MetaCogAgent: A Metacognitive Multi-Agent LLM Framework with Self-Aware Task Delegation
- URL: http://arxiv.org/abs/2605.17292v1
- Date: Sun, 17 May 2026 07:12:04 GMT
- Title: MetaCogAgent: A Metacognitive Multi-Agent LLM Framework with Self-Aware Task Delegation
- Abstract summary: We propose MetaCogAgent, a multi-agent large language model (LLM) framework where each agent is equipped with a Metacognitive Self-Assessment Unit.<n>We show that MetaCogAgent achieves 82.4% task accuracy while using 5% fewer API calls than AutoGen and 34% fewer than ensemble voting.
- Score: 11.486913474115402
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Multi-agent large language model (LLM) systems have shown promise for solving complex tasks through agent collaboration. However, existing frameworks assign tasks based on predefined roles without considering whether an agent can accurately assess its own competence boundaries, leading to overconfident execution on tasks beyond its expertise. Inspired by metacognition theory from cognitive science, we propose MetaCogAgent, a multi-agent LLM framework where each agent is equipped with a Metacognitive Self-Assessment Unit that evaluates task-capability alignment before execution. The framework introduces three contributions: (1) a self-assessment mechanism that estimates per-task confidence by combining verbalized uncertainty with historical capability profiles; (2) an adaptive delegation protocol that routes low-confidence tasks to better-suited agents through cross-agent evaluation; and (3) a capability boundary learning module that iteratively refines each agent's competence model via cybernetic feedback. Experiments on our constructed MetaCog-Eval benchmark (700 tasks across 5 cognitive dimensions) demonstrate that MetaCogAgent achieves 82.4% task accuracy -- 8.7% above the best routing baseline -- while using 5% fewer API calls than AutoGen and 34% fewer than ensemble voting. Ablation studies confirm that each metacognitive component contributes to overall system performance.
Related papers
- The Meta-Agent Challenge: Are Current Agents Capable of Autonomous Agent Development? [80.24951682268332]
We introduce the Meta-Agent Challenge (MAC), an evaluation framework designed to test the capacity of frontier models for autonomous agent development.<n>To ensure evaluation integrity, this framework is secured by multi-layer defenses against reward hacking.<n>We demonstrate that meta-agents rarely match human-engineered baseline policies, and the few that do are dominated by proprietary frontier models.
arXiv Detail & Related papers (2026-06-03T04:58:17Z) - Agent-First Tool API: A Semantic Interface Paradigm for Enterprise AI Agent Systems [1.3259123126940986]
We propose the Agent-First Tool API paradigm, which decomposes tool interactions into search, resolve, preview, execute, verify, and recover phases.<n>The paradigm is implemented and validated in a production multi-tenant platform serving 85 registered tools across 6 business domains.
arXiv Detail & Related papers (2026-05-11T13:30:43Z) - EvoAgent: An Evolvable Agent Framework with Skill Learning and Multi-Agent Delegation [37.26213009494508]
EvoAgent is an evolvable large language model (LLM) agent framework.<n>It integrates structured skill learning with a hierarchical sub-agent delegation mechanism.
arXiv Detail & Related papers (2026-04-22T03:00:28Z) - MARTI-MARS$^2$: Scaling Multi-Agent Self-Search via Reinforcement Learning for Code Generation [64.2621682259008]
Multi-Agent Reinforced Training and Inference Framework with Self-Search Scaling (MARTI-MARS2)<n>We propose a Multi-Agent Reinforced Training and Inference Framework with Self-Search Scaling (MARTI-MARS2) to integrate policy learning with multi-agent tree search.<n>We show that MARTI-MARS2 achieves 77.7%, outperforming strong baselines like GPT-5.1 on challenging code generation benchmarks.
arXiv Detail & Related papers (2026-02-08T07:28:44Z) - AgentIF-OneDay: A Task-level Instruction-Following Benchmark for General AI Agents in Daily Scenarios [49.90735676070039]
The capacity of AI agents to effectively handle tasks of increasing duration and complexity continues to grow.<n>We argue that current evaluations prioritize increasing task difficulty without sufficiently addressing the diversity of agentic tasks.<n>We propose AgentIF-OneDay, aimed at determining whether general users can utilize natural language instructions and AI agents to complete a diverse array of daily tasks.
arXiv Detail & Related papers (2026-01-28T13:49:18Z) - Towards a Science of Scaling Agent Systems [79.64446272302287]
We formalize a definition for agent evaluation and characterize scaling laws as the interplay between agent quantity, coordination structure, modelic, and task properties.<n>We derive a predictive model using coordination metrics, that cross-validated R2=0, enabling prediction on unseen task domains.<n>We identify three effects: (1) a tool-coordination trade-off: under fixed computational budgets, tool-heavy tasks suffer disproportionately from multi-agent overhead, and (2) a capability saturation: coordination yields diminishing or negative returns once single-agent baselines exceed 45%.
arXiv Detail & Related papers (2025-12-09T06:52:21Z) - MCP-Orchestrated Multi-Agent System for Automated Disinformation Detection [84.75972919995398]
This paper presents a multi-agent system that uses relation extraction to detect disinformation in news articles.<n>The proposed Agentic AI system combines four agents: (i) a machine learning agent (logistic regression), (ii) a Wikipedia knowledge check agent, and (iv) a web-scraped data analyzer.<n>Results demonstrate that the multi-agent ensemble achieves 95.3% accuracy with an F1 score of 0.964, significantly outperforming individual agents and traditional approaches.
arXiv Detail & Related papers (2025-08-13T19:14:48Z) - Visual Document Understanding and Question Answering: A Multi-Agent Collaboration Framework with Test-Time Scaling [83.78874399606379]
We propose MACT, a Multi-Agent Collaboration framework with Test-Time scaling.<n>It comprises four distinct small-scale agents, with clearly defined roles and effective collaboration.<n>It shows superior performance with a smaller parameter scale without sacrificing the ability of general and mathematical tasks.
arXiv Detail & Related papers (2025-08-05T12:52:09Z) - CodeAgents: A Token-Efficient Framework for Codified Multi-Agent Reasoning in LLMs [16.234259194402163]
We introduce CodeAgents, a prompting framework that codifies multi-agent reasoning and enables structured, token-efficient planning in multi-agent systems.<n>Results show consistent improvements in planning performance, with absolute gains of 3-36 percentage points over natural language prompting baselines.
arXiv Detail & Related papers (2025-07-04T02:20:19Z) - AgentOrchestra: Orchestrating Hierarchical Multi-Agent Intelligence with the Tool-Environment-Agent(TEA) Protocol [22.406849007798858]
We propose the Tool-Environment-Agent Protocol to integrate environments, agents, and tools into an unified system.<n>We introduce AgentOrchestra, a hierarchical multi-agent framework with a central planning agent that decomposes complex objectives and coordinates specialized agents.
arXiv Detail & Related papers (2025-06-14T13:45:37Z) - MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents [59.825725526176655]
Large Language Models (LLMs) have shown remarkable capabilities as autonomous agents.<n>Existing benchmarks either focus on single-agent tasks or are confined to narrow domains, failing to capture the dynamics of multi-agent coordination and competition.<n>We introduce MultiAgentBench, a benchmark designed to evaluate LLM-based multi-agent systems across diverse, interactive scenarios.
arXiv Detail & Related papers (2025-03-03T05:18:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.