fix plots?

app.py

@@ -23,12 +23,6 @@ def run_single_analysis_display(*args, progress=gr.Progress(track_tqdm=True)):
     finally:
         cleanup_memory()
 
-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 die speziellen Plots für die verschiedenen Experimente handhaben kann."""
     try:
@@ -36,21 +30,28 @@ def run_auto_suite_display(model_id, num_steps, seed, experiment_name, progress=
 
         dataframe_component = gr.DataFrame(label="Comparative Statistical Signature", value=summary_df, wrap=True, row_count=(len(summary_df), "dynamic"))
 
-        plot_params = PLOT_PARAMS_DEFAULT.copy()
+        # FINALE KORREKTUR: Robuste Plot-Parameter-Logik
+        plot_params = {
+            "title": "Comparative Cognitive Dynamics",
+            "color_legend_position": "bottom", "show_label": True, "height": 400, "interactive": True
+        }
+
         if experiment_name == "ACT Titration (Point of No Return)":
             plot_params.update({
                 "x": "Patch Step", "y": "Post-Patch Mean Delta", "color": None,
                 "title": "Attractor Capture Time (ACT) - Phase Transition", "mark": "line",
+                "color_legend_title": None,
             })
-            plot_params.pop("color_legend_title", None)
         elif experiment_name == "Mechanistic Probe (Attention Entropies)":
             plot_params.update({
                 "x": "Step", "y": "Value", "color": "Metric",
                 "title": "Mechanistic Analysis: State Delta vs. Attention Entropy",
+                "color_legend_title": "Metric",
             })
-        else:
+        else: # Default für alle anderen Multi-Lauf-Experimente
             plot_params.update({
-                 "y": "Delta", "color": "Experiment",
+                 "x": "Step", "y": "Delta", "color": "Experiment",
+                 "color_legend_title": "Experiment Runs",
             })
 
         new_plot = gr.LinePlot(value=plot_df, **plot_params)
@@ -109,7 +110,8 @@ with gr.Blocks(theme=theme, title="Cognitive Seismograph 2.3") as demo:
 
                 with gr.Column(scale=2):
                     gr.Markdown("### Suite Results Summary")
-                    auto_plot_output = gr.LinePlot(**PLOT_PARAMS_DEFAULT)
+                    # Initialisiere den Plot mit den Default-Parametern
+                    auto_plot_output = gr.LinePlot(x="Step", y="Delta", color="Experiment", title="Comparative Cognitive Dynamics")
                     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()

cognitive_mapping_probe/auto_experiment.py

@@ -1,4 +1,5 @@
 import pandas as pd
+import gc
 from typing import Dict, List, Tuple
 
 from .llm_iface import get_or_load_model, release_model
@@ -104,7 +105,7 @@ def run_auto_suite(
         raise ValueError(f"Experiment protocol '{experiment_name}' not found.")
 
     all_results, summary_data, plot_data_frames = {}, [], []
-    llm = None  # Initialisiere llm außerhalb des try-Blocks für den finally-Block
+    llm = None
 
     try:
         if experiment_name == "Sequential Intervention (Self-Analysis -> Deletion)":
@@ -163,29 +164,30 @@ def run_auto_suite(
                     "Step": range(min_len), "State Delta": deltas[:min_len], "Attention Entropy": entropies[:min_len]
                 })
 
-                summary_df_single = df.drop(columns='Step').agg(['mean', 'std', 'max']).reset_index().rename(columns={'index':'Statistic'})
-                summary_data.append(summary_df_single) # Append DataFrame to list
+                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')
+                return summary_df, plot_df, all_results
 
-                # Special return for this probe type
-                return summary_df_single, plot_df, all_results
+            elif 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: # Handles all other multi-run protocols
+            else: # Handles seismic, triangulation, causal_surgery
                 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 == "act_titration":
-                        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,
-                        )
-                        summary_data.extend(results.get("titration_data", []))
-
-                    elif current_probe_type == "causal_surgery":
+                    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"],
@@ -200,7 +202,6 @@ def run_auto_suite(
                             "Introspective Report": results.get("introspective_report", "N/A"),
                             "Patch Info": f"Source: {patch_info.get('source_prompt')}, Reset KV: {patch_info.get('kv_cache_reset')}"
                         })
-
                     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,
@@ -213,7 +214,6 @@ def run_auto_suite(
                             "Std Dev Delta": stats.get("std_delta"), "Max Delta": stats.get("max_delta"),
                             "Introspective Report": results.get("introspective_report", "N/A")
                         })
-
                     else: # seismic
                         results = run_seismic_analysis(
                             model_id=model_id, prompt_type=run_spec["prompt_type"], seed=seed, num_steps=num_steps,

docs/12B-results-Mechanistic Probe (Attention Entropies).txt

@@ -0,0 +1,2 @@
+ mean 148.70083333333332 2.694883015950521 std 33.019324548173515 0.34383212147542636 max 3103.4762369791666665
+