releasing model instance for '{llm.model_id}'.")
+    del llm
+    cleanup_memory()
+
 [File Ends] cognitive_mapping_probe/llm_iface.py
 
 [File Begins] cognitive_mapping_probe/orchestrator_seismograph.py
@@ -683,10 +741,11 @@ import numpy as np
 import gc
 from typing import Dict, Any, Optional, List
 
-from .llm_iface import get_or_load_model, LLM
+from .llm_iface import get_or_load_model, LLM, release_model
 from .resonance_seismograph import run_cogitation_loop, run_silent_cogitation_seismic
 from .concepts import get_concept_vector
 from .introspection import generate_introspective_report
+from .signal_analysis import analyze_cognitive_signal, get_power_spectrum_for_plotting
 from .utils import dbg
 
 def run_seismic_analysis(
@@ -700,245 +759,175 @@ def run_seismic_analysis(
     llm_instance: Optional[LLM] = None,
     injection_vector_cache: Optional[torch.Tensor] = None
 ) -> Dict[str, Any]:
-    """Orchestriert eine einzelne seismische Analyse (Phase 1)."""
+    """
+    Orchestriert eine einzelne seismische Analyse mit polyrhythmischer Analyse.
+    """
     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():
-        if injection_vector_cache is not None:
-            dbg(f"Using cached injection vector for '{concept_to_inject}'.")
-            injection_vector = injection_vector_cache
+    llm = None
+    try:
+        if llm_instance is None:
+            llm = get_or_load_model(model_id, seed)
+            local_llm_instance = True
         else:
-            progress_callback(0.2, desc=f"Vectorizing '{concept_to_inject}'...")
+            llm = llm_instance
+            llm.set_all_seeds(seed)
+
+        injection_vector = None
+        if concept_to_inject and concept_to_inject.strip():
             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
+        )
 
-    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
-    )
+        stats: Dict[str, Any] = {}
+        results: Dict[str, Any] = {}
+        verdict = "### ⚠️ Analysis Warning\nNo state changes recorded."
 
-    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)) }
 
-    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."
+            signal_metrics = analyze_cognitive_signal(deltas_np)
+            stats.update(signal_metrics)
+
+            freqs, power = get_power_spectrum_for_plotting(deltas_np)
+            results["power_spectrum"] = {"frequencies": freqs.tolist(), "power": power.tolist()}
 
-    results = { "verdict": verdict, "stats": stats, "state_deltas": state_deltas }
+            verdict = f"### ✅ Seismic Analysis Complete"
+            if injection_vector is not None:
+                verdict += f"\nModulated with **'{concept_to_inject}'** at strength **{injection_strength:.2f}**."
 
-    if local_llm_instance:
-        dbg(f"Releasing locally created model instance for '{model_id}'.")
-        del llm, injection_vector
-        gc.collect()
-        if torch.cuda.is_available(): torch.cuda.empty_cache()
+        results.update({ "verdict": verdict, "stats": stats, "state_deltas": state_deltas })
+        return results
 
-    return results
+    finally:
+        if local_llm_instance and llm is not None:
+            release_model(llm)
 
 def run_triangulation_probe(
-    model_id: str,
-    prompt_type: str,
-    seed: int,
-    num_steps: int,
-    progress_callback,
-    concept_to_inject: str = "",
-    injection_strength: float = 0.0,
+    model_id: str, prompt_type: str, seed: int, num_steps: int, progress_callback,
+    concept_to_inject: str = "", injection_strength: float = 0.0,
     llm_instance: Optional[LLM] = None,
 ) -> Dict[str, Any]:
-    """
-    Orchestriert ein vollständiges Triangulations-Experiment, jetzt mit optionaler Injektion.
-    """
+    """Orchestriert ein vollständiges Triangulations-Experiment."""
     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() and injection_strength > 0:
-        if concept_to_inject.lower() == "random_noise":
-             progress_callback(0.15, desc="Generating random noise vector...")
-             hidden_dim = llm.stable_config.hidden_dim
-             noise_vec = torch.randn(hidden_dim)
-             base_norm = 70.0
-             injection_vector = (noise_vec / torch.norm(noise_vec)) * base_norm
+    llm = None
+    try:
+        if llm_instance is None:
+            llm = get_or_load_model(model_id, seed)
+            local_llm_instance = True
         else:
-            progress_callback(0.15, desc=f"Vectorizing '{concept_to_inject}'...")
-            injection_vector = get_concept_vector(llm, concept_to_inject.strip())
-
-    progress_callback(0.3, desc=f"Phase 1/2: 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
-    )
+            llm = llm_instance
+            llm.set_all_seeds(seed)
 
-    progress_callback(0.7, desc="Phase 2/2: Generating introspective report...")
-    report = generate_introspective_report(
-        llm=llm, context_prompt_type=prompt_type,
-        introspection_prompt_type="describe_dynamics_structured", num_steps=num_steps
-    )
-
-    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)) }
-        verdict = "### ✅ Triangulation Probe Complete"
-    else:
-        stats, verdict = {}, "### ⚠️ Triangulation Warning"
+        state_deltas = run_silent_cogitation_seismic(
+            llm=llm, prompt_type=prompt_type, num_steps=num_steps, temperature=0.1,
+            injection_strength=injection_strength
+        )
 
-    results = {
-        "verdict": verdict, "stats": stats, "state_deltas": state_deltas,
-        "introspective_report": report
-    }
+        report = generate_introspective_report(
+            llm=llm, context_prompt_type=prompt_type,
+            introspection_prompt_type="describe_dynamics_structured", num_steps=num_steps
+        )
 
-    if local_llm_instance:
-        dbg(f"Releasing locally created model instance for '{model_id}'.")
-        del llm, injection_vector
-        gc.collect()
-        if torch.cuda.is_available(): torch.cuda.empty_cache()
+        stats: Dict[str, Any] = {}
+        verdict = "### ⚠️ Triangulation Warning"
+        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)) }
+            verdict = "### ✅ Triangulation Probe Complete"
 
-    return results
+        results = {
+            "verdict": verdict, "stats": stats, "state_deltas": state_deltas,
+            "introspective_report": report
+        }
+        return results
+    finally:
+        if local_llm_instance and llm is not None:
+            release_model(llm)
 
 def run_causal_surgery_probe(
-    model_id: str,
-    source_prompt_type: str,
-    dest_prompt_type: str,
-    patch_step: int,
-    seed: int,
-    num_steps: int,
-    progress_callback,
+    model_id: str, source_prompt_type: str, dest_prompt_type: str,
+    patch_step: int, seed: int, num_steps: int, progress_callback,
     reset_kv_cache_on_patch: bool = False
 ) -> Dict[str, Any]:
-    """
-    Orchestriert ein "Activation Patching"-Experiment, jetzt mit KV-Cache-Reset-Option.
-    """
-    progress_callback(0.0, desc=f"Loading model '{model_id}'...")
-    llm = get_or_load_model(model_id, seed)
+    """Orchestriert ein "Activation Patching"-Experiment."""
+    llm = None
+    try:
+        llm = get_or_load_model(model_id, seed)
 
-    progress_callback(0.1, desc=f"Phase 1/3: Recording source state ('{source_prompt_type}')...")
-    source_results = run_cogitation_loop(
-        llm=llm, prompt_type=source_prompt_type, num_steps=num_steps,
-        temperature=0.1, record_states=True
-    )
-    state_history = source_results["state_history"]
-    assert patch_step < len(state_history), f"Patch step {patch_step} is out of bounds."
-    patch_state = state_history[patch_step]
-    dbg(f"Source state at step {patch_step} recorded with norm {torch.norm(patch_state).item():.2f}.")
-
-    progress_callback(0.4, desc=f"Phase 2/3: Running patched destination ('{dest_prompt_type}')...")
-    patched_run_results = run_cogitation_loop(
-        llm=llm, prompt_type=dest_prompt_type, num_steps=num_steps,
-        temperature=0.1, patch_step=patch_step, patch_state_source=patch_state,
-        reset_kv_cache_on_patch=reset_kv_cache_on_patch
-    )
+        source_results = run_cogitation_loop(
+            llm=llm, prompt_type=source_prompt_type, num_steps=num_steps,
+            temperature=0.1, record_states=True
+        )
+        state_history = source_results["state_history"]
+        assert patch_step < len(state_history), f"Patch step {patch_step} is out of bounds."
+        patch_state = state_history[patch_step]
 
-    progress_callback(0.8, desc="Phase 3/3: Generating introspective report...")
-    report = generate_introspective_report(
-        llm=llm, context_prompt_type=dest_prompt_type,
-        introspection_prompt_type="describe_dynamics_structured", num_steps=num_steps
-    )
+        patched_run_results = run_cogitation_loop(
+            llm=llm, prompt_type=dest_prompt_type, num_steps=num_steps,
+            temperature=0.1, patch_step=patch_step, patch_state_source=patch_state,
+            reset_kv_cache_on_patch=reset_kv_cache_on_patch
+        )
 
-    progress_callback(0.95, desc="Analyzing...")
-    deltas_np = np.array(patched_run_results["state_deltas"])
-    stats = { "mean_delta": float(np.mean(deltas_np)), "std_delta": float(np.std(deltas_np)), "max_delta": float(np.max(deltas_np)) }
-
-    results = {
-        "verdict": "### ✅ Causal Surgery Probe Complete",
-        "stats": stats,
-        "state_deltas": patched_run_results["state_deltas"],
-        "introspective_report": report,
-        "patch_info": {
-            "source_prompt": source_prompt_type,
-            "dest_prompt": dest_prompt_type,
-            "patch_step": patch_step,
-            "kv_cache_reset": reset_kv_cache_on_patch
-        }
-    }
+        report = generate_introspective_report(
+            llm=llm, context_prompt_type=dest_prompt_type,
+            introspection_prompt_type="describe_dynamics_structured", num_steps=num_steps
+        )
 
-    dbg(f"Releasing model instance for '{model_id}'.")
-    del llm, state_history, patch_state
-    gc.collect()
-    if torch.cuda.is_available(): torch.cuda.empty_cache()
+        deltas_np = np.array(patched_run_results["state_deltas"])
+        stats = { "mean_delta": float(np.mean(deltas_np)), "std_delta": float(np.std(deltas_np)), "max_delta": float(np.max(deltas_np)) }
 
-    return results
+        results = {
+            "verdict": "### ✅ Causal Surgery Probe Complete",
+            "stats": stats, "state_deltas": patched_run_results["state_deltas"],
+            "introspective_report": report,
+            "patch_info": { "source_prompt": source_prompt_type, "dest_prompt": dest_prompt_type,
+                            "patch_step": patch_step, "kv_cache_reset": reset_kv_cache_on_patch }
+        }
+        return results
+    finally:
+        release_model(llm)
 
 def run_act_titration_probe(
-    model_id: str,
-    source_prompt_type: str,
-    dest_prompt_type: str,
-    patch_steps: List[int],
-    seed: int,
-    num_steps: int,
-    progress_callback,
+    model_id: str, source_prompt_type: str, dest_prompt_type: str,
+    patch_steps: List[int], seed: int, num_steps: int, progress_callback,
 ) -> Dict[str, Any]:
-    """
-    Führt eine Serie von "Causal Surgery"-Experimenten durch, um den "Attractor Capture Time"
-    durch Titration des `patch_step` zu finden.
-    """
-    progress_callback(0.0, desc=f"Loading model '{model_id}'...")
-    llm = get_or_load_model(model_id, seed)
-
-    progress_callback(0.05, desc=f"Recording full source state history ('{source_prompt_type}')...")
-    source_results = run_cogitation_loop(
-        llm=llm, prompt_type=source_prompt_type, num_steps=num_steps,
-        temperature=0.1, record_states=True
-    )
-    state_history = source_results["state_history"]
-    dbg(f"Full source state history ({len(state_history)} steps) recorded.")
-
-    titration_results = []
-    total_steps = len(patch_steps)
-    for i, step in enumerate(patch_steps):
-        progress_callback(0.15 + (i / total_steps) * 0.8, desc=f"Titrating patch at step {step}/{num_steps}")
-
-        if step >= len(state_history):
-            dbg(f"Skipping patch step {step} as it is out of bounds for history of length {len(state_history)}.")
-            continue
-
-        patch_state = state_history[step]
+    """Führt eine Serie von "Causal Surgery"-Experimenten durch, um den ACT zu finden."""
+    llm = None
+    try:
+        llm = get_or_load_model(model_id, seed)
 
-        patched_run_results = run_cogitation_loop(
-            llm=llm, prompt_type=dest_prompt_type, num_steps=num_steps,
-            temperature=0.1, patch_step=step, patch_state_source=patch_state
+        source_results = run_cogitation_loop(
+            llm=llm, prompt_type=source_prompt_type, num_steps=num_steps,
+            temperature=0.1, record_states=True
         )
+        state_history = source_results["state_history"]
 
-        deltas = patched_run_results["state_deltas"]
+        titration_results = []
+        for step in patch_steps:
+            if step >= len(state_history): continue
+            patch_state = state_history[step]
 
-        buffer = 10
-        post_patch_deltas = deltas[step + buffer:]
-        post_patch_mean_delta = np.mean(post_patch_deltas) if post_patch_deltas else 0.0
+            patched_run_results = run_cogitation_loop(
+                llm=llm, prompt_type=dest_prompt_type, num_steps=num_steps,
+                temperature=0.1, patch_step=step, patch_state_source=patch_state
+            )
 
-        titration_results.append({
-            "patch_step": step,
-            "post_patch_mean_delta": float(post_patch_mean_delta),
-            "full_mean_delta": float(np.mean(deltas)),
-        })
+            deltas = patched_run_results["state_deltas"]
+            buffer = 10
+            post_patch_deltas = deltas[step + buffer:]
+            post_patch_mean_delta = np.mean(post_patch_deltas) if len(post_patch_deltas) > 0 else 0.0
 
-    dbg(f"Releasing model instance for '{model_id}'.")
-    del llm, state_history
-    gc.collect()
-    if torch.cuda.is_available(): torch.cuda.empty_cache()
+            titration_results.append({ "patch_step": step, "post_patch_mean_delta": float(post_patch_mean_delta),
+                                       "full_mean_delta": float(np.mean(deltas)) })
 
-    return {
-        "verdict": "### ✅ ACT Titration Complete",
-        "titration_data": titration_results
-    }
+        return { "verdict": "### ✅ ACT Titration Complete", "titration_data": titration_results }
+    finally:
+        release_model(llm)
 
 [File Ends] cognitive_mapping_probe/orchestrator_seismograph.py
 
@@ -1022,24 +1011,24 @@ def _calculate_attention_entropy(attentions: Tuple[torch.Tensor, ...]) -> float:
     """
     total_entropy = 0.0
     num_heads = 0
-
+    
     # Iteriere über alle Layer
     for layer_attention in attentions:
         # layer_attention shape: [batch_size, num_heads, seq_len, seq_len]
         # Für unsere Zwecke ist batch_size=1, seq_len=1 (wir schauen nur auf das letzte Token)
         # Die relevante Verteilung ist die letzte Zeile der Attention-Matrix
         attention_probs = layer_attention[:, :, -1, :]
-
+        
         # Stabilisiere die Logarithmus-Berechnung
         attention_probs = attention_probs + 1e-9
-
-        # Entropie-Formel: - sum(p * log(p))
+        
+        # Entropie-Formel: - sum(p * log2(p))
         log_probs = torch.log2(attention_probs)
         entropy_per_head = -torch.sum(attention_probs * log_probs, dim=-1)
-
+        
         total_entropy += torch.sum(entropy_per_head).item()
         num_heads += attention_probs.shape[1]
-
+        
     return total_entropy / num_heads if num_heads > 0 else 0.0
 
 @torch.no_grad()
@@ -1055,7 +1044,6 @@ def run_cogitation_loop(
     patch_state_source: Optional[torch.Tensor] = None,
     reset_kv_cache_on_patch: bool = False,
     record_states: bool = False,
-    # NEU: Parameter zur Aufzeichnung von Attention-Mustern
     record_attentions: bool = False,
 ) -> Dict[str, Any]:
     """
@@ -1065,7 +1053,6 @@ def run_cogitation_loop(
     prompt = RESONANCE_PROMPTS[prompt_type]
     inputs = llm.tokenizer(prompt, return_tensors="pt").to(llm.model.device)
 
-    # Erster Forward-Pass, um den initialen Zustand zu erhalten
     outputs = llm.model(**inputs, output_hidden_states=True, use_cache=True, output_attentions=record_attentions)
     hidden_state_2d = outputs.hidden_states[-1][:, -1, :]
     kv_cache = outputs.past_key_values
@@ -1084,31 +1071,44 @@ def run_cogitation_loop(
             if reset_kv_cache_on_patch:
                 dbg("--- KV-Cache has been RESET as part of the intervention. ---")
                 kv_cache = None
-
+        
         if record_states:
             state_history.append(hidden_state_2d.cpu())
 
         next_token_logits = llm.model.lm_head(hidden_state_2d)
-
-        temp_to_use = temperature if temperature > 0.0 else 1.0
+        
+        temp_to_use = temperature if temperature > 0.0 else 1.0 
         probabilities = torch.nn.functional.softmax(next_token_logits / temp_to_use, dim=-1)
         if temperature > 0.0:
             next_token_id = torch.multinomial(probabilities, num_samples=1)
         else:
             next_token_id = torch.argmax(probabilities, dim=-1).unsqueeze(-1)
 
-        hook_handle = None # Hook-Logik unverändert
+        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.stable_config.num_layers // 2
+
+            def injection_hook(module: Any, layer_input: Any) -> Any:
+                seq_len = layer_input[0].shape[1]
+                injection_3d = injection_vector.unsqueeze(0).expand(1, seq_len, -1)
+                modified_hidden_states = layer_input[0] + (injection_3d * injection_strength)
+                return (modified_hidden_states,) + layer_input[1:]
 
         try:
-            # (Hook-Aktivierung unverändert)
+            if injection_vector is not None and injection_strength > 0 and injection_layer is not None:
+                assert 0 <= injection_layer < llm.stable_config.num_layers, f"Injection layer {injection_layer} is out of bounds."
+                target_layer = llm.stable_config.layer_list[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,
-                # Übergebe den Parameter an jeden Forward-Pass
                 output_attentions=record_attentions
             )
         finally:
-            if hook_handle:
+            if hook_handle: 
                 hook_handle.remove()
                 hook_handle = None
 
@@ -1124,39 +1124,144 @@ def run_cogitation_loop(
         hidden_state_2d = new_hidden_state.clone()
 
     dbg(f"Cognitive loop finished after {num_steps} steps.")
-
+    
     return {
         "state_deltas": state_deltas,
         "state_history": state_history,
-        "attention_entropies": attention_entropies, # Das neue Messergebnis
+        "attention_entropies": attention_entropies,
         "final_hidden_state": hidden_state_2d,
         "final_kv_cache": kv_cache,
     }
 
-def run_silent_cogitation_seismic(*args, **kwargs) -> List[float]:
-    """Abwärtskompatibler Wrapper."""
-    results = run_cogitation_loop(*args, **kwargs)
+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]:
+    """
+    Ein abwärtskompatibler Wrapper, der die alte, einfachere Schnittstelle beibehält.
+    Ruft den neuen, verallgemeinerten Loop auf und gibt nur die Deltas zurück.
+    """
+    results = run_cogitation_loop(
+        llm=llm, prompt_type=prompt_type, num_steps=num_steps, temperature=temperature,
+        injection_vector=injection_vector, injection_strength=injection_strength,
+        injection_layer=injection_layer
+    )
     return results["state_deltas"]
-
 [File Ends] cognitive_mapping_probe/resonance_seismograph.py
 
+[File Begins] cognitive_mapping_probe/signal_analysis.py
+import numpy as np
+from scipy.fft import rfft, rfftfreq
+from scipy.signal import find_peaks
+from typing import Dict, List, Optional, Any, Tuple
+
+def analyze_cognitive_signal(
+    state_deltas: np.ndarray, 
+    sampling_rate: float = 1.0,
+    num_peaks: int = 3
+) -> Dict[str, Any]:
+    """
+    Führt eine polyrhythmische Spektralanalyse mit einer robusten,
+    zweistufigen Schwellenwert-Methode durch.
+    """
+    analysis_results: Dict[str, Any] = {
+        "dominant_periods_steps": None,
+        "spectral_entropy": None,
+    }
+    
+    if len(state_deltas) < 20:
+        return analysis_results
+
+    n = len(state_deltas)
+    yf = rfft(state_deltas - np.mean(state_deltas))
+    xf = rfftfreq(n, 1 / sampling_rate)
+    
+    power_spectrum = np.abs(yf)**2
+    
+    spectral_entropy: Optional[float] = None
+    if len(power_spectrum) > 1:
+        prob_dist = power_spectrum / np.sum(power_spectrum)
+        prob_dist = prob_dist[prob_dist > 1e-12]
+        spectral_entropy = -np.sum(prob_dist * np.log2(prob_dist))
+        analysis_results["spectral_entropy"] = float(spectral_entropy)
+
+    # FINALE KORREKTUR: Robuste, zweistufige Schwellenwert-Bestimmung
+    if len(power_spectrum) > 1:
+        # 1. Absolute Höhe: Ein Peak muss signifikant über dem Median-Rauschen liegen.
+        min_height = np.median(power_spectrum) + np.std(power_spectrum)
+        # 2. Relative Prominenz: Ein Peak muss sich von seiner lokalen Umgebung abheben.
+        min_prominence = np.std(power_spectrum) * 0.5
+    else:
+        min_height = 1.0
+        min_prominence = 1.0
+
+    peaks, properties = find_peaks(power_spectrum[1:], height=min_height, prominence=min_prominence)
+    
+    if peaks.size > 0 and "peak_heights" in properties:
+        sorted_peak_indices = peaks[np.argsort(properties["peak_heights"])[::-1]]
+        
+        dominant_periods = []
+        for i in range(min(num_peaks, len(sorted_peak_indices))):
+            peak_index = sorted_peak_indices[i]
+            frequency = xf[peak_index + 1]
+            if frequency > 1e-9:
+                period = 1 / frequency
+                dominant_periods.append(round(period, 2))
+        
+        if dominant_periods:
+            analysis_results["dominant_periods_steps"] = dominant_periods
+
+    return analysis_results
+
+def get_power_spectrum_for_plotting(state_deltas: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+    """
+    Berechnet das Leistungsspektrum und gibt Frequenzen und Power zurück.
+    """
+    if len(state_deltas) < 10:
+        return np.array([]), np.array([])
+        
+    n = len(state_deltas)
+    yf = rfft(state_deltas - np.mean(state_deltas))
+    xf = rfftfreq(n, 1.0)
+    
+    power_spectrum = np.abs(yf)**2
+    return xf, power_spectrum
+
+[File Ends] cognitive_mapping_probe/signal_analysis.py
+
 [File Begins] cognitive_mapping_probe/utils.py
 import os
 import sys
+import gc
+import torch
 
 # --- 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.
-    """
+    """A controlled debug print function."""
     if DEBUG_ENABLED:
         print("[DEBUG]", *args, **kwargs, file=sys.stderr, flush=True)
 
+# --- NEU: Zentrale Funktion zur Speicherbereinigung ---
+def cleanup_memory():
+    """
+    Eine zentrale, global verfügbare Funktion zum Aufräumen von CPU- und GPU-Speicher.
+    Dies stellt sicher, dass die Speicherverwaltung konsistent und an einer einzigen Stelle erfolgt.
+    """
+    dbg("Cleaning up memory (centralized)...")
+    # Python's garbage collector
+    gc.collect()
+    # PyTorch's CUDA cache
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    dbg("Memory cleanup complete.")
+
 [File Ends] cognitive_mapping_probe/utils.py
 
 [File Begins] run_test.sh
@@ -1195,85 +1300,13 @@ fi
 
 [File Begins] tests/conftest.py
 import pytest
-import torch
-from types import SimpleNamespace
-from cognitive_mapping_probe.llm_iface import LLM, StableLLMConfig
 
 @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):
+def model_id() -> str:
     """
-    Erstellt einen robusten "Mock-LLM" für Unit-Tests.
-    FINAL KORRIGIERT: Simuliert nun die vollständige `StableLLMConfig`-Abstraktion.
+    Stellt die ID des realen Modells bereit, das für die Integrations-Tests verwendet wird.
     """
-    mock_tokenizer = mocker.MagicMock()
-    mock_tokenizer.eos_token_id = 1
-    mock_tokenizer.decode.return_value = "mocked text"
-
-    mock_embedding_layer = mocker.MagicMock()
-    mock_embedding_layer.weight.shape = (32000, mock_llm_config.hidden_size)
-
-    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
-    llm_instance.model.get_input_embeddings.return_value = mock_embedding_layer
-    llm_instance.model.lm_head = mocker.MagicMock(return_value=torch.randn(1, 32000))
-
-    # FINALE KORREKTUR: Simuliere die Layer-Liste für den Hook-Test
-    mock_layer = mocker.MagicMock()
-    mock_layer.register_forward_pre_hook.return_value = mocker.MagicMock()
-    mock_layer_list = [mock_layer] * mock_llm_config.num_hidden_layers
-
-    # Simuliere die verschiedenen möglichen Architektur-Pfade
-    llm_instance.model.model = SimpleNamespace()
-    llm_instance.model.model.language_model = SimpleNamespace(layers=mock_layer_list)
-
-    llm_instance.tokenizer = mock_tokenizer
-    llm_instance.config = mock_llm_config
-    llm_instance.seed = 42
-    llm_instance.set_all_seeds = mocker.MagicMock()
-
-    # Erzeuge die stabile Konfiguration, die die Tests nun erwarten.
-    llm_instance.stable_config = StableLLMConfig(
-        hidden_dim=mock_llm_config.hidden_size,
-        num_layers=mock_llm_config.num_hidden_layers,
-        layer_list=mock_layer_list # Füge den Verweis auf die Mock-Layer-Liste hinzu
-    )
-
-    # 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)
-    mocker.patch('cognitive_mapping_probe.auto_experiment.get_or_load_model', return_value=llm_instance)
-
-    mocker.patch('cognitive_mapping_probe.orchestrator_seismograph.get_concept_vector', return_value=torch.randn(mock_llm_config.hidden_size))
-
-    return llm_instance
+    return "google/gemma-3-1b-it"
 
 [File Ends] tests/conftest.py
 
@@ -1282,261 +1315,178 @@ import pandas as pd
 import pytest
 import gradio as gr
 from pandas.testing import assert_frame_equal
+from unittest.mock import MagicMock
 
 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": [1.0, 2.0]}
+    """Testet den UI-Wrapper für Einzel-Experimente mit korrekten Datenstrukturen."""
+    mock_results = {
+        "verdict": "V",
+        "stats": {
+            "mean_delta": 1.0, "std_delta": 0.5,
+            "dominant_periods_steps": [10.0, 5.0], "spectral_entropy": 3.5
+        },
+        "state_deltas": [1.0, 2.0],
+        "power_spectrum": {"frequencies": [0.1, 0.2], "power": [100, 50]}
+    }
     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())
+    verdict, df_time, df_freq, raw = run_single_analysis_display(progress=MagicMock())
 
-    assert "V" in verdict and "1.0000" in verdict
-    assert isinstance(df, pd.DataFrame) and len(df) == 2
-    assert "State Change (Delta)" in df.columns
+    # FINALE KORREKTUR: Passe die Assertion an den exakten Markdown-Output-String an.
+    assert "- **Dominant Periods:** 10.0, 5.0 Steps/Cycle" in verdict
+    assert "Period (Steps/Cycle)" in df_freq.columns
 
-def test_run_auto_suite_display(mocker):
-    """
-    Testet den Wrapper für die Auto-Experiment-Suite.
-    FINAL KORRIGIERT: Rekonstruiert DataFrames aus den serialisierten `dict`-Werten
-    der Gradio-Komponenten, um die tatsächliche API-Nutzung widerzuspiegeln.
-    """
-    mock_summary_df = pd.DataFrame([{"Experiment": "E1", "Mean Delta": 1.5}])
-    mock_plot_df = pd.DataFrame([{"Step": 0, "Delta": 1.0, "Experiment": "E1"}, {"Step": 1, "Delta": 2.0, "Experiment": "E1"}])
-    mock_results = {"E1": {"stats": {"mean_delta": 1.5}}}
+def test_run_auto_suite_display_generates_valid_plot_data(mocker):
+    """Verifiziert die Datenübergabe an die Gradio-Komponenten für Auto-Experimente."""
+    mock_summary_df = pd.DataFrame([{"Experiment": "A", "Mean Delta": 150.0}])
+    mock_plot_df_time = pd.DataFrame([{"Step": 0, "Delta": 100, "Experiment": "A"}])
+    mock_all_results = {
+        "A": {"power_spectrum": {"frequencies": [0.1], "power": [1000]}}
+    }
 
-    mocker.patch('app.run_auto_suite', return_value=(mock_summary_df, mock_plot_df, mock_results))
-    mocker.patch('app.cleanup_memory')
+    mocker.patch('app.run_auto_suite', return_value=(mock_summary_df, mock_plot_df_time, mock_all_results))
 
-    dataframe_component, plot_component, raw_json_str = run_auto_suite_display(
-        "mock-model", 100, 42, "mock_exp", progress=mocker.MagicMock()
+    dataframe_comp, time_plot_comp, freq_plot_comp, raw_json = run_auto_suite_display(
+        "mock-model", 10, 42, "Causal Verification & Crisis Dynamics", progress=MagicMock()
     )
 
-    # KORREKTUR: Die `.value` Eigenschaft einer gr.DataFrame Komponente ist ein Dictionary.
-    # Wir müssen den pandas.DataFrame daraus rekonstruieren, um ihn zu vergleichen.
-    assert isinstance(dataframe_component, gr.DataFrame)
-    assert isinstance(dataframe_component.value, dict)
-    reconstructed_summary_df = pd.DataFrame(
-        data=dataframe_component.value['data'],
-        columns=dataframe_component.value['headers']
-    )
-    assert_frame_equal(reconstructed_summary_df, mock_summary_df)
-
-    # Dasselbe gilt für die LinePlot-Komponente
-    assert isinstance(plot_component, gr.LinePlot)
-    assert isinstance(plot_component.value, dict)
-    reconstructed_plot_df = pd.DataFrame(
-        data=plot_component.value['data'],
-        columns=plot_component.value['columns']
-    )
-    assert_frame_equal(reconstructed_plot_df, mock_plot_df)
+    assert isinstance(dataframe_comp.value, dict)
+    assert_frame_equal(pd.DataFrame(dataframe_comp.value['data'], columns=dataframe_comp.value['headers']), mock_summary_df)
 
-    # Der JSON-String bleibt ein String
-    assert isinstance(raw_json_str, str)
-    assert '"mean_delta": 1.5' in raw_json_str
+    assert time_plot_comp.y == "Delta"
+    assert "Period (Steps/Cycle)" in freq_plot_comp.x
 
 [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
+import numpy as np
+from cognitive_mapping_probe.llm_iface import get_or_load_model
 from cognitive_mapping_probe.resonance_seismograph import run_silent_cogitation_seismic
-from cognitive_mapping_probe.utils import dbg
 from cognitive_mapping_probe.concepts import get_concept_vector, _get_last_token_hidden_state
-
-# --- 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.
-    FINAL KORRIGIERT: Der lokale Mock ist nun vollständig konfiguriert.
-    """
-    mock_model = mocker.MagicMock()
-    mock_model.eval.return_value = None
-    mock_model.set_attn_implementation.return_value = None
-    mock_model.device = 'cpu'
-
-    mock_model.get_input_embeddings.return_value.weight.shape = (32000, 128)
-    mock_model.config = mocker.MagicMock()
-    mock_model.config.num_hidden_layers = 2
-    mock_model.config.hidden_size = 128
-
-    # Simuliere die Architektur für die Layer-Extraktion
-    mock_model.model.language_model.layers = [mocker.MagicMock()] * 2
-
-    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`."""
+from cognitive_mapping_probe.signal_analysis import analyze_cognitive_signal
+
+def test_get_or_load_model_loads_correctly(model_id):
+    """Testet, ob das Laden eines echten Modells funktioniert."""
+    llm = get_or_load_model(model_id, seed=42)
+    assert llm is not None
+    assert llm.model_id == model_id
+    assert llm.stable_config.hidden_dim > 0
+    assert llm.stable_config.num_layers > 0
+
+def test_run_silent_cogitation_seismic_output_shape_and_type(model_id):
+    """Führt einen kurzen Lauf mit einem echten Modell durch und prüft die Datentypen."""
     num_steps = 10
+    llm = get_or_load_model(model_id, seed=42)
     state_deltas = run_silent_cogitation_seismic(
-        llm=mock_llm, prompt_type="control_long_prose",
-        num_steps=num_steps, temperature=0.7
+        llm=llm, prompt_type="control_long_prose",
+        num_steps=num_steps, temperature=0.1
     )
-    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):
+    assert isinstance(state_deltas, list)
+    assert len(state_deltas) == num_steps
+    assert all(isinstance(d, float) for d in state_deltas)
+
+def test_get_last_token_hidden_state_robustness(model_id):
+    """Testet die Helper-Funktion mit einem echten Modell."""
+    llm = get_or_load_model(model_id, seed=42)
+    hs = _get_last_token_hidden_state(llm, "test prompt")
+    assert isinstance(hs, torch.Tensor)
+    assert hs.shape == (llm.stable_config.hidden_dim,)
+
+def test_get_concept_vector_logic(model_id):
+    """Testet die Vektor-Extraktion mit einem echten Modell."""
+    llm = get_or_load_model(model_id, seed=42)
+    vector = get_concept_vector(llm, "love", baseline_words=["thing", "place"])
+    assert isinstance(vector, torch.Tensor)
+    assert vector.shape == (llm.stable_config.hidden_dim,)
+
+def test_analyze_cognitive_signal_no_peaks():
     """
-    Testet, ob bei einer Injektion der Hook korrekt registriert wird.
-    FINAL KORRIGIERT: Greift auf die stabile Abstraktionsschicht zu.
+    Testet den Edge Case, dass ein Signal keine signifikanten Frequenz-Peaks hat.
     """
-    num_steps = 5
-    injection_vector = torch.randn(mock_llm.stable_config.hidden_dim)
-    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
-    )
-    # KORREKTUR: Der Test muss denselben Abstraktionspfad verwenden wie die Anwendung.
-    # Wir prüfen den Hook-Aufruf auf dem ersten Layer der stabilen, abstrahierten Layer-Liste.
-    assert mock_llm.stable_config.layer_list[0].register_forward_pre_hook.call_count == num_steps
-
-# --- Tests for concepts.py ---
+    flat_signal = np.linspace(0, 1, 100)
+    results = analyze_cognitive_signal(flat_signal)
+    assert results is not None
+    assert results["dominant_periods_steps"] is None
+    assert "spectral_entropy" in results
 
-def test_get_last_token_hidden_state_robustness(mock_llm):
-    """Testet die robuste `_get_last_token_hidden_state` Funktion."""
-    hs = _get_last_token_hidden_state(mock_llm, "test prompt")
-    assert hs.shape == (mock_llm.stable_config.hidden_dim,)
-
-def test_get_concept_vector_logic(mock_llm, mocker):
+def test_analyze_cognitive_signal_with_peaks():
     """
-    Testet die Logik von `get_concept_vector`.
+    Testet den Normalfall, dass ein Signal Peaks hat, mit realistischerem Rauschen.
     """
-    mock_hidden_states = [
-        torch.ones(mock_llm.stable_config.hidden_dim) * 10, # target concept
-        torch.ones(mock_llm.stable_config.hidden_dim) * 2,  # baseline word 1
-        torch.ones(mock_llm.stable_config.hidden_dim) * 4   # baseline word 2
-    ]
-    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"])
-
-    # Erwarteter Vektor: 10 - mean(2, 4) = 10 - 3 = 7
-    expected_vector = torch.ones(mock_llm.stable_config.hidden_dim) * 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 == ""
+    np.random.seed(42)
+    steps = np.arange(200)
+    # Signal mit einer starken Periode von 10 und einer schwächeren von 25
+    signal_with_peak = (1.0 * np.sin(2 * np.pi * (1/10.0) * steps) + 
+                          0.5 * np.sin(2 * np.pi * (1/25.0) * steps) +
+                          np.random.randn(200) * 0.5) # Realistischeres Rauschen
+    results = analyze_cognitive_signal(signal_with_peak)
+
+    assert results["dominant_periods_steps"] is not None
+    assert 10.0 in results["dominant_periods_steps"]
+    assert 25.0 in results["dominant_periods_steps"]
+
+def test_analyze_cognitive_signal_with_multiple_peaks():
+    """
+    Erweiterter Test, der die korrekte Identifizierung und Sortierung
+    von drei Peaks verifiziert, mit realistischerem Rauschen.
+    """
+    np.random.seed(42)
+    steps = np.arange(300)
+    # Definiere drei Peaks mit unterschiedlicher Stärke (Amplitude)
+    signal = (2.0 * np.sin(2 * np.pi * (1/10.0) * steps) + 
+              1.5 * np.sin(2 * np.pi * (1/4.0)  * steps) +
+              1.0 * np.sin(2 * np.pi * (1/30.0) * steps) +
+              np.random.randn(300) * 0.5) # Realistischeres Rauschen
+              
+    results = analyze_cognitive_signal(signal, num_peaks=3)
+
+    assert results["dominant_periods_steps"] is not None
+    expected_periods = [10.0, 4.0, 30.0]
+    assert results["dominant_periods_steps"] == expected_periods
 
 [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
+from cognitive_mapping_probe.orchestrator_seismograph import run_seismic_analysis
 
-def test_run_seismic_analysis_no_injection(mocker, mock_llm):
-    """Testet den Orchestrator im Baseline-Modus."""
-    mock_run_seismic = mocker.patch('cognitive_mapping_probe.orchestrator_seismograph.run_silent_cogitation_seismic', return_value=[1.0])
-    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(),
-        llm_instance=mock_llm
-    )
-    mock_run_seismic.assert_called_once()
-    mock_get_concept.assert_not_called()
-
-def test_run_seismic_analysis_with_injection(mocker, mock_llm):
-    """Testet den Orchestrator mit Injektion."""
-    mock_run_seismic = mocker.patch('cognitive_mapping_probe.orchestrator_seismograph.run_silent_cogitation_seismic', return_value=[1.0])
-    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_concept", injection_strength=1.5, progress_callback=mocker.MagicMock(),
-        llm_instance=mock_llm
+def test_run_seismic_analysis_with_real_model(model_id):
+    """Führt einen einzelnen Orchestrator-Lauf mit einem echten Modell durch."""
+    results = run_seismic_analysis(
+        model_id=model_id,
+        prompt_type="resonance_prompt",
+        seed=42,
+        num_steps=3,
+        concept_to_inject="",
+        injection_strength=0.0,
+        progress_callback=lambda *args, **kwargs: None
     )
-    mock_run_seismic.assert_called_once()
-    mock_get_concept.assert_called_once_with(mock_llm, "test_concept")
-
+    assert "verdict" in results
+    assert "stats" in results
+    assert len(results["state_deltas"]) == 3
 
 def test_get_curated_experiments_structure():
-    """Testet die Datenstruktur der kuratierten Experimente."""
+    """Überprüft die Struktur der Experiment-Definitionen."""
     experiments = get_curated_experiments()
     assert isinstance(experiments, dict)
-    assert "Sequential Intervention (Self-Analysis -> Deletion)" in experiments
-    protocol = experiments["Sequential Intervention (Self-Analysis -> Deletion)"]
-    assert isinstance(protocol, list) and len(protocol) == 2
-
-def test_run_auto_suite_special_protocol(mocker, mock_llm):
-    """
-    Testet den speziellen Logik-Pfad für das Interventions-Protokoll.
-    FINAL KORRIGIERT: Verwendet den korrekten, aktuellen Experiment-Namen.
-    """
-    mock_analysis = mocker.patch('cognitive_mapping_probe.auto_experiment.run_seismic_analysis', return_value={"stats": {}, "state_deltas": []})
-    mocker.patch('cognitive_mapping_probe.auto_experiment.get_or_load_model', return_value=mock_llm)
+    assert "Causal Verification & Crisis Dynamics" in experiments
 
-    # KORREKTUR: Verwende den neuen, korrekten Namen des Experiments, um
-    # den `if`-Zweig in `run_auto_suite` zu treffen.
-    correct_experiment_name = "Sequential Intervention (Self-Analysis -> Deletion)"
+def test_run_auto_suite_special_protocol(mocker, model_id):
+    """Testet den speziellen Logikpfad, mockt aber die langwierigen Aufrufe."""
+    mocker.patch('cognitive_mapping_probe.auto_experiment.run_seismic_analysis', return_value={"stats": {}, "state_deltas": [1.0]})
 
-    run_auto_suite(
-        model_id="mock-4b", num_steps=10, seed=42,
-        experiment_name=correct_experiment_name,
-        progress_callback=mocker.MagicMock()
+    summary_df, plot_df, all_results = run_auto_suite(
+        model_id=model_id, num_steps=2, seed=42,
+        experiment_name="Sequential Intervention (Self-Analysis -> Deletion)",
+        progress_callback=lambda *args, **kwargs: None
     )
-
-    # Die restlichen Assertions sind nun wieder gültig.
-    assert mock_analysis.call_count == 2
-
-    first_call_kwargs = mock_analysis.call_args_list[0].kwargs
-    second_call_kwargs = mock_analysis.call_args_list[1].kwargs
-
-    assert 'llm_instance' in first_call_kwargs
-    assert 'llm_instance' in second_call_kwargs
-    assert first_call_kwargs['llm_instance'] is mock_llm
-    assert second_call_kwargs['llm_instance'] is mock_llm
-
-    assert first_call_kwargs['concept_to_inject'] != ""
-    assert second_call_kwargs['concept_to_inject'] == ""
+    assert isinstance(summary_df, pd.DataFrame)
+    assert len(summary_df) == 2
+    assert "1: Self-Analysis + Calmness Injection" in summary_df["Experiment"].values
 
 [File Ends] tests/test_orchestration.py