MetaCogAgent
Free while signed in. Answers cite the passages they came from.
MetaCogAgent equips a multi-agent system with metacognition, so each agent decides whether it should answer or delegate. The bottleneck in current multi-agent systems is over-delegation and under-delegation, and a metacognitive gate is a principled way to manage both. The Metacognitive Unit (MCU) at each agent produces confidence scores that drive routing to a delegation hub.
Confidence-driven routing: Each agent's MCU combines verbalized and profile-based confidence into a single score. Low-confidence tasks route to a delegation hub rather than getting answered anyway.
Self-aware specialization beats fixed routers: MetaCogAgent reaches 82.4% on MetaCog-Eval, versus 70.2% for a skill-fixed router and 65.3% for single-agent. Self-assessment and adaptive delegation each contribute material gains in ablations.
Emergent specialization: Distinct confidence profiles (high on coding, low on retrieval, etc.) emerge purely from feedback. No specialization is encoded beyond initial system prompts.
Why it matters: Multi-agent systems usually rely on fixed routers or simple round-robin schemes. A learned, uncertainty-aware delegation gate gives a primitive that adapts to task difficulty without retraining the routing layer.
Get next week’s papers.
The same picks and the same summaries, in your inbox. Free, and 176 issues deep.
Subscribe on Substack