{ "spec_version": "1.4.0-otel", "timestamp": "2026-04-24T00:13:03.355457Z", "trace_id": "4bf92f3577b34da6a3ce929d0e0e4736", "span_id": "00f067aa0ba902b7", "severity_text": "WARN", "severity_number": 13, "event_name": "swarm.agent.lifecycle.deprecated", "resource": { "service.name": "swarm-orchestrator", "service.version": "4.2.0", "deployment.environment": "production", "node.id": "worker-node-aws-us-east-1a-04" }, "attributes": { "agent.id": "RootNodeAgent", "agent.previous_version": "v29.x", "agent.target_version": "v30", "deprecation.reason": "Transitioning to v30 architecture.", "deprecation.action_required": true }, "metrics": { "agent.core_logic.function_count": 8, "agent.capabilities.count": 0 }, "artifacts": { "provenance": { "repository": "git+ssh://repo/core/agents/specialized/root_node_agent.py", "commit_sha": "a1b2c3d4e5f6g7h8i9j0", "docstring_hash": "sha256:d8c4b12389a..." }, "extracted_capabilities": [], "core_logic_signatures": [ { "name": "__init__", "ast_hash": "sha256:9cb36634fd66d088", "loc": 5, "source": "def __init__(self, config: Dict[str, Any] = None):\n super().__init__(config=config or {})\n self.max_depth = 5\n self.simulation_count = 10 # Number of MCTS rollouts\n self.exploration_weight = 1.41" }, { "name": "execute", "ast_hash": "sha256:5892703f01316ed9", "loc": 10, "source": "async def execute(self, **kwargs) -> str:\n \"\"\"\n Executes the agent's main logic.\n Expects 'problem_statement' in kwargs.\n \"\"\"\n problem = kwargs.get(\"problem_statement\")\n if not problem:\n return \"No problem statement provided.\"\n\n return self.solve(problem)" }, { "name": "solve", "ast_hash": "sha256:7df9acb05536c9d4", "loc": 30, "source": "def solve(self, problem_statement: str) -> str:\n \"\"\"\n Main entry point to solve a problem using MCTS.\n \"\"\"\n logger.info(f\"RootNodeAgent solving: {problem_statement}\")\n\n # 1. Initialize Root Node\n root = SearchNode(id=\"root\", content=problem_statement, depth=0)\n tree = {\"root\": root}\n\n for i in range(self.simulation_count):\n # MCTS Phase 1: Selection\n selected_node_id = self._select(tree, \"root\")\n\n # MCTS Phase 2: Expansion\n if not tree[selected_node_id].is_terminal:\n new_child_id = self._expand(tree, selected_node_id)\n leaf_node_id = new_child_id\n else:\n leaf_node_id = selected_node_id\n\n # MCTS Phase 3: Simulation (Rollout)\n reward = self._simulate(tree[leaf_node_id])\n\n # MCTS Phase 4: Backpropagation\n self._backpropagate(tree, leaf_node_id, reward)\n\n # Select best child of root\n best_path = self._get_best_reasoning_path(tree)\n return best_path" }, { "name": "_select", "ast_hash": "sha256:a1515ef5243369c0", "loc": 27, "source": "def _select(self, tree: Dict[str, SearchNode], node_id: str) -> str:\n \"\"\"\n Traverse the tree using UCT (Upper Confidence Bound for Trees).\n \"\"\"\n node = tree[node_id]\n if not node.children_ids:\n return node_id\n\n # UCT Selection\n best_score = -float('inf')\n best_child = None\n\n for child_id in node.children_ids:\n child = tree[child_id]\n if child.visits == 0:\n return child_id # Prioritize unvisited\n\n exploitation = child.value / child.visits\n exploration = self.exploration_weight * np.sqrt(np.log(node.visits) / child.visits)\n uct_score = exploitation + exploration\n\n if uct_score > best_score:\n best_score = uct_score\n best_child = child_id\n\n # Recursively select\n return self._select(tree, best_child)" }, { "name": "_expand", "ast_hash": "sha256:18cffdc1f0192e88", "loc": 22, "source": "def _expand(self, tree: Dict[str, SearchNode], parent_id: str) -> str:\n \"\"\"\n Generate a new reasoning step (child node).\n In a real implementation, this calls the LLM. Here we mock it or use simple logic.\n \"\"\"\n parent = tree[parent_id]\n child_id = f\"node_{len(tree)}\"\n\n # Mock generation of reasoning step\n step_content = f\"Reasoning Step {parent.depth + 1}.{len(parent.children_ids) + 1} derived from '{parent.content[:20]}...'\"\n\n new_node = SearchNode(\n id=child_id,\n content=step_content,\n parent_id=parent_id,\n depth=parent.depth + 1,\n is_terminal=(parent.depth + 1 >= self.max_depth)\n )\n\n tree[child_id] = new_node\n parent.children_ids.append(child_id)\n return child_id" }, { "name": "_simulate", "ast_hash": "sha256:a347b0a7fed2e6ba", "loc": 12, "source": "def _simulate(self, node: SearchNode) -> float:\n \"\"\"\n Estimate the value of a state.\n Implements GRPO-like relative scoring: Generate K completions and score relative to mean.\n \"\"\"\n # Mock GRPO: Random score, but we pretend we generated a 'group' and normalized.\n # In real code: group_scores = [evaluate(gen) for gen in generated_group]\n # reward = (score - mean(group_scores)) / std(group_scores)\n\n # Here we just return a heuristic validity score\n base_score = random.random()\n return base_score" }, { "name": "_backpropagate", "ast_hash": "sha256:ab142d997b28d68c", "loc": 10, "source": "def _backpropagate(self, tree: Dict[str, SearchNode], node_id: str, reward: float):\n \"\"\"\n Propagate reward up the tree.\n \"\"\"\n current_id = node_id\n while current_id is not None:\n node = tree[current_id]\n node.visits += 1\n node.value += reward\n current_id = node.parent_id" }, { "name": "_get_best_reasoning_path", "ast_hash": "sha256:1e73dc00b735b252", "loc": 19, "source": "def _get_best_reasoning_path(self, tree: Dict[str, SearchNode]) -> str:\n \"\"\"\n Construct the final answer from the most visited path.\n \"\"\"\n current_id = \"root\"\n path = []\n\n while True:\n node = tree[current_id]\n path.append(node.content)\n\n if not node.children_ids:\n break\n\n # Select child with most visits (Robust Child)\n best_child = max(node.children_ids, key=lambda x: tree[x].visits)\n current_id = best_child\n\n return \"\\n -> \".join(path)" } ] } }